Heterogeneous Traffic Environment Research Articles

Mixed Traffic Behaviour in Heavy Flow: Two-Lane Road Insights

The current paper outlines a comprehensive methodology for modeling speed data on two-lane roads during periods of heavy traffic characterized by heterogeneous vehicle types. In such scenarios, the presence of diverse vehicle types causes a significant departure from the normal speed distribution model. This deviation becomes particularly pronounced during heavy traffic flows due to the frequent interactions among vehicles within the traffic stream. Consequently, there arises a necessity to develop a modeling approach specifically tailored to such flow conditions. Drawing upon empirical data collected from a major intercity road in India, this study uncovers a notable skewness in speed data under heavy traffic conditions. This skewness primarily stems from the formation of vehicle platoons within the traffic stream, exerting a substantial influence on their speed characteristics. By scrutinizing the distribution of this data, the study concludes that a logarithmic transformation effectively aligns with the assumption of normality. This assertion is supported by various goodness-of-fit metrics, affirming the suitability of the proposed modeling approach for capturing the intricacies of speed behavior in heterogeneous traffic environments.

A preliminary analysis of base saturation flow rate during first loss time and saturated time at pretimed signalized intersections

Saturation flow rate is a key parameter essential for assessing signalized intersection capacity. The reference often used for signalized intersection planning in Indonesia is Indonesian Highway Capacity Guidelines (PKJI, 2023). In heterogeneous traffic environments, it has been observed that driver characteristics and behaviors exhibit significant variations. Therefore, this study aimed to investigate the prevailing saturation flow rate conditions at signalized intersections within Banda Aceh City. This exploration unfolded through two distinct scenarios, namely saturated time with and without initial lost time. For data acquisition, the focus was set on four signalized intersections, namely Jambo Tape, Pocut Baren, AMD Batoh, and Lhong Raya. This extraction procedure aimed to uncover insights into vehicle composition, movement patterns, and field saturation flow rate at these signalized intersections. In the aspect of analysis, a simple linear regression approach was adopted to recalibrate saturation flow rate coefficient of signalized intersections. This recalibration took into account vehicle compositions at the observation locations. The culmination of this analysis showed that in the scenario devoid of lost time under prevailing conditions, flow rate yielded S0 = 352 x Le. Meanwhile, for the scenario entailing lost time, the calculation produced was S0 = 294 x Le.

User Opinion on Factors Affecting Drivers Frequent Lane-Changing Behavior: A National Sample of Drivers in Pakistan

Abstract: In recent years massive traffic volume increase has demanded the implementation of traffic improvement plans in Pakistan. The urban Highway transportation networks are becoming congested in developed as well as in most of the developing countries. The main causes of unmanageable heterogeneous traffic congestion on the highway are an increase in population, change in lifestyle, and economic development of a country. To recognize the characteristics of a driver who frequently changes lanes and analyze the techniques of frequent lane-changing behavior. The researchers collected data via a questionnaire survey and analyzed the influence of traffic environment and driver characteristics violation on frequent lane-changing behavior on travel speed in detail by manually Excel statistics. Results showed that the impact of a heterogeneous traffic environment of two lanes and three lanes cars, and traffic flow density was sharply associated with frequent lane-changing behavior. The drivers were the main factors affecting frequent lane-changing behavior in heterogeneous traffic conditions, which was most popular during aggressive driving. Only 8.33% of drivers were never involved in this behavior. Most liable to frequent lane-changing behavior were males as compared to females, 31–40-year-old drivers, and those with low academic qualifications and private vehicle personalities. Among various traffic factors, changes in required speed had the most significant impact on how often lane changes occurred. The findings of the current study highlight the most significant traffic parameters that influence the lane change frequency. This research recommends strict enforcement of lane discipline to counter traffic congestion and the remedial measures that are to be taken for the improvement of better and sustainable road infrastructure conditions. Overall the research study will have practical implications for improving traffic management and safety on highways, and it provides a foundation for further research in the field of traffic-related technologies.

A jam-absorption driving system for reducing multiple moving jams by estimating moving jam propagation

Jam-absorption driving (JAD) is a novel connected and automated vehicle-based (CAV-based) control strategy that uses either a single or several absorbing cars to clear moving jams. However, a practical control system has not yet been developed. This study presents a real-time control system for operating JAD against multiple moving jams on freeway sections under a partial CAVs and heterogeneous (mixture of cars and trucks) traffic environment. The system dynamically predicts absorbing end points by estimating the propagation of multiple moving jams without using a traffic model. Subsequently, it selects suitable CAVs as absorbing cars in traffic via vehicle-to-infrastructure communication to absorb moving jams. A control model embedded with complete/synergetic JAD strategy decision-making and moving jams–CAVs matching process was developed to guarantee moving jams can be properly absorbed as early as possible. Simulation results demonstrated that the proposed JAD system successfully reduced successive multiple moving jams in the toy model (with two artificial successive moving jams) and hypothetical bottleneck (with series moving jams generated by a speed limit zone) scenarios. Moreover, sensitivity analysis indicated that the JAD system effectively reduced moving jams and enhanced traffic performance (e.g., about 10% fuel consumption reduction) under approximately 1% to 10% CAV market penetration rates (MPRs). This implies that the proposed JAD system can be implemented at low CAV MPRs in the future.

The Impact of Connected and Autonomous Vehicle Platoon’s Length on Expressway Traffic Flow Characteristics Based on Symmetry Lane Changing Rules

This study mainly investigates the maximum length of CAV (Connected and Autonomous Vehicle) platoons in a heterogeneous traffic flow environment. By employing MATLAB to simulate the heterogeneous traffic flow on expressways, this study focuses on the maximum platoon length of CAV platoons and explores their impact on the traffic flow characteristics on expressways. Firstly, based on four different car-following modes of heterogeneous traffic flow, F-STCA (Flexible–Symmetric Two-Lane Cellular Automata Model) and the symmetric lane-changing strategy, the study refines and improves the construction of the NaSch (Nagel and Schreckenberg) model introduced into the Gipps safety distance formula. The whole improvement process is based on the acceleration decay characteristics of vehicles on expressway. Secondly, the congestion situations under different maximum platoon lengths are compared using fundamental heat maps of the simulation data. The evolution of the fundamental diagram with changes in maximum platoon length is studied to investigate the impact and magnitude of maximum platoon lengths on the road capacity under different CAV permeabilities. Finally, the study explores the stability and safety of heterogeneous traffic flow involving CAV platoons using SD (Standard Deviation) and TERCRI (Time-Exposed Rear-End Collision Risk Index). The results show that when the CAV’s permeability does not reach a high level on heavily trafficked sections of the expressway, considering the overall average speed, the maximum platoon length should not be set too long and should be around five vehicles. This restriction does not apply when the CAV permeability approaches 100%.

Exploring the Traffic Congestion and Improving Travel Time Reliability Measures in Heterogeneous Traffic Environments: A Focus on Developing Countries

Exploring the Traffic Congestion and Improving Travel Time Reliability Measures in Heterogeneous Traffic Environments: A Focus on Developing Countries

Modeling and analysis of car-following models incorporating multiple lead vehicles and acceleration information in heterogeneous traffic flow

In the foreseeable future, a complex heterogeneous traffic environment will emerge as Connected and Autonomous Vehicles (CAVs) coexist with Human-driven Vehicles (HDVs). Consequently, understanding the impact of CAVs on car-following behavior and the operational characteristics of heterogeneous traffic flow becomes crucial before their widespread deployment. To tackle this challenge, this research proposes an improved car-following model based on the Intelligent Driver Model (IDM). The model incorporates the position, velocity, and acceleration information of both front and sub-front vehicles in the heterogeneous traffic flow. The impact of different types of information on the model's stability is verified through linear stability analysis while investigating the operational characteristics of traffic flow during vehicle start-up. Additionally, the car-following modes are classified based on the type of leading vehicle, and the corresponding following model is formulated. The results indicate that the improved model significantly improves traffic flow stability, particularly when considering acceleration information. Compared to the front vehicle, the influence of the sub-front vehicle on traffic flow stability is less significant, but their combined impact yields positive effects. Furthermore, the improved model reduces the start-up time of vehicles at signalized intersections by 7.9% and enables a smoother start-up process for vehicles. Moreover, CAVs can mitigate the impact of HDV's disturbances on the overall heterogeneous fleet operation by adjusting their spacing relative to the front vehicle. With an increasing penetration rate, the velocity fluctuation of the entire fleet decreases.

Driving safety field modeling focused on heterogeneous traffic flows and cooperative control strategy in highway merging zone

Connected and Automated Vehicles (CAVs) will coexist with Human-Driven Vehicles (HDVs) in the foreseeable future. Proactive safe controls indeed play a vital role in thus a complex heterogeneous traffic environment. This paper mainly constructed the safety field models for vehicles and environments respectively to address the problems of unclear rules of heterogeneous traffic flow and unclear safety mechanisms in the complex scenes of merging areas. Firstly, the action range of CAV vehicle field was constrained with the differential approximation algorithm to fill in the weakness of the infinite influence range of the existed vehicle field. Then the safety field model of HDV was also reconstructed by taking into account the driver’s environmental psychological tolerance with three indicators from driver experience, environmental visibility and trust. Meanwhile, the environmental field model was innovatively set up with the longitudinal distance variable to overcome the drawbacks of sudden changes in field strength as the number of lanes changes. Based on above mechanism models, this study further reconstructed the early lane-changing model that cored with the minimum distance model and the mandatory lane-changing model in the merging zone that cored with the driving safety index (DSI). Those models provided explicit judgment for the selection of vehicle lane-changing model and CAV behavior control in the merging zone. Finally, the simulation experiments show that the cooperative control strategy with a higher of 45% CAV penetration rate in low-density traffic (1200 veh/h) scenario outperforms than those strategies of SUMO or without early lane-changing in adapting the features of highway merge zone. While in a high-density traffic (1600 veh/h) scenario that with a fixed 30% CAV penetration rate, the time-average speed and space-average speed of the cooperative control strategy improved 15.56% and 25.15%, respectively.

Traffic Flow State Analysis Considering Driver Response Time and V2V Communication Delay in Heterogeneous Traffic Environment

In order to study the heterogeneous traffic environment generated by connected automated vehicles (CAVs) and human-driven vehicles (HDVs), the car-following model and basic graph model of the mixed traffic flows of connected automated vehicles and human-driven vehicles are constructed. Considering driver response time and the communication delay of the connected automated vehicles control system, the three-parameter variation law of traffic flow is summarized to solve the traffic congestion problem in heterogeneous traffic environments. Firstly, the probability of six scenarios in queues of cooperative adaptive cruise control (CACC) vehicles, adaptive cruise control (ACC) vehicles, and human-driven vehicles in heterogeneous traffic environments is analyzed. The car-following model is defined, and the parameters are calibrated, and then a fundamental diagram model of traffic flow balance is derived. On this basis, considering driver response time and the communication delay of the linear controller, a car-following model considering multi-party delay is updated and established, and the heterogeneous traffic flow analysis of the two types of delays in the model is carried out. Finally, the microscopic simulation environment is constructed based on SUMO 1.17.0 (Simulation of Urban Mobility) software. The results show that when the permeability (the proportion of connected automated vehicles in a traffic stream) exceeds 0.6, CAVs account for the main part in the heterogeneous traffic, which has a positive impact on the maximum flow and the optimal density and can effectively improve the maximum capacity of the road. The simulation results show that the updated car-following model is reasonable and accurate in dealing with driver response time and V2V communication delay.

Context‐aware trajectory prediction for autonomous driving in heterogeneous environments

AbstractThe prediction of surrounding agent trajectories in heterogeneous traffic environments remains a challenging task for autonomous driving due to several critical issues, such as understanding social interactions among agents and the environment, handling multiclass traffic movements, and generating feasible trajectories in accordance with real‐world rules, all of which hinder prediction accuracy. To address these issues, a new multimodal trajectory prediction framework based on the transformer network is presented in this study. A hierarchical‐structured context‐aware module, inspired by human perceptual logic, is proposed to capture contextual information within the scene. An efficient linear global attention mechanism is also proposed to reduce the computation and memory load of the transformer framework. Additionally, this study introduces a novel auxiliary loss to penalize infeasible off‐road predictions. Empirical results on the Lyft l5kit data set demonstrate the state‐of‐the‐art performance of the proposed model, which substantially enhances the accuracy and feasibility of prediction outcomes. The proposed model also possesses a unique feature, effectively dealing with missing input observations. This study underscores the importance of comprehending social interactions among agents and the environment, handling multiclass traffic movements, and generating feasible trajectories adhering to real‐world rules in autonomous driving.

Effect of vehicle size on crash risk in a heterogeneous traffic scenario: a bivariate extreme value approach

ABSTRACT Most traffic conflict indicators are defined for car-following scenarios where a follower vehicle interacts with a leader vehicle in one-dimensional space. However, vehicles do interact in a two-dimensional space especially in a heterogeneous traffic environment. Further, designating an interaction as risky depends on the interacting leader-follower (LF) pairs. Conflict indicators namely Time-to-Collision (TTC) and lateral gap which quantifies longitudinal and lateral interactions respectively, were computed from video recordings at four accident black spots on four-lane divided highways. Conflict in two-dimensional space was modelled for various LF-pairs using the Bivariate Extreme Value function of these two conflict indicators. Crash risk was estimated for each LF-pairs separately. Results show that cars and light commercial vehicles exhibit higher crash risk as compared to two-wheelers and motorized three-wheelers. The proposed framework can be used for more accurate risk assessment and calibration of collision warning systems in lane free mixed traffic conditions.

Data-Driven Prediction and Predictive Control Methods for Eco-Driving in Production Vehicles

This paper presents a study of perception and robust model-predictive control (MPC) strategies in realistic traffic environments, which are simulated using data from real-world driving experiments. In this paper, we consider a heterogeneous traffic environment, which includes human-driven vehicles, and study the performance of currently available automation in production vehicles. We then present a data-driven preceding vehicle's velocity and position prediction algorithm, and a robust MPC strategy that optimizes fuel consumption and takes into account the prediction errors. Data used in this paper are taken from experiments using a 2018 Cadillac CT6 vehicle. Simulation results show up to 6.39% energy efficiency improvement.

Overtaking risk modeling in two-lane two-way highway with heterogeneous traffic environment of a low-income country using naturalistic driving dataset

Introduction: Driver behavior related to overtaking maneuvers, which are considered a major safety risk determinant on two-lane two-way highway in low- and middle-income countries (LMIC), are an important subject of further analysis. This study evaluates safety risk in terms of nature and severity of probable conflicts during overtaking maneuvers on a bi-directional undivided two-lane highway in a heterogeneous traffic environment of a low-income country. Nature and severity of probable conflicts were defined with the application of surrogate safety proximity indicators in real-world naturalistic driving environment. Method: A risk severity model for overtaking maneuver was developed to better understand the significant factors associated with the probability of conflict and its severity during overtaking maneuver using discrete choice modeling approaches. The relevance of three alternate discrete outcome frameworks, namely multinomial logit (ML), ordered probit (OP), and mixed logit (MXL) models are addressed. The best fitted model is identified and estimated. The impact of the significant attributes was also evaluated. The study collected data from a section of two-lane highway in Bangladesh using naturalistic driving from both observational and computer vision techniques. A total of 46 explanatory variables related to overtaking maneuver are assessed. Results: Speed differential between overtaking and overtaken vehicles have a significant impact on the probability of severe conflicts. Moreover, the presence of a bus as an overtaking vehicle was found to contribute significantly to the severity of conflicts. Conclusions: The study makes substantial research contributions related to overtaking behavior and safety risk evaluation during overtaking in mixed traffic environment in low-income countries. The results can be used as a proactive tool for the evaluation of overtaking maneuvers and associated safety risk, and making policy decisions reducing safety risk during overtaking maneuver as well as overall safety, while acknowledging the limited resources and facilities in low-income countries.

Development of pedestrian safety index models for safety of pedestrian flow at un-signalized junctions on urban roads under mixed traffic conditions using MLR

In metropolitan areas of emerging countries, such as India, there has been a fast rise in the number of pedestrians and traffic clashes in recent decades. In order to improve the safety and security of pedestrian services under heterogeneous traffic conditions, it is important to improve pedestrian facilities along city roads. The research study was undertaken to develop Pedestrian Safety Index models using parameters such as crosswalk speed (m/sec), crosswalk markings, vehicle stream speed (km/hr), pedestrian volume, pedestrian safety scores /rankings and crosswalk length (m). Data have been collected and extracted through videographic technique at various un-signalized junctions in the Central Business Districts (CBD) zones and surroundings of Srinagar Metropolitan (SMA) Area, where increase in the number of pedestrian volumes was identified. The amount of pedestrian volumes and average pedestrian speed (m/sec) during peak hours were identified at some of these study locations. Pedestrians have been identified on the basis of different age categories. Pedestrian movements are classified according to the speed and direction of the crossing. The Pedestrian safety index models are developed using SPSS software and tested on dataset obtained at various locations in the Srinagar city. The developed Pedestrian Safety Score Index (PSSI) model results obtained for all selected locations in the city of Srinagar are three to four indicating the ordinary / average safe to danger situation of the PSI scores. The results of the PSSI and Ped ISI model demonstrate the estimation of the correct index value of pedestrian safety at various un-signalized junctions. This research will also help to enhance the pedestrian safety strategies, pedestrian volumes (ped/hr) and also improved pedestrian safety interventions on urban roads under heterogeneous traffic environments.

Guidance for Design and Implementation of Queue Jump Lane with Presignal for a Heterogeneous Traffic Environment

AbstractIn mixed traffic streams, especially near intersections, bus priority treatments are necessary to prevent buses from significant travel time losses. This paper aims to assist practitioners ...

Micro Level Speed Choice Behavior on a Rural Highway in an Heterogeneous Traffic Environment: Latent Class Modeling Approach

The modeling of driver speed choice behavior on a rural highway with heterogeneous traffic environment in a developing country is reported. The study explores the application of a latent class model as an alternative methodological approach, to relate factors such as road geometry, roadside environment, traffic mix and operational characteristics with speed choice at the micro level. In the conventional analysis based on lane-based homogeneous traffic environments, the impact of exogenous variables is kept constant across road segments; whereas those impacts might vary across different segments for different conditions. To check this possible variation, the study compares latent class linear regression with ordinary least square, as well as the random parameter linear regression model. The proposed models were estimated using data collected from two sections of a major national highway in Bangladesh. The entire roadway section was divided into several small segments and the critical segment length was estimated based on several sensitivity tests. The small segmental level exogenous variables were considered for the model building and estimation. The results obtained could be used to understand better the speed choice factors of drivers in the non-lane-based traffic environments of developing countries. Law enforcement agencies, traffic operation and maintenance officials, as well as roadway design and planning authorities, could gainfully use the findings of this study to inform more responsive speed management strategies.

A two-sided lateral gap continuum model and its numerical simulation for non-lane based heterogeneous traffic environment

The objective of this study is to model the complex behavior of non-lane based heterogeneous traffic which is predominantly occupied by vehicles with varying physical and dynamical characteristics and their staggered car following behavior. This study proposes a new continuum model by considering the properties of two-sided lateral gap in a non-lane based heterogeneous traffic stream. The model is built on a strong empirical background and modified car following theory. In particular, the model consists of density dependent disturbance propagation speed, viscosity term and a frictional clearance term to describe the complex vehicular interactions in a traffic stream. Viscosity term in the model represents the driver’s reaction to a sudden change in density and further, it helps in smoothening out the shock fronts. Frictional clearance term in the model considers the effect of slow moving vehicles on the dynamics of fast moving vehicles. The results of mathematical and numerical analysis show that the proposed model satisfies all the qualitative and physical properties of the real world system. Moreover, two sided lateral gap in the model improves the stability region of traffic flow and increases the capacity and density of traffic stream. In addition, it is able to dissipate the traffic perturbation rapidly when compared to one-sided lateral gap model. It is anticipated that the new model provides the basis for evaluating alternative transport policy measures and helps in analyzing the system performance in the future.

AdPS: Adaptive Priority Scheduling for Data Services in Heterogeneous Vehicular Networks

Data service scheduling is paramount in realization of Intelligent Transportation System (ITS). ITS services recon upon real-time information sharing among vehicular network components. To provide real time information services in vehicular networks, numerous challenges are imposed due to unique characteristics of such networks. For efficient information dissemination, there is a prime requirement of an adaptive scheduling algorithm for approximate vehicular dynamics information. In this work, a Dedicated Short Range Communication (DSRC) based vehicular communication model has been used for efficient data services in heterogeneous traffic environment. A novel Adaptive Priority data Service scheduling (AdPS) algorithm has been proposed to provide real-time data services, which is based on fuzzy logic request deadline estimation and request prioritization. The prioritized requests has been stored in multilevel queues according to the urgency of requests to provide simultaneous access. The proposed algorithm has been simulated for different traffic scenarios for a real city environment and its performance has been analyzed at different traffic instances. The proposed protocol ensures the real time service scheduling which provides fairness among the users.

Pore acceptance predictions of motorised Two-Wheelers during filtering at urban Mid-Block sections

Filtering of motorized two-wheelers (MTWs) is a common practice in dense urban heterogeneous traffic environments where they often tend to navigate through the available lateral spaces (commonly termed as ‘pore’ in the literature) described by the vehicles in-front. Considering the increased vulnerability of MTW riders in dense urban systems, proper evaluation and modeling of pore acceptance/rejection predictions of MTWs can essentially provide a safer driving environment to MTW riders and the surrounding vehicles in a cognitive architecture, augment the reliability and predictability of microsimulation models and ameliorate the overall traffic flow phenomena. The current study therefore investigates the applicability of Raff’s method, binary logit model and support vector machines (SVM) in predicting the pore acceptance decisions of MTW riders during filtering in urban mid-block sections. The results of the study suggested that SVM technique could be considered as a potential tool for estimating the pore acceptance and rejection predictions of MTW riders, which can be further implemented in a cooperative intelligent transport systems environment for an overall safe yet smooth flow of traffic.

Using a surrogate safety approach to prioritize hazardous segments in a rural highway in a developing country

Road safety is a global concern particularly in developing countries where some road sections are disproportionately more vulnerable in terms of the frequency and severity of crashes. Other than using historical crash data based reactive approaches, those sections need to be identified proactively, so that mitigation measures can be applied. Moreover, those approaches are sometimes questioned mainly due to data reliability issues in developing countries. The study reported here is aimed at highlighting the applicability of traffic conflict techniques as surrogate safety measures to identify those sections of a rural highway in a developing country, which are most likely at risk. An adapted framework is demonstrated to identify traffic conflicts using combined surrogate indicators acknowledging the limited resources and facilities in developing countries. A new model is put forwarded using a count data modelling approach. Both fixed and random parameters model derivatives have been explored as an alternative methodological approach to relate the factors affecting the number and probability of conflicts. The partial effects of individual independent variables were estimated to gain a better insight of their impact. The results show that the model can predict high risk segments in terms of probability of conflicts as well as safety risk, as well as prioritize road sections according to the likelihood of their safety level. The model provides a less expensive alternative to the collection of historical crash data in order to identify hazardous road locations or black spots on two-lane highways in developing countries.11A part of the paper was presented in the Transport Research Board (TRB) 97th Annual Meeting and an abridged version from that part was published in TRB online forum1. Mahmud, S., et al., Prioritizing Hazardous Road Sections Using Surrogate Safety Measures: A Count Data Modelling Application in a Heterogeneous Traffic Environment, in Transport Research Board (TRB) 97th Annual Meeting 2018, Transport Research Board (TRB) Washington DC. p. 18–02193. Link: http://amonline.trb.org/2017trb-1.3983622/t025-1.3995192/382-1.3995580/18-02193-1.3993134/18-02193-1.3995603?qr=1.