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.

Driving Behavior Shaping Model in Road Traffic System

In order to give a new way for modeling driving behavior, identifying road traffic accident causation and solving a variety of road traffic safety problems such as driving errors prevention and driving behavior analysis, a new driving behavior shaping model is proposed, which could be used to assess the degree of effect of driving error upon road traffic safety. Driver behavior shaping model based on driving reliability and safety analysis could be used to identify the road traffic accident causation, to supply data for driver's behavior training, to evaluate driving procedures, to human factor design of road traffic system.

Evaluation of driving behavior based on massive vehicle trajectory data

Based on the driver surveillance video data and controller area network(CAN)data,the methods of studying commercial vehicles’driving behavior is relatively advanced.However,these methods have difficulty in covering private vehicles.Naturalistic driving studies have disadvantages of small sample size and high cost,one new driving behavior evaluation method using massive vehicle trajectory data is put forward.An automatic encoding machine is used to reduce the noise of raw data,and then driving dynamics and self-organizing mapping(SOM)classification are used to give thresholds or the judgement method of overspeed,rapid speed change,rapid turning and rapid lane changing.The proportion of different driving behaviors and typical dangerous driving behaviors is calculated,then the temporal and spatial distribution of drivers’driving behavior and the driving behavior characteristics on typical roads are analyzed.Driving behaviors on accident-prone road sections and normal road sections are compared.Results show that in Shenzhen,frequent lane changing and overspeed are the most common unsafe driving behaviors;16.1%drivers have relatively aggressive driving behavior;the proportion of dangerous driving behavior is higher outside the original economic special zone;dangerous driving behavior is highly correlated with traffic accident frequency.

A new cellular automaton for signal controlled traffic flow based on driving behaviors

The complexity of signal controlled traffic largely stems from the various driving behaviors developed in response to the traffic signal. However, the existing models take a few driving behaviors into account and consequently the traffic dynamics has not been completely explored. Therefore, a new cellular automaton model, which incorporates the driving behaviors typically manifesting during the different stages when the vehicles are moving toward a traffic light, is proposed in this paper. Numerical simulations have demonstrated that the proposed model can produce the spontaneous traffic breakdown and the dissolution of the over-saturated traffic phenomena. Furthermore, the simulation results indicate that the slow-to-start behavior and the inch-forward behavior can foster the traffic breakdown. Particularly, it has been discovered that the over-saturated traffic can be revised to be an under-saturated state when the slow-down behavior is activated after the spontaneous breakdown. Finally, the contributions of the driving behaviors on the traffic breakdown have been examined.

Bifurcation analysis of visual angle model with anticipated time and stabilizing driving behavior

In the light of the visual angle model(VAM),an improved car-following model considering driver's visual angle,anticipated time and stabilizing driving behavior is proposed so as to investigate how the driver's behavior factors affect the stability of the traffic flow.Based on the model,linear stability analysis is performed together with bifurcation analysis,whose corresponding stability condition is highly fit to the results of the linear analysis.Furthermore,the time-dependent Ginzburg–Landau(TDGL)equation and the modified Korteweg–de Vries(m Kd V)equation are derived by nonlinear analysis,and we obtain the relationship of the two equations through the comparison.Finally,parameter calibration and numerical simulation are conducted to verify the validity of the theoretical analysis,whose results are highly consistent with the theoretical analysis.

Abnormal driving behavior identification based on direction and position offsets

Abnormal driving behavior identification( ADBI) has become a research hotspot because of its significance in driver assistance systems. However,current methods still have some limitations in terms of accuracy and reliability under severe traffic scenes. This paper proposes a new ADBI method based on direction and position offsets,where a two-factor identification strategy is proposed to improve the accuracy and reliability of ADBI. Self-adaptive edge detection based on Sobel operator is used to extract edge information of lanes. In order to enhance the efficiency and reliability of lane detection,an improved lane detection algorithm is proposed,where a Hough transform based on local search scope is employed to quickly detect the lane,and a validation scheme based on priori information is proposed to further verify the detected lane. Experimental results under various complex road conditions demonstrate the validity of the proposed ADBI.

Association of risky driving behavior with psychiatric disorders among Iranian drivers:A case-control study

Purpose:This study aimed to investigate the possible association between psychological disorders and riskydriving behavior(RDB)in Iran.Methods:This case-control study conducted in Shiraz,Iran in 2021.The case group included drivers with psychological disorders and the control group included those without any disorders.The inclusion criteria for selecting patients were:active driving at the time of the study,being 18-65 years old,having a driving license,having a psychological disorder including depression,bipolar disorder,anxiety spectrum disorder,or psychotic disorder spectrum confirmed by a psychiatrist,and completing an informed consent form.The exclusion criterion was the existence of conditions that interfered with answering and understanding the questions.The inclusion criteria for selecting the healthy cases were:active driving at the time of the study,being 18-65 years old,having a driving license,lack of any past or present history of psychiatric problems,and completing an informed consent form.The data were gathered using a researcher-made checklist and Manchester driving behavior questionnaire.First,partition around medoids method was used to extract clusters of RDB.Then,backward logistic regression was applied to investigate the association between the independent variables and the clusters of RDB.Results:The sample comprised of 344(153 with psychological disorder and 191 without confirmed psychological disorder)drivers.Backward elimination logistic regression on total data revealed that share of medical expenditure≤10%of total household expenditure(OR=3.27,95%Cl:1.48-7.24),psychological disorder(OR=3.08,95%Cl:1.67-5.70),and substance abuse class(OR=6.38,95%CI:3.55-11.48)wereassociatedwithhighlevelof RDB.Conclusion:Substance abuse,psychological illnesses,and share of medical costs from total household expenditure were found to be main predictors of RDB.Further investigations are necessary to explain the impact of different psychological illnesses on driving behavior.

A review of road safety evaluation methods based on driving behavior

Road traffic safety should be evaluated throughout the entire life-cycle of road design,operation,maintenance,and expansion construction.However,traditional methods for evaluating road traffic safety based on traffic accidents and conflict technology are limited by their inability to account for the complex environmental factors involved.To address this issue,a new road safety evaluation method has emerged that is based on driving behavior.Because drivers’behaviors may vary depending on the driving environment and their personal characteristics,evaluating road safety from the perspective of driver behavior has become a popular research topic.This paper analyzes current research trends and mainstream journals in the field of road safety evaluation of driving behavior.Additionally,it reviews the three most commonly used driving behavior data collection methods,and compares the advantages and disadvantages of each.The paper proposes the main application scenarios of road safety evaluation methods based on driving behavior,such as road design,evaluation of the effects of road appurtenances,and intelligent highways.Furthermore,the paper summarizes a driving behavior index system based on vehicle data,driver’s physiological and psychological data,and driver’s subjective questionnaire data.A comprehensive evaluation method based on the fusion of each index system is presented in detail.Finally,the paper points out current research problems and the future development direction of the road safety evaluation method based on driving behavior.

Thirty years of research on driving behavior active intervention:A bibliometric overview

To better understand the research focus and development direction in the field of driving behavior active intervention,thereby laying a scientific foundation for further research,we used the combination of topic words and keywords to retrieve relevant articles from the Core Collection Database of Web of Science(WOS).A total of 578 articles published from1992 to 2022 were finally obtained.Firstly,the time distribution characteristics,country distribution,institution distribution and main source journal distribution of published articles were explored.Then,by using the Cite Space and VOSviewer software,cited reference co-citation analysis,keyword co-occurrence analysis and burst detection analysis were carried out respectively to visually explore the knowledge base,research topic,research frontier and development trend of this field.The results indicate that the USA,Australia and China are the three most active countries in the studies of driving behavior active intervention.Accidental Analysis&Prevention,Transportation Research Part F:Traffic Psychology and Behavior,and Journal of Safety Research are widely selected journals for publications related to this field.The research frontiers in the field of driving behavior active intervention focus on:“traffic safety and crashes analysis,as well as enforcement intervention”,“driving risk and education for young drivers”,“information provision and driving behavior”,“workload and situation awareness for automated driving”.It is worth noting that in recent years,“warning system”,“time”,“work load”have become research hotspots in this field.To sum up,by a bibliometric overview of research on driving behavior active intervention over the past thirty years,this paper clarifies the development skeleton of this research field,determines its hot topics and research progress,and provides a reference for the follow-up exploratory scientific research in this field.

Bibliometric study and critical individual literature review of driving behavior analysis methods based on brain imaging from 1993 to 2022

Brain imaging methods have effectively revealed drivers’underlying psychological and neural processes when they perform driving tasks and promote driving behavior research in a more scientific direction.With research no longer limited to indirect inferences about external behavior,some researchers combine behavior and driver brain activity to understand the human factors in driving essentially.However,most researchers in the field of driving behavior still have little understanding of how brain imaging methods are used.This paper aims to review and analyze the application of brain imaging methods in driving behavior research,including bibliometric analysis and an individual critical literature review.Regarding bibliometric analysis,this field’s knowledge structure and development trend are described macroscopically,using data such as annual distribution of publications,country/region statistics and partnerships,publication sources,literature co-citation analysis,and keyword co-occurrence analysis.In a review of the individual critical literature,eight research themes were identified that examined driving behavior using brain imaging methods:substance consumption,fatigue or sleep deprivation,workload,distraction,aging brains,brain impairment and other diseases,automated/semi-automated environments,emotions influence and risk-taking,and general driving process.In addition,the study reports on six brain imaging methods and their advantages and disadvantages,involving electroencephalography(EEG),functional magnetic resonance imaging(fMRI),functional near-infrared spectroscopy(fNIRS),magnetoencephalography(MEG),positron emission tomography(PET),and transcranial magnetic stimulation(TMS).The contribution of this study is twofold.The first part relates to providing the researchers with a comprehensive understanding of the field’s knowledge structure and development trends.The second part goes beyond reviewing and analyzing previous studies,and the discussion section points out the directions and challenges for future research.

Safety Evaluation of Commercial Vehicle Driving Behavior Using the AHP–CRITIC Algorithm

To prevent and reduce road traffic accidents and improve driver safety awareness and bad driving be-haviors,we propose a safety evaluation method for commercial vehicle driving behavior.Three driving style clas-sification indexes were extracted using driving data from commercial vehicles and four primary and ten secondary safety evaluation indicators.Based on the stability of commercial vehicles transporting goods,the acceleration index is divided into three levels according to the statistical third quartile,and the evaluation expression of the safety index evaluation is established.Drivers were divided into conservative,moderate,and radical using K-means++.The weights corresponding to each index were calculated using a combination of the analytic hierarchy process(AHP)and criteria importance through intercriteria correlation(CRITIC),and the driving behavior scores of various drivers were calculated according to the safety index score standard.The established AHP-CRITIC safety evaluation model was verified using the actual driving behavior data of commercial vehicle drivers.The calculation results show that the proposed evaluation model can clearly distinguish between the types of drivers with different driving styles,verifying its rationality and validity.The evaluation results can provide a reference for transportation management departments and enterprises.

An analysis on older driver＇s driving behavior by GPS tracking data： Road selection, left/right turn, and driving speed

With the high older-related accident ratio and increasing population aging problem, understanding older drivers＇ driving behaviors has become more and more important for building and improving transportation system. This paper examines older driver＇s driving behavior which includes road selection, left/right turn and driving speed. A two-month experiment of 108 participants was carried out in Aichi Prefecture, Japan. Since apparently contradictory statements were often drawn in survey-based or simulators-based studies, this study collected not only drivers＇ basic information but also GPS data. Analysis of road selection demonstrates that older drivers are reluctant to drive on expressway not only in short trips but also in long trips. The present study did not find significant difference be- tween older drivers and others while turning at the intersections. To investigate the impact factors on driving speed, a random-effects regression model is constructed with explan- atory variables including age, gender, road types and the interaction terms between age and road types. Compared with other variables, it fails to find that age （60 years old or over） has significant impact on driving speed. Moreover, the results reflect that older drivers drive even faster than others at particular road types： national road and ordinary municipal road. The results in this study are expected to help improve transportation planning and develop driving assistance systems for older drivers.

Dangerous Driving Behavior Recognition and Prevention Using an Autoregressive Time-Series Model

Time headway is an important index used in characterizing dangerous driving behaviors. This research focuses on the decreasing tendency of time headway and investigates its association with crash occurrence. An autoregressive（AR） time-series model is improved and adopted to describe the dynamic variations of average daily time headway. Based on the model, a simple approach for dangerous driving behavior recognition is proposed with the aim of significantly decreasing headway. The effectivity of the proposed approach is validated by means of empirical data collected from a medium-sized city in northern China. Finally, a practical early-warning strategy focused on both the remaining life and low headway is proposed to remind drivers to pay attention to their driving behaviors and the possible occurrence of crash-related risks.

Capturing driving behavior Heterogeneity based on trajectory data

Driving behavior is heterogeneous for various drivers due to the different influencing factors as reaction time,gender,driving years and so on.Some existing works tried to reproduce some of the complex characteristics of real traffic flow by taking into account the heterogeneous driving behavior,and the drivers are generally divided into two classes(including aggressive drivers and careful drivers)or three classes(including aggressive drivers,normal drivers and careful drivers).Nevertheless,the classification approaches have not been verified,and the rationality of the classifications has not been confirmed as well.In this study,the trajectory data of drivers is extracted from the NGSIM datasets.By combining the K-Means method and Silhouette measure index,the drivers are classified into four clusters(named as clusters A,B,C and D,respectively)in accordance with the acceleration and time headway.The two-dimensional approach is applied to analyze the characteristics of different clusters.Here,one dimension consists of“Cautious”and“Aggressive”behaviors in terms of velocity and acceleration,and the other dimension consists of“Sensitive”and“Insensitive”behaviors in terms of reaction time.Finally,the fuel consumption and emissions for different clusters are calculated by using the VT-Micro model.A surprising result indicates that overly“cautious”and“sensitive”behaviors may result in more fuel consumption and emissions.Therefore,it is necessary to find the balance between the driving characteristics.

Determining driver perceptions about distractions and modeling their effects on driving behavior at different age groups

This study was aimed at determining the driving distractions which are perceived most hazardous and determining the effects of these distractions and age on speed and headway. A questionnaire survey was done to find out the opinion of drivers related to the most hazardous distraction. 639 responses were collected in the survey which were used to determine the top-rated distractions for drivers in Bahrain. Roadside observations were taken to observe the speed, headway, age and type of distraction for the driver. Speed was observed for 48 drivers while headway was observed for 36 drivers along with other parameters. The most hazardous distractions, according to the participants of the questionnaire, are using mobile phones, handling children, and accidents or incidents on the road. Further, the results of the two-way analysis of variance(ANOVA) test and regression analysis demonstrated that using mobile phones and age have a significant effect on both speed and headway. Speed tends to decrease with distraction for all age groups while decreasing the headway for young and middle-aged drivers. The effect of distraction is higher than the effect of age on speed, as well as headway. Texting has the most significant effect among distractions on headway. It is hereby recommended that policymakers should focus on increasing awareness and stringent law enforcement related to handling mobile phones and children, especially for young and middle-aged drivers.

Efficient normalization for quantitative evaluation of the driving behavior using a gated auto-encoder

Driving behavior normalization is important for a fair evaluation of the driving style.The longitudinal control of a vehicle is investigated in this study.The normalization task can be considered as mapping of the driving behavior in a different environment to the uniform condition.Unlike the model-based approach as in previous work,where a necessary driver model is employed to conduct the driving cycle test,the approach we propose directly normalizes the driving behavior using an autoencoder(AE)when following a standard speed profile.To ensure a positive correlation between the vehicle speed and driving behavior,a gate constraint is imposed in between the encoder and decoder to form a gated AE(gAE).This approach is model-free and efficient.The proposed approach is tested for consistency with the model-based approach and for its applications to quantitative evaluation of the driving behavior and fuel consumption analysis.Simulations are conducted to verify the effectiveness of the proposed scheme.

Driving rule extraction based on cognitive behavior analysis

In order to make full use of the driver’s long-term driving experience in the process of perception, interaction and vehicle control of road traffic information, a driving behavior rule extraction algorithm based on artificial neural network interface(ANNI) and its integration is proposed. Firstly, based on the cognitive learning theory, the cognitive driving behavior model is established, and then the cognitive driving behavior is described and analyzed. Next, based on ANNI, the model and the rule extraction algorithm(ANNI-REA) are designed to explain not only the driving behavior but also the non-sequence. Rules have high fidelity and safety during driving without discretizing continuous input variables. The experimental results on the UCI standard data set and on the self-built driving behavior data set, show that the method is about 0.4% more accurate and about 10% less complex than the common C4.5-REA, Neuro-Rule and REFNE. Further, simulation experiments verify the correctness of the extracted driving rules and the effectiveness of the extraction based on cognitive driving behavior rules. In general, the several driving rules extracted fully reflect the execution mechanism of sequential activity of driving comprehensive cognition, which is of great significance for the traffic of mixed traffic flow under the network of vehicles and future research on unmanned driving.

Modeling and TOPSIS-GRA Algorithm for Autonomous Driving Decision-Making Under 5G-V2X Infrastructure

This paper is to explore the problems of intelligent connected vehicles(ICVs)autonomous driving decision-making under a 5G-V2X structured road environment.Through literature review and interviews with autonomous driving practitioners,this paper firstly puts forward a logical framework for designing a cerebrum-like autonomous driving system.Secondly,situated on this framework,it builds a hierarchical finite state machine(HFSM)model as well as a TOPSIS-GRA algorithm for making ICV autonomous driving decisions by employing a data fusion approach between the entropy weight method(EWM)and analytic hierarchy process method(AHP)and by employing a model fusion approach between the technique for order preference by similarity to an ideal solution(TOPSIS)and grey relational analysis(GRA).The HFSM model is composed of two layers:the global FSM model and the local FSM model.The decision of the former acts as partial input information of the latter and the result of the latter is sent forward to the local pathplanning module,meanwhile pulsating feedback to the former as real-time refresh data.To identify different traffic scenarios in a cerebrum-like way,the global FSM model is designed as 7 driving behavior states and 17 driving characteristic events,and the local FSM model is designed as 16 states and 8 characteristic events.In respect to designing a cerebrum-like algorithm for state transition,this paper firstly fuses AHP weight and EWM weight at their output layer to generate a synthetic weight coefficient for each characteristic event;then,it further fuses TOPSIS method and GRA method at the model building layer to obtain the implementable order of state transition.To verify the feasibility,reliability,and safety of theHFSMmodel aswell as its TOPSISGRA state transition algorithm,this paper elaborates on a series of simulative experiments conducted on the PreScan8.50 platform.The results display that the accuracy of obstacle detection gets 98%,lane line prediction is beyond 70 m,the speed of collision avoidance is higher than 45 km/h,the distance of collision avoidance is less than 5 m,path planning time for obstacle avoidance is averagely less than 50 ms,and brake deceleration is controlled under 6 m/s2.These technical indexes support that the driving states set and characteristic events set for the HFSM model as well as its TOPSIS-GRA algorithm may bring about cerebrum-like decision-making effectiveness for ICV autonomous driving under 5G-V2X intelligent road infrastructure.

COLLISION AVOIDANCE DECISION-MAKING MODEL OF MULTI-AGENTS IN VIRTUAL DRIVING ENVIRONMENT WITH ANALYTIC HIERARCHY PROCESS

Collision avoidance decision-making models of multiple agents in virtual driving environment are studied. Based on the behavioral characteristics and hierarchical structure of the collision avoidance decision-making in real life driving, delphi approach and mathematical statistics method are introduced to construct pair-wise comparison judgment matrix of collision avoidance decision choices to each collision situation. Analytic hierarchy process （AHP） is adopted to establish the agents＇ collision avoidance decision-making model. To simulate drivers＇ characteristics, driver factors are added to categorize driving modes into impatient mode, normal mode, and the cautious mode. The results show that this model can simulate human＇s thinking process, and the agents in the virtual environment can deal with collision situations and make decisions to avoid collisions without intervention. The model can also reflect diversity and uncertainly of real life driving behaviors, and solves the multi-objective, multi-choice ranking priority problem in multi-vehicle collision scenarios. This collision avoidance model of multi-agents model is feasible and effective, and can provide richer and closer-to-life virtual scene for driving simulator, reflecting real-life traffic environment more truly, this model can also promote the practicality of driving simulator.

Driving skill classification in curve driving scenes using machine learning

Driver support and infotainment systems can be adapted to the specific needs of individual drivers by assessing driver skill and state. In this paper, we present a machine learning approach to classifying the skill at maneuvering by drivers using both longitudinal and lateral controls in a vehicle. Conceptually, a model of drivers is constructed on the basis of sensor data related to the driving environment, the drivers＇ behaviors, and the vehi- cles＇ responses to the environment and behavior together. Once the model is built, the driving skills of an unknown driver can be classified automatically from the driving data. In this paper, we demonstrate the feasibility of using the proposed method to assess driving skill from the results of a driving simulator. We experiment with curve driving scenes, using both full curve and segmented curve sce- narios. Six curves with different radii and angular changes were set up for the experiment. In the full curve driving scene, principal component analysis and a support vector machine-based method accurately classified drivers in 95.7 % of cases when using driving data about high- and low/average-skilled driver groups. In the cases with seg- mented curves, classification accuracy was 89 %.