Heterogeneous Traffic Environment Research Articles

Calibration of base saturation flow rate at pretimed signal intersection utilizing Bayesian linear regression considering morning and evening peak hour observations

Abstract Base saturation flow is a critical constraint used for evaluating the capacity of signalized intersections. The Indonesian Highway Capacity Manual (IHCM, 2023) is frequently used for signalized intersection planning in Indonesia. In heterogeneous traffic environments, the driver characteristics and behaviors exhibit significant variations. However, the base saturation flow was also influenced by traffic volume during the observation period. This study modeled the base saturation flow by analyzing differences in morning and afternoon peak hour data. The objective was to examine the base saturation flow at pretimed signal in Banda Aceh, with a focus on traffic volume fluctuations during peak hours. Four intersections were investigated in this study. Unmanned aerial vehicles were employed to record discharge flow pattern within targeted signalized intersection. Manual timing was conducted using stopwatches and geometric road data were collected through direct field measurements. Bayesian linear regression with a Gibbs sampling approach was utilized to recalibrate the saturation flow rate coefficients. The base saturation flow during the morning peak is 284 We, while in the afternoon peak, it is 285 We, with We representing the effective intersection approach.

Assessing motorcyclist safety at unsignalized intersection using automated trajectory data analysis

Objective In a developing country like India, where the share of motorcyclists is increasing exponentially, their road crashes are also rising at an alarming rate. The majority of these road crashes occur at unsignalized intersections. Therefore, the present study aims to analyze the safety of motorcyclists at unsignalized three-arm intersections under a heterogeneous traffic environment using a fully automated trajectory data extraction tool. Methods The study first analyses the most frequent types of interactions that occur between motorcyclists and other road users at unsignalized intersections. Then, the study examines the interactions between motorcyclists and other vehicles by analyzing the speed of both vehicles involved in these interactions. Lastly, the study employs a supervised classification technique, Support Vector Machine (SVM), to categorize these interactions into critical, mild, and safe based on surrogate safety indicators (for the proximity of interaction) and the maximum speed (for the severity of an interaction) at which the vehicles interact. Result The results indicate that rear-end conflict was the most frequently observed conflict at the unsignalized intersections. Further, the study emphasizes the crucial role of speed during interactions, particularly at higher speeds, where elevated threshold values of PET and TTC significantly influence the severity of the interaction. Conclusion Overall, the research provides an essential insight into motorcyclists’ safety in terms of critical conflicts at an unsignalized three-arm intersection. The findings of the research demonstrate the remarkable potential of fully automated trajectory data analysis software in evaluating safety at unsignalized intersections.

Phase transitions in a heterogeneous lattice hydrodynamic model involving both communication distance and memory time duration differences

An in-depth analysis of vehicle heterogeneity characteristics in intelligent heterogeneous traffic flows, involving connected autonomous vehicles and human-driven vehicles, is essential to reveal the evolutionary mechanism of congestion and to understand its interaction among traffic factors. It has significant theoretical value in guiding the future field deployment and testing of large-scale connected autonomous vehicles through the systematic analysis of the complex dynamic characteristics of intelligent heterogeneous traffic flows from a traffic engineering perspective, as well as traffic management and control in intelligent heterogeneous traffic flow environments. In this study, the lattice hydrodynamics model is applied to construct a corresponding traffic flow model for the differences of communication capability and historical information memory capability for heterogeneous vehicles. The roles of these heterogeneous properties and their interactions are investigated. Through linear stability analysis and nonlinear analysis, we in this study explore the influence of key traffic factors involving the differences of communication capability and historical information memory capability contributing to ameliorating intelligent heterogeneous traffic dynamics, and numerical simulations are carried out to verify the accuracy and reliability of theoretical analysis. An interesting discovery is that longer memory duration gets better, but the optimizing impact of memory duration on the system being marginally decreasing.

EVALUATING PEDESTRIAN CROSSING ATTRIBUTES AT INTERSECTIONS IN KABUL CITY, AFGHANISTAN: A COMPUTER VISION APPROACH

This study aims to evaluate pedestrian crossing attributes in heterogeneous traffic environments using computer vision. For this purpose, YoloV8 models were optimised to detect pedestrian crossing attributes. Moreover, an OpenCV-based Python programme was developed to track pedestrian trajectories manually. For accuracy, the inverse perspective mapping method is applied to obtain a bird's eye view. Finally, a heatmap of pedestrian trajectories was provided to visualise the pedestrian crossing attributes. The results show that more than three-quarters of pedestrians are engaging in noncompliance crossing behaviours at major intersections in Kabul City, Afghanistan. In addition, pedestrians tend to walk longer, more frequent routes at corners and outside of crosswalks. Furthermore, statistical analysis reveals that pedestrian crossing speed decreased by 5.8% when disobeying crossing rules, indicating the significant effect of pedestrian attributes on crossing speed. In conclusion, this study contributes to a better understanding of pedestrian behaviour in heterogeneous traffic environments using computer vision. The results would provide insightful information to traffic engineers and planners for traffic management.

A dynamic priority bus lane strategy in heterogeneous traffic environments

Allowing connected and automated vehicles (CAVs) to drive in bus lanes can enhance the utilization efficiency of exclusive bus lanes. However, this could also lead to a certain degree of heightened bus operating delays. In a connected and automated environment, vehicle-to-vehicle (V2V) communication is essential for implementing dynamic management strategies for bus lanes. This study focuses on urban arterial roads as the research scenario. Based on the characteristics of the car-following and lane-changing behaviors of human-driven vehicles (HDVs) and CAVs, a simulation platform for heterogeneous traffic flow is established. Four strategies for bus lane control are proposed: exclusive bus lanes (EBLs), bus and CAV mixed lanes (BCMLs), dynamic bus lanes (DBLs), and dynamic priority bus lanes (DPBLs). Through simulation experiments, an analysis is conducted to compare traffic operation characteristics under different bus lane strategies, assess the advantages of these strategies, and determine the traffic density conditions for their implementation. The results indicate that in comparison to the EBL strategy, the other strategies enhance traffic flows but result in different levels of travel delays for buses. Compared to the DBL and BCML strategies, the DPBL strategy is applicable under a broader range of traffic density conditions. Additionally, sensitivity analysis revealed that the effects of the V2V communication distance and bus flows on the applicability of DPBL strategies are relatively minor. These results provide a theoretical basis and practical guidance for future bus lane management.

FedGODE: Secure traffic flow prediction based on federated learning and graph ordinary differential equation networks

Traffic flow prediction (TFP) plays a key role in optimizing intelligent transportation systems and reducing congestion in smart cities. However, current centralized TFP systems suffer from several limitations: limited adaptability to localized traffic patterns, privacy concerns associated with sharing raw data, inability to provide real-time predictions, network inefficiency due to large data transmissions, and difficulties in handling heterogeneous traffic environments. FedGODE (Federated Graph Ordinary Differential Equations) model offers a comprehensive solution to these issues. By employing federated learning (FL), FedGODE enables Roadside-Units (local clients) to contribute to the model training process, addressing challenges of localized traffic patterns and ensuring a more accurate representation of diverse scenarios. FedGODE incorporates privacy-preserving mechanisms (local differential privacy, homomorphic encryption, and secure aggregation), mitigating privacy concerns. FedGODE facilitates real-time updates to the global model, ensuring adaptability to dynamic traffic conditions while reducing network inefficiencies by transmitting only model updates. FedGODE utilizes (average/median) aggregation methods to aggregate the client’s local updates. The performance of FedGODE was evaluated using six real-world traffic flow datasets and compared against nine baselines (ARIMA, VAR, FC-LSTM, STGCN, DCRNN, ASTGCN, Graph WaveNet, STG2seq, STGODE, and FedGRU). The results showed that FedGODE outperforms all baselines for short-and-long-term prediction. This approach makes FedGODE an effective solution for TFP scenarios, particularly in environments with heterogeneous traffic dynamics and privacy considerations.

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.