Effectively planning the behavior of autonomous vehicles (AVs) while accounting for their mechanical properties and traffic flow characteristics is a challenging task. This study evaluates different car-following models in combination with various controllers to model the longitudinal behavior of AVs. Controllers regulate a vehicle's speed, ensuring smooth acceleration in accordance with its planned path. Specifically, conventional models, interaction-based models, and artificial intelligence-based models were tested alongside standard controllers. The respective transfer functions of the system were derived, and the weights were tuned accordingly. Nanoscopic simulation runs were conducted to assess performance. The results indicate that the choice of car-following model and controller significantly impacts the longitudinal planning of AVs, with certain combinations demonstrating superior performance. This study thus provides a framework for identifying the optimal pairing of a car-following model and controller to enhance longitudinal behavior planning in AVs.

As automated vehicles (AVs) become increasingly popular, the question arises as to how cyclists will interact with such vehicles. This study investigated (1) whether cyclists spontaneously notice if a vehicle is driverless, (2) how well they perform a driver-detection task when explicitly instructed, and (3) how they carry out these tasks. Using a Wizard-of-Oz method, 37 participants cycled a designated route and encountered an AV multiple times in two experimental sessions. In Session 1, participants cycled the route uninstructed, while in Session 2, they were instructed to verbally report whether they detected the presence or absence of a driver. Additionally, we recorded participants’ gaze behaviour with eye-tracking and their responses in post-session interviews. The interviews revealed that 30% of the cyclists spontaneously mentioned the absence of a driver (Session 1), and when instructed (Session 2), they detected the absence and presence of the driver with 93% accuracy. The eye-tracking data showed that cyclists looked more frequently and for longer at the vehicle in Session 2 compared to Session 1. Additionally, participants exhibited intermittent sampling of the vehicle, and they looked at the area in front of the vehicle when it was far away and towards the windshield region when it was closer. The post-session interviews also indicated that participants were curious, but felt safe, and reported a need to receive information about the AV's driving state. In conclusion, cyclists can detect the absence of a driver in the AV, and this detection may influence their perception of safety. Further research is needed to explore these findings in real-world traffic conditions.

As automated vehicles (AVs) become more common, it is important to understand how human-driven vehicles (HDVs) would interact with them. This research investigated HDV gap acceptance behavior in mixed traffic with AVs at a priority intersection, focusing on how mixed traffic factors affect this behavior and overall traffic efficiency. Using a driving simulator, four scenarios were tested by varying AV driving style (less defensive, more defensive, and HDV-like) and AV recognizability (distinguishable or not from HDVs). Gap acceptance models were estimated based on the collected trajectory data. These models were then integrated into the SUMO microscopic traffic simulation platform, where a T-intersection network was set up. Simulation runs varied based on AV driving style, recognizability, penetration rate (0-75% in 25% increments), and whether HDV behavioral adaptation was considered. The results indicated increased minor road vehicle delays with higher AV penetration rates. Recognizable less defensive AVs, and more defensive AVs with high penetration rates caused the largest delays for minor road vehicles compared to other conditions. Ignoring behavioral adaptation led to a delay underestimation of up to 75% for minor road vehicles. In conclusion, there is behavioral adaptation in gap acceptance of HDVs in mixed traffic environments. Taking into account the behavioral adaptation is essential for accurately assessing traffic efficiency in mixed traffic conditions, and guiding AV deployment policies.

Traffic microsimulation is a commonly used tool in traffic engineering. Given its flexibility and cost-efficiency, it is increasingly used for evaluating traffic safety. In real life traffic, unsafety is in many cases due to human error in driving behaviour. In traffic microsimulations however, driving behaviour is highly dependent on the formulated driving behavioural models and the level of their realism. Most of these behavioural models were developed ignoring the inconsistencies and error proneness of human behaviour. A quantitative evaluation of the differences in the safety level between real life and simulated traffic, considering the mathematical formulation of different driver behavioural models, is lacking in the literature. The main aim of this study is to investigate the influence of different behavioural models’ formulations on the correlation between simulated and empirical surrogate safety measures’ outcomes. For this purpose, high-quality empirical trajectory data were used to calibrate and validate different driver behavioural models. SUMO (Simulation of Urban MObility), an open-source traffic microsimulation software, was used as a platform for calibrating, validating, and testing four distinct combinations of car following and lane changing models. The results show that, regardless of the behavioural model formulations used, the number of simulated traffic conflicts is overestimated. This is most likely due to a higher frequency of lane changes and an unrealistic distribution of traffic over the different lanes in microscopic traffic simulation. The severity of the simulated conflicts was shown to be reasonably accurate at an aggregate level but not significantly comparable at a microscopic level.

This paper presents a framework for monitoring highway traffic-stream measures using quality trajectory data of mixed (heterogeneous) traffic. The framework includes a new measure that reflects the attentiveness of the follower driver, called receptiveness angle, in the vehicle-following process. This measure is integrated with the traditional measures (distance gap between the leader and follower vehicles and their speeds) to model the probabilistic rear-end collision interactions between the two vehicles. To verify the proposed framework, two road sections in India with mixed traffic conditions, located along the same road, were used. One section has no construction activity (base section) and the other has construction activity. The verification consisted of two tasks. First, to trace the movements of the vehicles, trajectory data over the study sections were developed for three traffic-flow levels, where two flow levels between the two sections were comparable. Second, the trajectory data were used to verify the proposed framework which was evaluated for the traffic streams of the two sections at the three traffic-flow levels. The results showed that smaller vehicles in the traffic stream exhibited a higher receptiveness angle (paid less attention) compared to other vehicle classes. Interestingly, the study revealed variations in safety among the three traffic-flow levels. It was observed that the traffic stream was safer at stop-and-go conditions than at other flow conditions. Furthermore, due to the pre-cautioning measures for the construction section, vehicles in this section were more attentive than those in the base section.

This study originated with the intent of qualifying traffic string stability from empirical observations. A new responsiveness angle measure was developed to assess driver instincts under vehicle-following conditions. In this measure, the degree of the follower vehicle's attention towards its leader vehicle's actions is quantified. In understanding string stability in the traffic stream and assessing the propagation of disturbances, the newly conceptualized measure was used along with a discrete Fourier transform to measure the frequencies associated with responsiveness angle sequences. In this transform, a higher frequency of the angle depicts unstable conditions and vice versa. In assessing string stability from the empirical observations, vehicular trajectory data were developed from three study sections. Two study sections tended to have homogeneous lane-wise traffic, whereas the third section had mixed (heterogeneous) traffic. The results of the string stability analysis over the study sections showed that string stability varied with the change in traffic flow conditions, road geometries, and traffic flow type. In the case of free-flow conditions, the traffic streams were found to be stable with marginal disturbances in the responsiveness angle. From the analysis, it was observed that, in the case of study Section 3, around 26 instances of the stream were extremely unstable conditions (frequency equal to 10). For study Sections 1 and 2, the traffic stream was unsteady for 4 and 13 instances, respectively. However, as the traffic flow level rose, string stability deteriorated. This study demonstrated a novel approach to analyzing string stability based on actual traffic conditions that can be implemented in real time for traffic stream monitoring.

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.

Currently, the dilemma and option zones’ failures are independently used to analyze the intergreen interval at signalized intersections. Therefore, the present research work is initiated to integrate these failures. First, the dilemma and option zones are modelled using the first-order second-moment method. Then, game theory is used to model the association between the dilemma and option failures. The failure probabilities of the dilemma and option zones are evaluated for various traffic conditions using Monte-Carlo simulation considering the Nash equilibrium. Next, the overall system probability is analyzed, based on the combined dilemma and option failures, given different intergreen intervals, speeds, and coefficients of variation. Finally, the study proposes a methodology for identifying the intergreen interval to limit system failure. This would aid practitioners in designing traffic lights at intersections and keeping proper intergreen intervals to limit the dilemma and option failures.

The gradual deployment of automated vehicles on the existing road network will lead to a long transition period in which vehicles at different driving automation levels and capabilities will share the road with human driven vehicles, resulting into what is known as mixed traffic. Whether our road infrastructure is ready to safely and efficiently accommodate this mixed traffic remains a knowledge gap. Microscopic traffic simulation provides a proactive approach for assessing these implications. However, differences in assumptions regarding modeling automated driving in current simulation studies, and the use of different terminology make it difficult to compare the results of these studies. Therefore, the aim of this study is to specify the aspects to consider for modeling automated driving in microscopic traffic simulations using harmonized concepts, to investigate how both empirical studies and microscopic traffic simulation studies on automated driving have considered the proposed aspects, and to identify the state of the practice and the research needs to further improve the modeling of automated driving. Six important aspects were identified: the role of authorities, the role of users, the vehicle system, the perception of surroundings based on the vehicle’s sensors, the vehicle connectivity features, and the role of the infrastructure both physical and digital. The research gaps and research directions in relation to these aspects are identified and proposed, these might bring great benefits for the development of more accurate and realistic modeling of automated driving in microscopic traffic simulations.

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.

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.

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 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.

Automated driving systems, which can take over certain dynamic driving tasks from the driver, are becoming increasingly available in commercial vehicles. One of these automated driving systems widely introduced in commercial vehicles is Adaptive Cruise Control (ACC). This system is designed to maintain certain desired driving speeds and time headways as chosen by drivers and based on the settings available within the system. The properties and actual performance of these systems will affect the traffic flow and its stability. However, the specific properties and their workings are rarely publicly available. Therefore, the main aim of this paper is to test the actual performance of a commercial ACC system under different desired speed and distance gap settings, as well as driving modes in a car-following situation. For this purpose, a pilot field test was conducted in the Netherlands in which two identical commercial vehicles equipped with ACC systems were driven simultaneously. The first vehicle was used to create a prespecified speed profile by adapting the ACC system settings manually, while the second vehicle followed the lead vehicle when the ACC system was engaged to test its actual performance. The main findings indicate that the different system settings affect the car-following indicators, and system response times were found to be comparable to human response times. The eco mode was found to affect some of the car-following indicators, and it does not deteriorate safety below the safety level of driving with short headway setting in drive mode.

The present research aims to understand the safety over the midblock road sections and proposes a safety framework using the conventional Time to Collision (TTC) measure. In the present work, the safety framework underlines a supporting structure connecting the actions of the surrounding vehicles and assesses the collisions changes for a given subject vehicle. The Framework principally checks the likelihood of lateral overlap and the time gap between the subject vehicle and its surrounding vehicles. Later, for the trajectory data development, an automated trajectory data development tool is built with the help of image processing for generating the trajectory data from the study sections. In supporting the developed safety framework, the lateral movement of the vehicles is modeled precisely with the help of deep learning. Further, the conceptualized safety framework is tested with the developed trajectory data sets over the study sections. From the results, it is observed that, in mixed traffic, the collision points are over the entire geometry of the study section. In the case of homogeneous traffic, the collision instincts are clustered toward the median lanes. With the advancement of technology, trajectory data development can be a real-time exercise, and the safety framework can be implemented. By applying the study methodology, the critical spots over the road network can be flagged for better treatment and improve safety over the sections.

A turbo roundabout uses spiral circulatory roads for effectively counteracting the problems faced in modern multilane roundabouts. First developed in 1996, the turbo roundabout has an advantage over the conventional roundabout regarding capacity and safety. Turbo roundabouts are still in the developing phase in North America, but even in the European subcontinent where they exist in large numbers, reliable analytical studies on the critical parameters of roundabout visibility are lacking. Visibility (sight distance) helps to shape the geometry of the intersection and aids in safety. This paper presents the mathematical characteristics of the intersection geometry and intersection sight distance (ISD) of the turbo roundabout. Mathematical formulas are presented for the sight distance from the approaching vehicle to the conflicting-entering and circulating vehicles. The maximum lateral clearances to the conflicting vehicles are derived using mathematical optimization. The developed analytical method is verified graphically using AutoCAD. To assist in practical applications, design aids for the maximum lateral clearance are presented. The presented method and design aids should aid in promoting safety at turbo roundabouts.

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.

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.

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.

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.