# Evolution of Traffic Microsimulation and Its Use for Modeling Connected and Automated Vehicles

Authors: Narayana Raju; Haneen Farah
Journal: Journal of Advanced Transportation
 (2021)

---

## Abstract

Traffic microsimulation has a functional role in understanding the traffic performance on the road network. This study originated with intent to understand traffic microsimulation and its use in modeling connected and automated vehicles (CAVs). Initially, the paper focuses on understanding the evolution of traffic microsimulation and on examining the various commercial and open-source simulation platforms available and their importance in traffic microsimulation studies. Following this, current autonomous vehicle (AV) microsimulation strategies are reviewed. From the review analysis, it is observed that AVs are modeled in traffic microsimulation with two sets of strategies. In the first set, the inbuilt models are used to replicate the driving behavior of AVs by adapting the models’ parameters. In the second strategy, AV behavior is programmed with the help of externalities (e.g., Application Programming Interface (API)). Studies simulating AVs with inbuilt models used mostly VISSIM compared to other microsimulation platforms. In addition, the studies are heavily focused on AVs’ penetration rate impact on traffic flow characteristics and traffic safety. On the other hand, studies which simulated AVs with externalities focused on the communication aspects for traffic management. Finally, the cosimulation strategies for simulating the CAVs are explored, and the ongoing research attempts are discussed. The present study identifies the limitations of present CAV microsimulation studies and proposes prospects and improvements in modeling AVs in traffic microsimulation.


---

## Body

## 1. Introduction

Automated vehicles (AVs) are closer than ever to making their mark over the road network. Given that AVs will ultimately not require the involvement of humans, which is known to be a contributing factor in most accidents [1], both researchers and practitioners have great hopes that AVs will improve the performance characteristics of traffic and enhance its safety standards [2, 3]. However, verifying the assertions about AVs’ added benefits to traffic efficiency and safety is quite challenging. Traffic microsimulation (i.e., soft computing platform for modeling the detailed intervehicular interactions in a traffic stream) can help to understand the implications of AVs’ performance and communication aspects on traffic and support developing better traffic management strategies. Considering this, traffic microsimulation and its application to understanding the implications of AVs is vital. Therefore, this paper first describes the evolution of traffic microsimulation tools and then reviews the state-of-the-art in using traffic microsimulation for modeling the implications of Connected and Automated Vehicles (CAVs) on traffic efficiency and safety.The following sections include an introduction to traffic microsimulation (Section1.1), followed by an introduction to CAVs (Section 1.2) and finally identifying the research aim and method (Section 1.3).


### 1.1. Traffic Microsimulation

For setting the traffic signals, Webster [4] attempted a computer simulation technique to support the framework in developing optimum signal time for the intersections. This can be marked as one of the initial attempts in traffic microsimulation. To account for the randomness in traffic modeling, researchers have relied on Monte Carlo simulation techniques [5–7] and cellular automata models [8, 9]. Simultaneously, researchers have developed and integrated mathematical models, such as different car following models [10] and lane changing models [11], into different simulation tools [12, 13]. The basic functionality of the simulation is time and space, along with the system state. Both time and space can be categorized as discrete or continuous with a system state. In this direction, a series of simulation approaches have been developed over time. In microsimulation, models exhibit discrete-time and continuous space phenomenon.Advancements in computing and developments of new mathematical frameworks have led to major upgrades to microsimulation packages. Supported by advanced processing in computations, traffic microsimulation models are gradually found to be meticulous and realistic. Relying on empirical traffic data for analysis may not be feasible at all times, and implementation of proposed solutions to the traffic network without any checks can put human life at risk and cause unnecessary damage to the infrastructure. Traffic microsimulation offers numerous customization options for testing a variety of scenarios. Sensing the importance of microsimulation in understanding traffic characteristics, researchers have conducted numerous studies and developed various traffic microsimulation frameworks over the last few decades. These studies relate to various aspects of traffic, including among others car following [14], lane changing [15], signal control [16], traffic safety [17], traffic emissions [18], pedestrian flow [19], ramp metering [20, 21], roundabouts [22], toll operations [23], and truck parking operations [24]. To visualize this, the literature related to traffic microsimulation was identified by Scopus [25] search engine using the keyword “traffic microsimulation” and was then segregated into subareas resulting in the network plot shown in Figure 1. The size of the nodes and thickness of the links in Figure 1 depict the scale of the publications in that area, and the color depicts the clusters.Figure 1
Keyword network from traffic microsimulation studies.Figure1 shows that traffic microsimulation was utilized in all kinds of traffic studies, and numerous traffic flow modeling concepts were evolved in connection with microsimulation. Given the ease and effectiveness, both researchers and industry partners have extensively used microsimulation approaches in supporting their decision making. Furthermore, numerous researchers [26–30] strongly advocated the importance of calibration for the reliability of the microsimulation outcomes. It is observed that researchers of various backgrounds widely use microsimulation in their studies. New microsimulation platforms were also developed and upgraded over time with the purpose of commercialization. Traffic microsimulation platforms have found a place across various domains for searching for optimized solutions.


### 1.2. Connected and Automated Vehicles

Researchers from both academic and industrial backgrounds, over the last five decades, believed that automobiles will achieve the self-driving functionality and evolve as autonomous vehicles [31–33]. Given the advancements in computing and the availability of superior complex modeling, research on AVs has progressed. Currently, most of the automobile industries focus on developing their own AVs. Considering the potential market for AVs, multinational conglomerates showed a great interest in developing AVs [34]. To harmonize the definition of the level of automation, the Society of Automotive Engineers (SAE) has proposed six levels of autonomy, ranging from level 0 (no driving automation) to level 5 (full automation). The SAE levels of automation primarily differ in their Dynamic Driving Tasks (DDTs), Object and Event Detection and Response (OEDR), and Operational Design Domain (ODD). The ODD is the conditions under which Automated Driving Systems (ADSs) are designed to work. At level 0, drivers will perform all the DDTs and OEDR. At level 1, drivers will still perform some of the DDTs and be responsible for the OEDR. At level 2, the system will perform the lateral and longitudinal DDTs while the drivers will be still responsible for the OEDR. At higher levels, the system will be responsible for the DDTs and the OEDR. The ODD of levels 1 to 4 is limited. At level 5 automation, unconditional DDTs will be carried by the ADS and unlimited ODD.Past and ongoing research attempts have resulted in developing AVs with initial SAE level functionalities. Simultaneously, to assess AVs driving performance, AVs are being tested at various testbed conditions. Several researchers [35–37] strongly advocate that AVs can significantly impact the traffic characteristics of the road network. Unlike regular traffic, empirical data on AVs in traffic are still relatively scarce, and field experiments are limited given the risk for human life and potential damage to the infrastructure. Considering this, traffic microsimulation can play a huge role in assessing AVs impacts on the road network.


### 1.3. Research Aim and Method

The main aim of this paper is to understand the development of the research on traffic microsimulation in connection with modeling AVs. To achieve this aim, this study was carried out in two stages:(i)
Firstly, considering the significance of traffic microsimulation platforms in simulation studies, the progression of traffic microsimulation and microsimulation platforms were reviewed.(ii)
Secondly, the modeling aspects of AVs in traffic microsimulation and cosimulation with AV simulation tools were reviewed.In the present study, literature was mined in three different parts: traffic microsimulation, C/AV microsimulation, and cosimulation studies. To understand the trend of traffic microsimulation, literature related to traffic microsimulation was first identified and then segregated using the keywords reported in Table1 and the Scopus [25] search engine. Scopus database contains only the publications with Scopus indexing. The Scopus search engine provides advanced search options like authors, keywords, title, affiliation, journal title, avoid references, along with different conditional options for sorting the search. The keywords that were used for identifying relevant traffic microsimulation papers were microscopic simulation, microlevel simulation, microscopic traffic simulation, microlevel traffic simulation, simulation, traffic simulation, and vehicle simulation. Initially, articles were checked individually for keywords, titles, and abstracts. In the next phase, for a given search keyword, all the articles were sorted, and duplicates were eliminated. Further, from the sorted articles, each article was checked by the authors; this check included the title of the work, followed by the abstract (in this phase the authors marked the microsimulation studies and platforms); finally, for the unclear articles, the research methodology of each article was examined. On this basis, the authors identified the articles in the domain of microsimulation.Table 1
Adopted nomenclature in the study.
Adopted wordsSimilar words in the literatureMicrosimulationMicroscopic simulation, microlevel simulation, microscopic traffic simulation, microlevel traffic simulation, simulation, traffic simulation, vehicle simulationAutonomous vehicles (AVs)Self-driving vehicles, autonomous cars, automated cars, automated vehicles, robotic vehicles, autonomous unmanned surface vehicle,https://acronyms.thefreedictionary.com/Autonomous+Unmanned+VehicleDriving behaviorDriver behavior, vehicular behavior, vehicle behavior, (multi-)agent behavior, (multi-)agent trajectory, lane changing, mergingTraffic flowTraffic stream, stream flow, network flowTraffic safetyRoad safety, driver safety, safetyEmissionsEmission modeling, traffic emission, vehicular emissionTraffic managementVehicle management, network managementLiterature related to C/AVs and cosimulation was mined using Scopus [25], Google Scholar (2021), and Mendeley (2021) databases. Google Scholar is a basic search platform, where the search engine focuses on the article title and the abstract for the search word. Further, those articles can be added to the library and exported as .csv files for other analyses. Like Google Scholar, Mendeley offers similar functions based on its search engine. Both Google Scholar and Mendeley contain all kinds of publications.From the literature review, it was observed that researchers used different nomenclature in describing the same term. For example, some studies reported traffic microsimulation as traffic microscopic simulation, microlevel simulation, and microsimulation. To limit this lack of consistency, similar words were clustered into a unique word, as reported in Table1.The rest of the paper is structured as follows: Section2 presents the sources of traffic microsimulation studies, followed by the review of different microsimulation platforms in Section 3. Section 4 synthesizes the AVs traffic microsimulation studies, and Sections 5 and 6 summarizes the paper and introduces the prospects and future research directions for AVs traffic microsimulation.


## 1.1. Traffic Microsimulation

For setting the traffic signals, Webster [4] attempted a computer simulation technique to support the framework in developing optimum signal time for the intersections. This can be marked as one of the initial attempts in traffic microsimulation. To account for the randomness in traffic modeling, researchers have relied on Monte Carlo simulation techniques [5–7] and cellular automata models [8, 9]. Simultaneously, researchers have developed and integrated mathematical models, such as different car following models [10] and lane changing models [11], into different simulation tools [12, 13]. The basic functionality of the simulation is time and space, along with the system state. Both time and space can be categorized as discrete or continuous with a system state. In this direction, a series of simulation approaches have been developed over time. In microsimulation, models exhibit discrete-time and continuous space phenomenon.Advancements in computing and developments of new mathematical frameworks have led to major upgrades to microsimulation packages. Supported by advanced processing in computations, traffic microsimulation models are gradually found to be meticulous and realistic. Relying on empirical traffic data for analysis may not be feasible at all times, and implementation of proposed solutions to the traffic network without any checks can put human life at risk and cause unnecessary damage to the infrastructure. Traffic microsimulation offers numerous customization options for testing a variety of scenarios. Sensing the importance of microsimulation in understanding traffic characteristics, researchers have conducted numerous studies and developed various traffic microsimulation frameworks over the last few decades. These studies relate to various aspects of traffic, including among others car following [14], lane changing [15], signal control [16], traffic safety [17], traffic emissions [18], pedestrian flow [19], ramp metering [20, 21], roundabouts [22], toll operations [23], and truck parking operations [24]. To visualize this, the literature related to traffic microsimulation was identified by Scopus [25] search engine using the keyword “traffic microsimulation” and was then segregated into subareas resulting in the network plot shown in Figure 1. The size of the nodes and thickness of the links in Figure 1 depict the scale of the publications in that area, and the color depicts the clusters.Figure 1
Keyword network from traffic microsimulation studies.Figure1 shows that traffic microsimulation was utilized in all kinds of traffic studies, and numerous traffic flow modeling concepts were evolved in connection with microsimulation. Given the ease and effectiveness, both researchers and industry partners have extensively used microsimulation approaches in supporting their decision making. Furthermore, numerous researchers [26–30] strongly advocated the importance of calibration for the reliability of the microsimulation outcomes. It is observed that researchers of various backgrounds widely use microsimulation in their studies. New microsimulation platforms were also developed and upgraded over time with the purpose of commercialization. Traffic microsimulation platforms have found a place across various domains for searching for optimized solutions.


## 1.2. Connected and Automated Vehicles

Researchers from both academic and industrial backgrounds, over the last five decades, believed that automobiles will achieve the self-driving functionality and evolve as autonomous vehicles [31–33]. Given the advancements in computing and the availability of superior complex modeling, research on AVs has progressed. Currently, most of the automobile industries focus on developing their own AVs. Considering the potential market for AVs, multinational conglomerates showed a great interest in developing AVs [34]. To harmonize the definition of the level of automation, the Society of Automotive Engineers (SAE) has proposed six levels of autonomy, ranging from level 0 (no driving automation) to level 5 (full automation). The SAE levels of automation primarily differ in their Dynamic Driving Tasks (DDTs), Object and Event Detection and Response (OEDR), and Operational Design Domain (ODD). The ODD is the conditions under which Automated Driving Systems (ADSs) are designed to work. At level 0, drivers will perform all the DDTs and OEDR. At level 1, drivers will still perform some of the DDTs and be responsible for the OEDR. At level 2, the system will perform the lateral and longitudinal DDTs while the drivers will be still responsible for the OEDR. At higher levels, the system will be responsible for the DDTs and the OEDR. The ODD of levels 1 to 4 is limited. At level 5 automation, unconditional DDTs will be carried by the ADS and unlimited ODD.Past and ongoing research attempts have resulted in developing AVs with initial SAE level functionalities. Simultaneously, to assess AVs driving performance, AVs are being tested at various testbed conditions. Several researchers [35–37] strongly advocate that AVs can significantly impact the traffic characteristics of the road network. Unlike regular traffic, empirical data on AVs in traffic are still relatively scarce, and field experiments are limited given the risk for human life and potential damage to the infrastructure. Considering this, traffic microsimulation can play a huge role in assessing AVs impacts on the road network.


## 1.3. Research Aim and Method

The main aim of this paper is to understand the development of the research on traffic microsimulation in connection with modeling AVs. To achieve this aim, this study was carried out in two stages:(i)
Firstly, considering the significance of traffic microsimulation platforms in simulation studies, the progression of traffic microsimulation and microsimulation platforms were reviewed.(ii)
Secondly, the modeling aspects of AVs in traffic microsimulation and cosimulation with AV simulation tools were reviewed.In the present study, literature was mined in three different parts: traffic microsimulation, C/AV microsimulation, and cosimulation studies. To understand the trend of traffic microsimulation, literature related to traffic microsimulation was first identified and then segregated using the keywords reported in Table1 and the Scopus [25] search engine. Scopus database contains only the publications with Scopus indexing. The Scopus search engine provides advanced search options like authors, keywords, title, affiliation, journal title, avoid references, along with different conditional options for sorting the search. The keywords that were used for identifying relevant traffic microsimulation papers were microscopic simulation, microlevel simulation, microscopic traffic simulation, microlevel traffic simulation, simulation, traffic simulation, and vehicle simulation. Initially, articles were checked individually for keywords, titles, and abstracts. In the next phase, for a given search keyword, all the articles were sorted, and duplicates were eliminated. Further, from the sorted articles, each article was checked by the authors; this check included the title of the work, followed by the abstract (in this phase the authors marked the microsimulation studies and platforms); finally, for the unclear articles, the research methodology of each article was examined. On this basis, the authors identified the articles in the domain of microsimulation.Table 1
Adopted nomenclature in the study.
Adopted wordsSimilar words in the literatureMicrosimulationMicroscopic simulation, microlevel simulation, microscopic traffic simulation, microlevel traffic simulation, simulation, traffic simulation, vehicle simulationAutonomous vehicles (AVs)Self-driving vehicles, autonomous cars, automated cars, automated vehicles, robotic vehicles, autonomous unmanned surface vehicle,https://acronyms.thefreedictionary.com/Autonomous+Unmanned+VehicleDriving behaviorDriver behavior, vehicular behavior, vehicle behavior, (multi-)agent behavior, (multi-)agent trajectory, lane changing, mergingTraffic flowTraffic stream, stream flow, network flowTraffic safetyRoad safety, driver safety, safetyEmissionsEmission modeling, traffic emission, vehicular emissionTraffic managementVehicle management, network managementLiterature related to C/AVs and cosimulation was mined using Scopus [25], Google Scholar (2021), and Mendeley (2021) databases. Google Scholar is a basic search platform, where the search engine focuses on the article title and the abstract for the search word. Further, those articles can be added to the library and exported as .csv files for other analyses. Like Google Scholar, Mendeley offers similar functions based on its search engine. Both Google Scholar and Mendeley contain all kinds of publications.From the literature review, it was observed that researchers used different nomenclature in describing the same term. For example, some studies reported traffic microsimulation as traffic microscopic simulation, microlevel simulation, and microsimulation. To limit this lack of consistency, similar words were clustered into a unique word, as reported in Table1.The rest of the paper is structured as follows: Section2 presents the sources of traffic microsimulation studies, followed by the review of different microsimulation platforms in Section 3. Section 4 synthesizes the AVs traffic microsimulation studies, and Sections 5 and 6 summarizes the paper and introduces the prospects and future research directions for AVs traffic microsimulation.


## 2. Sources of Traffic Microsimulation Studies

Researchers across the world have used traffic microsimulation to understand the traffic characteristics, such as driving behavior, traffic management, and traffic safety. Traffic characteristics are varying in nature with different geographical, cultural, and economic conditions. For example, vehicles have proper lane discipline under homogeneous traffic conditions. In the case of cold climatic conditions, cars have dominant proportion in comparison to motorized two-wheelers. On the other hand, in Southeast Asia and South Asian countries, given the favorable conditions with added fuel mileage, the proportion of motorized two-wheelers is higher. Along similar lines, the physical characteristics of vehicles are varying in nature across the world. Furthermore, economic status of countries affects their advancements in transportation infrastructure. Differences in cultures and norms affect road users’ behavior. Road user behavior, which is the root source of understanding traffic performance, is highly diverse with different circumstances. As a result, solutions to traffic problems are never taking a unique form.To understand the geographical distribution of traffic microsimulation studies, the identified literature was mapped considering author’s affiliations and nationality. This would help in understanding the quantum of microsimulation studies sources. For visualization purposes, a radial plot is presented in Figure2. The rectangular bars represent the number of publications for each affiliation. It can be observed that traffic microsimulation studies were conducted all over the world, with a significant share by the United States of America (27%), China (26%), Germany (7%), and Netherlands (7%). Some of the major universities in these countries have contributed heavily to this research domain: in Netherlands, Delft University of Technology (211); in China, Tongji University (96), Beijing Jiaotong University (96), Southeast University Nanjing (80), and Tsinghua University (63); in the United States of America, Virginia Polytechnic Institute and State University (79) and University of California, Berkeley (57); in Germany, Technical University of Munich (56); and in Singapore, the National University of Singapore (51). In recent years, in addition to developed nations, microsimulation studies are slowly picking up as well in developing nations.Figure 2
Radial plot depicting the involved institutes across the nations.In the present context, around 1250 traffic microsimulation studies have been published in more than 50 scientific journals. The analysis was carried forward to understand the articles’ share in journals as shown in Figure3. Note that in Figure 3, only journals with a minimum of 15 published articles were considered. The Transportation Research Record (28%) has the largest share, followed by Transportation Research Part C: Emerging Technologies (10%), IEEE Transactions on Intelligent Transportation Systems (7%), and the Journal of Transportation Engineering (6%). Publications in other journals with smaller share are also presented in Figure 3. With respect to conference proceedings, IEEE International Conference on Intelligent Transportation Systems (ITSC) proceedings (177) and Transportation Research Procedia (80) have accumulated most studies in this domain.Figure 3
Traffic microsimulation work share of the journals with at least 15 papers.


## 3. Traffic Microsimulation Platforms

With the increase in computational efficiency and availability, researchers and industry partners used programming scripts and input parameters to integrate the traffic modeling concepts into simulation packages. Initially, simulation packages were limited to case-specific ones with a small set of programming scripts. Most of the simulation platforms in their early versions were basic, including the car following functionality on a straight midblock road section. Given the opportunities with the evolution in programming over the last three decades, developers and researchers have updated different microsimulation platforms. In addition, the evolution of new research concepts, such as car following and lane changing models and traffic signal optimization techniques, led to various inbuilt customization options in traffic simulation over time. To understand these better, present traffic microsimulation platforms were reviewed, and their details and characteristics are summarized in Table2.Table 2
Details of the microsimulation platforms.
PlatformDeveloperCountryCar following (CF) modelsLane changing models (LC)Programming interfaceExternal moduleExternal module interfaceNature of distributionReleaseParamics (two versions)Initial: University of EdinburghPresent: Quadstone Paramics,S-ParamicsUKFritzsche CFGap acceptance-based LCC++, Java, PythonExternal DLLsC++, MATLABCommercialInitial: 1990Stable: Paramics Discovery (2020), S-Paramics (SIAS group)VISSIMInitial: Karlsruhe UniversityPresent: PTV Planung Transport Verkehr AGDEWiedemann 74,Wiedemann 99Sparmann modelC++, Java, PythonCOM interface, External DLLsC++, MATLAB, PythonCommercialInitial: 1992Stable: PTV VISSIM 2021 (2020)MIXICTU Delft and Transport Research Center (AW) of RijkswaterstaatNLAutonomous Intelligent Cruise Control (AICC) and rule-based MIXIC CFRule-based MIXIC LCCSource code can be scripted—Open-sourceInitial: 1995Stable: MIXIC 1.3 (2006)MITSIMITS Lab, Massachusetts Institute of TechnologyUSAGazis–Herman–Rothery (GHR) CFUtility based LCC++ (runs in Linux)Source code can be scripted—Open-sourceInitial: 1996Stable: 2010AIMSUNInitial: TSS-Transport SimulationPresent: acquired by Siemens MobilityESGipps CFRule-based Gipps LCC++, Java, PythonAIMSUN MicroApiC++, Python, Delphi, C#CommercialInitial: 1997Stable: AIMSUN Next 20 (2020)CORSIMMcTrans Center, University of FloridaUSAPitt CFIntralink LCCRun-Time Extension (RTE)Visual basic scriptCommercialInitial: 1998Stable: TSIS-CORSIM 6.3 (2012)SUMOGerman Aerospace CenterDEKrauss, Daniel, IDM, IDMM, P. Wagner, B. Kerner, Wiedemann, ACC, CACCDK2008, LC2013, SL2015C++, Java, PythonTraciPython, MATLABOpen-sourceInitial: 2001Stable: SUMO 1.9.2 (2021)TransModelerCaliper CorporationUSAGeneral Motors, Gipps, longitudinal control, constant time gap, IDM, Wiedemann 74, Wiedemann 99Gap acceptance LC with discrete behaviorC++, Java, PythonCaliper ScriptGISDK, C#, .NetCommercialInitial: 2005Stable: TransModeler (2020)MOTUSTU DelftNLIDM+LMRSJava, MATLABSource code can be scripted—Open-sourceInitial: 2010Stable: 2015TRITONEUNICAL (TIS Group)ITGeneral Motors, Gipps, Van Aerde, GHR, Fritzsche, Wiedemann, Krauss Pitt, Giofrè models, etc.Mobil, Gipps, GiofrèNot identifiedSource code can be scripted—Open-sourceInitial: 2012Stable: TRITONE (2018)OpenTrafficSimDiTTlab, TU DelftNLIDM+LMRSJavaSource code can be scripted—Open-sourceInitial: 2016Stable: 2016Paramics [38] project was supported by the UK Department of Transportation and developed by the researchers of the University of Edinburgh, Scotland. The initial platform was purely based on the Hans-Thomas Fritzsche car following model [39]. Later, research inputs from the University of California, Irvine, helped in embedding numerous packages, which include traffic signals, ramp metering, and bottleneck in freeway scenarios. Finally, in the year 2005, the Quadstone Paramics acquired the Paramics simulation platform. On the other hand, another variant of Paramics, S-Paramics, was distributed by the SIAS Group [40]. Presently, both Quadstone Paramics and S-Paramics are identified as old and new Paramics, respectively. Like Paramics, VISSIM was developed by researchers of Karlsruhe University, Germany, with a psychophysical car following model [41] and other input packages which were added over time. Later, PTV group acquired VISSIM from Karlsruhe University and developed it further as PTV VISSIM. Under MIT’s Intelligent Transportation Systems (ITS) program in collaboration with Caliper Corporation, researchers of the Massachusetts Institute of Technology, USA, developed MITSIM [42, 43] with the idea of an open-source traffic microsimulation.There are many other platforms including AIMSUN [44], MIXIC [45], CORSIM [46, 47], SUMO [48, 49], TransModeler [50, 51], MOTUS [52, 53], TRITONE [28], and OpenTrafficSim [54]. Among these platforms, AIMSUN, CORSIM, and TransModeler are commercial, whereas SUMO, MOTUS, TRITONE, and OpenTrafficSim are open-source platforms. Other microsimulation platforms, such as Integration [55], SimTraffic [56], HUTSIM [57], THOREAU [58], FAUSIM [59], FOSIM [60], RuTSIM [61], and GLD traffic simulator [62], evolved from certain project ideas. It can be noted that most of the established microsimulation platforms were instigated in the 90s and were developed with the idea of simulating homogeneous lane-based traffic conditions.From the review of the different simulation packages, it is observed that most of traffic microsimulation platforms were initiated and developed by transportation researchers at their institutes within frameworks of specific projects. Later, in collaboration with industrial partners, these simulation platforms have further evolved, which led to the development of commercial traffic microsimulation platforms. On the other hand, some traffic microsimulation platforms were kept open in carrying the research. Most commercial variants provide free time-limited licenses for academics. Microsimulation platforms with explicitly available details are summarized in Table2.Further, based on the specific functionality (core behavioral models, optimizing computation, user groups), microsimulation platforms were developed over various programming interfaces. Most of the inbuilt models in simulation platforms are sufficient to test basic scenarios. On the other hand, in some cases, inbuilt models must be replaced by external behavioral logic/algorithms for the research analysis. Keeping this in mind, presently, external modules are offered in coding behavioral logic/algorithms. Those external modules give opportunities for researchers to bypass the inbuilt logic. Various programming interfaces are used in different microsimulation platforms as detailed in Table2. However, unlike microsimulation platforms with inbuilt models, the processing speed with external modules is lower; this again depends on the coded script’s logical flow.


### 3.1. Publication Share of Microsimulation Platforms

To better understand the existing research using different microsimulation platforms, literature on traffic microsimulation platforms was mined. The identified studies were later segregated by the year of publication. A Mekko chart was prepared, as shown in Figure4, to illustrate the results. The x-axis in Figure 4 represents the timeline in years, and the y-axis demonstrates the percentage share. The width of the rectangles depicts the scale of publications in that year. From the database, it is shown that initially the research work was limited to its developers. Later in early 2000s, those microsimulation platforms were open to researchers outside the development teams.Figure 4
Publication share of microsimulation platforms over the years.The number of publications was low initially, with a minimum of 17 publications in 2001, but gradually increased over the years to a record of a maximum of 376 publications in the year 2019. Unlike the increase in total publications, the share of the different microsimulation packages in these publications has varied over the years. Starting from 2001, CORSIM and Paramics have a fair share of the scientific publications whereas VISSIM and AIMSUN have a minor share. However, over time the shares of VISSIM and SUMO have increased, and in 2020 both had the biggest shares among other simulation platforms.On the other hand, AIMSUN has a relatively uniform number of publications over the years. Publications using TransModeler have been relatively stable with a relatively minor share. Publications from MITSIM, MOTUS, MIXIC, TRITONE, HUTSIM, Integration, DRACULA, SimTraffic, and OpenTrafficSim have been identified over time, with most of the publications being from developers’ backgrounds.This analysis signifies that microsimulation in traffic engineering research has picked up the pace in the previous two decades. Numerous researchers and industry partners are testing different logic and scenarios using microsimulation platforms for decision making in the present driving context. Simultaneously the variation in the platforms’ share can be attributed to various factors, including commercialization, user interface, supported traffic conditions, and user support.Based on the number of publications in traffic microsimulation, the top twenty-five publishers were identified. Later, the publications were labeled based on the utilized microsimulation platform. To express this better, a Sankey plot was generated considering the microsimulation platforms and the journals, as shown in Figure5. The width of the connection demonstrates the number of publications concerning the microsimulation package in that journal. The sizes of the head (microsimulation platform) and tail (publisher) nodes show the scale of publications. The analysis shows that Transportation Research Record, Transportation Research Part C, Journal of Transportation Engineering Part A, IEEE ITSC proceedings, Transport Research Procedia, and Applied Mechanics and Materials have a significant share of microsimulation publications. It is noticed that, with 523 publications, VISSIM has a major share in all journals. CORSIM (103), Paramics (93), SUMO (69), and AIMSUN (58) had also a relatively significant share in publications.Figure 5
Publication shares of microsimulation platforms over top 25 publishers.


## 3.1. Publication Share of Microsimulation Platforms

To better understand the existing research using different microsimulation platforms, literature on traffic microsimulation platforms was mined. The identified studies were later segregated by the year of publication. A Mekko chart was prepared, as shown in Figure4, to illustrate the results. The x-axis in Figure 4 represents the timeline in years, and the y-axis demonstrates the percentage share. The width of the rectangles depicts the scale of publications in that year. From the database, it is shown that initially the research work was limited to its developers. Later in early 2000s, those microsimulation platforms were open to researchers outside the development teams.Figure 4
Publication share of microsimulation platforms over the years.The number of publications was low initially, with a minimum of 17 publications in 2001, but gradually increased over the years to a record of a maximum of 376 publications in the year 2019. Unlike the increase in total publications, the share of the different microsimulation packages in these publications has varied over the years. Starting from 2001, CORSIM and Paramics have a fair share of the scientific publications whereas VISSIM and AIMSUN have a minor share. However, over time the shares of VISSIM and SUMO have increased, and in 2020 both had the biggest shares among other simulation platforms.On the other hand, AIMSUN has a relatively uniform number of publications over the years. Publications using TransModeler have been relatively stable with a relatively minor share. Publications from MITSIM, MOTUS, MIXIC, TRITONE, HUTSIM, Integration, DRACULA, SimTraffic, and OpenTrafficSim have been identified over time, with most of the publications being from developers’ backgrounds.This analysis signifies that microsimulation in traffic engineering research has picked up the pace in the previous two decades. Numerous researchers and industry partners are testing different logic and scenarios using microsimulation platforms for decision making in the present driving context. Simultaneously the variation in the platforms’ share can be attributed to various factors, including commercialization, user interface, supported traffic conditions, and user support.Based on the number of publications in traffic microsimulation, the top twenty-five publishers were identified. Later, the publications were labeled based on the utilized microsimulation platform. To express this better, a Sankey plot was generated considering the microsimulation platforms and the journals, as shown in Figure5. The width of the connection demonstrates the number of publications concerning the microsimulation package in that journal. The sizes of the head (microsimulation platform) and tail (publisher) nodes show the scale of publications. The analysis shows that Transportation Research Record, Transportation Research Part C, Journal of Transportation Engineering Part A, IEEE ITSC proceedings, Transport Research Procedia, and Applied Mechanics and Materials have a significant share of microsimulation publications. It is noticed that, with 523 publications, VISSIM has a major share in all journals. CORSIM (103), Paramics (93), SUMO (69), and AIMSUN (58) had also a relatively significant share in publications.Figure 5
Publication shares of microsimulation platforms over top 25 publishers.


## 4. Automated Vehicle Microsimulation

Over the years, traffic microsimulation platforms have proven their mantle and their extensive use for various research and industrial activities across different domains. Presently traffic microsimulation platforms have reached a level that can support decision making of practitioners. At the same time, researchers and industrial partners are foreseeing the deployment of AVs on the exiting road network in the near future. Researchers of various backgrounds are predicting the use of AVs for private transport [63], public transport [64], and freight transport such as autonomous trucks [65], as well as for ridesharing [66] such as autonomous shuttles [67]. An increasing number of research studies [68, 69] support the claims that AVs improve traffic safety and efficiency. However, currently quantifying the impacts of AVs in real life from a traffic engineering point of view is highly challenging. This is because of several reasons, such as exposing road users to potential risks, uncertainty regarding the suitability of current traffic management plans, and road authorities being uncertain yet regarding the minimal infrastructure requirements for safe operation of AVs in mixed traffic. Simultaneously, developing strategies and frameworks from only an empirical, experimental point of view is challenging from a cost-benefit point of view. Therefore, the impact of AVs on traffic flow operation must be first studied extensively using traffic microsimulation. With proper utilization of technology and AVs’ communication, a good quantum of benefits can be achieved from a traffic engineering point of view.Given this potential of microscopic simulation platforms in carrying the research in this domain, researchers across the world have already performed various AV studies using different microscopic simulation platforms. Particularly, in the second half of the previous decade, these kinds of studies have picked up. To understand the research flow in this domain, keyword analysis was performed on the literature database, and the visualization plot in Figure6 presents the findings. The size of the node and the thickness of the links in Figure 6 depict the scale of the work, and the colors depict different clusters. Similar to the regular traffic studies, AV microsimulation studies were also conducted in connection with many traffic related topics including ramp metering [3, 70], car following [71, 72], traffic signals [73], emissions [74], road safety [75], and mixed traffic [76].Figure 6
Keyword network of AV microsimulation studies.Most of the microscopic simulation studies have differentiated AVs and human-driven vehicles by their driving behavior and inferred that the driving behavior is the crux in modeling AVs. In the present context, most researchers (e.g., [77, 78]) unilaterally assumed that, in comparison to human-driven vehicles, the behavior of AVs is less stochastic. In addition, it is believed that AVs have good lane discipline and consistent behavior.To investigate this in more detail, the literature was reviewed. From the initial assessment, it is observed that researchers used two main types of strategies to model AVs in simulation platforms. The first strategy is about bringing consistency in AVs driving behavior with the inbuilt car following and lane changing models. Following this direction, AV’s behavior was regulated by adapting the model parameters. The second strategy is about coding AVs’ behavior and connecting it to a simulation software using Application Programming Interface (API). In the following sections, these two approaches and the related studies are further described and discussed.


### 4.1. AV Modeling by Inbuilt Models

In giving due weightage to inbuilt behavioral models, researchers modeled AVs by adapting the parameters of the inbuilt car following models in microsimulation platforms. For example, to understand the safety at intersections and roundabouts due to AVs, [79, 80] adapted the Wiedemann model’s behavior in VISSIM. It was found that AVs improve traffic safety and throughput of the study sections. With a similar framework, [81] modeled AVs at roundabouts. At a higher penetration rate of AVs, a 75% decrease in crash probability was observed compared to a base scenario with no AVs. Along similar lines, by adapting the psychophysical Wiedemann 74 and 99 car following models in VISSIM, various other studies were carried out for different test scenarios to understand the impacts of AVs. These studies included congested networks [82], traffic signals [83], roadway capacity [84], traffic stream characteristics [85], free speeds [86], operational performance [87], emission impacts [88], safety performance [89], unsignalized intersections [90, 91], and transitional impacts [92]. Other studies have adapted the inbuilt Krauss model in SUMO [93, 94] to model AVs. Zeidler et al. [95] tested the compatibility of inbuilt Wiedemann models for inducing the communication between AVs and compared it to field driven AVs. The focus of these mentioned studies, the behavioral models used, and the studies’ key findings are summarized in Table 3.Table 3
Details of AV microsimulation studies with inbuilt models.
PlatformStudyFocusConnectivity in AVsAV car following behavioral modelKey adjusted parameters to mimic AVKey findingsVISSIMMorando et al. [79, 80]Impact on safety due to AV at intersections and roundaboutsNoWiedemann 99(i) Headway time(ii) Following distance variation(iii) Look ahead distance(i) In case of intersection, AVs reduce the number of conflicts by 20% to 65% with AV penetration rates of between 50% and 100%(ii) For roundabouts, the number of conflicts is reduced by 29% to 64% with 100% AV penetration rateTibljaš et al. [81]Safety impacts of AVs at roundaboutsNoWiedemann 74, Wiedemann 99(i) Standstill distance(ii) Safety distance(iii) Following time variation(i) AVs reduce the crash rate at roundabouts at higher penetration rates (penetration rate higher than 75%)Stanek et al. [82]AV impacts on congested networksNoWiedemann 74, Wiedemann 99(i) Standstill distance(ii) Headway time(iii) Following distance variation(iv) Threshold for entering following(v) Speed dependency of oscillation(i) Substantial delay reduction is observed at 30% AV penetration rateWang and Wang, [83]Understanding AVs performance on single lane with traffic signal phaseNoWiedemann 99 with change in acceleration distributions(i) Desired speed and acceleration distribution(ii) Standstill distance(iii) Headway time(iv) Following distance variation(i) AVs show a conservative driving style(ii) Total flow rate of AVs in a signal time is the same as that of human-driven vehicles(iii) AVs manage to increase the flow rate in the middle of the green phaseMartin-Gasulla et al. [84]Impact on throughput due to CAVYes (based on time headway)Wiedemann 99(i) Headway time(i) Throughput increases at a CAV penetration rate of 44%Zeidler et al. [95]Test the potentiality of psychophysical CF models in replicating AV behaviorYes (based on time headway policy)Wiedemann 74, Wiedemann 99(i) Standstill distance(ii) Headway time(i) Behavior of AVs communicating with the leading vehicle is reproduced well in VISSIM(ii) Problems remain when simulating CAVs that do not communicate with their leaderAsadi et al. [85]Impacts of CAVs on traffic streamYes (connectivity is brought by changing the smooth behavior and number of observed vehicles)Wiedemann 99(i) Standstill distance(ii) Headway time(iii) Negative following threshold(iv) Positive following threshold(v) Speed dependency of oscillation(i) Congestion levels decrease with CAVsTafidis et al. [91]AVs operation at intersectionsNoWiedemann 74(i) Look ahead distance(ii) Look back distance(iii) Standstill distance(i) Greater improvement of performance at uncontrolled intersections is achievedHe et al. [86]Identifying the freeway speeds at low AV penetration ratesYes (connectivity is brought by changing the smooth behavior and number of observed vehicles)Wiedemann 99/along with lane change parameters(i) Observed vehicles(ii) Standstill distance(iii) Headway time(iv) Negative following threshold(v) Positive following threshold(vi) Cooperative lane change(i) The link speeds can be estimated by means of AVs even at small proportionsMathew et al. [87]Impact on operational performanceNoWiedemann 74(i) Simulation resolution(ii) Standstill distance(iii) Additive part of the safety distance(iv) Multiplicative part of the safety distance(v) Cooperative lane change(i) Given the high penetration of AVs, the operational performance is improvedTomás et al. [88]Emission impacts of AV on freewaysNoWiedemann 99(i) Standstill distance(ii) Headway time(iii) Negative following threshold(iv) Positive following threshold(v) Speed dependency of oscillation(vi) Minimum lane change headway(i) The penetration rate of AVs at 10% and 20% demonstrates nominal impact on the emissionsYang et al. [89]Understanding safety performance of Wyoming Connected vehicle Pilot Deployment ProgramYes (assumed normal headway distribution and number of observed vehicles)Wiedemann 99(i) Standstill distance(ii) Headway time(iii) Negative following threshold(iv) Positive following threshold(v) Speed dependency of oscillation(vi) Advanced Merging(vii) Cooperative lane Changing(i) improvement in safety is not significant at low penetration rates; in 85% of autonomous trucks, maximum safety benefits are achievedMaryam Mousavi et al. [90]Understanding safety at unsignalized intersectionsNoWiedemann 74(i) Standstill distance(ii) Additive part of the safety distance(iii) Multiplicative part of the safety distance(i) With connectivity of AVs and reduction in headways, the safety and throughput over the intersections increaseRezaei and Caulfield, [92]Examining the transition from human-driven vehicles to AVsNoWiedemann 99(i) Standstill Distance(ii) Safety distance(iii) Following time variation(i) With AV penetration rates of more than 60%, the traffic performance is enhancedRao and Park, [96]Managing CAVs on connected highwaysYes (parameters not explicitly mentioned)Wiedemann 99/along with lane change parameters(i) Standstill Distance(ii) Safety distance(iii) Following time variation(iv) Oscillation acceleration(v) Standstill acceleration(vi) Safety distance reduction factor(vii) Necessary lane change deceleration(viii) Maximum deceleration(ix) accepted deceleration(i) From 10% penetration of CAVs, there is a reduction in travel times over the road section(ii) The study highlights the optimal behavioral patterns for CAVs over the penetration levels for coordinated movement with non-CAVsSUMOQiong Lu et al. [93]AVs impact on capacityNoKrauss Model(i) Minimum gap(ii) Max acceleration(iii) Max deceleration(iv) Time headway(i) Capacity improves at high AV penetration rateJesús Mena-Oreja et al. [94]Platooning maneuvers in mixed trafficNoKrauss model(i) Time headway(i) The study reports that model parameter combination impacts the platooningIt is observed that there is inconsistency in the findings of the studies regarding the penetration rates of C\AVs at which there is an improvement in safety and traffic efficiency, with reported penetration rates ranging from 20 to 70%. Some studies demonstrated a deterioration in safety and drop in capacity standards at penetration rates below 30%; this is observed in all kinds of modeled road sections. In addition, the transition point of the penetration rate of AVs at which an improvement is observed is not consistent across the studies.The strategy of modeling AVs with inbuilt models has its advantages and disadvantages. When the inbuilt model parameters are tweaked for AVs, the microsimulation models typically run with fewer externalities; as a result, a consistent traffic movement will be observed at less computational capacity.Many AV simulation studies with inbuilt models used VISSIM and relied on its psychophysical car following models. The influential critical parameters to mimic AVs vary among the studies and depend on the specific circumstances being studied as summarized in Table3. It is inferred that very few studies considered the lane changing parameters for AV modeling. Furthermore, the modeled behavior of the AVs is highly oriented towards the inbuilt models; as a result, the simulated AVs can be less realistic in comparison to field behavior. Furthermore, there seems to be less weightage to modeling the communication of AVs. Connectivity which is the key in CAVs was simulated by means of adapting the parameters that determine the headway and the number of observed vehicles, with any predefined logic/algorithms. Considering the importance of communication aspects of AVs, this is a huge limitation in this direction.


### 4.2. AV Modeling by Externalities

The externalities of microsimulation platforms give prospects in scripting the external logic. During the run-time of the simulation process and with the help of Application Programming Interface (API), the inbuilt models can be overridden, and as a result the vehicle motion can be dictated by the data feeds. This gives researchers a considerable advantage in testing their models/frameworks. Considering the potential of applying external models, researchers applied predefined behavior logic of AVs. Given the flexibility in controlling the characteristics of the simulation objects during the run-time, communication logic was also induced among the vehicles. The basic idea of this kind of studies is that AVs have the added functionality of communication as CAVs and are controlled by the externalities, while the human-driven vehicles are operated by the inbuilt models. Based on this framework, researchers modeled CAVs on different road segments and modeled various AV maneuvers. To understand this better, the literature related to AV simulation studies in connection with externalities has been reviewed and is summarized in Table4 with details regarding the study focus, logic modeled, CAV behavior, and key findings.Table 4
Details of AV microsimulation studies with externalities.
PlatformStudyFocusIntroduced logicConnectivity in AVsCoded AV behaviorKey findingsParamicsGao et al. [97]Developing data driven adaptive framework for CAVsAdaptive dynamic programmingYesAdaptive dynamic logic and bringing intervehicle communication(i) The optimal controller strategy can asymptotically stabilize the connected vehicles(ii) The framework can be used for solving connected vehicle control problems with unknown system dynamicsOlia et al. [98]Evaluating the impacts of CAVs on the capacities of highway systems—NoGipps model(i) Maximum lane capacity of 6,450 vph per lane (300% improvement) is achievable if all vehicles are driven in a cooperative automated mannerVISSIMXie et al. [70]Modeling a merging strategy for freeway operations under connected autonomous environmentAn optimization-based ramp control strategyYesDictating the movement based on default models(i) The optimal control strategy demonstrated effective coordination among the merging vehicles and will improve the safety and mobility at undersaturated traffic conditionsVirdi et al. [99]Understanding the safety improvements in mixed traffic stream across the network elementsDeveloping Virdi CAV Control Protocol (VCCP) algorithm for modeling CAV behaviorYesAV following model (Talebpour a Mahmassani, 2016)(i) At low penetration rates of CAV, the potential conflicts will increase(ii) Results demonstrated that CAV operation seems to show a significant overall improvement in safety at midblock road sections(iii) With increase in CAV penetration rates, the potential conflicts reduced. The greater the priority, the more the reduction in conflictsPapadoulis et al. [75]Understanding the safety impacts of CAV on motorwaysDecision-making CAV control algorithmYesAssuming CAVs longitudinal time gap as 0.6 s and modeling the instant speeds(i) Results indicated that CAVs bring about compelling benefit to road safety as traffic conflicts significantly reduce even at relatively low market penetration ratesArd et al. [100]Studying the Anticipative Cruise Controller mechanism in two main different scenarios, under disconnected mode with human-driven vehicles and connected mode with other CAVsFormulated Model Predictive Control (MPC) framework to dictate the movement of the vehiclesYesControl-oriented longitudinal dynamics model(i) Automated vehicles perform at a 10%–20% higher energy efficiency over human-driven vehicles(ii) Simulated human-driven vehicles were found to drive up to 10% more energy-efficiently than they did in the baselineLiu and Fan [101]Impacts on freeway capacity due to CAVs—YesIDM(i) With an increase in CAV penetration rate, freeway capacity increases, and further the capacity increases significantly with the increase in speed limitsAIMSUNShladover et al. [102]Understanding the impacts of CACC on traffic flow—YesNissan ACC and CACC car following behavior (Bu et al., 2010)(i) Use of ACC was unlikely to change lane capacity significantly(ii) CACC was able to increase capacity greatly after its market penetration reached moderate to high percentages(iii) Capacity increase could be accelerated by equipping non-ACC vehicles with vehicle awarenessChou et al. [103]Coordinated merge control with vehicle-to-vehicle communicationV2V highway merge control algorithmYes—(i) Merging performance is found to be proportional to the CAV penetration rates(ii) Merge assist features to human-driven vehicles, even at less AV penetration rate, merge performance is enhancedYu et al. [104]Impacts of AV lanes in mixed traffic—NoIDM(i) Roadway capacity increases by 84% by providing AV lanes(ii) Deterioration in safety is reported when the market penetration of CAVs is less than 40Wang et al. [105]Effects of CACC on traffic flow—YesFollowing: IDM, lane changing: Gipps + Heuristic(i) CACC helped significantly attenuate traffic congestion and even remove congestion at AV penetration rate greater than 40 percentMakridis et al. [106]CAV impact on traffic flow and CO2 emissions—YesAV: Nissan ACC [102]CAVs (Van Arem et al., 2006)(i) CAVs can deteriorate the status of the network, and that connectivity is the key to improved traffic flow(ii) Emission-wise, the CAVs have the highest fuel consumption per km traveled among other types, while CAVs only marginally lower the overall consumption of human-driven vehiclesOlsson and Levin [107]Management of traffic intersection in the connected scenariosAutonomous Intersection Management (AIM)Yes—(i) AIM+ is able to reduce delay and increase average speed in comparison to conventional intersection management practicesSUMOJesús Mena-Oreja and Gozalvez [108]Platooning maneuvers in mixed trafficPlatooning Extension Plexe, PERMITYesCACC model(i) Identifying the importance of platooning in connected scenarioTimmerman and Boon, [109]Intersection managementProposing platoon forming algorithms and providing equal predefined delay to the vehicles over the intersectionYes—(i) Platoon forming algorithms with a low mean delay sometimes are relatively unfair, indicating a potential need for balancing mean delay and fairnessLiu et al. [110]Intersection managementProposing a cooperative scheduling mechanism for CAVs passing through an intersection, called TP-AIMYes—(i) TP-AIM mechanism significantly reduces the average evacuation time and increases throughput by over 60s and over 20%, respectively.(ii) The maximum delay in TP-AIM can be reduced to less than 10% of Adaptive and Fixed LightsDomingues et al. [111]Lane changing maneuvers and traffic congestion, fuel consumption, and CO2 emissionProposing a negotiation method for the vehicular merging and lane changing scenariosYesNegotiation logic for lane changing movement(i) Platooning has an important impact on reducing fuel consumption and CO2 emissions(ii) Platooning combined with lane-merging shows a significant increase in performanceMaske et al. [112]Improving the mixed traffic performance by intervehicle communicationBy means of deep reinforcement learning, intervehicle communication is carriedYesOptimum Velocity Model (OVM)(i) Traffic stream performance is improved by the intervehicle communicationLu and Kim [113]Intersection management with mixed trafficDiscrete-time occupancies trajectory-based intersection traffic coordination algorithm (DICA)Yes—(i) Proposed DICA is found to be computationally efficient(ii) At uncongested traffic, DICA has better traffic throughput than the optimized traffic control mechanismChouhan et al. [114]Modeling the lane sorting strategies for CAVsProposing an algorithm that converts traffic composed of vehicles located randomly in a set of lanes into sorted trafficYes—(i) The lane sorting strategies were proven to improve the density of traffic streamsLi et al. [115]Modeling right of way (RoW) scenarios for mixed traffic—YesTheoretical model(i) With proper RoW, the road capacities are improved at even a lower AV penetration rateLi and Liu [116]Investigating intersection management strategy for CAVsProposing a matrix-based algorithm for allocating the vehicle movementYes—(i) The proposed algorithm performs better in comparison to both adaptive and fixed traffic signal systemsChamideh et al. [117]Coordination of CAVs at intersectionsAn edge cloud controller is used to deliver services that provide traffic safety and efficiencyYesIntersection Coordination Unit (ICU) will allocate the vehicle speeds to move over the sections(i) Delay is reduced over the intersection, along with improvement in safety and flow over the intersections(ii) High execution time was reported in processing the coded algorithmsMOTUSWang et al. [72]Cooperative car following controlCooperative Controller and Distributed AlgorithmYesDeveloped CFC and C-CFC(i) The developed algorithms improved the jam characteristics in traffic networkXiao et al. [118]Converting existing High Occupancy Vehicle (HOV) lanes into CACC lanes—YesCACC levels(i) Converting to CACC lanes at low penetration less than 30 percent can decrease the congestion(ii) However, at medium CACC with penetration rates of 40 to 50 percent, the congestion is drastically alleviated due to a large share of traffic carried by CACC lanesIt is observed that researchers applied numerous logic systems/algorithms, including adaptive dynamic programming [97], optimization-based ramp control strategy [70], Virdi CAV Control Protocol [99], decision-making CAV control algorithm [75], Model Predictive Control [100], Autonomous intersection management [107], Platooning Extension Plexe [108], cooperative scheduling mechanism for CAVs [110], discrete-time occupancies trajectory-based intersection traffic coordination algorithm [113], lane sorting [114], matrix-based intersection management logic [116], and Cooperative Controller and Distributed Algorithm [72].The main aim of the algorithms mentioned above was to induce CAVs’ behavior and instigate communication among the vehicles for better traffic movement over the traffic network. Researchers tested the CAVs across various traffic facilities, including midblock sections, signalized intersections, unsignalized intersections, on- and off-ramps, etc. The behavior of the AVs was governed by various models including the Gipps model [119], Intelligent Driver Model (IDM) [120], Optimum Velocity Model (OVM) [121], Adaptive Cruise Control (ACC) [45], Cooperative Adaptive Cruise Control CACC [71], Theoretical model, and cooperative longitudinal following behavior.The focus of the researchers varied following the type of study segments. In midblock sections, researchers were focused on CAVs following behavior and lane changing behavior with platooning logic to maximize the improvement in safety and throughput. On the other hand, at intersections and ramps, studies focused on communication among CAVs so that CAVs coordinate themselves and create necessary gaps for traffic movement, with the idea of limiting the stop and go conditions and eliminating traffic signals. Unlike the studies of CAVs with inbuilt models, the studies were focused on the efficacy of the behavioral logic rather than studying the penetration impacts of CAVs. To a certain extent, most of these studies demonstrated a good efficacy of their logic/algorithms. With some externalities, microsimulation platforms such as VISSIM, Paramics, and AIMSUN performed the simulation runs with a single core in the computer processor. As a result, the processing time with those externalities was high. Therefore, the application to a bigger network will be computationally inefficient. Furthermore, most of the logic/algorithms were tested at isolated study segments; therefore, their performance must be quantified in future studies for a traffic network level with greater stochasticity. Finally, to express the reviewed literature in a better manner, visualization analysis was carried out as shown in Figure7.Figure 7
AV microsimulation studies over different platforms.


### 4.3. Cosimulation

While in traffic microsimulation, interactions among vehicles are modeled and traffic characteristics are quantified, traffic microsimulation tools do not offer an opportunity to model the C/AV mechanism, and the behavior of the C/AVs is mainly governed by the behavioral models rather than the accurate perception of the infrastructure environment. On the other hand, from an individual C/AV perspective, the automobile industry programs the C/AV behavior in four planning stages: global route planning, behavior planning, motion planning, and local planning. To identify the state estimation and localization, C/AVs are reinforced with numerous sensors for the perception. To model the performance of these sensors and the perception of the infrastructure environment, nanoscopic AV simulation tools are available. Nanoscopic AV simulation tools offer numerous customization options, including detailed maps, 3D vehicle models, sensors, vehicular physics-based models, and external programming interfaces for controlling the actors during the run-time. To better understand the nanoscopic AV simulation tools, a review analysis was performed, and the results are summarized in Table5.Table 5
Details of AVs nanoscopic simulation tools.
Nanoscopic simulation toolDeveloperNature of distributionLocal planningGlobal route planningAvailable sensors for perceptionBuilt environmentApplication program interfaceLatest releaseCosimulation with microsimulation toolsCarlaComputer Vision Center (CVC), Intel, Toyota Research institute, FutureweiOpen sourceYesYesCamera, lidar, radar, safety, GNSSUnreal EnginePython (3.7)Carla 0.9.12VISSIM, SUMOPreScanTASS International (Siemens)CommercialYesYesCamera, lidar, radar, safety, GNSSUnreal EngineMATLAB and SimulinkPrescan 2020.4VISSIM, SUMO, AIMSUNVIRES virtual Test DriveHexagonCommercialYesYesCamera, lidar, radar, safety, GNSSNot identifiedVTD APIVIRES VTD 2.2VISSIM, SUMOCarSimPresent: Mechanical Simulation Corporation Initial: University of Michigan Transportation Research InstituteCommercialYesYesCamera, lidar, radar, safetyUnreal EnginePython, MATLABCarSim 2021.1VISSIM, SUMOSummitNational University of SingaporeOpen sourceYesYesCamera, lidar, radar, safety, GNSSUnreal EnginePython (3.7)Summit 1.0SUMOOpenDSGerman Research Center for Artificial IntelligenceOpen sourceYesYesCamera, lidar, radar, safety, GNSSjMonkeyEngine 3.2Lightweight Java Game LibraryOpenDS 5.0VISSIM, SUMOVI-WorldSimVI-grade GmbHCommercialYesYesCamera, lidar, radar, ultrasonic, safety, GNSSUnreal EngineMATLABVI-WorldSimNot identifiedAutomotive Simulation Models (ASM)dSPACE GmbHCommercialYesYesCamera, lidar, radar, GNSSUnreal Engine 4MATLAB and SimulinkASM 9.6VISSIM, SUMOAltair simulationAltairCommercialYesYesradar, ultrasonic, and lidarNot identifiedMATLABAltair simulation—SCANeR studioAVSimulationCommercialYesYesCamera, lidar, radarUnreal EnginePythonSCANeR 2021.2Not identifiedDriving SimulationrFproCommercialYesYesCamera, lidarNot identifiedMATLAB and SimulinkDriving Simulation rFproVISSIM, SUMOAutonomous Vehicle SimulatorNICommercialYesYesCamera, lidar, radarUnreal EngineNot identifiedAutonomous Vehicle SimulatorNot identifiedCarMakerIPG AutomotiveOpen sourceYesYesCamera, lidar, radarNot identifiedMATLAB and SimulinkCarMaker 10.0VISSIM, SUMODriveSimNIVIDIAOpen sourceYesYesCamera, lidar, radar, ultrasonic, safety, GNSSNVIDIA RTXPythonNIVIDIA DriveSim 10.0—SVL SimulatorLGOpen sourceYesYesCamera, lidar, radar, ultrasonic, safety, GNSSUnity game enginePythonLGSVL Simulator 2020.06—veDYNATESIS GmbHCommercialYesNoCamera, lidar, radar, ultrasonic, safety, GNSS—MATLABveDYNA—DeepdriveVoyageOpen sourceYesYesCamera, lidar, radarUnity game enginePythonDeepdrive—CognataCognataCommercialYesYesCamera, lidar, radar photorealistic sensor modelsCognata enginePythonCognata simulation—MetamotoInitial: MetamotoPresent: Foretellix Ltd.CommercialYesYesLaser radar, camera, millimeter wave radar, ultrasonic radar, GPS, and IMUUnreal EnginePythonMetamoto—RightHookRightHookCommercialYesYesCamera, lidar, radar, ultrasonic, safety, GNSSUnity, UE4, or CryEnginePythonRightHook—Parallel DomainParallel DomainExternal Support: ToyotaCommercialYesYesCamera, lidar, radar, ultrasonic, safety, GNSSUnreal Engine 4, Houdini, Maya, 3DS Max, and Substance SuitePythonParallel Domain—51Sim-One51WorldOpen sourceYesYesCamera, lidar, radar, ultrasonic, safety, GNSSNot specified explicitlyPython51Sim-One—Pilot-D GaiaPilot-D AutomotiveCommercialYesYesCamera, lidar, radar, ultrasonic, safety, GNSSUnreal EnginePythonPilot-D Gaia 3.0—ESI Pro-SiVICESICommercialYesYesCamera, lidar, radar, ultrasonic, safety, GNSSUnreal EnginePythonPro-SiVIC 2020.0—PanoSimPanoSimCommercialYesYesCamera, lidar, radar, ultrasonic, safety, GNSSNot specified explicitlyPython, MATLABPanoSim 4.0—AAIAAI GmbHCommercialYesYesCamera, lidar, radar, ultrasonic, safety, GNSSUnity game enginePythonAAI—Baidu ApolloBaiduOpen sourceYesYesCamera, lidar, radar, ultrasonic, safety, GNSSApollo Game EnginePython, Baidu CloudApollo 6.0SUMOWaymo CarcraftGoogle WaymoOpen sourceYesYesCamera, lidar, radar, ultrasonic, safety, GNSSUnreal EnginePythonCarcraft—TAD SimTencentCommercialYesYesCamera, lidar, radar, ultrasonic, safety, GNSSTencent game enginePythonTAD Sim 2.0—CruzWayUniv. of California, Santa CruzOpen sourceYesYesNot specified explicitlyUnreal EnginePythonCruzWaySUMOGazeboUniv. Grenoble Alpes, InriaOpen sourceYesYesCamera, lidar, radar, ultrasonic, safety, GNSSNot specifiedPythonGazebo 3DSUMO3DCoAutoSimUniversidad Carlos III de Madrid (UC3M)Open sourceYesYesCamera, lidar, radar, ultrasonic, safety, GNSSUnity game enginePython3DCoAutoSimSUMOFrom the analysis, it is observed that more than thirty AV simulation tools are available in the market. They are commercial and open source in nature and are built using game engines [122, 123]. In general, game engines give the flexibility to place the actors over the 3D space. Based on their character, the behavior will be exhibited. By connecting the actors and agents, the simulation environment is built. The open-source unreal game engine [124] was found to be a popular game engine for building the simulation environment. Most simulation tools offer all kinds of basic sensors for modeling the AV perception. To control the actors during the simulation run-time, application programming interfaces are provided, mostly in python and MATLAB/Simulink.Interestingly most of these tools offer a cosimulation environment, in which different AV simulation tools are coupled with traffic microsimulation tools. During the cosimulation, the C/AV behavior is completely governed by the AV simulation tool, where the behavior of C/AVs is modeled with perception from the system, and the entire traffic behavior is governed by the traffic microsimulation tool. These cosimulation strategies help in simulating the C/AVs more accurately and improve the simulation to achieve a more realistic estimation of the traffic operation. The review analysis observed that SUMO and VISSIM are used more often among other traffic microsimulation platforms as cosimulation tools. Given the open-source nature, SUMO is favored in the AV cosimulations.The literature related to the C/AV cosimulation studies was reviewed and is summarized in Figure8 and Table 6. The review analysis revealed that the studies are limited in nature. The cosimulation studies picked up recently, and a major proportion of these studies focused on developing methodologies for the C/AVs cosimulation. Interestingly, researchers favored VISSIM to couple it with CarMaker and SUMO with Carla to carry the cosimulation.Figure 8
AV cosimulation studies over different platforms.Table 6
Details of AV cosimulation studies.
StudyStudy focusMicrosimulation toolAV simulation toolScripted logicAV behaviorAV perception sensorsFindingsKlischat et al. [125]∗Transferring the trajectories of vehicles controlled by a motion planner and the trajectories of other traffic participants between SUMO and CarlaSUMOCarlaLane change synchronizationLocal—The proposed algorithm for synchronizing lane changes with SUMO yields a more realistic behavior in line with motion planningSelvaraj, [126]Focusing on developing a framework for sensor- and communication-assisted vehicle dynamic for AVsSUMOCarMakerACC and CACC controllersLocal, motion, global—Analyzing the importance of the connected vehicles in vehicular safety applicationsZhu et al. [127]∗Evaluating the performance of a proportional-derivative (PD) steering controllerSUMOCarSim—Local—A new lateral control design procedure based on robust PD controller with parameter space design and add-on model regulator is introducedParanjape et al. [128]∗Generating town sized road networks and intersections for the modeling of AVs and pedestriansSUMOCruzWayTownSim, IntGen, Netgenerate, Sumo2UnrealLocal, motion—With the help of CruzWay, the road network is modeled in the form of mesh and navmesh and imported to SUMOWang et al. [105]∗Introducing a Python API between the planning module of the Apollo platform and the CommonRoad frameworkSUMOBaidu ApolloCommonRoadLocal, motion—Converting the road network and obstacle information from Apollo to CommonRoad and developing an interface with CommonRoad-SUMO for simulating the vehiclesAoki et al. [129]∗Cooperative perception scheme with deep reinforcement learning to enhance the detection accuracy for the surrounding objectsSUMOCarlaCIVSLocal, motionCamerasCIVS platform able to produce realistic 3D graphics, traffic model, vehicle model, sensor model, and communication among the vehiclesWang et al. [130]∗Hierarchical behavior and motion planning (HBMP) to explicitly model the behavior in learning-based solutionSUMOCarlaHBMPLocal, motionLidar, camerasGiving a methodology to work on hierarchical behavior and motion planning using the reinforced learningZofka et al. [131]∗Robot Operating System (ROS) for a mixed reality environmentSUMOCarlaROSLocal, motionFramework now enables the holistic simulation of traffic scenarios by coupling multiple domain expert models with real world entitiesSantonato, [132]Working on a framework for complete end to end simulation for AVsSUMOCarla—Local, globalLidar, camerasIdentifying the importance of cosimulation for AV simulationWang et al. [133]∗CommonRoad-RL, an open-source toolbox to train and evaluate RL-based motion planners for autonomous vehiclesSUMOCarlaCommonRoad-RLLocal, motion—Framework for an open-source toolbox to train and evaluate RL-based motion planning for autonomous vehiclesChada et al. [134]Cosimulation framework for testing predictive eco-driving assistance systems (EDAS) for commercial vehicles in urban environmentsSUMOCarMakerEco-driving assistance systems (EDAS)Local, motion—Proposed cosimulation framework has been tested on the driving simulator in real timeYuan, [135]∗Collective Multiagent Perception In combining SUMO and Carla for modeling the AVsSUMOCarlaCOMAPModeling the cooperative behavior for AVsLidar, camerasCollective perception proven to improve the localization accuracy for the AVsStudyStudy focusMicrosimulation toolNanoscopic simulation toolScripted logicAV behaviorAV perception sensorsFindingsFu et al. [136]∗Working on network-level autonomous driving simulator framework based on the Cellular-Vehicle-to-Everything (C-V2X) protocolSUMOCarlaLTE-V2XConnectivity at intersectionsCamerasCombining SUMO and Carla and working on cosimulation framework, demonstrating that framework is feasible for some advanced autonomous driving applications, such as multi-intersection vehicle scheduling and remote drivingXu et al. [137]∗Working on cooperative driving automation logic for the AVs at merging sectionSUMOCarlaOpenCDALocal, globalLidar, external communicationShowing the importance of OpenCDA for modeling cooperative behavior logic among the AVsSchmied, [138]∗Designing and comparing two different control concepts applied to Adaptive Cruise Control (ACC) and lane change assisted ACC (LC-ACC)VISSIMCarMakerModel Predictive Control (MPC)Local, motion—Mixed H2/H∞ and predictive control concepts have been introduced to increase performance of the ACC and LC-ACCNalic et al. [139]Cosimulation framework for systematic generation of scenarios for testing and validation of Automated Driving SystemsVISSIMCarMakerDynamic Data Exchange interfaceLocal, motion—Demonstrating the importance of cosimulation for realistic behavioral analysisWaschl et al. [140]Framework for testing realistic traffic conditionsVISSIMCarMakerPredictive Adaptive Cruise Control (P-ACC)Local—Considering the traffic control based on available V2X and I2V information or more sophisticated sensor modelsNalic et al. [141]Implementation of a cosimulation frameworkVISSIMCarMakerModel View Controller (MVC)Local, motion—cosimulation framework for testing ADSs using MATLAB/Simulink, CarMaker, and VISSIMNalic et al. [142]A novel stress testing methodVISSIMCarMakerExternal driver modelsLocal, motion—The developed stress testing method showed a significant increase of detected scenarios in the cosimulation environmentNalic et al. [143]Framework for testing AVs in traffic environmentVISSIMCarMakerDriver Model DLL interface (DMDI)Local, motion—Driver model framework∗Literature from arXiv and conference proceedings.The cosimulation research studies highly concentrated at local and motion planning of the C/AVs, and less weightage was given to the global planning. It can be noted that AV simulation tools use the detailed network and simultaneously couple it to the microsimulation which demands good computational power. During the cosimulation, the microsimulation runs are carried out by the externalities, reducing the simulation processing speed and impacting the run-time. Most AV simulation platforms provide inbuilt maps/networks; however, generating such detailed maps for the study section requires additional tools. Given this, AV cosimulation studies are highly dependent on the inbuilt maps/networks in the present context. Finally, currently the cosimulation is restricted to traffic volume, simulation time, and network size.


## 4.1. AV Modeling by Inbuilt Models

In giving due weightage to inbuilt behavioral models, researchers modeled AVs by adapting the parameters of the inbuilt car following models in microsimulation platforms. For example, to understand the safety at intersections and roundabouts due to AVs, [79, 80] adapted the Wiedemann model’s behavior in VISSIM. It was found that AVs improve traffic safety and throughput of the study sections. With a similar framework, [81] modeled AVs at roundabouts. At a higher penetration rate of AVs, a 75% decrease in crash probability was observed compared to a base scenario with no AVs. Along similar lines, by adapting the psychophysical Wiedemann 74 and 99 car following models in VISSIM, various other studies were carried out for different test scenarios to understand the impacts of AVs. These studies included congested networks [82], traffic signals [83], roadway capacity [84], traffic stream characteristics [85], free speeds [86], operational performance [87], emission impacts [88], safety performance [89], unsignalized intersections [90, 91], and transitional impacts [92]. Other studies have adapted the inbuilt Krauss model in SUMO [93, 94] to model AVs. Zeidler et al. [95] tested the compatibility of inbuilt Wiedemann models for inducing the communication between AVs and compared it to field driven AVs. The focus of these mentioned studies, the behavioral models used, and the studies’ key findings are summarized in Table 3.Table 3
Details of AV microsimulation studies with inbuilt models.
PlatformStudyFocusConnectivity in AVsAV car following behavioral modelKey adjusted parameters to mimic AVKey findingsVISSIMMorando et al. [79, 80]Impact on safety due to AV at intersections and roundaboutsNoWiedemann 99(i) Headway time(ii) Following distance variation(iii) Look ahead distance(i) In case of intersection, AVs reduce the number of conflicts by 20% to 65% with AV penetration rates of between 50% and 100%(ii) For roundabouts, the number of conflicts is reduced by 29% to 64% with 100% AV penetration rateTibljaš et al. [81]Safety impacts of AVs at roundaboutsNoWiedemann 74, Wiedemann 99(i) Standstill distance(ii) Safety distance(iii) Following time variation(i) AVs reduce the crash rate at roundabouts at higher penetration rates (penetration rate higher than 75%)Stanek et al. [82]AV impacts on congested networksNoWiedemann 74, Wiedemann 99(i) Standstill distance(ii) Headway time(iii) Following distance variation(iv) Threshold for entering following(v) Speed dependency of oscillation(i) Substantial delay reduction is observed at 30% AV penetration rateWang and Wang, [83]Understanding AVs performance on single lane with traffic signal phaseNoWiedemann 99 with change in acceleration distributions(i) Desired speed and acceleration distribution(ii) Standstill distance(iii) Headway time(iv) Following distance variation(i) AVs show a conservative driving style(ii) Total flow rate of AVs in a signal time is the same as that of human-driven vehicles(iii) AVs manage to increase the flow rate in the middle of the green phaseMartin-Gasulla et al. [84]Impact on throughput due to CAVYes (based on time headway)Wiedemann 99(i) Headway time(i) Throughput increases at a CAV penetration rate of 44%Zeidler et al. [95]Test the potentiality of psychophysical CF models in replicating AV behaviorYes (based on time headway policy)Wiedemann 74, Wiedemann 99(i) Standstill distance(ii) Headway time(i) Behavior of AVs communicating with the leading vehicle is reproduced well in VISSIM(ii) Problems remain when simulating CAVs that do not communicate with their leaderAsadi et al. [85]Impacts of CAVs on traffic streamYes (connectivity is brought by changing the smooth behavior and number of observed vehicles)Wiedemann 99(i) Standstill distance(ii) Headway time(iii) Negative following threshold(iv) Positive following threshold(v) Speed dependency of oscillation(i) Congestion levels decrease with CAVsTafidis et al. [91]AVs operation at intersectionsNoWiedemann 74(i) Look ahead distance(ii) Look back distance(iii) Standstill distance(i) Greater improvement of performance at uncontrolled intersections is achievedHe et al. [86]Identifying the freeway speeds at low AV penetration ratesYes (connectivity is brought by changing the smooth behavior and number of observed vehicles)Wiedemann 99/along with lane change parameters(i) Observed vehicles(ii) Standstill distance(iii) Headway time(iv) Negative following threshold(v) Positive following threshold(vi) Cooperative lane change(i) The link speeds can be estimated by means of AVs even at small proportionsMathew et al. [87]Impact on operational performanceNoWiedemann 74(i) Simulation resolution(ii) Standstill distance(iii) Additive part of the safety distance(iv) Multiplicative part of the safety distance(v) Cooperative lane change(i) Given the high penetration of AVs, the operational performance is improvedTomás et al. [88]Emission impacts of AV on freewaysNoWiedemann 99(i) Standstill distance(ii) Headway time(iii) Negative following threshold(iv) Positive following threshold(v) Speed dependency of oscillation(vi) Minimum lane change headway(i) The penetration rate of AVs at 10% and 20% demonstrates nominal impact on the emissionsYang et al. [89]Understanding safety performance of Wyoming Connected vehicle Pilot Deployment ProgramYes (assumed normal headway distribution and number of observed vehicles)Wiedemann 99(i) Standstill distance(ii) Headway time(iii) Negative following threshold(iv) Positive following threshold(v) Speed dependency of oscillation(vi) Advanced Merging(vii) Cooperative lane Changing(i) improvement in safety is not significant at low penetration rates; in 85% of autonomous trucks, maximum safety benefits are achievedMaryam Mousavi et al. [90]Understanding safety at unsignalized intersectionsNoWiedemann 74(i) Standstill distance(ii) Additive part of the safety distance(iii) Multiplicative part of the safety distance(i) With connectivity of AVs and reduction in headways, the safety and throughput over the intersections increaseRezaei and Caulfield, [92]Examining the transition from human-driven vehicles to AVsNoWiedemann 99(i) Standstill Distance(ii) Safety distance(iii) Following time variation(i) With AV penetration rates of more than 60%, the traffic performance is enhancedRao and Park, [96]Managing CAVs on connected highwaysYes (parameters not explicitly mentioned)Wiedemann 99/along with lane change parameters(i) Standstill Distance(ii) Safety distance(iii) Following time variation(iv) Oscillation acceleration(v) Standstill acceleration(vi) Safety distance reduction factor(vii) Necessary lane change deceleration(viii) Maximum deceleration(ix) accepted deceleration(i) From 10% penetration of CAVs, there is a reduction in travel times over the road section(ii) The study highlights the optimal behavioral patterns for CAVs over the penetration levels for coordinated movement with non-CAVsSUMOQiong Lu et al. [93]AVs impact on capacityNoKrauss Model(i) Minimum gap(ii) Max acceleration(iii) Max deceleration(iv) Time headway(i) Capacity improves at high AV penetration rateJesús Mena-Oreja et al. [94]Platooning maneuvers in mixed trafficNoKrauss model(i) Time headway(i) The study reports that model parameter combination impacts the platooningIt is observed that there is inconsistency in the findings of the studies regarding the penetration rates of C\AVs at which there is an improvement in safety and traffic efficiency, with reported penetration rates ranging from 20 to 70%. Some studies demonstrated a deterioration in safety and drop in capacity standards at penetration rates below 30%; this is observed in all kinds of modeled road sections. In addition, the transition point of the penetration rate of AVs at which an improvement is observed is not consistent across the studies.The strategy of modeling AVs with inbuilt models has its advantages and disadvantages. When the inbuilt model parameters are tweaked for AVs, the microsimulation models typically run with fewer externalities; as a result, a consistent traffic movement will be observed at less computational capacity.Many AV simulation studies with inbuilt models used VISSIM and relied on its psychophysical car following models. The influential critical parameters to mimic AVs vary among the studies and depend on the specific circumstances being studied as summarized in Table3. It is inferred that very few studies considered the lane changing parameters for AV modeling. Furthermore, the modeled behavior of the AVs is highly oriented towards the inbuilt models; as a result, the simulated AVs can be less realistic in comparison to field behavior. Furthermore, there seems to be less weightage to modeling the communication of AVs. Connectivity which is the key in CAVs was simulated by means of adapting the parameters that determine the headway and the number of observed vehicles, with any predefined logic/algorithms. Considering the importance of communication aspects of AVs, this is a huge limitation in this direction.


## 4.2. AV Modeling by Externalities

The externalities of microsimulation platforms give prospects in scripting the external logic. During the run-time of the simulation process and with the help of Application Programming Interface (API), the inbuilt models can be overridden, and as a result the vehicle motion can be dictated by the data feeds. This gives researchers a considerable advantage in testing their models/frameworks. Considering the potential of applying external models, researchers applied predefined behavior logic of AVs. Given the flexibility in controlling the characteristics of the simulation objects during the run-time, communication logic was also induced among the vehicles. The basic idea of this kind of studies is that AVs have the added functionality of communication as CAVs and are controlled by the externalities, while the human-driven vehicles are operated by the inbuilt models. Based on this framework, researchers modeled CAVs on different road segments and modeled various AV maneuvers. To understand this better, the literature related to AV simulation studies in connection with externalities has been reviewed and is summarized in Table4 with details regarding the study focus, logic modeled, CAV behavior, and key findings.Table 4
Details of AV microsimulation studies with externalities.
PlatformStudyFocusIntroduced logicConnectivity in AVsCoded AV behaviorKey findingsParamicsGao et al. [97]Developing data driven adaptive framework for CAVsAdaptive dynamic programmingYesAdaptive dynamic logic and bringing intervehicle communication(i) The optimal controller strategy can asymptotically stabilize the connected vehicles(ii) The framework can be used for solving connected vehicle control problems with unknown system dynamicsOlia et al. [98]Evaluating the impacts of CAVs on the capacities of highway systems—NoGipps model(i) Maximum lane capacity of 6,450 vph per lane (300% improvement) is achievable if all vehicles are driven in a cooperative automated mannerVISSIMXie et al. [70]Modeling a merging strategy for freeway operations under connected autonomous environmentAn optimization-based ramp control strategyYesDictating the movement based on default models(i) The optimal control strategy demonstrated effective coordination among the merging vehicles and will improve the safety and mobility at undersaturated traffic conditionsVirdi et al. [99]Understanding the safety improvements in mixed traffic stream across the network elementsDeveloping Virdi CAV Control Protocol (VCCP) algorithm for modeling CAV behaviorYesAV following model (Talebpour a Mahmassani, 2016)(i) At low penetration rates of CAV, the potential conflicts will increase(ii) Results demonstrated that CAV operation seems to show a significant overall improvement in safety at midblock road sections(iii) With increase in CAV penetration rates, the potential conflicts reduced. The greater the priority, the more the reduction in conflictsPapadoulis et al. [75]Understanding the safety impacts of CAV on motorwaysDecision-making CAV control algorithmYesAssuming CAVs longitudinal time gap as 0.6 s and modeling the instant speeds(i) Results indicated that CAVs bring about compelling benefit to road safety as traffic conflicts significantly reduce even at relatively low market penetration ratesArd et al. [100]Studying the Anticipative Cruise Controller mechanism in two main different scenarios, under disconnected mode with human-driven vehicles and connected mode with other CAVsFormulated Model Predictive Control (MPC) framework to dictate the movement of the vehiclesYesControl-oriented longitudinal dynamics model(i) Automated vehicles perform at a 10%–20% higher energy efficiency over human-driven vehicles(ii) Simulated human-driven vehicles were found to drive up to 10% more energy-efficiently than they did in the baselineLiu and Fan [101]Impacts on freeway capacity due to CAVs—YesIDM(i) With an increase in CAV penetration rate, freeway capacity increases, and further the capacity increases significantly with the increase in speed limitsAIMSUNShladover et al. [102]Understanding the impacts of CACC on traffic flow—YesNissan ACC and CACC car following behavior (Bu et al., 2010)(i) Use of ACC was unlikely to change lane capacity significantly(ii) CACC was able to increase capacity greatly after its market penetration reached moderate to high percentages(iii) Capacity increase could be accelerated by equipping non-ACC vehicles with vehicle awarenessChou et al. [103]Coordinated merge control with vehicle-to-vehicle communicationV2V highway merge control algorithmYes—(i) Merging performance is found to be proportional to the CAV penetration rates(ii) Merge assist features to human-driven vehicles, even at less AV penetration rate, merge performance is enhancedYu et al. [104]Impacts of AV lanes in mixed traffic—NoIDM(i) Roadway capacity increases by 84% by providing AV lanes(ii) Deterioration in safety is reported when the market penetration of CAVs is less than 40Wang et al. [105]Effects of CACC on traffic flow—YesFollowing: IDM, lane changing: Gipps + Heuristic(i) CACC helped significantly attenuate traffic congestion and even remove congestion at AV penetration rate greater than 40 percentMakridis et al. [106]CAV impact on traffic flow and CO2 emissions—YesAV: Nissan ACC [102]CAVs (Van Arem et al., 2006)(i) CAVs can deteriorate the status of the network, and that connectivity is the key to improved traffic flow(ii) Emission-wise, the CAVs have the highest fuel consumption per km traveled among other types, while CAVs only marginally lower the overall consumption of human-driven vehiclesOlsson and Levin [107]Management of traffic intersection in the connected scenariosAutonomous Intersection Management (AIM)Yes—(i) AIM+ is able to reduce delay and increase average speed in comparison to conventional intersection management practicesSUMOJesús Mena-Oreja and Gozalvez [108]Platooning maneuvers in mixed trafficPlatooning Extension Plexe, PERMITYesCACC model(i) Identifying the importance of platooning in connected scenarioTimmerman and Boon, [109]Intersection managementProposing platoon forming algorithms and providing equal predefined delay to the vehicles over the intersectionYes—(i) Platoon forming algorithms with a low mean delay sometimes are relatively unfair, indicating a potential need for balancing mean delay and fairnessLiu et al. [110]Intersection managementProposing a cooperative scheduling mechanism for CAVs passing through an intersection, called TP-AIMYes—(i) TP-AIM mechanism significantly reduces the average evacuation time and increases throughput by over 60s and over 20%, respectively.(ii) The maximum delay in TP-AIM can be reduced to less than 10% of Adaptive and Fixed LightsDomingues et al. [111]Lane changing maneuvers and traffic congestion, fuel consumption, and CO2 emissionProposing a negotiation method for the vehicular merging and lane changing scenariosYesNegotiation logic for lane changing movement(i) Platooning has an important impact on reducing fuel consumption and CO2 emissions(ii) Platooning combined with lane-merging shows a significant increase in performanceMaske et al. [112]Improving the mixed traffic performance by intervehicle communicationBy means of deep reinforcement learning, intervehicle communication is carriedYesOptimum Velocity Model (OVM)(i) Traffic stream performance is improved by the intervehicle communicationLu and Kim [113]Intersection management with mixed trafficDiscrete-time occupancies trajectory-based intersection traffic coordination algorithm (DICA)Yes—(i) Proposed DICA is found to be computationally efficient(ii) At uncongested traffic, DICA has better traffic throughput than the optimized traffic control mechanismChouhan et al. [114]Modeling the lane sorting strategies for CAVsProposing an algorithm that converts traffic composed of vehicles located randomly in a set of lanes into sorted trafficYes—(i) The lane sorting strategies were proven to improve the density of traffic streamsLi et al. [115]Modeling right of way (RoW) scenarios for mixed traffic—YesTheoretical model(i) With proper RoW, the road capacities are improved at even a lower AV penetration rateLi and Liu [116]Investigating intersection management strategy for CAVsProposing a matrix-based algorithm for allocating the vehicle movementYes—(i) The proposed algorithm performs better in comparison to both adaptive and fixed traffic signal systemsChamideh et al. [117]Coordination of CAVs at intersectionsAn edge cloud controller is used to deliver services that provide traffic safety and efficiencyYesIntersection Coordination Unit (ICU) will allocate the vehicle speeds to move over the sections(i) Delay is reduced over the intersection, along with improvement in safety and flow over the intersections(ii) High execution time was reported in processing the coded algorithmsMOTUSWang et al. [72]Cooperative car following controlCooperative Controller and Distributed AlgorithmYesDeveloped CFC and C-CFC(i) The developed algorithms improved the jam characteristics in traffic networkXiao et al. [118]Converting existing High Occupancy Vehicle (HOV) lanes into CACC lanes—YesCACC levels(i) Converting to CACC lanes at low penetration less than 30 percent can decrease the congestion(ii) However, at medium CACC with penetration rates of 40 to 50 percent, the congestion is drastically alleviated due to a large share of traffic carried by CACC lanesIt is observed that researchers applied numerous logic systems/algorithms, including adaptive dynamic programming [97], optimization-based ramp control strategy [70], Virdi CAV Control Protocol [99], decision-making CAV control algorithm [75], Model Predictive Control [100], Autonomous intersection management [107], Platooning Extension Plexe [108], cooperative scheduling mechanism for CAVs [110], discrete-time occupancies trajectory-based intersection traffic coordination algorithm [113], lane sorting [114], matrix-based intersection management logic [116], and Cooperative Controller and Distributed Algorithm [72].The main aim of the algorithms mentioned above was to induce CAVs’ behavior and instigate communication among the vehicles for better traffic movement over the traffic network. Researchers tested the CAVs across various traffic facilities, including midblock sections, signalized intersections, unsignalized intersections, on- and off-ramps, etc. The behavior of the AVs was governed by various models including the Gipps model [119], Intelligent Driver Model (IDM) [120], Optimum Velocity Model (OVM) [121], Adaptive Cruise Control (ACC) [45], Cooperative Adaptive Cruise Control CACC [71], Theoretical model, and cooperative longitudinal following behavior.The focus of the researchers varied following the type of study segments. In midblock sections, researchers were focused on CAVs following behavior and lane changing behavior with platooning logic to maximize the improvement in safety and throughput. On the other hand, at intersections and ramps, studies focused on communication among CAVs so that CAVs coordinate themselves and create necessary gaps for traffic movement, with the idea of limiting the stop and go conditions and eliminating traffic signals. Unlike the studies of CAVs with inbuilt models, the studies were focused on the efficacy of the behavioral logic rather than studying the penetration impacts of CAVs. To a certain extent, most of these studies demonstrated a good efficacy of their logic/algorithms. With some externalities, microsimulation platforms such as VISSIM, Paramics, and AIMSUN performed the simulation runs with a single core in the computer processor. As a result, the processing time with those externalities was high. Therefore, the application to a bigger network will be computationally inefficient. Furthermore, most of the logic/algorithms were tested at isolated study segments; therefore, their performance must be quantified in future studies for a traffic network level with greater stochasticity. Finally, to express the reviewed literature in a better manner, visualization analysis was carried out as shown in Figure7.Figure 7
AV microsimulation studies over different platforms.


## 4.3. Cosimulation

While in traffic microsimulation, interactions among vehicles are modeled and traffic characteristics are quantified, traffic microsimulation tools do not offer an opportunity to model the C/AV mechanism, and the behavior of the C/AVs is mainly governed by the behavioral models rather than the accurate perception of the infrastructure environment. On the other hand, from an individual C/AV perspective, the automobile industry programs the C/AV behavior in four planning stages: global route planning, behavior planning, motion planning, and local planning. To identify the state estimation and localization, C/AVs are reinforced with numerous sensors for the perception. To model the performance of these sensors and the perception of the infrastructure environment, nanoscopic AV simulation tools are available. Nanoscopic AV simulation tools offer numerous customization options, including detailed maps, 3D vehicle models, sensors, vehicular physics-based models, and external programming interfaces for controlling the actors during the run-time. To better understand the nanoscopic AV simulation tools, a review analysis was performed, and the results are summarized in Table5.Table 5
Details of AVs nanoscopic simulation tools.
Nanoscopic simulation toolDeveloperNature of distributionLocal planningGlobal route planningAvailable sensors for perceptionBuilt environmentApplication program interfaceLatest releaseCosimulation with microsimulation toolsCarlaComputer Vision Center (CVC), Intel, Toyota Research institute, FutureweiOpen sourceYesYesCamera, lidar, radar, safety, GNSSUnreal EnginePython (3.7)Carla 0.9.12VISSIM, SUMOPreScanTASS International (Siemens)CommercialYesYesCamera, lidar, radar, safety, GNSSUnreal EngineMATLAB and SimulinkPrescan 2020.4VISSIM, SUMO, AIMSUNVIRES virtual Test DriveHexagonCommercialYesYesCamera, lidar, radar, safety, GNSSNot identifiedVTD APIVIRES VTD 2.2VISSIM, SUMOCarSimPresent: Mechanical Simulation Corporation Initial: University of Michigan Transportation Research InstituteCommercialYesYesCamera, lidar, radar, safetyUnreal EnginePython, MATLABCarSim 2021.1VISSIM, SUMOSummitNational University of SingaporeOpen sourceYesYesCamera, lidar, radar, safety, GNSSUnreal EnginePython (3.7)Summit 1.0SUMOOpenDSGerman Research Center for Artificial IntelligenceOpen sourceYesYesCamera, lidar, radar, safety, GNSSjMonkeyEngine 3.2Lightweight Java Game LibraryOpenDS 5.0VISSIM, SUMOVI-WorldSimVI-grade GmbHCommercialYesYesCamera, lidar, radar, ultrasonic, safety, GNSSUnreal EngineMATLABVI-WorldSimNot identifiedAutomotive Simulation Models (ASM)dSPACE GmbHCommercialYesYesCamera, lidar, radar, GNSSUnreal Engine 4MATLAB and SimulinkASM 9.6VISSIM, SUMOAltair simulationAltairCommercialYesYesradar, ultrasonic, and lidarNot identifiedMATLABAltair simulation—SCANeR studioAVSimulationCommercialYesYesCamera, lidar, radarUnreal EnginePythonSCANeR 2021.2Not identifiedDriving SimulationrFproCommercialYesYesCamera, lidarNot identifiedMATLAB and SimulinkDriving Simulation rFproVISSIM, SUMOAutonomous Vehicle SimulatorNICommercialYesYesCamera, lidar, radarUnreal EngineNot identifiedAutonomous Vehicle SimulatorNot identifiedCarMakerIPG AutomotiveOpen sourceYesYesCamera, lidar, radarNot identifiedMATLAB and SimulinkCarMaker 10.0VISSIM, SUMODriveSimNIVIDIAOpen sourceYesYesCamera, lidar, radar, ultrasonic, safety, GNSSNVIDIA RTXPythonNIVIDIA DriveSim 10.0—SVL SimulatorLGOpen sourceYesYesCamera, lidar, radar, ultrasonic, safety, GNSSUnity game enginePythonLGSVL Simulator 2020.06—veDYNATESIS GmbHCommercialYesNoCamera, lidar, radar, ultrasonic, safety, GNSS—MATLABveDYNA—DeepdriveVoyageOpen sourceYesYesCamera, lidar, radarUnity game enginePythonDeepdrive—CognataCognataCommercialYesYesCamera, lidar, radar photorealistic sensor modelsCognata enginePythonCognata simulation—MetamotoInitial: MetamotoPresent: Foretellix Ltd.CommercialYesYesLaser radar, camera, millimeter wave radar, ultrasonic radar, GPS, and IMUUnreal EnginePythonMetamoto—RightHookRightHookCommercialYesYesCamera, lidar, radar, ultrasonic, safety, GNSSUnity, UE4, or CryEnginePythonRightHook—Parallel DomainParallel DomainExternal Support: ToyotaCommercialYesYesCamera, lidar, radar, ultrasonic, safety, GNSSUnreal Engine 4, Houdini, Maya, 3DS Max, and Substance SuitePythonParallel Domain—51Sim-One51WorldOpen sourceYesYesCamera, lidar, radar, ultrasonic, safety, GNSSNot specified explicitlyPython51Sim-One—Pilot-D GaiaPilot-D AutomotiveCommercialYesYesCamera, lidar, radar, ultrasonic, safety, GNSSUnreal EnginePythonPilot-D Gaia 3.0—ESI Pro-SiVICESICommercialYesYesCamera, lidar, radar, ultrasonic, safety, GNSSUnreal EnginePythonPro-SiVIC 2020.0—PanoSimPanoSimCommercialYesYesCamera, lidar, radar, ultrasonic, safety, GNSSNot specified explicitlyPython, MATLABPanoSim 4.0—AAIAAI GmbHCommercialYesYesCamera, lidar, radar, ultrasonic, safety, GNSSUnity game enginePythonAAI—Baidu ApolloBaiduOpen sourceYesYesCamera, lidar, radar, ultrasonic, safety, GNSSApollo Game EnginePython, Baidu CloudApollo 6.0SUMOWaymo CarcraftGoogle WaymoOpen sourceYesYesCamera, lidar, radar, ultrasonic, safety, GNSSUnreal EnginePythonCarcraft—TAD SimTencentCommercialYesYesCamera, lidar, radar, ultrasonic, safety, GNSSTencent game enginePythonTAD Sim 2.0—CruzWayUniv. of California, Santa CruzOpen sourceYesYesNot specified explicitlyUnreal EnginePythonCruzWaySUMOGazeboUniv. Grenoble Alpes, InriaOpen sourceYesYesCamera, lidar, radar, ultrasonic, safety, GNSSNot specifiedPythonGazebo 3DSUMO3DCoAutoSimUniversidad Carlos III de Madrid (UC3M)Open sourceYesYesCamera, lidar, radar, ultrasonic, safety, GNSSUnity game enginePython3DCoAutoSimSUMOFrom the analysis, it is observed that more than thirty AV simulation tools are available in the market. They are commercial and open source in nature and are built using game engines [122, 123]. In general, game engines give the flexibility to place the actors over the 3D space. Based on their character, the behavior will be exhibited. By connecting the actors and agents, the simulation environment is built. The open-source unreal game engine [124] was found to be a popular game engine for building the simulation environment. Most simulation tools offer all kinds of basic sensors for modeling the AV perception. To control the actors during the simulation run-time, application programming interfaces are provided, mostly in python and MATLAB/Simulink.Interestingly most of these tools offer a cosimulation environment, in which different AV simulation tools are coupled with traffic microsimulation tools. During the cosimulation, the C/AV behavior is completely governed by the AV simulation tool, where the behavior of C/AVs is modeled with perception from the system, and the entire traffic behavior is governed by the traffic microsimulation tool. These cosimulation strategies help in simulating the C/AVs more accurately and improve the simulation to achieve a more realistic estimation of the traffic operation. The review analysis observed that SUMO and VISSIM are used more often among other traffic microsimulation platforms as cosimulation tools. Given the open-source nature, SUMO is favored in the AV cosimulations.The literature related to the C/AV cosimulation studies was reviewed and is summarized in Figure8 and Table 6. The review analysis revealed that the studies are limited in nature. The cosimulation studies picked up recently, and a major proportion of these studies focused on developing methodologies for the C/AVs cosimulation. Interestingly, researchers favored VISSIM to couple it with CarMaker and SUMO with Carla to carry the cosimulation.Figure 8
AV cosimulation studies over different platforms.Table 6
Details of AV cosimulation studies.
StudyStudy focusMicrosimulation toolAV simulation toolScripted logicAV behaviorAV perception sensorsFindingsKlischat et al. [125]∗Transferring the trajectories of vehicles controlled by a motion planner and the trajectories of other traffic participants between SUMO and CarlaSUMOCarlaLane change synchronizationLocal—The proposed algorithm for synchronizing lane changes with SUMO yields a more realistic behavior in line with motion planningSelvaraj, [126]Focusing on developing a framework for sensor- and communication-assisted vehicle dynamic for AVsSUMOCarMakerACC and CACC controllersLocal, motion, global—Analyzing the importance of the connected vehicles in vehicular safety applicationsZhu et al. [127]∗Evaluating the performance of a proportional-derivative (PD) steering controllerSUMOCarSim—Local—A new lateral control design procedure based on robust PD controller with parameter space design and add-on model regulator is introducedParanjape et al. [128]∗Generating town sized road networks and intersections for the modeling of AVs and pedestriansSUMOCruzWayTownSim, IntGen, Netgenerate, Sumo2UnrealLocal, motion—With the help of CruzWay, the road network is modeled in the form of mesh and navmesh and imported to SUMOWang et al. [105]∗Introducing a Python API between the planning module of the Apollo platform and the CommonRoad frameworkSUMOBaidu ApolloCommonRoadLocal, motion—Converting the road network and obstacle information from Apollo to CommonRoad and developing an interface with CommonRoad-SUMO for simulating the vehiclesAoki et al. [129]∗Cooperative perception scheme with deep reinforcement learning to enhance the detection accuracy for the surrounding objectsSUMOCarlaCIVSLocal, motionCamerasCIVS platform able to produce realistic 3D graphics, traffic model, vehicle model, sensor model, and communication among the vehiclesWang et al. [130]∗Hierarchical behavior and motion planning (HBMP) to explicitly model the behavior in learning-based solutionSUMOCarlaHBMPLocal, motionLidar, camerasGiving a methodology to work on hierarchical behavior and motion planning using the reinforced learningZofka et al. [131]∗Robot Operating System (ROS) for a mixed reality environmentSUMOCarlaROSLocal, motionFramework now enables the holistic simulation of traffic scenarios by coupling multiple domain expert models with real world entitiesSantonato, [132]Working on a framework for complete end to end simulation for AVsSUMOCarla—Local, globalLidar, camerasIdentifying the importance of cosimulation for AV simulationWang et al. [133]∗CommonRoad-RL, an open-source toolbox to train and evaluate RL-based motion planners for autonomous vehiclesSUMOCarlaCommonRoad-RLLocal, motion—Framework for an open-source toolbox to train and evaluate RL-based motion planning for autonomous vehiclesChada et al. [134]Cosimulation framework for testing predictive eco-driving assistance systems (EDAS) for commercial vehicles in urban environmentsSUMOCarMakerEco-driving assistance systems (EDAS)Local, motion—Proposed cosimulation framework has been tested on the driving simulator in real timeYuan, [135]∗Collective Multiagent Perception In combining SUMO and Carla for modeling the AVsSUMOCarlaCOMAPModeling the cooperative behavior for AVsLidar, camerasCollective perception proven to improve the localization accuracy for the AVsStudyStudy focusMicrosimulation toolNanoscopic simulation toolScripted logicAV behaviorAV perception sensorsFindingsFu et al. [136]∗Working on network-level autonomous driving simulator framework based on the Cellular-Vehicle-to-Everything (C-V2X) protocolSUMOCarlaLTE-V2XConnectivity at intersectionsCamerasCombining SUMO and Carla and working on cosimulation framework, demonstrating that framework is feasible for some advanced autonomous driving applications, such as multi-intersection vehicle scheduling and remote drivingXu et al. [137]∗Working on cooperative driving automation logic for the AVs at merging sectionSUMOCarlaOpenCDALocal, globalLidar, external communicationShowing the importance of OpenCDA for modeling cooperative behavior logic among the AVsSchmied, [138]∗Designing and comparing two different control concepts applied to Adaptive Cruise Control (ACC) and lane change assisted ACC (LC-ACC)VISSIMCarMakerModel Predictive Control (MPC)Local, motion—Mixed H2/H∞ and predictive control concepts have been introduced to increase performance of the ACC and LC-ACCNalic et al. [139]Cosimulation framework for systematic generation of scenarios for testing and validation of Automated Driving SystemsVISSIMCarMakerDynamic Data Exchange interfaceLocal, motion—Demonstrating the importance of cosimulation for realistic behavioral analysisWaschl et al. [140]Framework for testing realistic traffic conditionsVISSIMCarMakerPredictive Adaptive Cruise Control (P-ACC)Local—Considering the traffic control based on available V2X and I2V information or more sophisticated sensor modelsNalic et al. [141]Implementation of a cosimulation frameworkVISSIMCarMakerModel View Controller (MVC)Local, motion—cosimulation framework for testing ADSs using MATLAB/Simulink, CarMaker, and VISSIMNalic et al. [142]A novel stress testing methodVISSIMCarMakerExternal driver modelsLocal, motion—The developed stress testing method showed a significant increase of detected scenarios in the cosimulation environmentNalic et al. [143]Framework for testing AVs in traffic environmentVISSIMCarMakerDriver Model DLL interface (DMDI)Local, motion—Driver model framework∗Literature from arXiv and conference proceedings.The cosimulation research studies highly concentrated at local and motion planning of the C/AVs, and less weightage was given to the global planning. It can be noted that AV simulation tools use the detailed network and simultaneously couple it to the microsimulation which demands good computational power. During the cosimulation, the microsimulation runs are carried out by the externalities, reducing the simulation processing speed and impacting the run-time. Most AV simulation platforms provide inbuilt maps/networks; however, generating such detailed maps for the study section requires additional tools. Given this, AV cosimulation studies are highly dependent on the inbuilt maps/networks in the present context. Finally, currently the cosimulation is restricted to traffic volume, simulation time, and network size.


## 5. Summary

Given the long research history and research activities, traffic microsimulation has evolved as a core area in traffic engineering. The research activities in the traffic microsimulation domain were found to be highly dynamic in nature with numerous traffic microsimulation studies emerging across the world. Therefore, understanding the flow of the research can help in carrying future research studies. Thus, this study attempted to understand the literature in the traffic microsimulation research domain. Initially, the literature was mined, and the analysis, in terms of the source of microsimulation studies, was performed. To realize this, the involved institutes and the scientific journals were reviewed. From the review analysis, it was observed that worldwide researchers addressed the traffic problems considering the unique traffic conditions in their own countries. Based on the analysis, it was found that few institutes from certain countries are heavily involved in this domain. Traffic microsimulation studies have found a place in more than 50 journals and numerous conferences. Among them, some specific journals gathered a significant share of simulation studies.For supporting the research activities and industrial applications, numerous traffic microsimulation platforms have evolved. In this paper, commercial and open-source microsimulation platforms were reviewed; from the review analysis, it was found that some specific platforms kept on evolving, while some others lost their share. Particularly in the previous decade, microsimulation studies have picked up the pace. As a result, a great number of microsimulation studies were conducted. Microsimulation platforms were upgraded over time to match the realistic field conditions as closely as possible. In the present context, microsimulation tools offer various customization options. To a certain extent, researchers can override the inbuilt models and test their frameworks, including advanced mathematical concepts, such as Artificial Intelligence logic.Despite the many advantages, there are certain limitations associated with microsimulation platforms. The calibration of the simulation models plays a significant role in having confidence in the simulation outcomes. Given the sophisticated inbuilt models, microsimulation platforms demand detailed empirical data regarding the vehicle trajectories from field conditions. At the same time, generating such kind of microscopic data may not be viable all the time. Due to this, the simulations are calibrated with certain assumptions, and as a result, the same assumptions will be reflected in the simulation outcomes. While performing the simulation runs with externalities, the processing falls to a single core in the computer processor in some microsimulation platforms. As a result, for medium-scale networks with heavy traffic flow conditions, the simulation run can overload a single processor’s core, leading to simulation crashing. Presently, this is a limitation in expanding microsimulation studies with externalities for a more extensive network with heavy traffic.


## 6. Future Prospects for AVS

Given the considerable innovations in automobiles and top multinational conglomerates’ involvement in research and innovation in this sector, anticipating AVs on our road network is becoming a realistic scenario for the near future. Additionally, this has brought new challenges for the researchers in the transportation domain. Traffic microsimulation platforms can be one of the dependable resources in understanding AVs’ impacts from a traffic engineering point of view. Particularly in the second half of the last decade, researchers focused heavily on understanding AVs’ effects on traffic performance. The literature examination shows that the research attempts in this direction have taken two significant approaches. According to the first approach, the researchers have used the inbuilt models from the simulation platforms and have adapted the model’s parameters to replicate the behavior of AVs. The second approach used by researchers is coding the CAVs’ behavior with the help of externalities for the simulation runs. Presently, both strategies have their advantages and limitations. Most studies have focused on the impacts of AVs penetration rates on safety and efficiency, with the majority predicting that, at low AV penetration rates, both safety and efficiency will be degraded. At higher AV penetration rates, the studies forecast significant improvements in safety and efficiency standards. It is inferred that the results across the different studies are not consistent; the AV transition penetration rates are varying across the studies and range from 20% to 70%. The results of these studies are highly dependent on the inbuilt models of the simulation platforms. On the other hand, with externalities, researchers focused on the communication aspects among CAVs and worked on platooning models at midblock sections, cooperative merging models at merging sections, and cooperative gap formation models at intersections. The present ongoing studies adapt the behavioral model parameters in such a manner that C/AVs will have smooth behavior in comparison to human-driven vehicles. At the same time, the calibration of the parameters for C/AVs demands trajectory data of C/AVs from field conditions, which is limited in nature.From the studies, it is concluded that most studies assume fully functional AVs in the simulation models. However, from a practical perspective, the functionality of AVs is discretized into five levels by the Society of Automotive Engineers (SAE). In supporting this, presently, AVs of initial levels find their place on our road networks. As a result, there can be a mixed proportion with different AV levels that can be observed. In existing studies, not much weight is given to these aspects. This forms a huge research gap in the present AV microsimulation studies. From recent investigations, it is observed that AVs’ presence can influence the behavior of human-driven vehicles. However, present microsimulation studies assume that human-driven vehicles will have the same usual interaction standards even with AVs, ignoring human behavioral adaption. This is once again one of the limitations of present AV microsimulation studies, which can be addressed by future studies.Considering the importance of C/AV motion planning in microsimulation, cosimulation frameworks have evolved in recent years. Cosimulation strategies show a good promise to model the C/AV behavior in a much-detailed manner combined with sensor perception. In the present context, most of the cosimulation studies are at initial levels and are highly focused on developing cosimulation frameworks. Given the computational constraint in cosimulation APIs, the cosimulation studies focus on a single vehicle with limited traffic volume and network. Cosimulation studies are highly focused on C/AV motion planning with less weightage to traffic characteristics as in microsimulation. However, with the ongoing attempts and cosimulation APIs, it can be extended with a higher number of C/AVs guided based on the sensors in a traffic stream and the human-driven vehicles with the behavioral models. These developments will help develop new and improved strategies for better management of mixed traffic. At the same time, the results can build enough confidence for practitioners in accepting the research outcomes.



---

*Source: 2444363-2021-09-25.xml*