Using vehicle trajectory datasets developed over a study section for three traffic flow levels, a rectangular hyperbolic relation between time-to-collision and relative speeds in vehicle-following behavior were observed. A new methodology for estimating the probable rear-end collisions in the given traffic stream is developed based on this relation. The vehicle-following behavior is examined in terms of the hysteresis phenomenon concerning the distance gap (DG) and relative speed (RS). Further, based on the follower's attention toward towards the leader vehicle, a novel surrogate safety measure, called Instantaneous Heeding Time (IHT), was conceptualized. This measure represents the time gap available based on the positions of the leader and follower vehicles. After exploring vehicle-following behavior, IHT, DG, and RS were used to estimate the rear-end collision probability. The applicability of the proposed methodology is tested using different thresholds (IHT, DG, and RS) and applied to the study section at the three traffic flow levels.

The present work introduced a framework of developing comprehensive extended vehicular trajectory data under heterogeneous non-lane-based traffic conditions like the NGSIM datasets in the United States. Due to the absence of automation and instrumentation, and even the lack of sensor deployment on roads in developing economies like India, it is even more challenging to study driver behavior. A new stitching-based algorithm was used for developing the extended trajectory database for three traffic-flow levels on a 535-m long section of an urban arterial. The algorithm was used to stitch the trajectory data over the segments such that the subject vehicle with continuous trajectory data points over the entire study stretch. The developed framework is a novel tool for establishing a trajectory dataset for mixed traffic, it should be of interest to researchers in developing and developed countries.

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 present research work is initiated, probing for safety levels, evolving as an outcome of analysis of micro-level interactions due to naturalistic vehicular movements on Indian expressways. The research work reported here, devised a micro-level parameter, namely instant perception time (IPT) as a safety measure, which is a function of vehicular interactions. As a step towards deriving IPT, video-graphic surveys were conducted and traffic data was collected on two intercity Indian expressways. As a part of this work, first microscopic trajectory data was developed for capturing naturalistic driving behavior under heterogeneous traffic environment. Thereafter, hysteresis plots were developed between leader-follower pairs using relative speed and spacing between the vehicles. By means of the developed hysteresis plots, a novel approach for identification and quantifying chances of rear-end crashes using follower's Instantaneous perception time (IPT), is proposed in the present study. To study the IPT approach under different flow conditions, well-calibrated and validated simulation model is also developed using real trajectory data collected for the prevailing traffic conditions observed on the study sections. From the obtained IPTs using simulation model, three cluster ranges are delineated pertaining to different levels of risks of crashes. The cluster-analysis suggests that the least values of space-headway (m) and IPT (s) (space headway < 10.91 m, IPT < 5.61 s) at a given location shows most severe chances of rear-end collision. While, lower to moderate values indicate (with thresholds: space headway in the range 10.91–18.5 m; IPT 5.61–11.91 s), whereas higher values of IPT (more than 12 s) suggest the least chances of rear-end collision. The methodological approach, results and findings thus developed under naturalistic driving environment, presented here can be very useful in understanding the operational deficiencies of traffic streams, road geometry, coupled with safety assessment of traffic streams on uninterrupted roadway facilities. Furthermore, traffic control or geometry improvement related decisions can also be taken at different unsafe locations using IPT as a risk measure to enhance safety.

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.