Road safety is one of the major concerns in the ever-growing traffic network. In addressing this, surrogate safety measures play a critical role in identifying collision instincts. Besides the added advantage of quantifying collision instincts in advance, surrogate safety measures have their limitations. For example, in some instances, those measures tend to show erroneous results. In this paper, a new surrogate safety measure Instant Heeding Time (IHT), is presented based on follower vehicle attention in the traffic streams. This new measure is integrated with a distance gap and the vehicles' speeds to assess probable rear-end collisions. Further, along with other safety measures, the developed safety framework is tested over a study section, with the help of trajectory datasets at three traffic flow conditions (free flow, capacity, and congested) under prevailing heterogeneous (mixed) traffic conditions. Based on the safety framework, it is observed that, in the case of free flow and capacity conditions, 23 and 55 probable rear-end collisions points are detected. At the congested conditions, no rear-end collision points are observed. Further, smaller vehicles in the traffic stream are associated with a higher number of rear-end collision instincts than other vehicle categories. The conceptualized safety framework can be applied on a real-time basis for monitoring the safety measures for vehicles in a mixed traffic stream.@en

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.@en

The paper presents the performance analysis of a well-designed truck parking terminal, which is planned for regulating truck traffic over a commercial port. The designed truck parking terminal is modeled using microscopic traffic simulation, which is validated based on the movement of vehicles to the parking bays. After validation, various scenarios were created to evaluate parking terminal performance by varying the parking volumes and number of operational parking bays. The operational efficiency of the parking terminal for design scenarios was evaluated using parking performance measures that included parking load, average parking duration, parking turnover, and load-to-capacity ratio (parking index). For the design peak load of 4,200 vehicles/day with a uniform arrival rate, the operational efficiency was found to be about 73%. Interestingly, it was observed that with an increase in the number of operational parking bays, the parking efficiency decreased for the given volume level. Considering this phenomenon, a methodology was developed to identify the optimum number of parking bays under varying demand-supply scenarios. The developed methodology can help identify the optimum number of parking bays for existing and future (expansion) conditions. Furthermore, this study highlights the importance of using simulation in evaluating operational and design aspects of transportation facilities, where the need for repeated empirical observations is eliminated. As such, this study should be of interest to traffic engineers and practitioners interested in the efficient operation of parking terminals.@en

This study aims to model traffic flow under weak lane based heterogonous (mixed) traffic conditions. Unlike homogeneous traffic, when a follower (subject) vehicle in mixed traffic moves closer to its leader vehicle, it tends to adjust its longitudinal movement or change its lane and acts discretely. Due to this phenomenon, traffic flow modeling under such conditions is always challenging. A new driver behavioral logic is conceptualized for the vehicles' movement within a combination of surrounding vehicles. In which the following behavior was dissected with the lateral shift distance between vehicles. Two car-following models for homogeneous traffic conditions, the IDM and Gipps models were adapted with relevant lateral behavior parameters to different vehicle classes under mixed-traffic conditions. The new driving behavior logic was incorporated externally in place of default logic. The results showed that the performance of the adapted models was better accurate than the classical models. @en

This paper proposes a novel approach for examining rear-end collisions between successive vehicles in a traffic stream. In this approach, a new safety measure of the follower driver's attentiveness is proposed, referred to herein as instantaneous heeding time (IHT), reflecting the subject follower's heeding nature concerning its leader. A safety framework that integrates the IHT with the distance gap and the instantaneous follower's speed is presented. The applicability of the framework is demonstrated using an Indian-traffic trajectory database (developed in this study) and the homogeneous traffic database of the next generation simulation (NGSIM) project developed in the United States (U.S.). Five study sections in India and two study sections in the U.S. are analyzed for three traffic-flow levels. For Indian traffic, the results show that motorized two-wheelers (MTW) have degraded road safety due to the unrestrained lateral crisscross movements. Due to the presence of MTW, the Indian-traffic stream operates in a disorderly fashion, thereby increasing the probability of rear-end collisions with other vehicle classes. Further, the importance of implementing cautioning measures for drivers that reduce the probability of collisions is demonstrated. Besides, the NGSIM application results confirmed the proposed framework's applicability to both Indian and homogeneous traffic conditions. In practice, the proposed framework can be used in real-time to monitor the driver's aggressive instincts.@en

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.@en

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.@en

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.@en

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.@en

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.@en

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.@en

The diverse nature of vehicle categories and the resultant lane discipline in mixed (heterogeneous) traffic cause complex spatial interactions. As a result, the driving behavior process in mixed traffic conditions is meaningfully different, where both longitudinal and lateral movements of the vehicles continuously occur. Under prevailing homogeneous traffic conditions in developed countries, driving behavior is partially discrete, where following longitudinal behavior and outboard lane-change models can model traffic behavior. However, the established car-following and lane-change models cannot be directly used in shaping mixed traffic conditions. Such conditions also warrant the use of high-quality microlevel vehicular trajectory data. Accordingly, realizing this need, vehicular trajectory data for different traffic flow conditions were developed. The data were used to extract the parameters required for modeling the vehicles' positions using machine learning algorithms. Three established supervised machine learning algorithms (k-NN, random forest, and regression tree) and deep learning are selected to model mixed traffic conditions. The parameters which influence longitudinal and lateral movements are identified using Spearman correlation analysis. Furthermore, simulation runs are performed using the python language. The performance of the algorithms is evaluated both at the microscopic and macroscopic levels using relevant traffic indicators. The results show that a deep learning model and k-NN tend to replicate better-mixed traffic conditions than random forest and regression trees. @en

The article describes modeling vehicular movements using supervised machine learning algorithms with trajectory data from heterogeneous non-lane-based traffic conditions. The trajectory data on the mid-block road section of around 540 m is used in the study. Supervised machine learning algorithms are employed to model the vehicular positions. A set of parameters were identified for modeling the longitudinal and lateral positions. With the set of parameters, the algorithm’s potentiality for mimicking vehicular positions is evaluated. It was identified that supervised machine learning algorithms would model the vehicles’ positions with accuracy in the range of 20–60 mean absolute percentage error. The k-NN algorithm was marginally edging past all algorithms and acted as a promising candidate for modeling vehicular positions.@en

The research work is attempted in capturing the following behavior of vehicles under Indian traffic conditions. In this context, two access-controlled mid-block sections on multi-lane urban roads were selected in studying the driving behavior. Later with the help of trajectory data from the two study sections, time-space plots are developed for the vehicles’ movement in the same lane as well as from adjacent lanes for studying non-lane based movement. Based on close inspection on time-space plot of vehicles, vehicles pairs which are in the tentative following conditions are distinguished along with the vehicles in adjacent lanes, which may affect the tentative leader–follower pairs. From the distinguished leader–follower pairs, vehicle-following behavior is thoroughly investigated. The investigation is carried out by identifying relationship (hysteresis plots) between relative distance versus relative velocity plots among the leader–follower vehicle for the vehicles in the same as well as in adjacent lane that is nearby position. Hysteresis phenomenon (representative of vehicle interaction) for vehicles under following behavior is examined in quantifying the following behavior of vehicles.@en