Influences of Mixed Traffic Flow and Time Pressure on Mistake-Prone Driving Behaviors among Bus Drivers

Bus safety is a matter of great importance in many developing countries, with driving behaviors among bus drivers identified as a primary factor contributing to accidents. This concern is particularly amplified in mixed traffic flow (MTF) environments with time pressure (TP). However, there is a lack of sufficient research exploring the relationships among these factors. This study consists of two papers that aim to investigate the impact of MTF environments with TP on the driving behaviors of bus drivers. While the first paper focuses on violated driving behaviors, this particular paper delves into mistake-prone driving behaviors (MDB). To collect data on MDB, as well as perceptions of MTF and TP, a questionnaire survey was implemented among bus drivers. Factor analyses were employed to create new measurements for validating MDB in MTF environments. The study utilized partial correlation and linear regression analyses with the Bayesian Model Averaging (BMA) method to explore the relationships between MDB and MTF/TP. The results revealed a modified scale for MDB. Two MTF factors and two TP factors were found to be significantly associated with MDB. A high presence of motorcycles and dangerous interactions among vehicles were not found to be associated with MDB among bus drivers. However, bus drivers who perceived motorcyclists as aggressive, considered road users’ traffic habits as unsafe, and perceived bus routes’ punctuality and organization as very strict were more likely to exhibit MDB. Moreover, the results from the three MDB predictive models demonstrated a positive impact of bus route organization on MDB among bus drivers. The study also examined various relationships between the socio-demographic characteristics of bus drivers and MDB. These findings are of practical significance in developing interventions aimed at reducing MDB among bus drivers operating in MTF environments with TP.

Potential Application of iRap for Road Safety Assessment under Mixed Traffic Condition Case Study: Le Van Viet Street in Ho Chi Minh City

Road transport safety policies have emphasized road infrastructure safety design and engineering as a core function.However,in developing countries like Vietnam,this approach has been slower to adopt,resulting in substandard roads.In-depth studies of accident locations indicate that road environment factors contribute significantly to road accidents in Vietnam and road design features are associated with specific accident types and hazards.Proactive and reactive approaches,such as road safety audit,inspection,assessment,and treatment of hazardous locations,are necessary to ensure that the road and its environment are safe.This paper provides an overview of road safety in Vietnam in general,and Ho Chi Minh in particular,including its factors and characteristics,as well as road infrastructure safety improvements.The iRap tool for road safety inspection and assessment is highlighted as a potential method for systematically analyzing road infrastructure deficiencies and providing targeted countermeasures to improve road safety under mixed traffic conditions.

Interaction-Aware Cut-In Trajectory Prediction and Risk Assessment in Mixed Traffic

Accurately predicting the trajectories of surrounding vehicles and assessing the collision risks are essential to avoid side and rear-end collisions caused by cut-in.To improve the safety of autonomous vehicles in the mixed traffic,this study proposes a cut-in prediction and risk assessment method with considering the interactions of multiple traffic participants.The integration of the support vector machine and Gaussian mixture model(SVM-GMM)is developed to simultaneously predict cut-in behavior and trajectory.The dimension of the input features is reduced through Chebyshev fitting to improve the training efficiency as well as the online inference performance.Based on the predicted trajectory of the cut-in vehicle and the responsive actions of the autonomous vehicles,two risk measurements are introduced to formulate the comprehensive interaction risk through the combination of Sigmoid function and Softmax function.Finally,the comparative analysis is performed to validate the proposed method using the naturalistic driving data.The results show that the proposed method can predict the trajectory with higher precision and effectively evaluate the risk level of a cut-in maneuver compared to the methods without considering interaction.

Effects of Driver Response Time Under Take‑Over Control Based on CAR‑ToC Model in Human–Machine Mixed Traffic Flow

The take-over control(ToC)of human–machine interaction is a hotspot.From automatic driving to manual driving,some factors affecting driver response time have not been considered in existing models,and little attention has been paid to its effects on mixed traffic flow.This study establishes a ToC model of response based on adaptive control of thought-rational cognitive architecture(CAR-ToC)to investigate the effects of driver response time on traffic flow.A quantification method of driver’s situation cognition uncertainty is also proposed.This method can directly describe the cognitive effect of drivers with different cognitive characteristics on vehicle cluster situations.The results show that when driver response time in ToC is 4.2 s,the traffic state is the best.The greater the response time is,the more obvious the stop-and-go waves exhibit.Besides,crashes happen when manual vehicles hit other types of vehicles in ToC.Effects of driver response time on traffic are illustrated and verified from various aspects.Experiments are designed to verify that road efficiency and safety are increased by using a dynamic take-over strategy.Further,internal causes of effects are revealed and suggestions are discussed for the safety and efficiency of autonomous vehicles.

Safety of motorised two wheelers in mixed traffic conditions:Literature review of risk factors

The ownership of motorised two wheelers(MTWs)has been on the rise across various countries across the globe.The growth has been especially higher in developing countries which have typical traffic characteristics and higher populations.This steady rise has resulted in increased accident and fatalities.This abrupt increase warranted attention from the researchers to carry out specific studies for MTWs,which have a very different behaviour as compared to cars in terms of physical and dynamic parameters.Moreover,the unique traffic patterns usually found in the developing countries pose an additional challenge to the researchers,since the conventional focus of transportation safety researchers was a homogeneous car-based traffic.Many such studies have been attempted,especially in the recent decades,which have considered various risk factors related to MTW safety.However,the studies have considered different sets of risk factors and have given surprising and even conflicting results.Therefore,a comprehensive review of the diverse studies needs to be carried out which incorporates all the risk factors considered in previous research.This study reviews such research papers which have analysed various risk factors related to safety of MTWs,especially in heterogeneous,non-lane based traffic.Specifically,this paper aims to incorporate results from those studies and highlight the conclusions from state of the art.The paper also discusses about the research gaps that are crucial for MTW safety in mixed traffic conditions.The review will be useful for researchers working in the field of MTW safety and for policy implementation and analysis.

Analyzing driver's response to yellow indication subjected to dilemma incursion under mixed traffic condition

Under mixed traffic conditions prevailing on Indian roads,drivers show complex response when faced with yellow signal because lane assignment gets dynamic in nature.The present study analyzes the effect of surrounding vehicles on response of the drivers while facing dilemma at intersections.Although dilemma zone definitions hold true in case of homogeneous traffic mix,a statistical analysis is performed to check the consistency across the definitions under mixed traffic condition.Present study shows a significant difference in percentage of red light running in comparison to homogeneous traffic as reported by various studies.For carrying out the research,study locations are chosen in such a way to reflect diversity in road geometry,traffic composition and signal characteristics.The results deduced in this study indicate a strong correlation between the driver's decision making choice and the effect of presence of surrounding vehicle at the onset of yellow signal.The effect of critical time analysis has been found out to be one of the parameters other than critical distance in categorizing driver's aggressiveness while facing the yellow signal.In the process of identifying the statistical significance of dilemma zone definitions,it has been found that under heterogeneous traffic condition,drivers behave differently as compared to homogenous traffic when it comes to dilemma zone.It is observed that the percentage of vehicles crossing the intersection when faced with dilemma by violating the red light is 11.6%according to dilemma zone definition I whereas the definition II has yielded about 10.8%violation covering different vehicle types.The above violation figures derived based on the above definition is somewhat higher as compared to homogeneous traffic condition which is observed to be of the order of 5%-6%.

Multi-agent reinforcement learning for cooperative lane changing of connected and autonomous vehicles in mixed traffic

Autonomous driving has attracted significant research interests in the past two decades as it offers many potential benefits,including releasing drivers from exhausting driving and mitigating traffic congestion,among others.Despite promising progress,lane-changing remains a great challenge for autonomous vehicles(AV),especially in mixed and dynamic traffic scenarios.Recently,reinforcement learning(RL)has been widely explored for lane-changing decision makings in AVs with encouraging results demonstrated.However,the majority of those studies are focused on a single-vehicle setting,and lane-changing in the context of multiple AVs coexisting with human-driven vehicles(HDVs)have received scarce attention.In this paper,we formulate the lane-changing decision-making of multiple AVs in a mixed-traffic highway environment as a multi-agent reinforcement learning(MARL)problem,where each AV makes lane-changing decisions based on the motions of both neighboring AVs and HDVs.Specifically,a multi-agent advantage actor-critic(MA2C)method is proposed with a novel local reward design and a parameter sharing scheme.In particular,a multi-objective reward function is designed to incorporate fuel efficiency,driving comfort,and the safety of autonomous driving.A comprehensive experimental study is made that our proposed MARL framework consistently outperforms several state-of-the-art benchmarks in terms of efficiency,safety,and driver comfort.

Impact of connected and autonomous vehicles on traffic safety of mixed traffic flow:from the perspective of connectivity and spatial distribution

Equipped with high driving automation and advanced communication technologies, connected and autonomous vehicles (CAV) areexpected to possess a shorter reaction time and a wider vision,which are promising to improve traffic safety and efficiency. However,little attention has been paid to the effect of connectivity and spatial distribution on the safety performance of mixed traffic flow. Inthis paper, we attempt to investigate the impact of CAV on traffic safety considering these factors. To this end, a car-following modelfor CAV is proposed first. Then, the cooperative driving strategy for CAVs is designed. Precisely, the feedback gains of the informationare adjusted in real-time and are designed based on the derived stability criterion of the mixed traffic flow. Microscopic simulations ofmixed traffic flow in traffic oscillation are designed and conducted to explore how the distribution and connectivity of CAV affect thesafety performance ofmixed traffic flow. Simulation results show that increasing the penetration rate of CAV is promising to shift thesafety performance ofmixed traffic flow. In addition, the safety performance of mixed traffic flow is related to the spatial distributionand communication range of CAV. Besides, increasing communication range does not inevitably improve the safety performance ofmixed traffic flow when the penetration rate of CAV is low. Moreover, it is also found from the spatial–temporal trajectory of themixedtraffic flow that introducing CAV can mitigate the propagation of the stop-and-go wave and increase the throughput.

Analysis of motorcycle microscopic characteristics at roundabouts under mixed traffic condition:A case study of Vietnam

Recently,the demand for comprehension of mixed traffic in developing countries,particularly at roundabouts,which are highly interactive road junctions,has increased.Thus,we analyzed mixed traffic at roundabouts,considering Vietnam as a case study.The two main objectives of this study were to characterize the mixed traffic in Vietnam and to determine the microscopic characteristics of motorcycles at roundabouts.First,efforts were made to clarify the two constitutions of mixed traffic(the performance rule and the presence of small-sized vehicles),and the term“motorcycle-oriented mixed traffic”was defined.Even when satisfying the two fundamental constitutions,this traffic state has unique features,e.g.,only one type of non-lane-based vehicle(the motorcycle)and the predominance of motorcycles in the traffic composition(91.7%).Second,four microscopic characteristics of motorcycles were obtained from a large dataset:the motorcycles’continuous changes in speed,the relationship between the turning angle rate and the speed,the critical gap,and the following space.The relationship between the turning angle rate and the speed was first formulated as a power curve.The critical gap of motorcycles was estimated as a small value(1.25 s)in the case study.The following spaces varied with respect to the speed and had an oval shape.The smallest lateral and longitudinal dimensions were 1.5 and 1.74 m,respectively.While all the findings are meaningful,they are restricted to the case study(Ho Chi Minh City,Vietnam).

Traffic signal control in mixed traffic environment based on advance decision and reinforcement learning

Reinforcement learning-based traffic signal control systems (RLTSC) can enhance dynamic adaptability, save vehicle travelling timeand promote intersection capacity. However, the existing RLTSC methods do not consider the driver’s response time requirement, sothe systems often face efficiency limitations and implementation difficulties.We propose the advance decision-making reinforcementlearning traffic signal control (AD-RLTSC) algorithm to improve traffic efficiency while ensuring safety in mixed traffic environment.First, the relationship between the intersection perception range and the signal control period is established and the trust region state(TRS) is proposed. Then, the scalable state matrix is dynamically adjusted to decide the future signal light status. The decision will bedisplayed to the human-driven vehicles (HDVs) through the bi-countdown timer mechanism and sent to the nearby connected automatedvehicles (CAVs) using the wireless network rather than be executed immediately. HDVs and CAVs optimize the driving speedbased on the remaining green (or red) time. Besides, the Double Dueling Deep Q-learning Network algorithm is used for reinforcementlearning training;a standardized reward is proposed to enhance the performance of intersection control and prioritized experiencereplay is adopted to improve sample utilization. The experimental results on vehicle micro-behaviour and traffic macro-efficiencyshowed that the proposed AD-RLTSC algorithm can simultaneously improve both traffic efficiency and traffic flow stability.

Analyzing influence of bicycles on traffic flow using microscopic simulation approach

Mixed traffic consisting of cars and bicycles is a typical pattern of urban traffic in China.To study the impact of bicycles on traffic performance,a microscopic simulating model based on the principle of cautious driving and collision free was proposed.The interaction between cars and bicycles were described by lateral friction and overlapping driving.The dynamical features of speed and time-headway were investigated by numerical simulations.The results show that bicycles have a significant impact on travel speed and time-headway.The effect of bicycles can cause different results in the free flow phase and congestion phase respectively.The results also indicate the necessity to mitigate the interaction between motorized vehicles and non-motorized vehicles.

Bicycle capacity of borrowed-priority merge at unsignalized intersections in China

To investigate bicyclists' behavior at unsignalized intersections with mixed traffic flow,a bicycle capacity model of borrowed-priority merge was developed by the addition-conflict-flow procedure.Based on the actual traffic situation,the concept of borrowed priority,in which the major-road bicycles borrow the priority of major-road cars to enter the intersections when consecutive headway for major-steam cars is lower than the critical gap for minor-road cars,was addressed.Bicycle capacity at a typical unsignalized intersection is derived by the addition-conflict-flow procedure.The proposes model was validated by the empirical investigation.Numerical results show that bicycle capacity at an intersection is the function of major-road and minor-road car streams.Bicycle capacity increases with increasing major-road cars but decreases with increasing minor-road cars.

Modeling lateral placement and movement of vehicles on urban undivided roads in mixed traf?c:A case study of India

In India,the majority of urban roads are undivided where the behavior of flows in a particular direction is predominantly influenced by the opposing traffic.Due to lack of lane segregation,the vehicles in ongoing direction occupy the opposing lane,which increases the lateral interactions between vehicles.These lateral interactions are influenced by various parameters such as vehicle types,driver behavior and vehicular speeds.Study of such complex interactions plays an important role in evaluating various management measures using microscopic simulation models.The lateral characteristics of vehicles,such as placement,separation and movement,act as necessary input for simulation models.The present study aims to analyze and model the lateral characteristics of vehicles on two-lane urban undivided roads.To achieve this,traffic flow data were collected from an urban undivided mid-block section in Bangalore City,India,using video graphic technique.Multiple linear regression model was developed for predicting the lateral placement of subject vehicle and it was found that lateral placement of subject vehicle is influenced by types and speeds of subject and opposing vehicles.Lateral separation for different types of ongoing(subject)and opposing pairs was also analyzed.The results show that both the ongoing and opposing vehicles have less freedom to move laterally when their sizes increase and hence,lateral separation decreases.The choice of path of vehicles’lateral shifts(left,current and right)on urban undivided roads was modeled using multinomial logistic regression.Lateral shift of a vehicle is influenced by speeds of subject vehicle and leader vehicle in current path,speed of leader vehicle in target path,and lateral gap between leader vehicles in current path and target path.

Merging control strategies of connected and autonomous vehicles at freeway on-ramps:a comprehensive review

Purpose–On-ramp merging areas are typical bottlenecks in the freeway network since merging on-ramp vehicles may cause intensive disturbances on the mainline traffic flow and lead to various negative impacts on traffic efficiency and safety.The connected and autonomous vehicles(CAVs),with their capabilities of real-time communication and precise motion control,hold a great potential to facilitate ramp merging operation through enhanced coordination strategies.This paper aims to present a comprehensive review of the existing ramp merging strategies leveraging CAVs,focusing on the latest trends and developments in the research field.Design/methodology/approach–The review comprehensively covers 44 papers recently published in leading transportation journals.Based on the application context,control strategies are categorized into three categories:merging into sing-lane freeways with total CAVs,merging into singlane freeways with mixed traffic flows and merging into multilane freeways.Findings–Relevant literature is reviewed regarding the required technologies,control decision level,applied methods and impacts on traffic performance.More importantly,the authors identify the existing research gaps and provide insightful discussions on the potential and promising directions for future research based on the review,which facilitates further advancement in this research topic.Originality/value–Many strategies based on the communication and automation capabilities of CAVs have been developed over the past decades,devoted to facilitating the merging/lane-changing maneuvers at freeway on-ramps.Despite the significant progress made,an up-to-date review covering these latest developments is missing to the authors’best knowledge.This paper conducts a thorough review of the cooperation/coordination strategies that facilitate freeway on-ramp merging using CAVs,focusing on the latest developments in this field.Based on the review,the authors identify the existing research gaps in CAV ramp merging and discuss the potential and promising future research directions to address the gaps.