# IoT-Enabled Smart Sustainable Cities: Challenges and Approaches
## 4 Smart and Sustainable Urban Mobility
* Provide several _easy-to-use_ visual consultation mechanisms for mobility data, through various representation technologies, based on the roles of the different users accessing them [119]. Considering the communication between IoT devices--deployed within city limits and monitoring the different aspects of the citizens' life--and the infrastructure in which the data processing is performed, it can be useful to introduce a particular class of IoT nodes, denoted as IoT Hubs or IoT GWs, characterized by enhanced capabilities of local computing. In turn, these IoT Hubs should be further enhanced with _on-Edge_ capabilities, in order to provide innovative ways to handle a sustainable mobility as expected in modern smart cities. Furthermore, IoT GWs have to cope with the following different aspects, on both local and remote sides:  
* Inform drivers and people on the street with dynamic signals (directly on the road) based on the environmental conditions sensed by local sensors; examples of these signals are speed limits, presence of speed meters, indications on traffic condition, and planning for public transportation [120]. * Collect traffic flow information through sensors deployed on the road, thus estimating some key features characterizing the mobility conditions: amount of cars passing a specific segment and density of trucks, cars, and motorcycles [121]. * Counting people's density and flows moving in particular areas, through the usage of heterogeneous sensing nodes, based on heterogeneous sensors (such as optical and proximity sensors), and using IEEE 802.11 or BLE communication protocols [122]. As an example, these sensing elements could be placed in bus/train stations, cultural (e.g., museums) and crucial and strategic areas (e.g., train stations and airports). In order to avoid resource-constrained IoT nodes from heavy processing tasks, yet benefiting from the possibility to extract overall features based on the data arriving from each local sector of the city, some processing activities can be delegated to Edge and Cloud infrastructures. Illustrative examples of these activities are the following. * Prediction of free parking slots, delay of buses at bus stops, bikes to be shared from a given point, traffic flow at some points. * Data visualization for mobility and transport control room operators, to transmit alarms in the case of early warning conditions and to monitor the current global traffic situation [123], in order to predict road traffic congestion and suggest alternative ways to reach a Point of Interest (POI) [124]. * ML-aided algorithms execution on traffic data, in order to suggest alternative paths for people in certain conditions, e.g., to avoid queues in congested areas and to promote new and alternative tours. * AI-based algorithm execution for user behavior and engagement classification, in order to better understand how citizens use the city infrastructure and propose changes to promote virtuous behaviors [125]. Considering the _green_ evolution in the sustainable mobility of a smart city (as briefly discussed in Section 3.10), it is interesting to analyze how it is possible to improve the quality of life of citizens when they deal with different types of vehicles (e.g., public buses, vans, private cars, motorcycles, bicycles, and scooters) [126], as shown in Figure 12.  
Figure 11: Data traffic’s pipeline from the field to the processing arena. Thanks to this need for a better life quality of people living in a city, it is possible to conclude that a smart sustainable urban mobility model will need to follow the following principles [128, 129]. * Flexibility: multiple modes of transportation allow travelers to choose which ones work best for a given situation. * Efficiency: the trip gets the travelers to their destination with minimal disruption and in as little time as possible [130]. * Integration: the full route is planned door-to-door, regardless of which modes of transportation are used. * Clean Technology: transportation moves away from pollution-causing vehicles to zero-emission ones.