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.

The research work reported here investigates driving behavior under mixed traffic conditions on high-speed, multilane highways. With the involvement of multiple vehicle classes, high-resolution trajectory data is necessary for exploring vehicle-following, lateral movement, and seeping behavior under varying traffic flow states. An access-controlled, mid-block road section was selected for video data collection under varying traffic flow conditions. Using a semi-automated image processing tool, vehicular trajectory data was developed for three different traffic states. Micro-level behavior such as lateral placement of vehicles as a function of speed, instant responses, vehicle-following behavior, and hysteresis phenomenon were evaluated under different traffic flow states. It was found that lane-wise behavior degraded with increase in traffic volume and vehicles showed a propensity to move towards the median at low flow and towards the curb-side at moderate and heavy flows. Further, vehicle-following behavior was also investigated and it was found that with increase in flow level, vehicles are more inclined to mimic the leader vehicle’s behavior. In addition to following time, perceiving time of subject vehicle for different leading vehicles was also evaluated for different vehicle classes. From the analysis, it was inferred that smaller vehicles are switching their leader vehicles more often to escape from delay, resulting in less following and perceiving time and aggressive gap acceptance. The present research work reveals the need for high-quality, micro-level data for calibrating driving behavior models under mixed traffic conditions.

This research work is focused on modeling vehicle following behavior under mixed traffic conditions. Vehicle-following behavior can be potentially utilized in building real-time drivers’ assistance systems or identifying collision escaping time thresholds for varying roadway and traffic conditions. For this purpose, initially, vehicular trajectory data was developed over the road sections in India. Thereafter, based on hysteresis phenomenon among the vehicles, vehicles in the following conditions are identified. Further to model the following the behavior of vehicles, selected car-following models, such as Wiedemann-74, Wiedemann-99, Gipps, Bando and Intelligent Driver Model (IDM) are calibrated for followers (subject vehicle) from each vehicle category. The model’s validity is tested by comparing the observed position and modeled position of the follower under varying conditions. Subsequently, to test the car-following model’s efficacy in replicating the following behavior, simulation runs were performed using VISSIM 9.0. For this purpose, the car following models (other than Wiedemann models) were coded in VISSIM using external driving behavior program. The models were then tested using calibrated vehicle-dependent following-behavior parameters. Based on the simulation runs, the comparison was made with derived traffic parameters for macroscopic validation. For better understanding hysteresis plots were also compared for different vehicle-following models, using simulated trajectory data. Further, simulated hysteresis for different vehicle types was compared with related plots made using actual trajectory data. It was well-established that psychophysical models (Wiedemann), multi-regime (Gipps), and IDM car-following models resemble Indian traffic behavior reasonably well, whereas single-regime car-following model, optimal velocity model fails in replicating the actual following-behavior.

The present research work provides a methodology of calibration of the vehicle-following behavior in VISSIM using vehicle trajectory data obtained under non-lane-based mixed traffic conditions. In this context, two access-controlled mid-block sections, one on the multi-lane urban road with six-lane-divided carriageway and the second one with eight-lane-divided carriageway are identified for extracting high-quality vehicle trajectory data. Later, using trajectory data, lane-wise time-space plots were developed for the vehicles’ movement simultaneously in the same lane as well as from adjacent lanes for studying non-lane-based movement.The investigation is carried out by developing a relationship (hysteresis plots) between relative distance versus relative velocity among the leader–follower vehicle for the vehicles in the same as well as in adjacent lane that is nearby position. Hysteresis phenomenon for vehicles under the following behavior is examined.Data about identified potential vehicular pairs are used for calibration of well-known psychophysical car-following models.

The research work presented is originated with the intent of studying driving behavior, especially in a lateral direction under the influence of construction work activities under prevailing heterogeneous traffic conditions. Initially, a midblock road section is selected, and then macroscopic traffic characteristics were evaluated. Later, Mumbai metro-rail construction work was started on the same road section. As a result, it was planned to study the variation in traffic-flow characteristics for road sections with-and-without construction work zone at microscopic as well as macroscopic levels. For this purpose, video graphic surveys were conducted, and macroscopic characteristics were evaluated for both study sections (with-and-without construction work zone). The results showed that at given flow levels, the stream speeds were dropped from 70 to 50 kph, followed by a drop in efficiency in terms of per-lane capacity. To sense this impact more deeply, vehicular trajectory data were developed over road sections at three different traffic-flow levels. The driving behavior is investigated using different variables, such as longitudinal following behavior, following times, lateral amplitude, and lateral placement, followed by lateral direction correlation analysis. From the investigation, the results show that the following-hysteresis plots for vehicles are found to be slender in a construction work-zone, revealing the conservative approach in driving behavior. Furthermore, following-time analysis also supports the same interpretation. Given the smaller size of vehicles, the vehicle tends to possess extra lateral freedom and hence is responsible for the seeping phenomenon through a porous media in the traffic stream. As an impact of reduced road width, capacity (per-lane) values are found to be higher in the base section than the section having a construction work zone. Further, based on the conceptualized framework, safety in the traffic stream over the study sections was evaluated. Finally, the importance of cautioning drivers the construction work activity in advance was well revealed from the safety analysis of this work.