Handbook of Multimodal
Application of Sensing, Networking and Big Data Analysis SATREPS Project of “Smart Cities Development for Emerging Countries by Multimodal Transport System Based on Sensing, Network and Big Data Analysis of Regional Transportation" Handbook of Multimodal
Application of Sensing, Networking and Big Data Analysis SATREPS Project of “Smart Cities Development for Emerging Countries by Multimodal Transport System Based on Sensing, Network and Big Data Analysis of Regional Transportation"
Today, cities around the world are facing various environmental problems. In order to continue sustainable development in the future, it is necessary to streamline all activities in the city and form a society without waste and congestion. To achieve this goal, many cities are aiming to realize smart cities. This handbook introduces the technologies necessary to realize such a smart city in India and the methods of low carbonization using these technologies. Much of the content presented in this handbook is the result of the project entitled "Smart Cities Development for Emerging Countries by Multimodal Transport System Based on Sensing, Network and Big Data Analysis of Regional Transportation," which is one of the Science and Technology Research Partnership for Sustainable Development (SATREPS) implemented by Japan International Agency (JICA) and Japan Science and Technology Agency (JST). This project is a joint research between the Indian Institute of Technology Hyderabad, Nagoya Electric Works Co. Ltd., and Nihon University and was carried out from 2016 to 2022 with the city of Ahmedabad in India as the study area. In addition, faculty members from the Tokyo Institute of Technology and the University of Tokyo also participated in this research. As shown in Appendix, many experiments were conducted on the campus of the Indian Institute of Technology Hyderabad as well as Ahmedabad. As the title suggests, this research group focuses on the essential traffic among the various elements that make up a smart city and utilizes the latest technologies such as information and communication technology, image analysis technology, and AI technology to multi-purpose. The goal is to realize modal, environmentally- friendly transportation and reduce greenhouse gas emissions. This handbook consists of two parts of this handbook. Part I presents the basic ideas for smart cities, multimodal transport, and greenhouse gas reduction. Part II presents the advanced technology developed in this project and demonstrated in the field. In the last Appendix, we present the contents of the demonstration experiment conducted on the campus of the Indian Institute of Technology Hyderabad as a test bed. The technologies introduced in this handbook are still under development, but they are essential technologies for promoting multimodal transport and realizing smart cities, and after that, smart cities will be introduced in other cities. We strongly believe that it will help consider it. Finally, we would like to thank Ahmedabad Municipal Corporation (AMC), Ahmedabad Traffic Police, BRTS-Ahmedabad Janmarg Limited, Gujarat Metro Rail Corporation (GMRC) Limited, Ahmedabad Auto Rickshaw company (G-Auto), JICA, JST, and other related parties for their cooperation as the project's leader.
Dr. Tsutomu TSUBOI
Project Leader of SATREPS "Smart Cities Development for Emerging Countries by Multimodal Transport System Based on Sensing, Network and Big Data Analysis of Regional Transportation"
Part I
Role of Multimodal Transport for Smart City

Part II    Advanced Technologies to Realize Multimodal Transport

Appendix
Chapter A1 Testbed for Technology Development . Preface 3 Contributors of Handbook 84 86 Project Members Role of Multimodal Transport for Smart City
Today, 55% of the world's population lives in urban areas, and this share is expected to grow to 68% by 2050. Most of the world's economic activity, energy consumption and greenhouse gas emissions are carried out in cities. Therefore, in order to significantly reduce greenhouse gas emissions, it is necessary to streamline activities in urban areas and reduce energy consumption. “Smart cities” play a vital role in achieving these goals. Smart cities cover all urban activities, but are particularly concentrated in development in areas such as energy, mobility, environment, resource recycling, and living. In these fields, activities can be visualized based on ICT, Al, Big Data analysis, Digital twin, etc., and at the same time, digital transformation can be promoted to improve the efficiency of urban activities dramatically. In particular, this handbook covers efforts in the field of mobility are important elements for realizing smart cities. People's movements are essential for all activities in the city, and newer transportation systems are needed to achieve efficient urban activities. Transportation of goods is also a basic function of urban activities. Realizing an efficient logistics system is one of the efforts in the transportation field. In the field of mobility, many efforts are already underway that are expected to lead to the realization of smart cities. The most obvious example is MaaS, or Mobility as a Service, which is an organic combination of various transportation services. By combining information from multiple transportation services and visualizing services through big data analysis, we can promote the multimodal and further digital transformation to significantly transform the service itself and make it energy efficient transportation system.
What kind of concept is
a smart city?
A smart city was defined by the Ministry of Land, Infrastructure, Transport and Tourism in Japan as "sustainable cities or districts where overall optimization is achieved for various problems faced by cities, management (planning, maintenance, management, operation) is carried out while utilizing new technologies such as ICT". By gathering various residents and companies in the city, economic activity becomes active, and an attractive space is formed, but on the other hand, It causes many urban problems such as the occurrence of traffic congestion, deterioration of public security, environmental pollution, waste generation, increase in energy consumption, etc. In smart cities, by utilizing ICT, the activities of each inhabitant and the activities of each company / organization are aligned, creating and nurturing effective and efficient industries, using energy, maintaining public order, children and the elderly. We will be able to watch over people activities, eliminate traffic congestion, and take measures against natural disasters.
What is the difference
from the conventional
city planning?
Conventional city planning has been centered on making hardware such as designation of land use, installation of roads and lifelines, construction of buildings, etc. For this reason, it was difficult for urban functions to change significantly in response to a change in society. Thus, the residents and businesses living there have to be working in accordance with the city. In the future, as society changes drastically due to many technological innovations, such inflexible cities cannot accept this change. On the other hand, in smart cities, we will change the city functions according to various social conditions that change with the times and the values and needs of society, and perform optimal city management. ICT is the basis of a system that flexibly responds to a change in a society. Recently, there are signs of change in the concentration of population in large cities, such as the spread of telework triggered by the corona disaster. Smart cities that utilize ICT can change urban functions flexibly responding to such rapid social changes.
Smart city from the
perspective of
transportation system
As mentioned above, the realization of smart cities will be achieved through efforts in many fields related to urban activities, but especially achivements in the transportation field is an important key to the realization of smart cities. This is because the movement of people and goods is indispensable for urban activities, and unless transportation is made smarter, the overall urban activities will not be made smarter. Many technological innovations have been made to improve the sophistication of transportation systems by utilizing information and communication technology. These technological innovations will be incorporated into the realization of smart cities.
1: What is smart transportation?
The transportation system in the city is not limited to automobiles and vehicles and roads and railroads, but is a system that includes overall movement including control and operation management of these. Therefore, the traffic system consists of elements such as traffic condition monitoring, traffic information collection, traffic condition analysis, traffic information provision to drivers, traffic control, and public transportation operation management, all of which must be connected by utilizing ICT so that the transportation system becomes smarter.
2: Efforts to make
transportation smarter
Since the 1970s, when the increase in automobile traffic caused the problem of traffic congestion in cities, mechanisms have been introduced in many cities to monitor traffic conditions, provide traffic information, and provide appropriate traffic control. Until now, ultrasonic and loop coil vehicle detectors have been installed in various parts of the city to monitor traffic conditions and control signals, which have been put into practical use in many cities. SCAT, SCOOT, MODERATE, etc. are widely introduced in cities around the world as typical signal control methods. Recently, these systems themselves are also becoming smarter by utilizing ITC. Infrared detectors and image detectors and mobile communications such as Bluetooth and WiFi with GPS are being used to monitor traffic conditions. In addition to utirize detected traffic infomation to control traffic signals, the method of providing it to the driver as traffic information has become widespread. Today, many cars are equipped with car navigation systems, and it is common to provide guidance on the optimal route based on traffic information. With the spread of digital maps, such information provision has become more sophisticated, and route searches and the like are generally performed on mobile terminals and the like based on traffic conditions. Such route search using mobile terminals is becoming widely used when using public transportation such as taxis, buses, and trains other than private cars. The location information of taxis, buses, and trains is also being used for operation management, which is important for achieving efficient operation and redusing traffic congestion. Furthermore, even in emergency vehicles and freight transport vehicles, these traffic information are utilized and used for optimal route selection, etc., contributing to the realization of efficient transportation.
3: ITS development
Many technologies have been developed that enable safer and more efficient road transportation systems by using such ITCs for information collection and operation of transportation systems. That is the intelligent Transportation system or ITS. It is expected that the use of ICT will reduce external diseconomies such as automobile congestion, air pollution, and traffic accidents, and some applications have begun. ITS targets mainly traffic on roads, including pedestrians, which is not sufficient from the perspective of making the city smarter.
4: Expectations for CASE
Currently, it is said that automobile society is entering a period of change once every 100 years. The four keywords that symbolize this are connectivity (C), autonomous driving (A), sharing economy (S), and electric vehicle (E). Connectivity means that all vehicles are connected to the Internet, information is exchanged as a whole, and each vehicle can be controlled as a system. Underlying this technology is the development of ICT and IoT technologies. Autonomous driving is the control of vehicles by machines, and this technology is also supported by sensing technology and control technology. The sharing economy, as typified by car sharing, is the idea of sharing with society without owning a vehicle. For the operation of car sharing, technology using conventional ICT such as vehicle management and reservation system is indispensable. An electric vehicle is a vehicle that is driven by a motor rather than an engine. Naturally, it will greatly contribute to the reduction of greenhouse gases, but the electrification of vehicles will make it easier to connect to Internet pictures and control by ITC. The technology developed as the above-mentioned ITS is an elemental technology that supports this CASE. The lean and energy-efficient automotive society brought about by these CASEs is essential to the realization of smart cities.
5: Promotion of
multimodal by MaaS
At the same time as automobiles, other transportation modes will also be connected to the Internet, so the entire transportation system can be operated and used as a single system, and the selection of transportation modes becomes more efficient. MaaS makes it possible to use all of these transportation modes as a single transportation system using ICT. It is expected that MaaS will enable users to select the most suitable combination of transportation modes according to the situation and pay as a single system, and will reduce unnecessary travel and realize more efficient transportation. This really means realizing multimodal using ICT. In order to realize MaaS, in addition to general traffic information, big data analysis that centrally collects and analyzes all traffic information such as operation information such as public transportation and information provision that enables multimodal selection Various technologies that have been utilized are required. Technology development in this area is exactly one of the main themes of this handbook.
Achievement of SDGs
Targets by Smart City
Sustainable Development Goals (SDGs) are set forth in the 2030 Agenda for Sustainable Development, which was unanimously adopted by member countries at the UN Summit in September 2015, until 2030. It is an international goal to aim for a sustainable and better world. It consists of 17 goals and 169 targets for achieving socio-economic and environmental ambitions. Multiple goals below are involved in better cities, transportation, water and sewage, realization of energy systems, and solving global warming problems. Goal 6: Ensure availability and sustainable management of water and sanitation for all Goal 7: Ensure access to affordable, reliable, sustainable and modern energy for all Goal 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation Goal 11: Make cities and human settlements inclusive, safe, resilient and sustainable Figure 1-1 The four aspects of the Sustainable Development Goal [1] Goal 12: Ensure sustainable consumption and production patterns Goal 13: Take urgent action to combat climate change and its impacts The original purpose of SDG is to create an inclusive, safe, resilient, sustainable city where people can live properly, and to create prosperity and social welfare without harming the environment (Figure 1-1) [1]. Goal 11 directly contributes to the promotion of the smart city targeted by this book and is designed to address the challenges of most cities. To enjoy a convenient modern life in cities with reduced impact on the environment, products and services should be delivered considering multitude of perspectives. In particular, policies and plans should target a balance between economic growth, social equity and environmental preservation within a city over the long term in connection to the Triple Bottom Line (Figure 1-2) [1]. Accordingly, the governing agencies require a strategy that covers the design, implementation, evaluation and enhancement of urban systems. In this connection, Information and Communication Technologies (ICT) represent a great opportunity to rethink a new urban configuration [2] through eliminating redundancy in urban operations and services, and integrating urban domains, facilities and networks [3]. The success of achieving SDG depends especially on the local government. They should look to transportation, city planning, mitigating and reducing disaster risk, and protecting the environment to configure urban measures by the next 15 years.
1: Sustainable smart city
While the cities around the world are trying to achieve the smart city tag, those especially of developed countries are aiming to obtain the tag without prioritizing sustainability aspect [4]. Conversely, smart city should produce sustainable and livable places for all instead of overemphasizing providing sophisticated digital technology services to the citizens [5]. Such lack of integration of environmental, economic and social aspects in achieving a smart city lead to the term “Smart Sustainable City” to strengthen this linkage. Not surprisingly, cities of developed countries have been more successful in becoming smart than those of emerging economies. In this connection, the there are no sophisticated tools that bring all types of cities in all kinds of contexts into a comparable framework. In fact, a mechanism to evaluate in terms of their contribution to sustainability is still very scarce. Given the scale, complexity and mutuality of sustainability challenges, cities need integrated strategies to deliver sustainable, effective services and engage citizens to make better choices in their daily lives. To achieve this, suitable platforms are necessitated to gather and assimilate huge data from multiple sources; this would allow a real-time access of the demand and availability of services, both for governing agencies and citizens.
2: Inclusivity
In developing countries, the population growth rates are high where local agencies lack enough resources to handle the integration of these new settlers who are especially near poor and marginalized [6]. Most often, the slums represent a territorial exclusion due to the lack of equipped spaces. Social sustainability is achievable only when there is suitable environment for human interaction, communication and access to facilities [7]. Appropriate use of ICT can enhance interaction with citizens, increase public service efficiency and thus facilitate transparent governance [8]. In fact, involving the people in decision-making is one of the desirable features of a sustainable society. Digital participation enables residents to comment and enquire proposals within administration and politics. In addition, it also gives the citizens an opportunity to make their own suggestions and enable t making efficient plans and strategic goals that incorporate citizen experience. Accordingly, the products and services should balance the technical advancement of infrastructure with social engagement and empowerment.
3: Urban safety
Urban safety encompasses activities such as managing traffic, quick response to emergencies or having safe infrastructure, and protection from crime. The smart technologies accessible to law enforcement are also advancing in various fronts [5]. Digitalization is revolutionizing policing and assisting authorities do more with data gathered. Many cities around the world now have command centers that have provision real-time information assimilation and sharing across agencies. Traffic accidents can be significantly reduced using smart technologies. Video surveillance technologies are also coming up with accident detections and predictions thus enabling the riders to avoid accidents. Improving infrastructure for pedestrian and cyclist can promote greener commute along with safety [4]. Improving traffic flow with intelligent signals can reduce risky driving at intersections. Advancement in technology of autonomous vehicles can also decrease traffic accidents. The health of buildings and bridges can be continuously monitored with sensors. Similarly, the covers of man holes when interfaced with sensors can avoid accidents during rainy seasons. Encrypting, stamping, and safeguarding footage taken from body-worn cameras in a certified cloud environment could protect it from being altered or hacked.
4: Environmental sustainability
ICT can bring down GHG emissions, lower water consumption and reduce the volume of solid waste in cities. GHG emissions come from three major sources in cities: transportation, buildings and waste. In general, the most effective ways that smart city technologies can help decrease emissions involve reducing electricity usage and thus generators. Several measures developed to improve urban transportation have secondary effects on reducing the vehicular emissions. Those with the potentially significant effect involve reducing private vehicle usage and shifting to a greener mode of transport. This can be achieved by using advanced digital technology to provide better services in public transportation. Intelligent traffic lights, which are designed to smooth the flow of traffic, have the additional benefit of minimizing the time that vehicles spend idling. Cities can use technology in using water efficiently. Water wastage can be restricted by keeping a limit of usage-per-household. This approach could be made more effective if digital tools are used in making the consumption more transparent. Water consumption tracking, which uses advanced metering combined with feedback messages to the consumer, can increase awareness and nudge people toward responsible consumption. In fact water leakages can also be avoided by deploying appropriate sensors. Smart irrigation technology can tune the water use in public spaces by considering weather, soil conditions, and sunlight patterns. Similarly, some cities have reduced the volume of solid waste by implementing effective recycling programs based on the said ICT technology. For instance, charging households according the weight and type of the waste disposal enables better waste management. In addition, illegal dumping should also be monitored. More importantly, using ICT, one can create more awareness about appropriate waste disposal by displaying on billboards.
5: Urban resilience
UN-Habitat defines resilience as “the ability to resist, absorb and accommodate to the effects of a hazard, in a timely and efficient manner" [9]. Thus, cities with resilience can withstand, adapt, and recover in a from any kind of challenges they face, either planned or unplanned. The agencies anticipate hazards and adopt strategies unemployment The cities can adopt ICT to handle shocks such as natural disasters and terrorist attacks. For instance, sensors deployed around cities can be used to maintain detect the said calamities and develop warning and management systems. Cybersecurity is also another vital aspect underpinning resilience, due to the increasing digitalization of critical infrastructures. Cities that effectively use new technologies, and successfully imbed them into urban planning and design will be more resilient to future challenges. In summary, to make the cities smart and sustainable, sensors should be deployed in all necessary domains (physical systems). Next data should be collected and analyzed. Subsequently, agencies to intervene and improve the physical systems. Generally, the physical systems together with said components are together are considered as cyber-physical systems (CPS). Smart city can infact be visualized as multi-level CPS (Figure 1-3).
What are challenges in
this Handbook?
As mentioned above, promoting multimodal using ICT is a very important initiative for realizing smart cities. Efficient movement not only achieves efficient use of limited urban space, but also minimizes the energy required for movement in cities and significantly reduces greenhouse gases and roadside air pollutants emitted. In addition, the spread of EVs, which is expected in the future, will further promote the computerization of automobiles, so it is expected that the efficiency of movement will be further improved by connecting to the Internet. However, since many of these technologies such as ICT, digitization, AI, big data analysis and Digital ware currently being developed individually, even if these technologies evolve, an efficient transportation system will not be realized. Therefore, the next challenge is how to combine these technologies to build a more efficient transportation system. And how to connect this to the realization of smart cities. The M2Smart project is a research project under SATREPS, which was launched in collaboration with Indian Institute of Technology Hyderabad, Nagoya Electric, and Nihon University with such an awareness of the problem. The purpose of this handbook is to realize a multi-modal transportation system, promote smart cities by it, and further greenhouse effect by sharing the results of research members discussing and developing technology in the M2Smart project widely with society. It is to contribute to the reduction of gas. In the handbook, Part 1 will first present the basic concept of smart cities, the realization of multimodal transportation systems that support smart cities, and the effects of reducing greenhouse gas emissions in the situation in India. Next, in Part 2, we will introduce various technologies that are the basis for realizing a multi-modal transportation system that will lead to the realization of smart cities. Many of the technologies and their applications introduced here are the technologies and application methods developed by the M2Smart project, but at the same time, we will also introduce general technology development trends. At the end of this handbook, we will introduce our efforts with the test bed installed on the campus of Indian Institute of Technology Hyderabad. The purpose of this testbed is to operate the technology developed in the project in a space that imitates the real space and verify the technology. It is dangerous to put new technology directly into the traffic that is moving violently every day. The approach of verification using such a test bed is very effective, and we will introduce its contents. [1] Elkington, J.: Cannibals with forks: The triple bottom line of 21st century business. 1997. [2] Bibri, S. E. & J. Krogstie. Big data analytics and context–aware computing for smart sustainable cities of the future. Proceedings of the NOBIDS Conference, Vol.15, 2016. [3] Batty, M., K. W. Axhausen, F. Giannotti, A. Pozdnoukhov, A. Bazzani, M. Wachowicz, G. Ouzounis & Y. Portugali. Smart cities of the future. The European Physical Journal Special Topics Vol.214, pp.481–518, 2012. [4] Hoon, H., & S. Hawken: Introduction: Innovation and identity in next-generation smart cities. City, culture and society, Vol.12 pp.1-4, 2018. [5] Yountaik, L., H. Han & S. H. Lee.: Sejong Smart City: On the Road to Be a City of the Future. International Conference on Computers in Urban Planning and Urban Management. Springer, 2019. [6]  Bolay, J. C., J. Chenal, & Y. Pedrazzini. Learning from the slums: the habitat of the urban poor in the making of emerging cities. Springer, London, UK, 2016. [7] Monfaredzadeh, T. & R. Krueger. Investigating social factors of sustainability in a smart city. Procedia Engineering, Vol.118, pp.1112-1118, 2015. [8] Francesco, B., M. Tregua, and C. C. Amitrano. Co-governing smart cities through living labs. Top evidences from EU. Transylvanian Review of Administrative Sciences, Vol.50 pp.21–37, 2015. [9] UN-Habitat: https://unhabitat.org/programme/city-resilience-profiling-programme, 2012.
In recent years, many countries and cities around the world have seen many efforts toward the realization of smart cities. As introduced in Chapter 1, the concept of smart city covers many areas. Therefore, when preparing smart city guidelines, many governments and cities start by defining the future city image and at the same time selecting key areas. It also establishes comprehensive approaches from the physical, institutional, social and economic aspects. As India is no exception, the Indian government launched the "Smart Cities Mission (SCM)" in 2015 to promote cities that provide core infrastructure, a clean and sustainable environment, and give a decent quality of life to their citizens through the application of ‘smart solutions’ [1]. Urban areas in India are expected to house 40% of the population and contribute 75% of GDP by 2030. This requires comprehensive development of physical, institutional, social, and economic infrastructure. The government mentioned in the guideline for the smart city, "All are important in improving the quality of life and attracting people and investments to the City, setting in motion a virtuous cycle of growth and development. Development of Smart Cities is a step in that direction." [2]. Thus, realizing smart cities is an important mission for sustainable urban development in India. This chapter describes the objectives and mechanism of the SCM as based on the cases of smart cities in India. According to the guideline for the SCM, the mission objective is to promote cities that provide core infrastructure and give a decent quality of life to Indian citizens, a clean and sustainable environment, and the application of Smart Solutions. "Smart Solutions" enable cities to use technology, information, and data to improve infrastructure and services, as shown in Figure 2-1 [2]. The purpose of the Smart Cities Mission is to drive economic growth and improve the quality of life of people by enabling local area development and harnessing technology, especially technology that leads to Smart outcomes. Area-based development will transform existing areas (retrofit and redevelop), including slums, into better-planned ones, thereby improving the liveability of the whole city. New areas (greenfield) will be developed around cities to accommodate the expanding population in urban areas. The application of Smart Solutions will Figure 2-1 Smart Solutions in Smart Cities Mission [2] Figure 2-2 Area-based Development in Smart City Mission [1] Figure 2-3 Strategies in Sm art Cities Mission [3] GREEENFIELD enable cities to use technology, information, and data to improve infrastructure and services. Comprehensive development will improve quality of life, create employment and enhance incomes for all, especially the poor and the disadvantaged, leading to inclusive Cities. As shown in Figure 2-2, the strategic components of area- based development in the Smart Cities Mission are city improvement (retrofitting), city renewal (redevelopment), and city extension (greenfield development), plus a Pan-city initiative in which Smart Solutions are applied, covering larger parts of the city. The SCM proposal enlisted two development strategies: Pan-City development and Area-based Development (Figure 2-3). Pan-city development envisages application of selected smart solutions to the existing city wide infrastructure. This would be refining and managing the existing infrastructure and services for affective productivity. Second is the area-based development which consists of three strategic components; Retrofitting, Redevelopment and Greenfield. Retrofitting model is the planning of an existing built up area (consisting of more than 500 acres) considering objectives of that place to achieve smart city objectives, to make the existing area more efficient and liveable. Redevelopment is creating a new layout for and existing area (more than 50 acres) with enhanced infrastructure, using mixed use and increasing density. Greenfield is to develop a . previously vacant area (more than 250 acres) into affordable housing for the poor. The Indian Government envisages the following elements as integral parts of every Smart City: Assured electricity supply . with at least 10% of the energy requirements met through renewable energy. . Adequate water supply with the recycling of waste water, harvesting and the reuse of storm water. Adequate urban transport with an emphasis on no-motorized transport, pedestrian-friendly pathways, intelligent traffic management and smart parking facilities. Enhanced citizen experiences . with engaging usage of open spaces and a safe environment for women, children and the elderly. Indian central government shortlisted 100 potential smart cities and asked for specific city proposals from the state government. The state government consulted all the associated professionals, prepared a proposal/master plan, and responded to the central government. The evaluation was done based on feasibility in terms of resources, economy, and practicality by experts. All the proposals were scored on common grounds. Between June and July 2015, all states and union territories (UT) within the country were required to submit nominations of cities for consideration in the 'India Smart Cities Challenge.' From these submissions, the Ministry of Urban Development (MoUD) selected 100 cities in August 2015. The selection criteria gave equal weightage to the urban population of the state/UT and the number of statutory towns in the state. In January 2016, MoUD announced 20 cities as winners from the first phase of the Smart City Challenge. The selection process consisted of extensive reviews of city-level and proposal-level criteria—by three independent panels of experts—of Smart City Proposals submitted by each city. In May 2016, MoUD released the second list of an additional 13 cities, selected on a fast- track basis to be included in the first phase of the SCM. These 33 cities from the two lists were to receive funding to be developed as model smart cities. In September 2016, in the second round of the Smart Cities Challenge, the government announced the third list of 27 additional cities, bringing the number of selected Smart City Proposals to 60. The final list of 40 cities was released at the end of June 2017. The location of selected 100 smart cities is described in the map of Figure 2-4. These cities are selected through passing the process of examination of a smart city proposal. The selection process is as follows; According to the guideline [2], each aspiring city competes for selection as a Smart City in what is called a ‘City Challenge’. There are two stages in the selection process as described in Figure 2-5. Stage 1 of the competition: Shortlisting of cities by States. The State/UT begins with shortlisting the potential Smart Cities on the basis of conditions precedent and scoring criteria and in accordance with the total number allocated to it. The first stage of the competition will be intra-state, in which cities in the State will compete on the conditions precedent and the scoring criteria laid out. These conditions precedent have to be met by the potential cities to succeed in the first round of competition and the highest scoring potential Smart Cities will be shortlisted and recommended to participate in Stage 2 of the Challenge. Stage 2 of the competition: The Challenge round for selection. In the second stage of the competition, each of the potential 100 Smart Cities prepare their proposals for participation in the 'City Challenge'. This is a crucial stage as each city's Smart City Proposal is expected to contain the model chosen, whether retrofitting or redevelopment or greenfield development or a mix thereof, and additionally include a Pan-City dimension with Smart Solutions. By a stipulated date, to be indicated by Ministry of Urban Development (MoUD) to the States/UTs, proposals will be submitted to MoUD for all these 100 cities. These will be evaluated by a Committee involving a panel of national and international experts, organizations and institutions. The winners of the first round of Challenge will be announced by MoUD. Thereafter, while the winning cities start taking action on making their city smart, those who do not get selected will start work on improving their SCPs for consideration in the second round. Ahmedabad is the largest city in Gujarat, and the administrative capital of Ahmedabad district. The city is at present divided into six zones namely, central, east, west, north, south and new west zone. Each zone is further split into nine wards. There are 64 wards. It serves as the seat of Gujarat High Court and is the seventh largest metropolitan area in India. Ahmedabad is an important economic and industrial hub and is reported to be one of the fastest growing cities of the decade. As shown in Figure 2-6, the Sabarmati River cutting the city from the middle plays a vital role in providing the urban life as well as fragmenting the city into two. The main motive of designing of any kind of strategies or policies for this city would be the providing a strong connection between the fragments [5]. This city was selected among the first 20 Smart Cities in India under the Smart Cities Mission of the Ministry of Housing and Urban Affairs. Ahmedabad has scored 66.81% for the Smart City Plan submitted on 15th of Dec 2015. In spite of being the 2nd largest city on the list, this city stands at 6th in rank in the country Handbook of Multimodal Transport for Smart City Application of Sensing, Networking and Big Data Analysis Figure 2-5 Smart City Selection Process [2] Letter to all state governments to shortlist potential Smart Cities based on Stage-I criteria according to number of Smart Cities distributed across states /UTs  by the MoUD. This is the first stage of Intra-State competition. On the basis of response from States/UTs, the list of potential 100 Smart Cities is announced. The second stage of All India competition begins. Each potential Smart City prepares its proposal assisted by a consultant (from a panel prepared by MoUD) and a hand-holding External Agency (various offers received such as World Bank, ADB, GEF, USTDA, JICA, DFID, AFD,KfW, UN Habitat) By stipulated date Stage 2 proposals submitted. Evaluation by a panel of experts. Selected cities declared – Round 1 Smart Cities Selected cities set up SPV and start implementation of their SCP. Preparation of DPRs, tenders etc. Other cities prepare to improve their proposal for next round of the Challenge Figure 2-6 Map of Ahmedabad for its Smart City Plan. In Ahmedabad, several smart city initiatives have been introduced, ranging from transit management, city surveillance, e-governance, Integrated Command and Control Centers, digital payments in utilities, and many more, as described in Figure 2-7 [6]. Two projects are taken up as Pan City Proposals as follows: (a) Smart Transit – Integrated Transit Management Platform with Common Card Payment System – a web and mobile based application to ease access to public transit systems, provide real time tracking and plan trips & journeys. It intends application of smart solution — technology, information and data – to the existing and proposed transit infrastructure (BRTS, AMTS, GSRTC and proposed Metro) to improve public transit (b) Command Control Centre (CCC) with OFC network – The CCC will be an integrated system that will operate and manage multiple city service operations including real time monitoring and help in improving services delivery & governance. OFC’s primary function will be to connect all AMC offices, city civic centers, urban health centers, schools and municipal buildings, thus reducing future bandwidth costs. Other applications are: - Integration of existing control rooms - Traffic / AMTS / BRTS / E-governance / Pollution - Emergency & Disaster Response System - Incident Management System with support from Fire, Police, Traffic, AMC and other departments - Traffic control: Traffic management, offence tracking at major junctions & smart parking - Environment & Climate monitoring - air quality monitors and automatic rain gauge stations Figure 2-7 Ahmedabad Smart City Proposal [6] [ 1 ]  Ministry of Housing and Urban Affairs. : https://smartcities.gov.in/, 2021. [2] Ministry of Urban Development: Smart Cities Mission Statement & Guideline, https://smartnet.niua.org/sites/default/files/ resources/smartcityguidelines.pdf, 2015. [3] Alankrita, S.: Shaping Indian Cities: Planning and Design with Smart City Technologies. Master Thesis of Delft University of Technology, 2017. [4] Housing and Land Rights Network: India's Smart Cities Mission: Smart for Whom? Cities for Whom? Published in New Delhi, 2018. [5] Ahmedabad Smart City Mission: https://ahmedabadcity.gov.in/portal/smartcitymission.jsp#smartCitiesMissionDiv, 2021. [6] Amdavad Municipal Corporation: https://ahmedabadcity.gov.in/portal/jsp/Static_pages/pi_smartcity.jsp#, 2022. Chapter 3
The realization of smart cities is able efficiently to use the mix of transport modes, e.g. public transport, private vehicles, bicycles, pedestrians, and rail. However, many of these modes currently act independently of each other with no information sharing between them. Recent developments and applications of information technology have made it possible to share information among multiple transportation modes and seamlessly connect them as a single transportation mode. This is generally referred to as "multimodal transport." Multimodal transport has relevance to emerging economies which are characterized by inadequate public transport and a large informal sector of transport operators. This chapter describes the summary of how to realize a smart city through multimodal transport. Conventional planning mainly supports automobile dependency, which refers to transport patterns mostly focused on automobile travel over other modes [1]. It strives to maximize traffic speeds and minimize congestion using a specifically-developed set of tools. However, in the cities of developing economies like India, increased dependence on private vehicle usage has started causing frequent congestion rendering traffic planning very challenging [2]. Consequently, the environmental impact of urban transportation has been huge creating long-lasting effects like climate change [3]. Accordingly, transportation planning has now expanded to include more emphasis on greener choices of modes like non-automobile modes and public transportation. A transportation system must handle diverse demands. For example, any conscious rider should have the feasibility to take a greener route if he wants to. Moreover, economically and socially disadvantaged people, in particular, need diverse mobility options: walking and cycling for local travel, public transit or combinations for longer trips, and automobiles (ridesharing) when necessary. As a result, many agencies worldwide are recognizing the diversity of travel demands and the importance of more multimodal ways. Multimodal transport refers to linking of multiple modes of transport (buses, Metro, auto-rickshaws, bicycle, etc.) to provide a more seamless end-to-end transport from source to destination with within the economic constraints. Multimodal transport planning requires tools for evaluating the quality of each mode, such as Level-of-Service standards which can be used to indicate problems and ways to improve each mode, as shown in Table 3-1. For example, London’s overall public transport network is characterized by a well-established rail network complemented by extensive bus and ferry networks. These networks are integrated by multimodal stations designed to ease interchange for high volumes of passengers. At major stations, purpose-built bus interchanges have been developed to be within walking distance of the railway and underground stations, often operated by bus station staff and furnished with real-time information systems [4]. To facilitate multimodal transportation particularly focusing on needed to encourage public transit usage, a number of different kinds of integration are required: Integration with land-use . planning: Building public transit links first and then developing mixed land-use commercial/ residential centres around them thereby reducing the need to travel Integration within and between different modes of transport ▷ Physical Integration: facilitating direct, comfortable, convenient, and safe access to public transport ▷ Fare Integration: Enabling the public transit user to pay ONCE for a journey involving different transport ▷ Route Integration—Facilitating logical interchange points where passengers are able to transfer from one vehicle or mode to another conveniently and safely ▷ Information Integration— Enabling a 'one-stop-shop' for public transit users, cyclists, and walkers to gain information on any journey they wish to conduct using these modes ▷ Institutional Integration— Ensuring that different public transit providers see themselves as part of a network and provide links to other types of transit, walking, and cycling
While there is a need to improve the said multimodal transport infrastructure, such interventions are generally constrained by financial resources. Since the transportation system has to handle colossal ridership, it may not result in the expected acceptance of multimodal transport unless the infrastructure additions have good coverage. Hence, the agencies should carefully plan when and where to invest in the infrastructure to target the gradually increasing acceptance of ridership optimally. To complicate matters, the above-mentioned deployments for non-motorized vehicles face geographical restrictions. It may not be possible to create separate weather-protected shelters everywhere as the cities are often ill-planned. Hence, non-motorized pathways may lack the required coverage or quality. Moreover, the hotter temperatures may cause the riders to withdraw from walking or cycling. Thus, within the financial resources, for the agencies to promote multimodal transport, auto rickshaws are generally used as feeder services or for shorter trips. In this context, the success of multimodal transport lies in the quality of the last mile connectivity. Developing its infrastructure can significantly improve public transportation usage and hence greener mobility. To this end, one needs to analyze the riders' opinions about the current mode transits and last-mile connectivity to improve accordingly. Against this backdrop, we conducted an experiment to analyze the change in riders’ opinions with and without seamless last-mile connectivity. The experiment was conducted near the ISKCON crossroads BRT station, which can provide the shortest and eco-friendliest routes using all possible modes. Specifically, the application was to track the modal choice behavior of the riders (Figure 3-1). Riders getting down at the bus station were approached and briefed about the participants with the help of flyers. Further, they were asked to fill out a questionnaire about their details. Of them, potential participants were selected. With this setup, participants were dropped from station to office in the morning and office to station in the evening for one week. The experiment lasted for two weeks, and the participants were divided into two sets one for each week. Care was taken to ensure that no delay was incurred in the participant's commute. Moreover, all the participants were requested to use the MMA app throughout their rides to analyze their driving behavior. A questionnaire was taken from the participants on different aspects like ride comfort, and transit delay before and after the experiment to examine the effect of seamlessness on riders’ opinions. All the participants were given certificates as a token of appreciation. Mobility as a Service (MaaS) integrates various transport services into a single mobility service accessible on demand. A MaaS operator facilitates a diverse menu of transport options to meet a customer’s request, be they public transport, ride-, car- or bike- sharing, taxi or car rental/lease, or a combination thereof. For the user, MaaS can offer added value by using a single application to provide access to mobility with a single payment channel instead of multiple ticketing and payment operations. This chapter focuses on what MaaS is and how to achieve it in developing countries from the perspective of multimodal transport in the smart city. When we look at Figure 3-1 Auto-rickshaw with a flag of the experiences the reality of developing countries, it is easy to find many difficulties in improving urban mobility. There is a mismatch between rapid economic growth and enhancing urban nobilities. For example, the growth of vehicle registration is dramatically high because of the rapid growth of its economy. On the other hand, infrastructure development funding is not enough to support such massive demand for vehicle mobility. From this situation, the following negative impact of transportation stands out. (1) heavy traffic congestion, (2) loss of production time, (3) unnecessary fuel consumption, (4) air pollution, (5) increasing accidental risk, and (6) problems with personal health. Figure 3-2 shows the MaaS system total configuration [5]. Inside of the dark color circle is traffic function as MaaS basic function. And each inside function is related to three major applications–municipal support, life support, and tourism support. The MaaS is a total life support system by mobility assist. Therefore, MaaS has various service applications, and a smartphone application is one of MaaS for navigation, transport selection, and ticketing for transportation. In terms of MaaS application, several new companies have started a kind of MaaS plant form system using a smartphone application for searching for proper transportation selection. In our program, we also demonstrate a multimodal application (MMA), and some example snapshots of MMA are shown in Figure 3-3. Once residents input their destination, MMA calculates potential transportation selection on their smartphone. Among those transport selections, users can easily find the most appropriate mode based on their interests, such as fast- reaching destinations, economical transportation, or easy access transport. In general, such a MaaS application is suitable for a trial, but there are significant challenges in promoting a business. The major transportation in India is motorcycles, auto-rickshaws, then private vehicles and local city buses. After this transportation, some major cities will implement BRT or Bus Rapid Transport and Metro railway. It is difficult to define what kind of level is good enough for transportation availability for MaaS. At least, there should prepare some public transportation capability. Figure 3-2 MaaS system total configuration [5] lt is famous Delhi Metro and that of other major cities such as Chennai, Gurgaon, Mumbai, Jay poor, Pune, etc. In Ahmedabad, there are two lines under development. Public transportation is so-called mass transit which means it carries a lot of passengers' transport capability. There are some operation management points for these mass transit expansion and usability. (1) time punctual operation, (2) easy ticketing, (3) connectivity with other transportation, including last one-mile connectivity, and (4) comfortability. Those operational management points can manage by a smartphone application. Traffic management is a crucial function of urban mobility services. There are two main parts–one is traffic information distribution, and the other is traffic control such as traffic signals—and all management items should be under traffic policy. In general, several methods will support traffic information in each region. Variable Message Signs (VMS) will be one of the traffic information delivery systems. Figure 3-4 VMS in Downtown, Ahmedabad Figure 3-4 shows an example of VMS in Ahmedabad. There are also other several methods such as traffic information radio and/or broadcast television service. And smartphone application is generally widely used with navigation system. [1] Weiner, E.: Urban transportation planning in the United States: An historical overview. US Department of Transportation, 1997. [2] Alam, M.A., and F. Ahmed: Urban transport systems and congestion: a case study of Indian cities. Transport and Communications Bulletin for Asia and the Pacific, Vol.82, pp.33–43, 2013. [3] Bhargab, M. and S. Sadhukhan: Urban public transportation system in the context of climate change mitigation: Emerging issues and research needs in India. In Mitigating climate change, pp.75–91. Springer, Berlin, Heidelberg, 2013. [4] Seaborn, C., J. Attanucci, and N.H.M. Wilson: Analyzing multimodal public transport journeys in London with smart card fare payment data. Transportation Research Record, Vol.2121, No.1, pp.55–62, 2009. [5] Hirabayashi, T. and Y. Yamada: Future of tourism 2022-2031. Nikkei BP Publish, Vol.1, p.223, 2021. Chapter 4
More than 90 % out of 16 GHG gas that cause global warming is carbon dioxide. Therefore, reducing carbon dioxide emissions from our activities is the central policy for low carbonization and the most important effort to curb global warming. This carbon dioxide is also emitted by our fundamental activities such as breathing, etc., but the main cause is that it burns fossil fuels consisting of hydrocarbons, an oxidative action that combines carbon with oxygen through the process of obtaining energy. Therefore, Carbon dioxide is emitted from all activities that use fossil fuels as energy. Next to the industrial sector, which requires a lot of energy, and the household sector, which is involved in our daily activities, carbon dioxide emissions from the internal combustion engine- powered transportation sector are high. In particular, transportation such as automobiles is powered by an internal combustion engine that uses gasoline and diesel as its main energy sources, so it is one of a major source of carbon dioxide emissions in cities that require a lot of travel. Even in the case of trains and electric vehicles, which have become increasingly popular in recent years, carbon dioxide is emitted if thermal power is used to generate electricity from view point of life cycle. Therefore, in promoting smart cities, the realization of a transportation system that is efficient, that is, reduces unnecessary movement as much as possible, is the key to reducing greenhouse gas emissions. Currently, cities consume over two- thirds of the world’s energy and account for more than 70% of global carbon dioxide emissions. Thus, achieving smart cities and reducing greenhouse gas emissions in cities is the most important step in combating global warming. Naturally, carbon dioxide is emitted from various activities in industry and homes even in cities, but the proportion of the amount emitted from transportation is very large. In particular, as cities grow in size, they become more active, have a wider range of travel, become more crowded, consume more energy, or emit more carbon dioxide. It is expected two-thirds of the global population will settle in towns and cities by 2050. As shown in Figure 4-1 [1], 90% of this urban growth will take place in less developed regions such as East Asia, Sub-Saharan Africa, and South Asia including India. In such areas, urbanization is largely unplanned, fueling the continuous growth of mostly poor informal settlements. Currently home to some 1 billion people, informal settlements are where the impact of climate change is most acute. This situation will be more serious. Thus, urban development, especially smart city development will play significant role in climate action. Urban density can also create the possibility for a better quality of life and a lower carbon footprint, for example through improved standards in house building and more efficient infrastructure and planning. This is one of the goals of smart cities. But the most important measure is how to achieve an efficient transportation system. This will significantly reduce emissions from transportation, which is the main source of carbon dioxide emissions, and reduce carbon emissions throughout the city. In order to measure the low-carbon effect of the realization of smart cities, it is first necessary to consider how to estimate the amount of carbon dioxide emitted from current cities. Generally, there are two possible methods for estimating carbon dioxide emissions: a bottom-up approach and a top-down approach (Figure 4-2). Due to the variety of activities taking place in cities, it is quite complicated to estimate overall emissions with a bottom-up approach. Basically, the amount of activity for each activity is grasped, and this is multiplied by the emission factors to obtain it. If it can be organized as an inventory for each activity, it can be accumulated and the total emission amount can be estimated, but finding all of them is a huge task. In addition, there is a boundary problem. Since there are various exchanges between cities and other regions, it becomes a difficult problem to define how much emissions are limited to cities. On the other hand, the top-down approach is a method of estimating carbon dioxide emissions based on energy consumption of the city as a whole. For example, if you know the energy consumption of the country as a whole, there is a way to estimate the contribution of the city from the ratio of the inhabitants living in the city and the industry in which it is located. Alternatively, if the carbon dioxide emissions of the country as a whole are estimated, it is possible to estimate the contribution in the same way mentioned above. If the energy consumption of each city is directly known, it is not so difficult to estimate the carbon dioxide emissions from it. If the above statistics are not available, carbon dioxide can be estimate the amount of emissions by analyzing the relationship between some activity and carbon dioxide emissions in other cities and extrapolating the activity of the target city to this relationship. However, when adopting a top-down approach, it is difficult to estimate the effectiveness of individual reduction measures. Therefore, this method can be used when the emission whole is calculated as a frame. The amount of carbon dioxide emitted from each activity can be calculated by multiplying the basic amount of activity by the emission factor, as shown in Figure 4-3. When estimating carbon dioxide emissions in the transportation field, the emission factor is shown as emissions per unit distance, per vehicle, or per person, based on an inventory of emissions. In the case of automobiles, the amount of fossil fuel consumed varies greatly depending on the driving conditions, so the amount of carbon dioxide emissions also changes. In such cases, the emission factor is expressed as a function such as velocity. For such emission factor, after creating a driving mode in each city, carry out chassis dynamometer test according to this mode to measure carbon dioxide emissions, and create a curve showing emission factor by speed. On the other hand, the amount of activity is calculated by the product of traffic volume and mileage. Therefore, it is generally expressed as a kilometer or a person kilometer for each road section. In addition, if the emission source is a function of velocity, the average velocity for each road section is also required. These traffic volumes and average speeds can be calculated by calculating the traffic volume distribution. If you want to consider changes in carbon dioxide emissions due to vehicle driving conditions, apply an emission model to the driving conditions of each vehicle in a micro traffic simulation to estimate changes in carbon dioxide emissions in considerable detail. It is also possible to do. When estimating carbon dioxide emissions at the national or regional level, a top-down approach is employed. Among estimation formulas based on a top-down approach as shown in Figure 4-4, the Kaya formula is the most widely known. In this formula, total CO 2 emissions can be expressed by multiplying (1) CO 2 emissions per energy consumption, (2) energy efficiency of economic activities, (3) economic level per capita, and (4) population. By using this formula, it is possible to explicitly incorporate the population size and economic level of each country into the estimation of carbon dioxide emissions, and it is possible to analyze the impact of economic development on the increase in carbon dioxide emissions. In this formula, except for (2) Energy efficiency of economic activity, each value can be obtained relatively easily by basic statistics such as economic statistics and demographic statistics. It should be noted that (2) Energy efficiency of economic activities is affected by the technological level of each country. When estimating emissions as a city, it is conceivable to apply the Kaya equation and input data for the city bell to estimate, as in the case of estimation at the national level. Alternatively, it is conceivable to divide the estimation results at the national level into the ratio of the economic activity level of each city to obtain the emissions at the city level. The former method cannot be estimated without statistics showing economic activity at the city level. It should also be noted that the energy efficiency of economic activity can vary between cities and other regions, or from city to city. There are many estimates of GHG emissions for cities in the world. For example, Moran, et al. [2] estimated carbon footprint of 13,000 cities including Indian cities based on carbon footprint per capita. Carbon footprints in top 25 India cities are in Table 4-1. Since this is estimation of top-down approach, Figure 4-4 Top-down Approach it is hard to identify the contribution by sectors. On the other hand, Ramachandra, et al. [3] estimate uses a bottom-up approach to estimate GHG emissions in major cities. In Table 4-2, it can be seen that the ratio of emissions from road traffic is 13% to 56%, which varies greatly depending on the city, but the ratio of road traffic to the total emissions is large.
Table 4-2 GHG emission in Major India Cities [3]
[1] National Institute of Corrections: 2018 Revision of World Urbanization Prospects. https://info.nicic.gov/ces/2018/global/ population-demographics/2018-revision-world-urbanization-prospects-0, 2018. [2]  Moran D., K. Kanemoto, M. Jiborn, R. Wood, J. Többen, and K. C. Seto: Carbon Footprints of 13 000 Cities. Environmental Research Letters, Vo.13, No.6, 064041, 2018. [3] Ramachandra T.V., B.H. Aithal and K. Sreejith: GHG Footprint of Major Cities in India. Renewable and Sustainable Energy Reviews, Vol.44, pp.473–495, 2015. Advanced Technologies to Realize Multimodal Transport
Video/image processing, and in particular digital image processing, is the processing of a digital image through some algorithm using a computer. Video sensors have become particularly important in traffic applications mainly due to their fast response, easy installation, maintenance, and their ability to monitor wider areas. Traffic applications like monitoring, prediction etc use Al based methods for video image processing and analysis. In order to develop intelligent automated traffic applications, it is useful to use robust AI based methods. Traffic applications such as vehicle detection, vehicle segmentation, vehicle tracking and counting and speed estimation for careful analysis of traffic congestion can be developed by applying object detection, semantic segmentation, object tracking and object counting techniques. Recent high-performance image processing using AI is based on Deep Learning (DL), which is an end- to-end process of feature extraction and recognition. Various DL architectures have been proposed, but the basic one is the Deep Neural Network (DNN), which is a deep version of the Neural Network (NN). Especially, in the field of image processing, it is effective to use the model called Convolutional Neural Network (CNN). A CNN (LeCun, 1998) is a NN that takes advantage of the image locality, where neighboring pixels have a strong relationship with each other, but the relationship becomes weaker as the distance increases. CNN is inspired by Neocognitron (Fukushima, 1982), a proposal inspired by neurophysiological findings on the primary visual cortex. Both Neocognitron and CNN are hierarchical NNs, consisting of a layer for feature extraction and a layer for obtaining an abstract representation by subsampling. CNN is the basis and probably the best choice for current Al-based image processing. As shown in Figure 5-1, Video/ image processing consists of three main components: object Sensing Technology (that includes object detection, object instance segmentation), object tracking, and object counting. The object sensing module identifies objects in a given frame of video and returns a list of bounding boxes or segmented areas around the objects to the tracker. The tracker uses the bounding boxes to track the objects in subsequent frames. The detector is also used to update the trackers periodically to ensure that they are still tracking the objects correctly. The counter counts objects when they leave the frame or makes use of a counting line drawn across a road. The counting method is based on the object’s regional bounding box marks and the virtual reference line. This technique assumes that the object movement is in a direction. For counting, each detected object in the detection step is assigned with a unique label and tracked until it reaches the virtual line. In this work, we have used five different object class labels, namely auto, car, motorcycle, bus and truck. And all these labels are categorized as vehicle objects and will be used in the counting system. Detailed explanation of the individual modules is given in the subsequent sections. Object detection is a computer vision and image processing technology, which deals with detecting instances of semantic objects of certain classes (like person, car, bus, truck, etc) in a digital image/video. Currently, the most popular approach is to use a CNN to solve both detection and classification tasks. Figure 5-1 Overview of Video/Image processing There are majorly two types of object detection: Two-stage and One-stage. Initial works in this task were all two-stage detectors like RCNN [5], Fast RCNN [4], Faster RCNN [6] and R-FCN [7]. These methods have high localization and object recognition accuracy, but a low inference speed. For real-time detection, one-stage detectors like YOLO [9,10,11], SSD [12] and RetinaNet [13] are used. These methods have high inference speed, but comparatively low accuracy compared to two-stage detectors. This trade-off of detection accuracy vs inference speed made one-stage detectors more practical to use for real-time applications like autonomous vehicles, video surveillance, traffic monitoring, etc.jj IoU (Intersection over Union) It is the extent of overlap between the ground truth from the training data and the prediction from the model. Greater the IoU, better the predictions. IoU is used in both detection and segmentation tasks. In general, an IoU > 0.5 is considered to be good. R-CNN: The Region-based CNN (R-CNN) approach to bounding- box object detection is to attend to a manageable number of candidate object regions and evaluate convolutional networks independently on each Rol. R-CNN was extended to allow attending to Rols on feature maps using RolPool, leading to faster speed and better accuracy. Faster R-CNN advanced this stream by learning the attention mechanism with a Region Proposal Network (RPN). Faster R-CNN is flexible and robust to many follow-up improvements, and is the current leading framework in several benchmarks. Faster R-CNN: We begun by briefly reviewing the Faster R-CNN detector. Faster R-CNN consists of two stages. The first stage, called a Region Proposal Network (RPN), proposes candidate object bounding boxes. The second stage, which is in essence Fast R-CNN, extracts features using RolPool from each candidate box and performs classification and bounding-box regression. The features used by both stages can be shared for faster inference. We refer readers to for latest, comprehensive comparisons between Faster R-CNN and other frameworks. YOLO (You Only Look Once) is one of the single stage object detection algorithms introduced to achieve real-time performance. Our applications like vehicle detection, and counting require getting real-time detections from the video feed. YOLOv4 is the state-of-the-art object detector. It is YOLOv3 + Bag of freebies + Bag of Specials. YOLOv5 is the fastest object detector till date. It is YOLOv3 + Image data augmentation techniques. Achieves more than 300 FPS (faster than YOLOv4). Three kinds of augmentations: scaling, color space adjustments, and mosaic augmentation. Driven by the effectiveness of RCNN, many approaches to instance segmentation are based on segment proposals. Earlier methods resorted to bottom-up segments. DeepMask and following works learn to propose segment candidates, which are then classified by Fast R-CNN. In these methods, segmentation precedes recognition, which is slow and less accurate. Likewise, Dai et al. proposed a complex multiple-stage cascade that predicts segment proposals from bounding-box proposals, followed by classification. Instead, our method is based on parallel prediction of masks and class labels, which is simpler and more flexible. Most recently, Li et al. combined the segment proposal system in an object detection system for "fully convolutional instance segmentation" (FCIS). The common idea is to predict a set of position sensitive output channels fully convolutionally. These channels simultaneously address object classes, boxes, and masks, making the system fast. But FCIS exhibits systematic errors on overlapping instances and creates spurious edges, showing that it is challenged by the fundamental difficulties of segmenting instances. Mask R-CNN adopts the same two-stage procedure, with an identical first stage (which is RPN). In the second stage, in parallel to predicting the class and box offset, Mask R-CNN also outputs a binary mask for each Rol. This is in contrast to most recent systems, where classification depends on mask predictions. Our approach follows the spirit of Fast R-CNN that applies bounding-box classification and regression in parallel (which turned out to largely simplify the multi- stage pipeline of original R-CNN. Formally, during training, we define a multi-task loss on each sampled Rol as L = Lcls + Lbox + Lmask. The classification loss Lcls and bounding- box loss Lbox are identical as those defined in. The mask branch has a Km2 - dimensional output for each Rol, which encodes K binary masks of resolution m × m, one for each of the K classes. To this we apply a per-pixel sigmoid, and define Lmask as the average binary cross-entropy loss. For an Rol associated with ground-truth class k, Lmask is only defined on the k-th mask (other mask outputs do not contribute to the loss). Our definition of Lmask allows the network to generate masks for every class without competition among classes; we rely on the dedicated classification branch to predict the class label used to select the output mask. This decouples mask and class prediction. This is different from common practice when applying FCNs to semantic segmentation, which typically uses a per-pixel softmax and a multinomial cross- entropy loss. In that case, masks across classes compete; in our case, with a per-pixel sigmoid and a binary loss, they do not. We show by experiments that this formulation is key for good instance segmentation results. Tracking gives information about speed, distance and location which helps in solving sub-tasks like speed estimation, vehicle tracking. Tracking more than one object in the video raises several challenges, including correct mapping of identity to each object throughout the video, frequent occlusions, initialization and termination of tracks, and similar appearance of different objects. To address these difficulties/challenges, we have used: 1. Siamese neural networks to correctly map identities to correct objects. 2. Memory module to store state of object for a long time. It helps in re-initiating the state of an object that has lost due to occlusion. 3. Segmentation mask module for an accurate representation of objects. It is a class of neural network architectures that contain two or more identical subnetworks with same parameters and weights. It is used to find the similarity of the inputs by comparing its feature vectors. Euclidean distance is calculated between these two feature vectors to find similarity. Triplet loss function(L) is used to train the network which calculates loss by taking Anchor(A) the original image, Positive(P) the matching image and Negative(N) the non matching image. Network should learn parameters such that the euclidean distance is small if inputs are similar and large if inputs are dissimilar. Where, f(A), f(P), f(N): feature vectors of A,P, N and m is margin which avoids zero solution. For the task of object tracking, we modify siamese neural networks where instead of treating it as a similarity learning network, we treat it as a network which finds cross correlation between the feature vectors of the two sister networks. Figure 5-2 represents the architecture to track multiple objects: 1. We use YOLO object detection for the initial frame only to output bounding boxes of objects of interest. We call them exemplar images. 2. We use a siamese neural network fΘ (ResNet-50 base) to extract features for each object (Feature Map X). 3. For each frame i after the first frame: a  For each object j, we take the region in frame i, which corresponds to the location of the larger crop of the bounding box of j in frame i-1. We call them the search regions. b We then send these search regions into fΘ to obtain features for each object’s search region (Feature Map Y). c We use depthwise cross- correlation (*d) between feature maps of respective objects to obtain an activation map representing the location of an object in frame i (Feature Map Z). d We use fully convolutional nets (hΦ) to obtain a mask for each object for accurate pixel-wise tracking. e bo outputs a bounding box corresponding to the highest score sψ. We maintain an in memory data structure which stores the state of all objects that are being tracked at current instance. We add an object's state if it is new and we remove the object’s state if it does not appear for more than 5 seconds in video (we assume that average time of occlusion is 5 seconds, which may vary based on the situation). This helps to track objects under short term occlusions. The widely applied methods of object counting mainly include object detection and object tracking. The earliest object detection was to extract the moving targets from the image sequence and identify the extracted targets. The object detection method during this period mainly included background subtraction method, frame difference method, and optical flow method. However, the background subtraction method uses the weighted average method for background update, and the effect of background update will affect the accuracy of object detection. Moreover, the frame difference method is greatly affected by the object speed and the time interval of continuous frames. Furthermore, the optical flow method is a pixel-level density estimation, which is not suitable for real-time applications due to its large computation. In recent years, to solve the impact of complicated scenarios towards accurate target detection, machine learning methods and classification methods have been widely used before deep learning becomes the mainstream of computer vision. However, machine learning methods and classification methods have the disadvantages of high time complexity, weak region selection, and poor robustness of manually extracted features. Consequently, deep learning methods are presented for target detection, which demonstrates that features extracted by the deep convolutional neural networks are superior to hand-crafted features. Vision-based object counting is an interesting computer vision problem tackled by different techniques. As per the taxonomy accepted in, the counting approach could be broadly classified into five main categories: counting by frame differencing, counting by detection, counting by density estimation and deep learning based counting. The first three counting methods are environmentally sensitive and generally don’t perform very well in occluded environments or videos with low frame rates. While counting by density estimation follows a supervised approach, they perform poorly in videos that have a larger perspective and contain oversized objects. Density estimation based methods are also limited in their scope of detection and lack object tracking capabilities. Finally, out of all these counting approaches, deep learning based counting techniques have had the greatest developments in recent years. The advancement in their built architectures have significantly improved the vehicle counting performance. In this study, we mainly focus on studying counting methods that are founded on deep learning based architectures. Unlike machine learning, the existing target detection systems based on deep learning can be divided into proposal-based methods, such as R-CNN, SPP-net, Fast R-CNN, Faster R-CNN and Mask R-CNN, and proposal-free methods, such as Single Shot Multibox Detector (SSD) and You Only Look Once (YOLO). Unlike the proposal-based methods, YOLO uses the method of setting the default box and the method of dividing the input image into a fixed grid to predict the target location and classification quickly, which makes the process of training and detecting much faster than the R-CNN series. On the whole, the comprehensive use of the effective deep learning models and dataset is of critical importance to improve the speed and accuracy of object detection. Tirtl device (as ground truth) is used for vehicle counting at IITH main gate. As shown in Figure 5-3, our vehicle counting algorithm complements the Tirtl device. Given a video stream from a surveillance camera, our model would process each frame in the video to provide positions and state information of multiple objects. In Figure 5-4 (a), we track and segment general objects like bus, person, vehicle. In Figure 5-4 (b), we track and segment only cars. All images shown are contiguous frames from a video. We assign a unique identity for each object (which can be observed in two contiguous frames in Figure 5-4 (a) and Figure 5-2) and are represented using a bounding box. For more accurate representation of objects, we also segment the object along the bounding box which is evident from figures. We developed an integrated system for other tasks like, (a) Real-Time Detection of Motorcyclist without Helmet using Cascade of CNNs on Edge-device, (b) smart parking system that helps in identifying the empty parking bays, and (c) Defining Traffic States using Spatio-temporal Traffic Graphs using Ahmedabad Aerial-view Dataset that helps in achieving the final objective of traffic congestion prediction and optimization to reduce vehicular carbon emission. Figure 5-3 Tracking output of general objects. (a) & (b) are two frames in the video Figure 5-4 Tracking output of 7 cars. (a) & (b) are two frames in the video The real-time detection of traffic rule violators in a city-wide surveillance network is a highly desirable but challenging task because it needs to perform computationally complex analytics on the live video streams from a large number of cameras, simultaneously. In this work, we propose an efficient framework using edge computing to deploy a system for automatic detection of bike-riders without helmets as shown in Figure 5-5 and Figure 5-6. First, we propose a novel robust and compact method for the detection of motorcyclists without helmets using convolutional neural networks (CNNs). Then, we scale it for the real-time performance on an edge- device by dropping redundant filters and quantizing the model weights. To reduce the network latency, we place the detector module on edge-devices in the cameras. The edge-nodes send their detected alerts to a central alert database where the end users access these alerts through a web interface. To evaluate the proposed method, we have collected two datasets of real traffic videos, namely, IITH Helmet 1 which contains sparse traffic and IITH Helmet 2 which contains dense traffic. The experimental results show that our method achieves a high detection accuracy of ≈ 95% while maintaining the real time processing speed of ≈ 22f ps on Nvidia-TX1. An intelligent transportation system is the integration of various advanced technologies such as intelligent computing, network communications, visual representation, visual-based analysis, efficient sensor electronics. Since, motorcycles are an affordable and daily mode of transport, there has been a rapid increase in motorcycle casualties due to the fact that most of the motorcyclists do not wear the helmet which makes it an ever-present danger every day to travel by motorcycle. The main contributions of this paper are, a) Design of a robust and reliable method for detection of moving motorcyclists in real-time using convolutional neural network (CNN) under the various challenging conditions, such as viewpoint, illumination effects, weather change, etc., b) Acceleration of the CNN model used to detect motorcyclists in real-time on the limited-resource embedded device. c) Develop lightweight but powerful CNN model for efficient classification of head (i.e. violator) and helmet, with very limited set of training samples. d)Use of an Edge-computing framework to overcome the communication overhead and network latency. Figure 5-5 Block diagram of proposed framework for the detection of motorcyclists without Helmet As described in Figure 5-7, the proposed framework for real-time detection of motorcyclists driving without helmets is able to perform in diverse surveillance conditions. Also, there is a significant reduction in the number of false alarms because of the use of cascaded CNNs. The placement of the detector modules in the vicinity of the capturing devices in an edge-computing framework reduces the communication overhead and solves the issue of network latency. The experimental results show the efficacy of the proposed approach. Outdoor parking lot vacancy detection systems have attracted a great deal of attention in the last decade due to many practical applications. While parking in itself does not seem to be a preeminent concern, we do not realize that it has significant implications when we consider the bigger picture of the entire driving experience and the psychology of the driver together. The following is a non-exhaustive list of the impact a bad parking experience could lead to: (i) Increased emissions of harmful gases due to continual search for a vacant spot for parking. (ii) Increased driver frustration due to inability to find a suitable parking spot that could subsequently lead to issues on the road or elsewhere, such as running a red signal. (iii) Cascading effect of not finding a vacant spot, with each new entrant into the parking area, the confusion and maneuvering space decreases. This creates chaos which can take anywhere from minutes to hours to resolve. (iv) Leads to parking on roads, which again causes congestion on roads, which leads to additional traffic, congestion, and frustration, increasing both pollution and risk of road accidents. The currently existing solutions to manage parking lots are as follows: • Manual Management: Pros: – Simple to implement. It just requires hiring someone to work the parking area. — No dependence on other resources, i.e., power, internet, etc. Cons: lt— Impossible to scale to large and (or) multi-floor parking facilities. — Prone to human errors. – Human limitations, memory, logic, etc. • Sensor Based Management: Pros: – Automatic system, no human management required. – Reliable system, i.e., low error rate. Cons: — Not extensible, new parking areas cannot be added as and when required without bringing new sensors, placing them, and connecting them to the grid, following which software changes need to be made to make it aware of the new spaces found. – Not compatible with existing infrastructure, new hardware in sensors and connection options (wires, wireless, etc.) will be required. As shown in Figure 5-8, we propose a computer-vision solution to guide the drivers attempting to park their vehicles using computer-detected available parking slots. The proposed solution solves most of the problems mentioned in the solutions as mentioned earlier while keeping most of their advantages. • Computer vision is lightning fast with modern-day processors. • The entire parking system will be automated, requiring bare minimum human intervention, if any. • Completely compatible with existing infrastructure. Cameras already exist in most parking lots. • Extensible seamlessly, as long as the new area is in the camera’s visual field, no new efforts need to be put in it. In the case of the completely new area, cameras may need to be added. Server Side The server side has two main components: • A flask server. • A deep learning model in TensorFlow. The flask server essentially acts as an abstraction layer between the deep learning model and any application that uses the information. In our project, we have created the server so that the application can query the server at a predetermined API call using a URL and a port at which the server has exposed an endpoint. On receiving this query, the server determines the current status of the parking lot using the learned model and thereby sends the summary of this information in the form of a JSON object. The model we have used to demonstrate is a straightforward one. It has the following steps: • The parking lot is segmented into bays, i.e., parking slots. This is ideally done through a segmentation process using a Mask R-CNN, for example. • Proceeding from the above, we do a binary classification of whether a slot is occupied or vacant. The server delivers the summary of the above output to the application.
We have also made an android application to demonstrate how an end-user would benefit from our project. The application essentially queries the server for the status information of the parking lot and, with this information, presents the driver with parking assistance in the most straightforward way. Ideally, using shortest path algorithms like Dijkstra’s, we can let the driver know the nearest position to park. The prototype presented was merely a beginning, and this project has a vast scope for innovation and enhancement. Some immediate measures we thought of are as follows: • Add segmentation to the deep- learning model. • Add real-time communication between CCTV cameras and the server. • Use graph algorithms in both app and server to make parking more systematic and faster. • Implement a GPS so that as soon as the driver approaches a parking lot, the parking lot can be identified and the server can be queried strictly for this parking lot automatically, finally delivering a notification to the driver's device. • Add security measures in which sensitive information like the license plates and faces can be auto-blurred. As described in Figure 5-9, Different types of traffic states (a) clumping - the vehicles bunch together rapidly, (b) neutral - the vehicles maintain the same relative speed and gap, and (c) unclamping- the vehicles disperse away rapidly. Intersections are one of the main sources of congestion and hence, it is important to understand traffic behavior at intersections. Particularly, in developing countries with high vehicle density, mixed traffic type, and lane-less driving behavior, it is difficult to distinguish between congested and normal traffic behavior. In this work, we propose a way to Figure 5-8 Proposed architecture of Smart parking system understand the traffic state of smaller spatial regions at intersections using traffic graphs. The way these traffic graphs evolve over time reveals different traffic states - a) congestion is forming (clumping), b) congestion is dispersing (unclamping), or c) traffic is flowing normally (neutral). We train a spatio-temporal deep network to identify these changes. Also, we introduce a large dataset called EyeonTraffic (EoT) containing 3 hours of aerial videos collected at 3 busy intersections in Ahmedabad, India. Our experiments on the EoT dataset show that the traffic graphs can help in correctly identifying congestion- prone behavior in different spatial regions of an intersection. Spatial regions considered for annotation at each of the three intersections (Paldi, Nehru Bridge and APMC) in the EoT dataset. Red denotes clumping, yellow denotes neutral, and blue denotes unclamping. Each way is denoted by a number. Intersections are a major cause of congestion in urban networks especially in the case of lane-less mixed traffic where a large number of smaller vehicles bunch together at stop signs in an irregular fashion. Determining the level of congestion at intersections is mainly performed in existing works by counting the number of vehicles [1]. While this is suitable for lane-based traffic, these methods are not applicable to high irregular traffic density with varying sizes of vehicles in developing countries like India. Furthermore, there is a high propensity for lateral movements and low gap maintenance in such traffic that may indicate congestion but may actually denote normal traffic flow. The aforementioned issues make it imperative to define a set of traffic states specifically for understanding mixed lane-less traffic. Hence, in this work, we propose a novel characterization of traffic states using traffic graphs. The evolution of these interactions can be best represented using traffic graphs [2] that change over time. Particularly, we observed that the traffic graph structure of a particular spatial region changes when too many vehicles congregate or disperse in a short span of time. The temporal pattern of changing neighbors is best represented using the adjacency matrix of the traffic graph. So, we propose to learn the spatio-temporal pattern of these adjacency matrices using a network consisting of Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) units. In this work, an approach to identify traffic states in lane-less traffic using temporal changes in the traffic graph was proposed. We showed that a spatio-temporal CNN-GRU network trained on the adjacency matrix of a traffic graph can identify clumping, unclumping, and neutral traffic states in various spatial regions of an intersection. Further, we showed the effectiveness of the proposed approach on a large annotated aerial dataset called EyeonTraffic that covered 3 intersections in Ahmedabad, India. In the future, we would like to apply this approach to predict the onset of congestion by observing the changes in traffic behavior. Figure 5-10 Annotation at the three intersections in the EoT dataset [1] Y. LeCun, L. Bottou, Y., Bengio, and P. Haffner (1998) Gradient-based learning applied to document recognition, Proc. IEEE, Vol. 86, No. 11, pp.2278–2324. [2] K. Fukushima and S. Miyake (1982) Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position, Pattern Recognit., Vol. 15, No. 6, pp. 455–469. [3] Roy, Debaditya, K. Naveen Kumar, and C. Krishna Mohan. "Defining Traffic States using Spatio-temporal Traffic Graphs." In 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), pp. 1–6. IEEE, 2020. [4] Singh, Dinesh, C. Vishnu, and C. Krishna Mohan. "Real-Time Detection of Motorcyclist without Helmet using Cascade of CNNs on Edge-device." In 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), pp. 1–8. IEEE, 2020. [5] P. Chakraborty, Y. O. Adu-Gyamfi, S. Poddar, V. Ahsani, A. Sharma,and S. Sarkar, "Traffic congestion detection from camera images using deep convolution neural networks," Transportation Research Record, vol. 2672, no. 45, pp. 222–231, 2018. [6]  L. Zhao, Y. Song, C. Zhang, Y. Liu, P. Wang, T. Lin, M. Deng, andH. Li, "T-gcn: A temporal graph convolutional network for traffic prediction," IEEE Transactions on Intelligent Transportation Systems, 2019. [7] Y.-B. Lin and C.-P. Young, "High-precision bicycle detection on single side-view image based on the geometric relationship," Pattern Recognition, vol. 63, pp. 334–354, 2017. [8] Wang, Qiang, Li Zhang, Luca Bertinetto, Weiming Hu, and Philip HS Torr. "Fast online object tracking and segmentation: A unifying approach." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1328–1338, 2019. [9]  Li, Bo, Junjie Yan, Wei Wu, Zheng Zhu, and Xiaolin Hu. "High performance visual tracking with siamese region proposal network." In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR), pp. 8971–8980, 2018. [10] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once: unified, real-time target detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1804–1812, 2016.
Vehicle detection is the essential technology to monitor traffic statuses such as traffic volume, travel speed, and traffic density in a city. Traditionally, a Loop-coil detector or ultrasonic detector has been used as the vehicle detector to monitor traffic states. However, the cost of installing such a vehicle detector is costly. So, vehicle detection by a Closed- circuit television camera (CCTV) is one of the practical approaches. The performance of the camera has improved, and even inexpensive cameras can now shoot with high resolution. Also, identifying a vehicle from the video image became reliable because of the recent development of image processing technology. Besides, the image data compression technology and transfer technology have improved dramatically. Due to the above technological innovations, CCTV vehicle detection technology has become a very inexpensive and deployable technology. This technology has already been introduced in many developed cities, but some problems could be solved when introduced in developing cities. One is how to detect mixed traffic, including small type vehicles such as an auto-rickshaw, a motorcycle, etc. Since those vehicles, it is challenging to observe each of these vehicles' conditions, as they often run as a group without following the lane. In developing countries, the traffic is highly heterogeneous and often densely populated. With the help of CCTV feed from Ahmedabad, the vehicle detection dataset with classes like motorcycle and auto-rickshaw, in addition to the classes like car, bus, truck, etc were annotated as part of the project. This collection and annotation of data specific to Indian roads will help boost the performance of the vehicle detection model because of the similarity of distributions of training data and testing data (data from Ahmedabad). The project's case study field is the west side of Ahmedabad city of Gujarat state in India, where it is one of the cities rapidly economic grow and heavy traffic congestion by transportation demand. Closed-circuit television (CCTV), also known as video surveillance, uses video monitoring cameras to transmit a signal to a specific place on a limited set of monitors. It differs from broadcast television in that the traffic signal is not openly transmitted, though it may employ point-to-point (P2P), point-to- multipoint (P2MP), or mesh wired, wireless links. Though almost all video cameras fit this definition, the term is most often applied to those used for surveillance in areas that may need monitoring, such as banks, stores, and other areas where security is needed. In this program, we installed a 360-degree direction local controlled movement with a 4K resolution camera as shown in Figure 6-1. In this section, programs were developed to draw reference lines and detect vehicles in each frame. The programs allow users to draw reference lines in the first frame of a video file, programs were developed to provide threshold values to sort out potential detected objects. The outputs of a processed image are rectangular bounding boxes and the score of those boxes. The score means a confidence level for a detected object, i.e., how confident YOLO is that the box contains a valid object. This value lies in the range of 0 to 1. The higher the score value, the higher the confidence that an object is indeed an object of interest. The bounding boxes are processed using a non-maximal suppression method in YOLO. The controlling factor in this process is a threshold value (a factor to screen out bounding boxes with low confidence levels), which means that bounding boxes having a score value higher than threshold are accepted for further processing. Those that have values below threshold are Figure 6-1 Monitoring Camera, 360-degrees 4K Camera (photo by Zero Sum Ltd.) eliminated. The sorted bounding boxes are sent to the tracker program to track detected vehicles. In this step, the sorted bounding boxes are tracked frame by frame. For the tracking process, Kalman Box Tracker was used. In this research, programs were developed to draw a centerline on each bounding box. Since a video file consists of thousands of frames, an object must be tracked from one frame to another to determine its direction of movement. This tracker compares the current frame with the immediately previous frame using the pixel variance of the frame. When it finds a similarity in pixel values, it updates the object (i.e., bounding box) and memorizes it for consideration in the next frame. Then, it compares the updated frame to the next frame. The tracker titles each tracked bounding box by a numeric value such as 1, 2, 3, etc. In this way, the tracker tracks an object from frame to frame. Rectangles are drawn around each tracked object on the computer screen. The tracked rectangles are used for vehicle counting. Vehicle counting plays a significant role in vehicle behavior analysis and traffic density detection for better traffic optimization. Accurate estimation of the number of vehicles on the road is an important endeavor in the intelligent transportation system (ITS). An effective measure of on-road vehicles can have a plethora of applications in transportation sciences including traffic management, signal control and on-street parking. In the earlier days before the rise of machine learning, the process of vehicle counting was done manually. It was performed by a person standing by the roadside; using an electronic device to record the data using a tally sheet. In some cases, the person may do the counting by observing video footage captured by city cams or closed-circuit television (CCTV) placed above the road or highway. Although the manual method provides high accuracy, it requires an extensive amount of human resources. Besides, it tends to be error-prone, especially on severe traffic flow and multiple road lines. Therefore, manual calculations are usually performed with only a small sample of data, and the results are extrapolated for the whole year or season for long-term forecasts. Existing sensor methods and the traditional image processing method have the problems of difficulty in installation, high cost, and low precision, which resulted in serious road damage, expensive information construction, and poor vehicle counting accuracy. Therefore, it is of great theoretical and practical significance to make full use of existing monitoring resources and apply the methods of deep learning and computer vision to study the video-based vehicle counting method for traffic monitoring and traffic optimization. The vehicle counting system is made up of three main components: a detector, tracker and counter. The detector identifies vehicles in a given frame of video and returns a list of bounding boxes around the vehicles to the tracker. The tracker uses the bounding boxes to track the vehicles in subsequent frames. The detector is also used to update the trackers periodically to ensure that they are still tracking the vehicles correctly. The counter counts vehicles when they leave the frame or makes use of a counting line drawn across a road. The counting method is based on the vehicle regional bounding box marks and the virtual reference line. This technique assumes that the vehicle movement is in a direction. For counting, each detected vehicle in the detection step is assigned with a unique label and tracked until it reaches the virtual line. In this work, we have used five different class labels, namely auto, car, motorcycle, bus and truck. And all these labels are categorized as vehicle objects and will be used in the counting system. After that, each vehicle position is checked whether it has crossed the horizontal reference line (red line) at the y-axis as drawn in figure. If it passes the line, then it will be counted as one. Figure 6-2 is the output on the Paldi junction in Ahmedabad for an interval of 15 sec during the night conditions, red line is the reference line and vehicles are counted from both the directions and the value is updated at the left topmost corner. Figure 6-3 is the output on the IIT Hyderabad main gate for an interval of 15 sec, the two red lines are the reference lines at both the directions and vehicles are counted from multiple directions and the value is updated at the left topmost corner. Figure 6-4 shows the vehicle distribution for 20 min at Paldi junction during the peak interval of the day and also the pie chart explains the vehicle type distribution during that interval. Motorbikes contribute to the highest occupancy of 46% on the road followed by Auto which contribute 29% of the road traffic during the peak interval. Based on the results of this chapter, we are considering the deployment of our system to other cities as a future task. Secondly, it is necessary to deal with the night-time problem of systems using vehicle detection by image sensors. In the following, we first explain the possible problems in applying the method described in this chapter to other cities. Next, we present two methods that are generally considered to be effective in addressing this problem, based Figure 6-2 Vehicle counting on Paldi junction data (Ahmedabad) for reference line Figure 6-4 Vehicle count distribution with multiple vehicle types at Paldi junction on simple experimental results. Furthermore, we present the results of using ToF sensors as one of the solutions to the problem of vehicle detection at night by image sensors. Expansion to other cities The performance of CNN is highly dependent on the quality and quantity of training data. Because of the specific appearance of the target environment, it is more important to prepare data that matches the target than to prepare general large-scale data. However, the cost of preparing enough training data is a major problem. There are two techniques, called Transfer Learning and Data augmentation, they may be useful in extending the results of this case study to other cities. These are approaches that can be applied to a variety of methods without major changes to the framework. A simple experiment will show the effectiveness of these techniques. In order to achieve the best performance in traffic image analysis and traffic measurement, it is useful to have a sufficient amount of training data in the target environment, but the cost of this task is prohibitive. To reduce this cost, we have tested the effectiveness of transfer learning for training a CNN. Transfer learning is a method of efficiently and effectively training a model to fit a new task or data (domain) by reusing the results of training on another related task or data (domain). Typical transfer learning in DNN has two main approaches: one is to retrain the parameters of the whole model Figure 6-3 Vehicle counting on IITH data for multiple directions (Fine Tuning), and the other is to learn only the parameters of the output layer, which is newly prepared for a new task or data. In this verification, we adopt M2Det [ 1 ], which has been proposed as a robust object detection model for small objects. M2Det consists of a backbone network, a multi-level feature pyramid network (MLFPN) and a prediction layer. M2Det is characterized by a multi-level feature pyramid (hierarchical structure of feature maps), which consists of multi- scale and multi-level feature maps. By training M2Det on large-scale data, backbone network and MLFPN of M2Det, are expected to acquire feature extraction methods that are generally effective for object detection, including vehicle detection. Therefore, we first train M2Det on large scale data for generic object detection. Then, a vehicle detection model tuned to the target junction in India is built by transfer learning, where only the prediction layer, which is the output layer of M2Det, is trained using a small-scale data set of the target junction. The M2Det model, which is trained on the COCO dataset [2] for generic object detection consisting of 80 classes, is transfer learned under two different conditions. One condition is trained using a large-scale dataset, the India driving dataset (IDD), and the other condition is trained using
output by the system. The correct results are the out results minus the incorrect counting. The results suggest the effectiveness of using small data sets created in the target environment rather than large-scale data sets. Data Augmentation is one of effective methods to increase object detection performance. It is almost impossible to manually collect enough numbers of image data to achieve object recognition engine, because the data scale often could be several thousand or more. In outdoor object recognition like vehicle and pedestrian recognition, the “appearance” changes remarkably depending on the sunshine conditions, in addition to the movement of the object itself. It is costly and not realistic to learn all vehicle data corresponding to this change. For the issue, the data augmentation is applied. In it, artificially adding deformation according to the expected change to the seed image and inflating the training data is used as a seed of data acquired very coarsely for the change. We focused Yolo V3 [4], well known object detection and classification method, and apply it to detect auto-rickshaw with using the data augmentation. We compare the detection accuracy (recall and false positive rate) between learning auto-rickshaw with very few 250 samples and learning it by applying data augmentation, where rotated and brightness changed data is artificially generated up to 17,800 samples. Figure 6-5 show an example of the augmentation for the rickshaw image. Table 6-3 shows the our own small-scale dataset (Paldi). IDD [3] is a vehicle detection dataset provided by Indian Institute of Information Technology, Hyderabad. The Paldi dataset was annotated by us using 100 images from the video of the target Paldi junction. Table 6-1 shows the number of vehicles in each dataset. The result of the vehicle counts for each training condition on a 30-minute video of the Paldi junction are shown in the Table 6-2. The out results are the vehicle count results
Figure 6-6 Example of Improvement for Auto-Rickshaw Detection Figure 6-7 A New Detected Rickshaw Examples result. As expected, the recall ratio increased from 46% to 70%, and the false positive rate decreased from 2.5% to 1.6% simultaneously. In this experiment, number of correct auto- rickshaw, should be detected, is 82. And, detected number for small data and augmented data are 38 and 58, respectively. At the same time, false detect number for both is 1, during detect number for small data and augmented data are 39 and 59, respectively. Figure 6-6 shows the comparison of auto-rickshaw detection. In the Right Result Augmentation is used, and the bottom-left rickshaw is correctly detected. Figure 6-7 shows anew detected rickshaw examples with the augmentation. These rickshaws cannot be detected in small data set. Image-type vehicle detectors are affected by ambient light, e.g. headlights at night. We believe that an effective solution to this problem is the supplementary use of laser ranging image sensors (ToF sensors), which are not affected by lighting. irradiates near-infrared pulse and measures the distance from the time it takes for the laser reflected from the object. Since the near-infrared pulse uses a coaxial optical system that irradiates and receives light at points, it is resistant to the effects of ambient light and can measure distance values day and night. The 3D sensor has a disturbance resistance of 200,000 lux or more for about 130,000 lux of direct sunlight in midsummer, and can be used outdoors at an operating temperature of −10℃ to +50℃. Figure 6-8 shows the installation position and detection range assumed in this study. The installation position was on the shoulder, and the shooting direction was an incident angle of 90 degrees with respect to the vehicle running in the lane. The maximum detection range is 15 m. Calculations show that if the distance from the sensor to the vehicle is at least 2.1 m, it is possible to detect vehicles up to 70 km/h. The background subtraction method of image processing can be used for vehicle detection. Figure 6-9 shows a captured image of the ToF sensor and an image of background subtraction. The distance information to the sensor is expressed in color Figure 6-9 Image of ToF Sensor and Background Subtraction Method Figure 6-10 Detection Result Sample of Lane 3 for the same two-dimensional image as the camera image. The closer the distance, the redder the color, and the farther the distance, the bluer the color. From the background image on the left, the moving object can be extracted as shown in the image on the right by taking the subtraction from the image containing the moving object in the middle. However, due to the characteristics of the sensor, the movement of the background is taken as noise, so we devised ways to eliminate noise, such as preparing multiple back-ground images. Here, the extracted moving object is to be determined whether a vehicle or not. The width of the moving object is calculated as the vehicle length, and those below the threshold are removed. We also check the ground contact conditions at the bottom of the moving object, because the tires are contacted with ground absolutely. A moving object is detected as a vehicle only when the vehicle length judgment and the ground contact condition are satisfied. The vehicle judgment is performed only in the first lane. And the ground contact condition cannot be confirmed in the second and third lane due to the distance is far. Therefore, it is deter-mined whether or not a vehicle based on the size of the moving object. The vehicle detection performance at night (after 21:00) was tested on a one-way three-lane road in Japan. The results of the experiment showed that the detection accuracy of the ToF sensor was 89% on average. An example of detection in lane 3 is shown in Figure 6-10. In particular, the accuracy was 100% in the traffic measurement in the front lane, confirming the effectiveness of the sensors as vehicle detectors. The results show that the 3D sensor is a promising complement to the image sensor, which has a problem of accuracy degradation due to headlights. [1]   Zhao, Q., T. Sheng, Y. Wang, Z. Tang, Y. Chen, L. Cai, and H. Ling (2019) M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network, The Thirty-Third AAAl Conf. on Artificial Intelligence, pp. 9259–9266. [2]  Lin, Y.T., M. Maire, S. Belongie, L. Bourdev, R. Girshick, J. Hays, P. Perona, D. Ramanan, C. L. Zitnick, and P. Dollár: Microsoft COCO: Common Objects in Context, In ECCV 2014, pp.740–755, 2014. [3] Varma, G., A. Subramanian, A. Namboodiri, M. Chandraker, C. V. Jawahar: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments, IEEE Winter Conference, on Applications of Computer Vision, pp. 1743-1751, 2018.
An increase in private-vehicle ownership is one of the primary reasons for pollution and greenhouse gas emissions. According to an article by the European Commission for Sustainable Urban Mobility [1], buses emit 20% less carbon monoxide, 10% fewer hydrocarbons, and 75% less nitrogen oxide per passenger mile compared to automobiles with a single occupant. Day-by-day increasing pollution shows a strong need to promote mass transit. However, as per the Singh [2], the share of public transport in work trips is only 18.1%. One of the key causes for such low share is the lack of comfort. Thus, to promote mass transit, basic conveniences are to be provided. Hence to enhance the comfort in the system, this work targets to improve travel time prediction using a GPS dataset. As shown in Figure 7-1 (a) and (b), passenger buses are mounted with GPS devices to track the speed, distance, elapsed time, and the real-time location in terms of latitude, and longitude. The dataset used for this study is collected from Figure 7-1 (a) GPS Data Logger; (b) Study vehicle used for data collection (b) a stretch segmented based on the traffic interruptions at various points in that stretch. The data is prepared by mapping the GPS coordinates of the bus with the road junction to understand the location of the bus in transit and calculating the actual time the bus spent at various road segments. This information is inputted into various regression models such as Support Vector Regressor (Cortes, et al. [3]), Random Forest (Breiman [4]), Gradient Boosting (Friedman [5]) and Extreme Gradient Boosting (Chen, et al. [6]) to make predictions. The tracked information is also stored in the database for updating the model with new train data. We implement two models, one for the prediction of travel time of the whole stretch second for prediction at various links on the stretch. This information can be sent to the bus stop information display board to notify passengers about the current location of the bus and expected arrival time. A study stretch of 26 km is selected near Hyderabad city on the four-lane divided national highway (NH-65), which is shown in Figure 7-2. The study section is divided into rural and sub-urban categories, which consist of 12 intersections, 13 mid-block openings, and 4 gentle curves. The data is collected for the passenger buses by mounting a high-end GPS data logger, as shown in Figure 7-1. The instrument captures the continuous positional coordinates, speed, distance, acceleration, heading, and slip angle along with the video data at a frequency of 10 Hz. The data collection was carried out for five weeks, ensuring clear and dry weather. Total 92 trip data were collected, comprising 69 travel hours and 2,116 kilometers. The latitude and longitudinal information is used to track the
bus over the selected route and at various junctions on the route. To account the effect of different traffic conditions on bus travel time, the study stretch is divided into segments characterized by important junctions on the route, namely IITH Main Road (I), Isnapur (II), Patancheru (II), and BHEL-X-Road (IV) (Figure 7-2). The parameters used in the models (Table 7-1) are selected based on the travel time variations observed. The notable effect of time of travel can be discerned from the statistics thus, the time of travel is considered a parameter for predicting travel time. Travel time also varies concerning the day of travel being weekday or weekend. As there is no significant trend observed on different weekdays, a parameter called weekday/weekend is used to differentiate between weekdays and weekends. The direction of travel is also considered as a parameter to account the travel time variations due to changes in traffic conditions with respect to the direction of travel. For link travel time, the study stretch is segmented, and a unique ID is assigned to each segment. Along with the selected parameters, the segment ID, distance, and the time required to travel the previous. Input parameters/predictor variables have very high importance on the accuracy of the model. Table 7-2 and Table 7-3 shows the importance and ranking of the features used as input parameters. In the link travel time prediction, the feature importance of distance is evident as it is directly proportional to travel time. However, it is interesting to see the impact of travel time of the previous segment on the current segment. Other than these, time of travel also seemed to be an important feature. As mentioned earlier, the error function used to evaluate the accuracy of models is MAPE. We also use RMSE to evaluate the performance of models. The comparative analysis of the four methods discussed in earlier sections is shown in Figure 7-3 and Figure 7-4. Figure 7-3 shows the performance of models in all the segments. In segments (links) I, II, and IV, we can observe a clear improvement in accuracy using XGBoost. In segment Ⅲ, we can observe that SVR and GB outperform RF and XGBoost, while XGBoost gives a better overall performance, as shown in Figure 7-4. As mentioned in the methodology section, ensemble methods have the power to combine several weak learners to form one strong learner. Gradient Boosting and Random Forest work based upon this principle and thus are stronger than single models like Support Vector Regressor. Using the principles of Gradient Boosting, XGBoost is built with additional custom regularization terms, which makes it more powerful. As per the current literature, less amount of work has been done using the ensemble models for the prediction of the arrival time of buses, especially for Indian traffic conditions. Some literature talks about the comparison of Random Forest and Gradient Boosting for travel time prediction on time series data (Zhang, et al. [7], Gupta et al., [8]). As per the mentioned studies, Gradient Boosting and Random Forest both performed well in predicting travel time. However, Gradient Boosting was able to understand the complex relationship between the parameters giving an overall superior performance to Random Forest. As per the best knowledge of the authors, Extreme Figure 7-4 Results for travel time prediction (10-fold cross-validation) Gradient Boosting has not been evaluated in the present studies on GPS data for bus arrival time prediction. In this study, we explored XGBoost to model travel time on GPS data using various parameters such as weekday, time of travel, road segment on which the bus is running, the direction of running, and travel time of the previous segment, etc. to implicitly learn the traffic patterns. Incorporating the mentioned parameters, the Extreme Gradient Boosting is found to predict significantly better than other benchmark models such as Gradient Boosting Machine, Random Forest, and Support Vector Regression with MAPE between 5.03% to 22.55% for link travel time prediction and 7.35% for prediction at a complete stretch. One issue with Extreme Gradient Boosting is related to optimization of parameters. As discussed in the model-optimization section, the results given by the model are significantly affected by the parameters. The tradeoff between time for training and accuracy of the model is to be considered while selecting the parameters. To summarize, XGBoost has potential to work considerably well in predicting travel time on highways. In recent times when numerous data collection sources are available, it is not trivial to find a specific model that works well on every kind of data. However, ensemble models have capability to combine multiple diverse models to form a strong model. Powerful model like XGBoost can prove very influential in such cases. In this study, the indirect parameters like weekday and time are considered along with time taken on previous segments to address spatial correlation. Exploring the impact of temporal correlation on prediction accuracy by considering time series data along with spatial correlation is the future scope of this work. [1] European Court of Auditors: Sustainable Urban Mobility in the EU: No Substantial Improvement Is Possible Without Member States' Commitment. https://www.eca.europa.eu/Lists/ECADocuments/SR20_06/SR_Sustainable_Urban_ Mobility_EN.pdf, 2020. [2] Singh, J.:  City Public Transportation Development in India. https://www.intelligenttransport.com/transport-articles/21458/ city-public-transportation-india/, Retrieved from 2016. [3] Cortes, C., and V., Vapnik: Support Vector Machine. Machine learning, Vol. 20, No.3, pp.273–297, 1995. [4] Breiman, L.: Random Forests. Machine learning, Vol.45, No.1, pp.5–32, 2001. [5]  Friedman, J. H.: Stochastic Gradient Boosting. Computational Statistics and Data Analysis, Vol. 38, No.4, pp.367–378, 2002. [6] Chen, T., & C., Guestrin: XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM KDD International Conference on Knowledge Discovery and Data Mining, pp.785–794, 2016. [7]  Zhang, Y., and A., Haghani: A Gradient Boosting Method to Improve Travel Time Prediction. Transportation Research Part C: Emerging Technologies, Vol.58, pp.308–324, 2015. [8] Gupta, B., S. Awasthi, R. Gupta, L. Ram, P. Kumar, B. R. Prasad, and S. Agarwal: Taxi Travel Time Prediction Using Ensemble-based Random Forest and Gradient Boosting Model. In Advances in Big Data and Cloud Computing, Springer, Singapore, pp.63-78, 2018. Chapter 8
In many cities of India, shared auto- rickshaws are the predominant mode of intermediate transportation. Auto-rickshaws are three-wheelers used for passenger transport, often classified as Intermediate Public Transport (IPT). Auto-rickshaws have popularity because they provide affordable and convenient mobility for last-mile connectivity. As per the National Sample Survey Office [1], the auto-rickshaws are the most preferred mode of transport in urban and rural India after busses. The Indian Ministry of Road Transport Highways [2] reported that around half a million auto-rickshaws are registered annually. In Telangana, where the capital city of Hyderabad is located, registered auto-rickshaws are 435,507 as of March 2020 [3]. The greenhouse gas emissions in India consist of 70% CO₂ and 30% non-CO 2 (methane, nitrous oxide, F-gas) emissions [4]. Also, these rickshaws contribute around 10% of India's carbon dioxide (CO 2 ) emissions [5]. To compute the impact of these auto-rickshaws on urban and rural air quality and greenhouse gas levels, it is necessary to quantify vehicular emissions. Measurement of vehicle exhaust emissions on the road is crucial for successful air pollution management in the transportation industry. In this work, a portable emission measurement system (PEMS) was used to measure CO2 and CO for three-wheeler auto- rickshaws in urban and rural traffic scenarios under real-world driving circumstances. The study’s findings may be utilized to understand better the emission variables associated with on-road transportation in a particular city or area. Existing policymakers may find the identified emission factors helpful in implementing a traffic control system. The detailed research methodology adopted in this study to capture the emission trends under real-world driving conditions from diesel auto- rickshaws for urban and rural traffic scenarios is shown in Figure 8-1. Sangareddy is a town and the administrative headquarters of the Sangareddy district in Telangana, India, with a population of 72,344 people distributed over 13.70 square kilometers. Its population density is 5300 persons per sq. km. The data was collected over a 14-kilometer- long test route in Sangareddy town, including feeder, rural, and urban routes with varying traffic levels. As illustrated in Figure 8-2, the study stretch has geometric components such as mid-block openings, uncontrolled junctions, and signalized intersections, with no steep slope, as shown in Figure 8-2. The measured emission concentration recorded in % volume from the gas analyzer was converted into grams per second (g/s) by assuming a constant exhaust flow rate. The calculation of emission rate in g/s is shown in Equation 1. Erc = C * Fv * p (1) Erc  : Emission rate of pollutant C in g/s C : Instantaneous concertation of pollutant C in % volume Fv    : Exhaust flow rate in L/sec P : Density of the pollutant in g/L The exhaust flow rate from the auto- rickshaw was calculated based on the engine size and the average engine speed (rpm), as shown in Equation 2. Fv = E × R/2 : Engine size in liters E : Engine speed in revolution R per second (rps) The average engine speed is assumed to be 2500 rpm for the auto-rickshaws. The speed and emission data on urban and rural roads were checked with a normality test. The data do not follow the normal distribution (p < 0.05). Further, a Wilcoxon signed- rank test was conducted pairwise between speed and emission data on urban and rural roads. The results showed a significant difference in emissions between the two road types at a 5% significance level. Based on the above finding, separate models were developed for urban and rural roads. The relationship between the average emission rates of CO 2 and CO with speed for urban and rural traffic conditions is discussed in this section. Firstly, the data was divided into a bin of 5 kmph such that the bin includes all the speed values observed during the entire data collection. The curve fitting is performed using the statistical package of social science (SPSS) software by considering the emission rates as the dependent variable and speed as the independent variable. Figure 8-1 The detailed experimental methodology adopted in this study captures the emission trend The data was fitted with quadratic polynomial function and validated by obtaining the various goodness of fit. Since the graphical fits are highly subjective, numerical fit with the goodness of fit statistics is preferred. All the plots of the emission rates concerning speed for urban and rural traffic conditions followed a second-order polynomial fit shown in Equation 3; the same is depicted in Figure 8-3. y = constant + v 1 x + v 2 x 2 (3) Where, y : the average CO 2 emission in (g/s) : the speed in kmph x v, and v,  : the coefficients. The emission factors are high at very low speeds of 5 to 10 kmph. The emission factors in urban traffic are approximately 1.2 times higher than in rural traffic. The average emission factors of CO₂ were found to be around 5% higher in urban traffic than in rural traffic for a speed range of 5–10 kmph and a speed range of 40–45 kmph, respectively, Figure 8-3 (a) and (b)). The average CO emission factors were found to be approximately 30% higher in urban traffic than in rural traffic for a speed of 5, 20, and 30 kmph, respectively (Figure 8-3 (c) and (d)). The average emission factors of CO were found to be 7% higher in urban traffic than rural traffic for a speed (25 and 35 kmph) and approximately 12% higher in urban traffic than rural traffic for a speed of 10–15 kmph (Figure 8-3 (c) and (d)). Also, the emissions factors for all the speed bands were considerably higher than prescribed emission standards (BS values), as shown in Figures 8-3. As seen in Table 8-1, emission factors obtained from our studies varied significantly from the emission factors given by ARAI (Automotive Research Association of India). The variation in emission factor values may be because ARAI emissions were obtained in a laboratory test using the standard Indian driving cycle (IDC). The variation in emissions may also depend on vehicle age, vehicle maintenance, vehicle kilometers traveled, etc. Thus, the studies which use the laboratory-based emission factor to estimate emission inventories may underestimate the total emissions of CO from diesel auto-rickshaw. The emission factors values obtained from our study were found to be 5.28 and 4.73 times higher than prescribed BS-III and BS-IV emission standards, as shown in Table 8-1. The emission from auto-rickshaws in urban areas is a severe problem Table 8-1 Comparison of the real-world emission factors with emission standards.
*indicates the results obtained from this study that researchers have not received adequate attention to. The present study quantified the emissions from diesel auto-rickshaws for urban and rural traffic conditions. Unlike laboratory tests, the real-world tests conducted in this study measure the emissions in real-time; thus, typical driving conditions were captured for Indian urban and rural roads. The result shows that the CO emission from the diesel auto-rickshaws is significantly higher than prescribed emission standards (BS values). Also, the emission rate for urban traffic conditions is slightly higher than in rural traffic. The findings from the study may be helpful to the local policymakers in implementing a proper traffic management system, therefore improving the city’s air quality. The emission standards prescribed by the Automotive Research Association of India (ARAI) are based on laboratory testing of the vehicles, and there has been limited prominence on real-world emissions monitoring. Thus, it is crucial to verify emission control system performance and durability, not just in laboratory certification tests of new engines but also in the real-world driving conditions, which will be needed for emissions standard formulations. Finally, a combination of the policy decision, including congestion pricing, encouraging public transport services, and carpooling, would be necessary to reduce emissions drastically. Introducing e-rickshaw in urban traffic with proper charging infrastructure could be a possible replacement for fossil fuel-based auto rickshaws to improve the city's air quality. [1] NSSO: Motor Vehicles – Statistical Year Book India 2016. 2016. [2] Ministry of Road Transport and Highways (MORTH): Road Accidents in India 2020. Transport Research Wing of Ministry of Road Transport and Highways of India. Statistics and Programme Implementation, 2020. [3] RTA: Growth of Vehicles. Government of Telangana State Transport Department, 2020. [ 4 ]   Olivier, J.G.J., and J. A. H. W. Peters: Trends in Global CO 2 and Total Greenhouse Gas Emissions: 2020 Report. 85pp., 2020. [5] Climate and Clean Air Coalition, Three wheels United '2018, https://www.ccacoalition.org/en/newsmedia. Retrieved from 2018.
Traffic management technologies are available, such as electric traffic information signboards, "Variable Message Sign" or "VMS," and traffic video monitoring cameras on roads. The VMS is generally used on highways to display traffic conditions such as traffic congestion conditions, warning information like traffic accidents, road conditions, etc. The traffic video monitoring camera detects and collects the traffic volume. VMS was deployed at least as early as the 1960s on the New Jersey Turnpike. The NJ Turnpike’s signs of that period, and up to around 2012, were capable of displaying a few messages in neon, all oriented around warning drivers to slow down: "REDUCE SPEED," followed by a warning of either construction, accident, congestion, ice, snow, or fog at a certain distance ahead. As for Japan, the first VMS system has installed on local roads in 1966 and expanded to use VMS for Metropolitan express highways in 1973. The purpose of VMS is mainly to provide traffic conditions, including traffic jams and accidents, and so on. The system of VMS is the same as that of the original, which means VMS shows the traffic condition information based on monitoring and analysis of the traffic monitoring condition. The system has been upgraded daily according to electronics technology development. The display is changed from a bulb lamp to LEDs with a multi-color diode type. The traffic information is collected through a traffic highway camera, and analysis information is provided by network infrastructure through the traffic control center. The network infrastructure is the most well-developed technology— high-speed transmission and more data capacity to transmit. The VMS used in this program, multi-color LED type display with a 3G cloud Network communication system is installed in the city (Figure 9-1). The display has two sections; No need for a traffic center: . Use internet access from the center through the 3G network. Digital signage capability by the half of display as commercial use with charge. The above feature allows cost- effective VMS system in India. Figure 9-1 Variable Message Sign in Ahmedabad, India Application of Sensing, Networking and Big Data Analysis Figure 9-2 (a)-(e) shows some examples of mobile VMS products in the industry; the others are portable VMS for warning for road works and/ or construction notices for drivers. In terms of road safety, transport policy or software technology is also important, as well as hardware technology. In the previous section, we see several VMS-related hardware technologies. In order to make that hardware technology useful, it is necessary to learn how to use this hardware equipment. Figure 9-3 (a)-(c) shows some examples of road safety operations with mobile VMS. It is necessary to set the appropriate arrow signs, flag-waving notice, color corns setting, etc. This kind of software plays a crucial role in road safety. In other words, road safety hardware preparation is not enough to support road safety operations. Here is one more important factor for VMS. It displays contents to inform several messages and or pictograms for traffic congestion; road works, weather information, strong wing, slip caution, detour message, etc. Figure 9-4 shows Figure 9-2 Highway patrol vehicle and Portable VMS for road works Figure 9-5 Road condition display several display patterns used for mobile VMS. This display pattern is defined by each transportation authority of countries and or road operators in general. Sometimes, it is necessary to determine appropriate display contents by own organization. Therefore, it is important for localization with the local municipalities. In terms of traffic condition display, Figure 9-5 (a) and (b) show one example of a display for road condition. In general, road condition indicates by colors pattern such as heavy congestion in red color, slight congestion in yellow, and smoothness in green. This tree-level road condition is quite common in many countries. Next point is how to share traffic condition to drivers efficiently through VMS. Here is an important to share traffic information not only current condition but also future prediction, even if the traffic condition forecast is not 100 % guarantee. In case of Ahmedabad city for example, typical traffic congestion will occur in the evening time from 17:00 to 20:00 and its location will same area. One of typical traffic condition in Ahmedabad city, especially west side of the city because the west side of city is new business area and highly crowded by shopping mall, restaurants hotels, and business offices. Figure 9-6 shows traffic condition spatial analysis result from 17:00 to 20:00 in October 2020. Figure 9-6 (a) is west side of the city, (b) is traffic congestion by occupancy level at 17:00, (c) is at 18:00, (d) is at 19:00, and (e) is at 20:00. All traffic occupancy is collected by traffic monitoring cameras (CCTV). The most congested condition occurs around 19:00. This condition is almost same during our monitoring observation during 2019 to 2021. In order to identify traffic congested area, it is useful to use spatial analysis method such as Moran’s l parameter. Figure 9-7 shows the result of Moran's I at 19:00. The lower part red shadow area is highly congested rerated area which means these area is most congestion area at 19:00 and there is something special reason of the traffic congestion. This Moran’s l analysis is based on 1 kilo-meter mesh in this area. When size of mesh is smaller, then it becomes clear for traffic congestion related reason. This kind of traffic monitoring and spatial analysis becomes useful for future traffic congestion forecast and finding traffic congestion reasons. This is recommendation for this traffic congestion spatial analysis in traffic control center and or traffic management center in future. Here is a interesting additional traffic congestion analysis example from Figure 9-6 Traffic congestion condition in Ahmedabad in October 2020. Figure 9-8 Shock Wave model for traffic congestion analysis our project. There are two types of traffic congestion which is well- known in traffic theory. The shock wave model is used as Equivalent situation from wide road to narrow road model shown in Figure 9-8. As shown in Figure 9-9, one is (a) Forward forming wave and the other is (b) Backward recovering wave, which are categorized by Shock Wave theory of traffic flow analysis. Also, these two types of traffic congestion are illustrated in Figure 9-9. Forward forming wave means forward traffic stream is congested and this happens at the beginning of traffic congestion occurs. On the other hand, Backward recovering wave means an opposite condition and backward traffic is congested after heavy traffic congestion occurred. In another words, there is a dead-lock traffic congestion in Backward recovering wave condition. In Table 9-1, it shows the typical traffic congestion condition shock wave result based on traffic occupancy parameter. All the data is based on actual measurement in October 2020. From traffic flow theory, the occupancy parameter becomes more than 25% under traffic congestion condition. There are two points of backward recovering wave which are Camera #2 at 22:00 and Camera #11 at 19:00. From this analysis, the followings are concluded. In Ahmedabad, the traffic congestion happens mostly at the evening time frame and the deadlock traffic congestion happens at the dedicated location. This analysis tells us very important message. There is a potential solution of traffic congestion improvement because its traffic congestion occurs at the dedicated time zone frame and dedicated location. And this congestion condition imagines road side parking at the evening time zone and dedicated area. As a summary of this Chapter, VMS becomes common use for traffic management. There are many kind of VMS equipment in market. On the other hand, it is different story for effective use of these equipment. As it is described, software technology for road management is important as well as hardware technology. This software technology comes from each counties experience. In case of developing countries such as India, it is better to learn from experienced county such as Japan. Japan has more than 60 years' experience to traffic management and fighting with traffic accidents and fatality. Even in Japan, traffic accidents and fatality are still issues. Therefore, as a conclusion in this chapter, we recommend to establish local consortium who manages road safety and traffic condition monitoring including display content and road operation center. There is more related information scenario in Chapter 3 MaaS section.
* C < 0 condition Chapter 10
Transport sustainability and its evaluation are essential for urban transport planners and policymakers. In developing counties like India, transportation system improvement, including public transportation, is crucial for economic growth. These countries are facing rapid economic growth, especially for the transportation of human beings and commercial goods. Moreover, due to the limited improvement of road infrastructure development because of unbalancing speed between the demand of goods and material delivery and infrastructure improvement. Their economic growth generates more personal vehicle demand which results in traffic congestion, high energy consumption, proliferate delays and increased pollution levels in cities. Public transportation has more role of transportation to address this issue for more sustainable and safe transportation. In terms of public transportation such as BRT, Light Rail Transit (LRT), and METRO, it is more critical their connectivity for smooth transit. This connectivity generates Multimodal Transport capability. This Multimodal Transport is capable of providing seamless travel to the commuter. This will help the commuter to decide to use the public transport network as a matter of choice. It is equally important that the public transport system be user-friendly so that the commuter uses it voluntarily. The most crucial aspect is multimodal integration; Physical integration, network integration, fare integration, Information integration, and institutional integration. In general, there is no timetable and punctual operation in developing countries. Public transit local station stops are sometimes far from travelers' destinations, and they may wish to use private vehicles. Therefore, the first and last one-mile problems are critical for travelers choosing their vehicle or public transportation. As several studies in other countries like Canada, Australia, and New Zealand, most people in the U.S. are not comfortable walking more than one-quarter mile. Transit experts and consultants believe that micro- mobility is the solution that can bridge this symbolic distance. This message gives us a transportation connectivity design. understandable that even one- quarter mile walks hard under high-temperature conditions. On the other hand, India has good short transit capability like rickshaws at an affordable cost. This short-distance transportation capability is also a key role in multimodal transportation. To create smooth transport connectivity, ICT or information communication technology can help provide appropriate transportation information. In India, ICT, such as smartphones, is a popular personal tool for receiving information as a multimodal application. Figure 10-1 shows the overview of the multimodal APP system. As for Multimodal application, the following function is key. Data acquisition . Navigation function . Provide modal selection . A smartphone-based navigation app named "M2SmartNavi" for Ahmedabad was created. Since Android is the mainstream smartphone market in India, we chose an app for Android. The main functions are as follows. Obtain GPS location information from the user's smartphone. However, location information can only be obtained while the app runs and the navigation is used. Also, considering the battery consumption of the smartphone, we decided to acquire it every second. By continuously acquiring location information, we aimed to collect it as the user’s travel route data and utilize it to eliminate traffic congestion. In that case, if the user's attribute information is also added, it will be an advantage for the user by providing the route according to the individual attribute. On the other hand, it will be helpful for city planning on the administrative side by analyzing the traffic behavior history in detail. Therefore, it is a troublesome operation for the user, but only when the application is started for the first time, the following registration screen displayed, age, whether or not you own a private car, frequency of use of public transportation, etc. About Personal attributes: Nickname, Gender, Age, Profession, Income, Family structure, Educational background About owning a private car / Motor cycle: Figure 10-1 Overview of Multimodal APP System Own or not, Use or not, Vintage, Displacement About frequently used transportation: Usually, Commuting, Traveling, Shopping, Business, Pickup, and Others. The main feature of the Navigation function is to provide route selection to users based on their requirements. The navigation function of this application was developed based on the development environment of HERE’s “HERE SDK for Android (Premium)." The main screen image of the app is shown in the figure (Figure 10-2 (a), (b), and (c)). The navigation user first inputs the destination to the destination. Figure 10-3 Display of eco-index in route selection Figure 10-4 Examples of navigation function in multimodal App As shown in Figure 10-3, any of the following input methods can be used as the destination input method. Selection by free text search . Select from facility genres . Select from history . Selection by tapping directly on . the map Users can now choose the mode of transportation and route from private cars, Auto rickshaws, city buses, BRTs, and a combination of these. As a feature of this application, it is connected to the BRT operation management system, and it is possible to continuously calculate the route based on the real-time driving position information for the BRT. Multiple calculated routes (up to about 10) are presented on the navigation screen, with the distance for each route, estimated arrival time, cost, and eco-index. The eco-index was determined by an original calculation method from the estimated fuel consumption in each mode and the index value of India. To make it easier for users to understand visually, we decided to display the number of leaf marks according to the degree of CO 2 reduction. By switching tabs at the top of the screen, the preferential route can be selected from general road precedence, expressway precedence, and distance precedence by a user. Figure 10-4 shows an example of using M2SmartNavi. Once selected navigation is selected, the user can see the total transit mode to the destination, the real navigation guide on the map screen, and the feedback screen at the end of navigation. So far, about ten people have conducted five field experiments for two weeks separately, and more than 1,000 travel data have been obtained. We tried to visualize and analyze GPS data acquired from the user’s smartphone, so here is an example. ArcGIS Pro from ESRI (Environmental Systems Research Institute) indicates all personal travel records on a map and each person's record by color. An example of each person’s trip is shown in Figure 10-5. In December 2021, a three-week field experiment was conducted with the cooperation of a local university. At this time, the user was asked to record the following items as an activity diary (movement history) during the experiment period, both for personal attributes. Actual time Point of departure Destination Purpose Transportation use Actual degree of congestion Figure 10-6 shows the OD (Origin-Destination) arrow graph displayed on GIS for 170 trips obtained in this experiment. By collating with the activity diary mentioned above, it is possible to visualize by purpose or means of transportation. Actual time Point of departure . . Destination Figure 10-5 User movement history obtained from Multimodal App (each color represents an individual) Figure 10-7 Relationship between trips obtained by the app and traffic volume obtained by the traffic sensor Figure 10-8 Congestion status due to feedback from users . Purpose Transportation use . Actual degree of congestion . This section describes an example of traffic behavior analysis using these data. In Figure10-7 (a), a number of the total trips is shown, and traffic condition is shown in Figure 10-7 (b) by traffic volume and occupancy. These graphs show two peaks of trips in the morning and the evening. This is mainly for commuting from home to the office or school in the morning. And there is traffic congestion occurs by occupancy above 30% level. In the evening, more congestion occurs by more than 40% occupancy. This is unique data in this program which comes from each user’s feedback about their feeling of traffic congestion condition image by personal judgment level between 1 through 5 (Figure 10-8). Figure 10-9 Origin to destination arrow graph (in each feedback at all times) Level 1 means heavy congestion, level 2 indicates congestion, level 3 represents a fair condition, level 4 means smooth traffic, and level 5 means very smooth traffic. From this feedback, travelers feel heavy traffic congestion in the morning while commuting, and even traffic condition in the evening is worse. Figure 10-9 (a) shows the heavily congested and slightly congested trip mapping. One typical heavy crowded trips occur at trips crossing over the river. The gray shadow area is the Sabarmati River in Ahmedabad. This means traffic congestion occurs at each end of the river junction because both sides of the intersection become traffic congestion in each direction for vehicles that cross the bridge. Figure 10-9 (b) shows that smooth traffic travel occurs on one side of the area where the trip does not cross the river. This leads to traffic congestion occurring at the junction of each end of the river, as previously mentioned. This chapter shows multimodal application validness for analyzing traffic conditions and travelers’ feedback for their daily trips. From this experience, users' feedback function through smartphones provides real users’ messages about how they feel daily travel, travel conditions, and road conditions. This information is helpful for civil design and points of improvement for urban transportation. Commercial smartphone-based navigation systems such as Google navigation are becoming very popular these days. One advantage of M2SmartNavi tells us transportation connectivity capability by using BRT operation timing in this case. And there is a CO2 emission reduction indication by the green leaf log in M2SmartNavi, which will have several future capabilities to count CO2 emission reduction efforts from residents and potential incentives when the green leaf count will be used as eco-money in the city. This eco-money has potential for smart parking, public transportation fee, and or goods purchased in the future. To make M2SmartNavi at the commercial level, it is necessary to software maintenance, update the smartphone operation system, expanding other transportation support such as city buses, auto-rickshaw, or taxis. Those operations and maintenance should be handled under a specific city-level organization such as smart city development companies or some transportation consortiums, including transportation stakeholders. This section describes the possibility of utilizing multimodal apps in emerging countries and problems in terms of operation and gives a future outlook. Task for expansion: Use of incentive . Feedback to users Long-term use merit Figure 10-10 Visualization with GIS dashboard tools Task for operation: Support smartphone OS . Software update and version . up maintenance Server support . • Running cost support Alignment with other system: Vehicle dispatch support . Parking reservations . . Payment function Merit for users: Last one-mile support . Seamless connectivity Merit for municipality: . Collect user data Real time visualization . Modal shift and CO 2 reduction . Figure 10-10 shows one of example for visualization of real time transportation condition by using dashboard of GIS, which has been developed in this program.
Lot of traffic related data of different modalities gets generated in transportation setup, especially in urban regions and smart cities. The traffic cameras installed at different junctions capture traffic videos and send them to central servers. There are cameras with additional processing capabilities that can count the number of vehicles at different time instances, and also systems that can detect speed, headway etc. Often users report traffic-related incidents or scenarios through social media. All these data can be helpful to generate several insights about current and future traffic scenarios including but not limited to data-driven identification of traffic bottleneck, seasonality in traffic volume, route planning, and also identifying and alerting about current or future traffic incidents. The traffic related big data can be of various modalities such as video (feeds from traffic cameras), stream of numbers (such as counts reported by traffic counters), text and image (traffic-related posts by users and city governance). Such data can be stored in appropriate storage for further analysis. As part of the project, traffic cameras were set up at different parts of the city. The locations of the cameras are shown in Figure 11-1. The cameras additionally gave information about the number of vehicles that crossed the junctions at specific intervals, vehicle speed, length of the vehicles etc. This information was extracted and analyzed. For example, Figure 11-2 shows the variation of speed and the variation of vehicle length at two junctions, as captured by the cameras. We see that the average speed drops down after 10 am, which is generally true due to work schedules of offices and businesses. The speed decreases further around 3 pm, which is generally not a busy hour. However, we see from Figure 11-3 that the number of larger vehicles (possibly heavy vehicles) increase slightly around that time. These vehicles are generally slower due to the load. This mixed traffic leads to a slowdown in the speed. The plots also corroborate the fact that vehicle speeds are generally higher Figure 11-1 The region considered for the study related to traffic counts
Figure 11-4 Root Mean Squared Error vs actual counts ■ LSTM ■ Secial LSTM on weekends due to lesser traffic on the road. This may also indicate the necessity of deploying traffic personnel on road to regulate the traffic at these hours. Appropriate forecasting of future traffic can help in predicting congestions, which can in turn help in making various decisions. For example, based on this information civic bodies can decide on deployment of traffic personnel, adjust signal timings, and individuals can make appropriate routing decisions. Such actions reduce congestion and the waiting/plying time of vehicles on the road, thereby reducing the amount of emission. However, traffic forecasting, which deals with the prediction of traffic at different future timesteps based on past traffic information, is an extremely challenging task due to the highly complex traffic patterns. The traffic forecasting problem has often been posed as a time series prediction problem, where the input is the traffic volume (e.g. vehicle count) at the different past timestamps (say 7 am, 7:15 am, 7:30 am etc.) and the output is the expected traffic volume at that junction at some future timestamp (e.g. 9 am). These traffic counts can be obtained from the traffic counters (either from the traffic cameras directly) or by analyzing the traffic videos captured from different junctions. Time series modeling can be performed using different techniques. One can use the ARIMA based statistical models for the same or use the recent modeling techniques involving LSTMs and stacked-LSTMs. These techniques only consider the past counts for the future predictions. However, the traffic volume is also impacted by the spatial layout of the junctions, and traffic from one junction is influenced by (and itself influences) traffic at other junctions. Hence advanced methods, that consider spatial information also while making the future traffic predictions can give better predictions. To verify the possibility of making good predictions for future traffic volume, a case study was performed based on traffic data collected from the Ahmedabad city. The performance of different models for this prediction task are shown in Table 11-1. Here, RMSE stands for Root Mean Square Error and MAPE stands for Mean Average Percentage Error. Since both are error metrics, lower values indicate better performance. From the above comparison, we see that the social LSTM that considers the spatial information, and the deep transformer model that considers higher order interaction between counts can predict the traffic volume with more accuracy. The column plots shown in Figure 11-4 and Figure 11-5 indicate that as the count of actual vehicles increases, the misprediction count (RMSE) also increases slightly, however the percentage error comes down considerably. These observations mean that by employing appropriate methods, it is possible to predict the volume of future traffic on the roads. This can help in better preparedness of intelligent decision making for predicted high congestion levels at future timestamps. Macroscopic Fundamental Diagram or MFD is one of key performance indicator to express area traffic states. MFD expresses the interaction between area traffic states which connecting the total number of cars on the road at any given time (the accumulation) with the rate at which trips reach their destinations (the output). The area is set as group of detectors in Figure 11-6. Paldi area (Yellow boundary) is a targeted area of estimation form other three area. Both MFD indicators were estimated with high accuracy in comparison with observed data form traffic detectors in Figure 11-7. MFD is available for local government and its traffic agency to manage and control traffic in the viewpoint of area. Dynamic Inflow restrictions can be applied the areas where traffic is concentrated and it can be said that traffic can be dispersed spatially, leading to efficient use of the road network as stock. Often road traffic congestion can be induced by incidents or events like road accidents, infrastructure damages, rallies, protests, adverse weather events, disabled vehicles, roadway debris, daily rush hours etc. Detection of these incidents on time or ahead of time wherever possible will help the traffic authorities to alleviate road congestion problem, and can help the commuters as they can pre-plan their trip accordingly. As mentioned earlier, several advanced devices such as loop detectors, GPS probe vehicles, cameras etc. installed on transportation network can help detect traffic congestion. But, due to rapid growth of transportation networks the cost of procurement, installation and maintenance of these sensors also increases. On the other hand, with more and more people joining social media and posting information about real information from the ground, obtaining traffic related information from social media has become a possibility. Such information would complement the information which can be obtained from the hardware devices in the traffic network. It is observed that civic authorities as well as general people or groups often publish traffic related data in social media sites like Twitter. The Twitter handles of Traffic Police of several major Indian cities are highly active and give periodic updates and alerts about traffic flow. Users also report about congestions, road blockades, diversions. Interestingly, it has also been observed that often reports or news about future incidents like rallies, processions, marches etc. that can affect traffic are posted in social media well ahead in time. The impact of such events on the traffic can be detected by the cameras, loop detectors and other hardware sensors only at the time of the impact. Active consumption and Figure 11-8 Sample tweets about traffic scenario or incidents filtering of social media data can thus complement the information given by the hardware sensors. As it can be seen from the tweets shown in Figure 11-8, many posts contain information about current situation, situation due to recent past incident and also about future Figure 11-9 Workflow of a system for utilizing social media posts for traffic information extraction events that can impact traffic. Often these posts contain specific location information to get an idea of which region will be impacted. Timely extraction of such posts, and then identification of the nature of impact, time, and location can be helpful for traffic planning. As part of the project, a system was developed to consume social media posts and firstly determine whether they are about traffic incidents. Important fine-grained information from the posts were extracted. The same information can then be broadcast of notified to users. A brief workflow of the system is shown in Figure 11-9. For example, consider the following tweets (real tweets extracted from social media): 1. "All are invited to attend the rally for filing Nomination Papers of Dr Bhagwanth Rao BJP candidate of Hyderabad parliament Constituency on 25/ 3/ 2019 at 9am from Bhagyalaxmi temple Charminar" 2. "#Pedestrians account for 40% of all #road #traffic deaths in #Bengaluru. It is critical to design our #streets for #pedestrians, save lives - @akhilasuri talks to @ChristinMP_TOI" 3. "Surgery of Tumor 23.5 + kg at Gujarat Cancer Research Institute:GCRI Ahmedabad #oncology #surgery #oncosurgery #tumor #Cancer #India #Gujarat #Ahmedabad @GCRI_1972 @civilhospamd @PariseemaD @bonestumor @MoHFW_GUJARAT" Among the above tweets, the first tweet is related to traffic incidents. The third one is not related to traffic incident. Although the second tweet belongs to traffic policy, it is not related to traffic incident. A well trained machine learning system is able to classify such contents. Also from the fist tweet, specific information like event type="rally", date="25-Mar-2019", Time="9 am", and location="Bhagyalaxmi temple Charminar" can be extracted. The mobility performance is able to be provided by Big Data Annalistic. The department, related of traffic management within existing resources, collect, analyze, and summarize highway congestion data and make it available upon request to regional transportation planning agencies, congestion management agencies, and transit agencies. The mobility performance by the department with big data shall be opened not only these management agencies but also to the public. The citizen is ability to know their current traffic state and environmental impact so that they have an opportunity to change their mobility for better mobility of themselves and sustainable choice for better environmental impact. A lot of countries and region has been trying to make a mobility performance report. As a example of California department of Transportation, United State of America, data is collected in real- time from nearly 40,000 individual detectors spanning the freeway system across all major metropolitan areas of California. These big data opened the two ways:Website and reports. The website, called Performance Measurement System (PeMS), is available for related public agencies, private-traffic information suppliers, researchers/engineers and citizen. The accessibility to the big data is enhanced. On the other hand, annual mobility performance report is legislatively mandated by Government Code in the case of U.S.A. The department of transportation, CA, issues annual and quarterly reports, bottle neck mapping and et al through the website. The big data and mobility performance should be recognized as shared wisdom toward smart transport and city in the viewpoint of both public agency and citizen. Smart systems that can help people to take transport related decisions can be helpful for various aspects. For example, on-demand information regarding public transport schedule or current running status, information about congestion along different routes, accurate estimate of travel time along junctions, information about planned or unplanned events or incidents along traffic route etc. can be helpful for people to take transport/commute related decisions. Although this information can be obtained from multiple sources, this information is scattered over different special purpose applications and websites. People tend to use multiple mobile based applications or websites to get transit schedules for different modes of transport. Online schedules in many instances are clunky, involving multi-step input attributes to gain a simple answer. Many have pdf-based schedules or separate links to each metro/rail line. In some instances, there are separate sites for bus and rail. Going to each individual source according to the exact information need, and forming an appropriate instruction by filling out input fields in a web-based interface or app (e.g. for finding train schedule or running status), or searching in a map (e.g. finding nearby points of interest), or going through large text to figure out (e.g. prior information about traffic diversion in a route, pdf-based schedule) requires lots of effort from the user. It is desirable to streamline these different types of activities and bring them under one common interface where users can seek information in a natural way through conversations. Due to the ease of interactions, such conversational systems will make the task of information seeking easier for everyone, and especially for non- tech-savvy users. Moreover, it will save the user from taking multiple steps (opening the app/website, entering specific search query in the form expected by the service etc.). Getting timely information about public transport services can help towards better adoption of public transport. Efficient route finding and alerts regarding traffic related incidents can help people from taking appropriate routes thereby reducing congestion and traffic delays. Hence, development of such AI-powered conversational systems for smart transportation can be beneficial for implementation of smart cities and urban transportation systems. Towards this goal of developing a Al-powered conversation system for transport queries, suitable dataset is required. Unfortunately, there are no datasets for this purpose that fits the necessity of a multilingual country like India, where people prefer communicating in regional languages. Often in a single utterance there are both regional language and English language words. Handling such code-mixed utterances in a conversation requires special attention. As a part of the project, we have created a dataset and also an experimental system for a transport domain conversational system. We call the dataset as mTransDial. The system can address user queries belonging to multiple categories such as place-search, train-search, distance-search, traffic etc. A brief description of the query intents are provided below: Place_search: It contains queries for searching nearby Points of Interest like petrol pumps, toll plazas, shops, restaurants etc. Train_search: It contains queries to find bus/train/metro from a source to destination place. Distance_search: It consists of queries to find distance from a source to a destination, and time required to reach destination. Traffic: Queries about traffic information and travel alert on a particular place/city are added under this intent. Greeting: This intent is added to wake up the dialog assistant and has queries like "hi", "hello" etc. Thank you: This intent is for the dialog system to understand that the user has got the answer and the dialog system can check the need for further assistance. Goodbye: Queries in this intent are to end the conversation.
Table 11-2 A few sample queries from dataset and the corresponding English translations OOS: These are the out-of-scope queries and are added in the dataset so that our domain specific dialog system is fault tolerant to out of domain queries. A few sample queries from the dataset, from the above intents are shown in Table 11-2. We expect the system to receive queries where non-standard spellings are used (like plz for please, tym for time etc.). To assist in the development of road transport domain dialog system, we created a prototype that shows the usability of mTransDial in this domain. To develop this prototype we use Rasa framework, an open- source machine learning framework to develop conversational chatbots. This framework has two components (a) Rasa NLU responsible for understanding the user intent and extracting entities and (b) Rasa Core which decides the set of actions to be performed based on the previous user inputs. We used a subset of the English data to build this prototype. Based on Rasa NLU, we annotated this data subset with the following entity information: Source_loc, Destination_ loc, Near_loc, point_of_interest. The Source_loc and Destination_loc entities are to be extracted if the query is of Train search or Distance_ search intent class. For Place search intent we extract Near_loc, Figure 11-10 Sample response for train search query Figure 11-11 Sample response for place search query point_of_interest entities to search for the nearby point of interests around the location provided by the user. From Rasa NLU pipeline we used CRF entity extractor to extract the relevant entities from the intents. After intent classification and extraction of entities we performed relevant sets of action based on the intents. If the classified intent is Place_search or Distance_search we call an external map-based API (here, MapmyIndia [https://www. mapmyindia.com]) to find the points of interest near to the Near_loc extracted or to find distance between Source_loc and Distance_loc. If the classified intent is Train_search we use GTFS (General Transit Feed Specification, a common format for public transportation schedules and associated geographic information) to fetch the relevant bus/metro/ train schedule. Figures 11-10 and Figure 11-11 show the responses of the system for train search and place search queries. It can be seen that the system can handle user queries posted in natural language and provide appropriate responses. In this chapter we assess the use of big data analysis for understanding traffic flows and demonstrate how this can be used for providing appropriate intervention suggestions for decision making at multiple levels. It is understood that lowering the congestions will lead to lesser carbon emission. Making people better informed about the current and future traffic can make them plan accordingly. Being properly informed about public transportation will also increase their trust on the transport system and may motivate them to adopt public transportation. At a higher level, the city authorities can use insights from big data analytics for civic and traffic planning which can lead to both short- and long-term benefits to the traffic infrastructure, and congestion handling. Chapter 12
Intersection crashes, particularly right-angle crashes play a significant role in traffic accidents. According to the Indian Ministry of Road Transport and Highways, 449,002 road crashes were recorded in 2019, out of which 28% of the crashes occurred at the intersections [1]. Unsignalized intersections account for 20.6% of total intersection crashes and most of these resulted in serious injuries or fatalities. Crashes at unsignalized intersections mainly occur when a vehicle on minor road attempts to enter or cross the intersection with an unsafe gap condition. Poor sight distance, wrong judgment in gap acceptance by minor road vehicles, and priorities not being respected by the road users are the causes for the crashes at unsignalized intersections. To reduce frequency and severity of crashes and enhance road safety, Intersection Conflict Warning System (ICWS) is found to be effective. ICWS consists of an activated warning sign and sensors which detect the vehicles approaching the intersection. It warns the drivers on the minor road about presence of vehicles on major road and informs the drivers on the major road about presence of vehicles on the minor road. This situation results in reduced conflicts as drivers get alert about the possible approaching vehicle, resulting in lower intersection approach speeds and improved driver gap acceptance. This chapter shows to evaluate the performance of ICWS for a typical uncontrolled intersection by examining the responses of major road drivers towards the designed warning system. Different scenarios were designed to assess the behaviour of the drivers on the major road approach and driving performance measures such as speed and acceleration were evaluated. The experimental methodology for evaluating the response of major road drivers towards intersection conflict warning system is shown in Figure 12-1. The study area selected is of 2.1 km road stretch starting from Figure 12-1 Methodology for evaluating ICWS at uncontrolled intersections IITH hostel circle (point A) to the main gate (point B). It consists of uncontrolled intersection, roundabout, and side roads. Intersection conflict warning system was installed at a four-legged uncontrolled intersection, which is 230 meters away from the hostel circle (point A). A loop detector was installed on a minor road and a warning signboard installed on the major road. Twenty-nine participants with age ranging from 22 to 57 years were recruited for this study. Socio demographic characteristics such as age, gender, education qualification, driving license and occupation were collected. Four scenarios were designed to evaluate the response of major road drivers towards intersection conflict warning system. In the first scenario, no vehicle was present on the minor approach and therefore warning sign was not activated on the major road (Figure 12-2 (a)). In the second scenario, crossing vehicle was present on minor road and warning sign was not activated on the major road. In the third scenario, crossing vehicle was present on the minor approach and entering traffic warning signal was given to the major road approach as shown in the Figure 12-2 (b), however no prior information was given to the participant driver regarding the entering vehicle from minor approach. In the fourth scenario, the participant driver was educated about the deployed warning system and was asked to drive through the intersection with entering traffic warning signal on. The fourth scenario was designed to assess the drivers understanding of the warning system by examining their responses towards the system. A passenger car instrumented with video box was used for collecting the data. The video box (v- box) consists of a GPS receiver that records the position movements at 10 Hz and has two video cameras for recording videos as desired. One camera fixed to the windscreen records the road features and moment when the warning sign is activated. The second camera placed below the steering wheel records the driver's brake movements. Figure 12-3 represents the image of an instrumented vehicle used for the study. Participants were asked to sign on the consent form before they start the experiment. Each participant has to perform four test drives in order to provide responses to the four different scenarios. At the end of the experiment, participants were asked to fill a post signage questionnaire form to assess the degree to which the warning system helped them in crossing the intersection safely. Further, they were asked to complete a signage questionnaire in which they have to rank the warnings signs that they think suitable for placing near uncontrolled intersection from better to worse.
Drivers speed and acceleration profiles were extracted at a distance of 80 meters from the uncontrolled intersection. One way ANOVA was employed to study the statistical differences in driving performance against three scenarios. A post-hoc test Tukey's Honesty Significant Difference was conducted to determine any interaction exists between the two scenarios. A significance level α = 0.05 was considered in the study. To assess the best warning sign from the signage questionnaire form filled by drivers borda count method was employed. The summary statistics of driving performance measures and one-way ANOVA results are shown in Table 12-1. A statistical difference was observed among four scenarios (F = 9.30, p = 1.52E-05) for mean speed. The post comparison revealed that the difference between the mean speed of scenario 2 and scenario 4 (p = 0.0001) as well as the difference between the mean speed of scenario 3 and scenario 4 (p = 0.002) was statistically significant. This demonstrates that the drivers with a better understanding of the warning system resulted in lower mean speed in scenario 4 compared to scenario 2 and scenario 3. There was no significant difference observed between the mean speed of scenario 2 and scenario 3. The reason could be that drivers did not pay much attention to the warning system in scenario 3, and the mean speed for scenario 2 and scenario 3 was almost the same. A statistical difference was observed among four scenarios (F = 9.24, p = 1.62E-05) for mean acceleration. The post comparison test revealed that the difference between mean acceleration of scenario 2 and scenario 4 as well as the difference between the mean acceleration of scenario 3 and scenario 4 was statistically not significant. Figure 12-4 represents the boxplot of mean speed of all drivers for four scenarios. The quartiles of the box presented here are the 15 th and 85th percentile, and median is 50th percentile. The vertical line in the figure represents the intersection where a minor road vehicle enters. From the figure we can observe that the mean speed of all drivers for scenario 1 was almost constant. For scenario 2 there was a slight reduction in speed when they approached the intersection. In scenario 3 there was a reduction in speed compared to scenario 2 when they approach the intersection. In scenario 4 drivers tend to reduce more speed compared to scenario 2 and scenario 3 when they approach the intersection. This indicates that the drivers with a better understanding of the warning system could respond before they approach the intersection and can avoid sudden braking at the intersection. A post signage questionnaire form consists of two sets of warning signs i.e., dynamic and static warning signs. In dynamic warning signs when a vehicle is detected by the sensor, warning light starts blinking and message will be displayed. In static warning signs when a vehicle is detected by the sensor, only warning light blinks and message was already displayed. Four different warning signs were designed for both dynamic and static, and participants were asked to rank the signs from best to worse. Figure 12-5 represents the image of a signage questionnaire form. A ranking analysis was carried out to identify the best warning sign based on the ranking order provided by drivers in the signage questionnaire form. Borda count method was considered to evaluate the rank data. In the dynamic warning signboard, the first sign got more points, which has a LED light and message to be displayed as ‘ENTERING TRAFFIC’, and a sign symbol representing a four-legged intersection with vehicles approaching on the minor road. For static sign warning signboard, second sign got more points, which has a LED light and message displayed as ‘LOOK FOR ENTERING TRAFFIC’ and sign symbol representing intersection with the direction of approach of minor road vehicles. Further, a question was asked to select either dynamic or static signs that they think more suitable. 78% of drivers opted for dynamic signboards explaining that Figure 12-4 Average speed profiles of drivers for different scenarios (a) Scenario 1 (b) Scenario 2 (c) Scenario 3 (d) Scenario 4 the activation of LED light blinking and display of message when they approach the intersection attracts their attention to the signboard and make them more cautious. The chapter analyzed the responses of major road drivers towards the conflict warning system installed at a typical four-legged uncontrolled intersection. Results indicate a statistical difference among four scenarios for mean speed and mean acceleration was observed. The average mean speed of drivers for scenario 4 was lowered by 6 km/h compared with scenario 2. This implies that the drivers after being educated by the warning system could result in lower mean speed because drivers were alert about the approaching vehicle and responded before they approached the intersection. A post signage questionnaire form results show a positive response from drivers saying that the warning system would be helpful in reducing collisions at uncontrolled intersections. When an accident takes place at intersection there will be a delay in the movement of vehicles results in congestion and an increase in more fuel consumption and CO 2 emissions will be observed. With the help of ICWS we can reduce accidents, traffic congestion, CO 2 emissions and ensure safe travel of people on road. ICWS also avoids sudden braking behaviour of drivers which results in reduction in COF emissions. When driver applies sudden braking behaviour a number of deceleration events takes place which can increase fuel consumption and CO₂ emissions. [1] Ministry of Road Transport and Highways: Road accidents in India 2019. 197pp, 2020. Appendix Chapter A1
Testbeds are composite abstractions of systems that are used to study system components and interactions in order to learn more about the underlying system’s importance. They’re made up of prototypes and real-world system parts, and they’re used to show how a system’s elements work. The testbed provides a realistic hardware-software environment in which components can be tested without having access to the final system. The testbed allows for a better knowledge of the system’s functional needs and operational behaviour. Traffic congestion is a huge issue all throughout the world, resulting in slower speeds, longer travel times, and longer lines of vehicles. It not only increases the fuel consumption but consequently leads to increase in carbon dioxide emissions, outdoor air pollution as well as increase in the exposure time of the passengers. Intelligent Transportation System (ITS) technologies acts as a solution to reduce travel time, congestion, emissions and crashes and improves safety, driving experience and road capacity. To reduce CO 2 emissions by using ITS technologies such as Advance traffic management system a testbed was constructed in IIT Hyderabad to assess the efficiency of technologies in reducing CO 2 emissions. The primary objective of the testbed has been focused on improving safety and also increasing traffic mobility. In addition, it may helpful to reduce the transportation related environmental impacts that include pollutant emissions which leads to poor air quality as well as energy consumption and greenhouse gas emissions. Figure A-1 represents the IITH testbed. The components of IITH testbed are 1. Advance Driver Assistance System for Safe Merging deployed near IITH main gate 2. Speed Enforcement Warning System deployed near faculty towers 3. Intersection Conflict warning System deployed near Academic block A 4. Bluetooth and Wi-Fi sensors and Gas sensors are deployed on NH-65. Testbed help in developing advance traffic management system, advance traveller information system and reduce CO 2 emissions. In India, rural highways have numerous minor roads intersecting them and carry mixed traffic. According to MORTH, 449,002 road crashes were recorded in 2019, out of which 20.6% of the crashes occurred at uncontrolled intersections. Crashes at intersections are more likely to be angle crashes which have a higher risk of serious or fatal injury. Intersection Conflict Warning System is one such ITS technology that helped to reduce crashes at intersections. The objectives of ICWS is to evaluate the safety, effectiveness and efficiency of ICWS in order to promote safe gap acceptance of vehicle at an uncontrolled intersection and to reduce the frequency of crashes at uncontrolled intersection by warning drivers on the major and minor approach for cross traffic presence. The working principle of ICWS is discussed below. ICWS gives a warning to drivers as they provide real-time information about intersection conditions to driver to reduce intersections crashes. Static signing, detection and dynamic elements are used to provide substantial warnings to drivers at intersections where poor sight distance or gap acceptance have contributed to high crash rates. The intersection conflict warning system combines warning lights with roadside detectors. This system collects real-time vehicle data and transmits the results to drivers in a timely manner via the warning lights. Conflict warning lights are set on the major road and the minor road, respectively and has two states: continuously flashing or off. When no conflict is detected, the warning light remains off, meaning that vehicles may pass safely. When the system detects a conflicting vehicle on the major road it warns the drivers on the minor road about the presence of vehicle on the major road by activating warning lights. Similarly, when the system detects a vehicle entering from minor road it informs the major road drivers about the presence of vehicle on the minor road by activating warning lights. This situation results in reduced conflicts as drivers get alert about the possible approaching vehicle, resulting in lower approach speeds and improved driver gap acceptance. The type of sensors deployed at Intersection Conflict Warning System was described in the next section. Figure A-2 and Figure A-3 represents Figure A-1 IITH testbed A-block Figure A-3 Traffic merging signs with warning lights the intersection conflict warning system installed near A block, IITH and warning signs on major and minor road. Speeding has been implicated as a major contributing factor in all fatal vehicle crashes. Law enforcement in the recent years has increased with technologies like Global Positioning System (GPS) based vehicle tracking system, Speed gun, Speed Camera System to catch speeding motorist and to improve road safety as well as fleet management application. The Speed Enforcement Warning System (SEWS) is the latest technology used to monitor driver’s compliance with the speed limit. The objectives of speed enforcement warning system are to design speed warning system that will monitor the vehicle speed by warning the drivers. When driver exceed the pre-set speed limit board (30 kmph) and to automatically record the number plate from ANPR (Automatic number plate recognition) and camera for further action that will be stored as a data base in controller room. Speed enforcement is a system that monitors local speed of the driver by warning, so that driver can maintain optimum speed. Automatic number plate Recognition (ANPR) is a mass surveillance method that performs optical character recognition on images to read the license plate of the vehicle. It is also used for detection of average speed of the vehicles. Sign boards are placed alongside road to inform speed limit to the drivers. Loop detectors are installed prior to sign boards to classify vehicle types. Lidar speed board detects the speed of vehicle that is shown with speed limit sign board. ANPR is installed to detect the number plates of vehicle that are going above speed limit and it’ll send that data to server. Figure A-4, A-5 and A-6 represents the speed enforcement warning system near faculty and staff housing at IITH. Figure A-4 Speed Enforcement Warning System Near Faculty and Staff Housing, IITH Figure A-5 Automatic Number Plate Recognition (ANPR) Figure A-6 Speed warning signal system Driver assistant system is the primary elements for addressing repeatedly occurring freeway congestion on main line. The system consists of traffic signal on minor and major approach. The system controls the rate of vehicle entering into mainline, thereby allowing the freeway to carry the maximum volume at a uniform speed, such that freeway throughput is maximized. The objectives of driver assistance system for safe merging were to control the number of vehicles that are allowed to enter the freeway and to reduce the risk of accidents arising out of sudden driver decisions. 6: Working principle of Driver Assistance system It is a method by which traffic seeking to gain access to a busy highway is Figure A-7 Advance driver assistance system for safe merging near IITH main gate controlled at the access point via traffic warning signs. It reduces overall freeway congestion by managing the amount of traffic entering the freeway and by breaking up platoons that make it difficult to merge onto the freeway. The system warns the driver on the minor road (IITH exit) based on the traffic approaching on a major road in real-time condition and vice-versa. Virtual loop camera sensor detects the real-time vehicle position of the minor road approaching traffic and sends the signal to the traffic entering sign present on NH-65 (major road) by flashing amber light. For minor approach vehicles to safe merging into NH-65, TIRTL (The Infrared Traffic Logger) and loop detector sensor embedded on major approach detects the vehicles and sends the signal to conflicting traffic by flashing red light to minor road. Figure A-7, A-8 and A-9 represents the driver assistance system for safe merging installed near IITH main gate The type of sensors deployed for different technologies in the IITH testbed are described below. AGD traffic radar sensor was deployed on one leg of intersection conflict warning system where traffic goes towards IITH main gate. It is a frequency modulated continuous wave radar with detection range up to 150 meters. Radar sensor detects traffic volume, speed, occupancy, presence, vehicle type and direction of motion of vehicle. Radar sensor detection rate and their images are shown in Table A-1 and Figure A-10.
Figure A-10 AGD Traffic Radar
ExLSR III laser sensor was deployed on one leg of intersection conflict warning system where traffic goes towards academic block. It has a laser class of 1-905 nm and a scanning angle of 96 degrees. Laser sensor can be used for vehicle count and classification of vehicle. Laser sensor detection rate and their images are shown in Table A-2 and Figure A-11. Inductive loop detectors were installed in IITH at an intersection minor road approach where conflict warning system was deployed and on NH64 road for an assistant driver warning system. Inductive loop detector can measure speed, volume, occupancy and length of the vehicle. Inductive loop detector detection rate and their images are shown in Table A-3 and Figure A-12.
AUS 6003 Ultrasonic sensor was installed in IITH at an intersection minor road approach where conflict warning system was deployed and it uses ultrasonic technology for the detection of the presence of vehicles and their height / profile. Ultrasonic sensor was installed at a height of 5–7 meters from ground surface with sensor facing downward direction. Ultrasonic sensor can classify vehicles based on their height, measure speed and traffic volume. The image of ultrasonic sensor was shown in Figure A-13. TIRTL had been installed on NH 64 for Assistant Driver Warning System. It gives the vehicle-by-vehicle data consisting of timestamp, vehicle speed and vehicle type in real time through an ethernet interface. TIRTL detection rate and its image is shown in Table A-4 and Figure A-14. FLIR traffic camera was installed at IITH exit gate for an assistant driver warning system. It consists of both CMOS camera and video detector which acts as a vehicle presence detector. When a vehicle passes through this camera it detects the vehicle presence and information is send to controller unit. TrafiCam allows you to exactly position and verify the vehicle presence detection zones. TrafiCam detection rate and its image is shown in Table A-5 and Figure A-15. Testbed is mainly used for testing the developed technologies and to provide insights that would be helpful Table A-4 TIRTL detection rate
before deploying it in the real-world traffic. This chapter presents about Intelligent Transportation System (ITS) testbed deployed in IITH. The main objective of ITS testbed is to reduce accidents, congestion and CO₂ emissions by deploying various ITS technologies such as Advanced traffic management, Advanced traveller information and Bluetooth/Wi-Fi sensors and improve safe, secure and efficient travel to road users. The technologies that are deployed in testbed and their objectives and working principles was discussed in detail. ICWS technology helps to reduce collisions at uncontrolled intersections, Speed enforcement warning system helps drivers to travel in a safe speed, Driver assistance system for safe merging helps the drivers intend to merge the highway by avoiding sudden braking behaviours and can reduce accidents and Wi-Fi/ Bluetooth sensors and Gas sensors installed on NH-65 provides about the information of vehicles travelling on Highway. The testbed deployed in IITH may influence the driving behaviour and would aid in the development and implementation of efficient eco driving solutions to minimize vehicular emissions. Furthermore, IITH testbed may help the designers, traffic management system and policy makers to have an idea about the countermeasures needed to be taken to reduce congestion, accidents and greenhouse gases. Contributors of Handbook
Nagoya Electric Works Co., LTD. Chapter 11 Tetsuhiro ISHIZAKA Nihon University Maunendra Sankar DESARKAR    Indian Institute of Technology Hyderabad Chapter 12 Yashasvi RACHAKONDA
India Members (Indian Institute of Technology Hyderabad)
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భారతీయ పొంకేతిక విజాన పంప్ హిదరాలార్ गारतीय प्रोदोशिकी संरखान हेदरासार Indian Institute of Yechnology Hyderabad Tokyo Institute of Technology SATREPS Project of “Smart Cities Development for Emerging Countries by Multimodal Transport System Based on Sensing, Network and Big Data Analysis of Regional Transportation"