Homogeneous Traffic Conditions Research Articles

Dynamic traffic signal control for heterogeneous traffic conditions using Max Pressure and Reinforcement Learning

Optimization of green signal timing for each phase at a signalized intersection in an urban area is critical for efficacious traffic management and congestion mitigation. Many algorithms are developed, yet very few are for cities in developing nations where traffic is characterized by its heterogeneous nature. While some of the recent studies have explored different variants of Max Pressure (MP) and Reinforcement Learning (RL) for optimizing phase timing, the focus is limited to homogeneous traffic conditions. In developing nations, such as India, control systems, like fixed and actuated, are still predominantly used in practice. Composite Signal Control Strategy (CoSiCoSt) is also employed at a few intersections. However, there is a notable absence of advanced models addressing heterogeneous traffic behavior, which have a great potential to reduce delays and queue lengths. The present study proposes a hybrid algorithm for an adaptive traffic control system for real-world heterogeneous traffic conditions. The proposed algorithm integrates Max Pressure with Reinforcement Learning. The former dynamically determines phase order by performing pressure calculations for each phase. The latter optimizes the phase timings for each phase to minimize delays and queue lengths using the proximal policy optimization algorithm. In contrast to the past RL models, in which the phase timing is determined for all phases at once, in the proposed algorithm, the phase timings are determined after the execution of every phase. To assess the impact, classified traffic volume is extracted from surveillance videos of an intersection in Ludhiana, Punjab and simulated using Simulation of Urban Mobility (SUMO). Traffic volume data is collected over three distinct time periods of the day. The results of the proposed algorithm are compared with benchmark algorithms, such as Actuated, CoSiCoSt, and acyclic & cyclic Max Pressure, Reinforcement Learning-based algorithms. To assess the performance, queue length, delay, and queue dissipation time are considered as key performance indicators. Of actuated and CoSiCoSt, the latter performs better, and thus, the performance of the proposed hybrid algorithm is compared with CoSiCoSt. The proposed algorithm effectively reduces total delay and queue dissipation time in the range of 77.07%–87.66% and 53.95%–62.07%, respectively. Similarly, with respect to the best-performing RL model, the drop in delay and queue dissipation time range from 55.63 to 77.12% and 22.13 to 43.7%, respectively, which is significant at 99% confidence level. The proposed algorithm is deployed on a wireless hardware architecture to confirm the feasibility of real-world implementation. The findings highlight the algorithm’s potential as an efficient solution for queues and delays at signalized intersections, where mixed traffic conditions prevail.

Assessing the impact of reaction time on the crossing and merging conflicts and identifying suitable reaction time to detect the critical conflict

ABSTRACT Due to the increased vehicular traffic in developing countries, safety in mixed traffic conditions has become a concern. When critical speed was used with surrogate safety measures to identify critical conflicts, studies assumed some driver reaction time without any analysis. This study aims to assess the impact of different reaction times on the Percentage of Critical Conflicts (PCC) and identify the suitable reaction time for critical conflict identification. Post Encroachment Time was used in this study to assess the safety of crossing and merging traffic at unsignalized T-intersections. It was observed that an increase of 1 s reaction time increases 26.92% of PCC. PET extremes of different reaction times were modelled using Generalized Extreme Value distribution. The 1-second reaction time was found suitable as the estimated crash frequencies were similar to the actual field crashes. This approach could also be employed in the homogenous traffic conditions for critical conflict identification.

Vehicle Delay Model for Signalized Midblock Crosswalk Under Mixed Traffic Conditions

For signalized locations, delay is one of the key parameters in assessing the level of service and redesigning the traffic signals. Around the world, Highway Capacity Manual (HCM) and Webster delay models are commonly used in calculating the vehicle delay at signalized intersections, notwithstanding that these models assume homogeneous traffic conditions and lane discipline. Furthermore, it is worth noting that previous studies have mostly focused on developing delay models for signalized intersections, whereas signalized midblock crosswalks were overlooked by past researchers. In view of this, the current study presents a modified vehicle delay model for signalized midblock crosswalks (SMCs) under mixed traffic conditions. Mixed traffic conditions refers to diverse vehicles, complex interactions, and no lane discipline on the road—conditions which are usually noticed in developing nations. The HCM vehicle delay model is modified in the current study by incorporating factors related to mixed traffic conditions and SMCs. To test the accuracy of the proposed model, it was validated with the field delay using data collected at other SMCs in Hyderabad, India. Field delay was calculated by plotting the measured queue length against the signal cycle length. The performance metrics of root mean square error (RMSE), mean absolute percentage error, Nash–Sutcliffe efficiency, RMSE-observations standard deviation ratio, and percent bias were used to test the accuracy of the proposed model. Results revealed that the proposed model yielded the lowest error rates and the highest correlation coefficients between modeled and actual vehicle delay. The maximum error between the field delay and the modeled delay was found to be less than 5%.

Proposing an effective approach for traffic safety assessment on heterogeneous traffic conditions using surrogate safety measures and speed of the involved vehicles

Objective The vehicular traffic in the cities is becoming more complex in developing countries like India due to the rising population and rapid urbanization. With the annually increasing road accidents, a study on the effective safety assessment of heterogeneous traffic conditions is needed. The study aims to evaluate the safety of heterogeneous traffic by spotting the critical conflicts with respect to the speed of the involved vehicles. Methods The current study proposed Critical Following Speed instead of using a single threshold value for safety assessment in mixed traffic. Critical Following Speed was proposed by comparing the stopping and the available distances between the involved vehicles and used to identify the critical conflicts. With this, the study uses the speeds of both the leading and the following vehicles to judge the nature of the conflict. Three unsignalized four-legged intersections (S-1, S-2, and S-3) and two straight road sections (S-4 and S-5) were selected as the study area in Trichy, India. Post Encroachment Time (PET) and Time to Collison (TTC) were used as surrogate indicators to assess the crossing or merging and rear-end conflicts, respectively. Results The average PET and TTC values were between 1.25 and 1.73 s in the study locations. The overall percentage of critical conflicts indicated the non-safer crossing and merging maneuvers in three locations. The other two locations were experiencing safe rear-end conflicts, as the percentages of critical conflicts were below 4%. Various combinations of leading and following vehicle types were examined for the contribution of critical conflicts. The proposed methodology was validated with the 4-year accident data and a good relation was obtained. Conclusions Fast-moving vehicles were responsible for the less safe maneuvers with a higher collision probability in all the study locations. The correlation between critical conflicts and road accidents shows the effectiveness of the proposed approach in the traffic safety assessments for mixed traffic. This approach could be employed even in countries with homogenous traffic conditions instead of using a single threshold value. The correlations also show the potential of the proposed Critical Following Speed as a surrogate safety indicator for safety evaluation in the future.

Does anisotropy hold in mixed traffic conditions?

Traditionally, car-following models have focused on the interaction between leader and follower vehicles. However, some researchers have explored the significance of incorporating upstream vehicle information and considering non-anisotropic traffic flow patterns for homogeneous traffic conditions. This paper presents a comprehensive investigation into the impact of upstream vehicle information on car-following models for mixed traffic conditions. We employ three different approaches, macroscopic, microscopic, and nonparametric modeling, using empirical data to examine the evidence for the existence of non-anisotropy in mixed traffic conditions. We investigate the macroscopic evidence of fundamental diagrams, revealing complex interactions among heterogeneous traffic. The propagation of congestion can exceed the travel speed of the traffic stream itself, emphasizing the intricate nature of traffic dynamics. Our findings demonstrate that incorporating upstream vehicle information significantly enhances the prediction accuracy of car-following models. Particularly in scenarios where heavy vehicles are present upstream, the inclusion of upstream information proves to be crucial. Further, we conduct a microscopic analysis using triplet trajectories involving heavy vehicles to validate our findings, utilizing Edie’s definition. The microscopic validation supports the presence of non-anisotropic fundamental diagrams and reinforces the need to consider upstream interactions in car-following models.

Performance Evaluation of a Dynamic Signal Control System for Mixed Traffic Conditions Using Sparse Data

Dynamic signal control systems combine traditional traffic signals with an array of sensors to intelligently control vehicle and pedestrian traffic. Under mixed traffic conditions similar to that prevailing in India, where there are a variety of vehicle classes moving without lane discipline, implementation of such dynamic signal control systems is challenging. This is because automated sensors such as loop detectors used for data collection under homogeneous traffic conditions do not work well under mixed traffic conditions. Under such conditions, the possibility of utilizing sparse data obtained with respect to sampled travel times from different sensors is currently being explored. The present study focuses on developing a dynamic signal control system using sparse data collected using radio frequency identification (RFID) sensors under mixed traffic conditions. An existing delay equation was reformulated as an optimization problem for the dynamic signal control. The evaluation of the dynamic signal control system was carried out using a calibrated VISSIM microsimulation model. Python programming was used to integrate the dynamic signal control algorithm through the COM interface in VISSIM. The results indicated a good degree of improvement in overall average intersection delay of 12.6% compared to fixed time signal control. Thus, the proposed optimization method using delay from RFID sensors is suitable for implementing dynamic signal control under mixed traffic conditions.

ORGANIZATION OF TRAFFIC AT UNCONTROLLED INTERSECTIONS

This paper presents a study of drivers' gap acceptance behavior at an uncontrolled intersection. A 4-leg uncontrolled intersection located in a semi-urban area of Ahmedabad, Gujarat was selected to study the gap acceptance behavior of drivers. Video recording technique was adopted for data collection and analysis. Various available methods are used to calculate the critical gap for minor road passing and right turn movements. Gaps are analyzed for three different vehicle types. A comparison is made between the various existing methods of critical gap detection and their critical analysis is presented. The result shows that except for the behavior elimination approach, all other methods cannot provide the real scenario of the critical gap under mixed traffic conditions because all these methods are developed for homogeneous traffic conditions.

A conflict-based safety assessment technique for rear-end crash risk at signalized intersections in a lower-middle-income country: A comparison between homogeneous and heterogeneous traffic conditions

Rear-end crashes at signalized intersections are attributed to different traffic behavior and operational characteristics. However, the influences of homogenous traffic conditions in developed countries and heterogeneous traffic conditions in low and middle-income developing countries like India are rarely explored. This study aims to examine the spatial–temporal characteristics of rear-end crash risk at signalized intersections characterized by lane-disciplined homogeneous and non-lane-based heterogeneous traffic conditions. In particular, this study has applied a conflict-based safety assessment framework to compare the rear-end crash risks across these traffic conditions. Vehicular trajectory data of vehicles at four signalized intersections, one in the homogeneous and three in heterogeneous traffic conditions, is analyzed. Rear-end conflicts position and time of occurrence in the upstream and downstream section of the stop-line are identified and modeled by extreme value models. Results show that the vehicles in heterogeneous traffic conditions are more aggressive, resulting in a higher number of conflicts and probable crashes. The presence of smaller-sized vehicles in dominating proportions and disregard of lane discipline with a higher degree of lateral movement are responsible for more traffic conflicts. The estimated extreme value models suggest that the minimum rear-end crash risk probability at signalized intersections with heterogeneous traffic conditions is about three times higher than that at signalized intersections with homogeneous traffic conditions. The observation is similar for crash risks estimated from both surrogate safety measures used in this study. The weak lane discipline and irregular lateral movement of vehicles are responsible for higher crash risks in heterogeneous traffic conditions.

Comparison of Pedestrian Delay Models at Signalized Midblock Crossings Under Mixed Traffic Conditions

In the field of transportation engineering, one of the most important factors for the traffic engineers and road users (pedestrian and vehicle) is the delay which governs the LOS of a facility. Past studies shows that various delay models like HCM, Webster, and Austroads work well for the homogeneous traffic conditions. However, Limited studies were done on mixed traffic conditions like in India and in other developing countries. Apart from that, all the models were developed based on the conditions of signalized intersection whereas signalized midblock crosswalks were completely overlooked by the researchers. The present study aims to compare pedestrian field delay at signalized midblock sections with the existing models. Six mid-block crossings have been selected from Hyderabad, India and data was collected using video-graphic technique. Field Pedestrian waiting delays were measured by plotting queue length against cycle time and area under the curve was calculated using Simpson's 1/3rd rule. Field crossing delay was calculated as the difference between actual crossing time and ideal crossing time. The performance of all the models were evaluated using t-test and other performance indices like MAPE, RMSE and R2. It was observed from the statistical tests that there is a significant difference between the means of field delay and delay by existing models. Also all the existing models yielded an error of more than 20% with an R2 value of less than 0.3. Calibrated results can be used in the modelling of new pedestrian delay for signalized midblock crosswalks.

Implementing the Maximum Likelihood Method for Critical Gap Estimation under Heterogeneous Traffic Conditions

Gap acceptance analysis is crucial for determining capacity and delay at uncontrolled intersections. The probability of a driver accepting an adequate gap changes over time, and in different intersection types and traffic circumstances. The majority of previous studies in this regard have assumed homogeneous traffic conditions, and applying them directly to heterogeneous traffic conditions may produce biased results. Moreover, driver behavior concerning critical gap acceptance or rejection in traffic also varies from one location to another. The current research focused on the estimation of critical gaps considering different vehicle types (cars, and two- and three-wheelers) under heterogenous traffic conditions at uncontrolled crossings in the city of Peshawar, Pakistan. A four-legged uncontrolled intersection in the study area was used to investigate drivers’ gap acceptance behavior. The gaps were investigated for various vehicle types: two-wheelers, three-wheelers, and cars. For data collection, a video recording method was used, and Avidemux video editing software was used for data investigation. The study investigated the applicability of the maximum likelihood (MLM) method to analyzing a vehicle’s critical gap. MLM estimation results indicate that the essential critical gap values for car drivers are in the range from 7.45 to 4.6 s; for two-wheelers, the critical gap was in the range from 6.78 to 4.7 s; and for three-wheelers, the values were in the range from 6.3 to 4.9 s. At an uncontrolled intersection, the proposed method’s results can assist in distinguishing between different road user groups. This study’s findings are intended to be useful to both researchers and practitioners, particularly in developing countries with similar traffic patterns and vehicle adherence patterns at unsignalized intersections.

Identification of railroad ballast fouling through statistical process control on ballast particle movement

Ballast fouling is an undesirable condition which adversely impacts track stability. As early detection of this condition is critical for the safety of train operations, various inspection technologies have been developed to provide information about state of the ballast and its substructure. Several studies have been conducted with the aim of providing a method for automatic and continuous monitoring of the track system. Ongoing efforts are focused on the development of techniques that enable real-time analysis of the ballast condition while remaining compatible with existing technologies. As an attempt toward that end, this study was designed to utilize statistical pattern recognition technique with data input from an innovative wireless sensor called RTS SmartRockTM (referred as SmartRock in this paper). This method was implemented in a field experiment on two sections of railroad track with homogenous traffic but different track conditions: one with clean ballast and the other with mud pumping. Four SmartRock sensors were installed at different locations on each section. With time-history data from the SmartRocks, an unsupervised statistical model was developed and evaluated for damage detection. The algorithm used was successful in distinguishing between data sets obtained from different ballast conditions. The results of the study show potential suitability of this method for timely detection of ballast fouling, thereby facilitating maintenance planning of rail tracks for a safer and more efficient transportation system.

Delay in Oversaturated Flow Condition at Signal Controlled Intersection under Heterogeneous Traffic Scenario

The delay in travel time varies based on the traffic flow conditions. Undersaturated flow condition requires less time to clear the traffic at the stop line of a signal than oversaturated traffic flow. Several models are available to estimate the delay due to oversaturated flow conditions at intersections but they are mainly based on homogeneous traffic conditions. To address this issue, a modified delay model is developed for oversaturated flow conditions at signal controlled intersections under heterogeneous traffic scenarios. Different ranges of volume capacity ratio have been considered and the duration of the red phase has been added while developing the model. The queue length concept was used for measuring the field delay. The model was generated using the data from three cities and validation was done based on the data of four different cities. Ninety-five percent accuracy in delay estimation was observed using the modified delay model.

Data-Driven Approach for Modeling the Nonlane-Based Mixed Traffic Conditions

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.

Selection of open or closed boundaries in a cellular automata model for heterogeneous non-lane-based traffic

Cellular automata (CA) simulation models developed for traffic are either closed or open boundary type. The selection and difference of boundaries has been studied extensively for ideal and single-lane homogeneous traffic conditions. However, the effect of these on multi-lane-heterogeneous traffic still needs attention because most of the traffic observed in many parts of the world is not single-lane homogeneous traffic. It is evident from multiple studies that open and closed boundaries affect the simulation results. Moreover, these require different inputs for simulation. This study attempts to evaluate the difference in the results of open and closed boundary simulations in heterogeneous non-lane-based traffic. The methodology discussed in this study is relatable to the field conditions. The present study includes some of the common but often ignored features in the model such as seepage of small-sized vehicles. Furthermore, this study also includes some of the previously unnoticed features while modeling the non-lane-based traffic at intersections. The modeling of open boundaries simulation can be better and easy in most of the situations compared to the closed boundaries. Closed boundary simulation results for flow-density curve show a smooth trend, whereas open boundary simulation results are scattered as observed in the field. This study further concludes that the size of the vehicle does not change the fundamental diagrams except when other characteristics such as seepage, lane change, and different maximum speeds for different modes are considered. The study used field observed influence zone of intersections to decide the dimension of intersection in the simulation model.

Sensing technologies for traffic flow characterization: From heterogeneous traffic perspective

Importance of detailed traffic flow characterization is immense for achieving an intelligent transportation system. As such, great efforts in existing literature have gone into proposing different solutions for traffic flow characterization. Among these, first generation intrusive sensors such as pneumatic tube, inductive loop, piezoelectric and magnetic sensors were both labor intensive and expensive to install and maintain. These sensors were able to provide only vehicle count and classification under homogeneous traffic conditions. Second generation non-intrusive sensors based solutions, though a marked improvement over intrusive sensors, have the capability to only measure vehicle count, speed and classifications. Furthermore, both intrusive and non-intrusive sensor based solutions have limitations when employed under congested and heterogeneous traffic conditions. To overcome these limitations, a compute vision based solution has been proposed for traffic flow characterization under heterogeneous traffic behaviour. The proposed solution was field tested on a complex road configuration, consisting of a two-way multi-lane road with three U-turns. Unlike both intrusive and non-intrusive sensors, the proposed solution can detect pedestrians, two/ three wheelers and animal/human driven carts. Furthermore, detailed flow parameters such as vehicle count, speed, spatial/temporal densities, trajectories and heat maps were measured.

Older Drivers' Performance at Intersections: An Experimental Study in Italy

Longer life expectancy is leading to an increase in the average age of the European population. In 2018, 20% of the population was over 65 years old. In 2070, 30.3% of the population will be over 65. Older people prefer to walk in their neighborhoods but they rely on cars as their primary mode of transportation, which allows them to maintain their autonomy over long distances. However, age-related physical impairments can have a significant impact on driving skills, including the ability to properly visualize driving scenarios. Numerous studies have shown that older drivers have significant safety problems at standard intersections rather than roundabouts (40% of fatal crashes occur at standard intersections among drivers 70 years and older). In this work, it was decided to study a specific parameter representative of the behavior of all road users when preparing for the typical maneuvers at stop-controlled intersections (crossing, right-turn and left-turn). This parameter is the gap accepted by elderly road users at different stop-controlled intersections and under homogeneous traffic conditions. For this purpose, a research campaign was organized with 37 elderly drivers performing all possible maneuvers at 9 intersections in Italy (Eastern Sicily). The experimentally determined values of the critical gap for left-turn and crossing maneuvers were found to be compatible with those of the main internationally used reference manuals for road design. The value of the critical 50^th percentile gap (G₅₀) for the right-turn maneuver was similar to that for the left-turn maneuver. This is an indication of a very similar level of difficulty for older drivers in performing both turning maneuvers. The results of this study have highlighted the need to take into account the different operational difficulties associated with the possible maneuvers at intersections. The theoretical approaches of the Italian legislation are carried out, based on a criterion that does not distinguish according to the way in which the three maneuvers, which proves to be totally inadequate. This suggests a complete revision of the criteria for checking sight distances at stop-controlled intersections.

Deep Learning and YOLOv3 Systems for Automatic Traffic Data Measurement by Moving Car Observer Technique

Macroscopic traffic flow variables estimation is of fundamental interest in the planning, designing and controlling of highway facilities. This article presents a novel automatic traffic data acquirement method, called MOM-DL, based on the moving observer method (MOM), deep learning and YOLOv3 algorithm. The proposed method is able to automatically detect vehicles in a traffic stream and estimate the traffic variables flow q, space mean speed vs. and vehicle density k for highways in stationary and homogeneous traffic conditions. The first application of the MOM-DL technique concerns a segment of an Italian highway. In the experiments, a survey vehicle equipped with a camera has been used. Using deep learning and YOLOv3 the vehicles detection and the counting processes have been carried out for the analyzed highway segment. The traffic flow variables have been calculated by the Wardrop relationships. The first results demonstrate that the MOM and MOM-DL methods are in good agreement with each other despite some errors arising with MOM-DL during the vehicle detection step due to a variety of reasons. However, the values of macroscopic traffic variables estimated by means of the Drakes’ traffic flow model together with the proposed method (MOM-DL) are very close to those obtained by the traditional one (MOM), being the maximum percentage variation less than 3%.

Measuring Control Delays at Signalized Intersections in Mixed Traffic Conditions

Abstract Control delay is the key performance indicator of a signalized intersection that defines the level of service. Several models have been developed in previous research work for estimating control delays, but many of them were based on homogeneous traffic conditions. In the present study, an Open Street Map (OSM) tracker mobile application was used to measure control delays from the field. A non-linear model was developed in the present study for estimating control delays in mixed traffic conditions using a MATLAB fitting tool. The field delay is compared with the developed non-linear model delay along with the Indian Highway Capacity manual (INDO HCM) and Highway Capacity Manual (HCM) models. The control delay estimated using the model developed in the present study shows a close relation with the field delay obtained using an OSM tracker when compared to that obtained using the INDO HCM and HCM models. Therefore, the OSM tracker mobile application can be used as a field control delay measuring technique.

Customizing the following behavior models to mimic the weak lane based mixed traffic conditions

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.

Empirical approach for identifying potential rear-end collisions using trajectory data

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.