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Accurate traffic flow prediction can effectively improve traffic efficiency and safety. This has become a trending topic in intelligent transportation systems. However, the occurrence of traffic accidents will cause great fluctuations of traffic flow in a short time, thus affecting the accuracy of flow prediction. This paper analyses the influence of traffic accident information on traffic flow, and proposes a grey convolutional neural network called G-CNN for traffic flow prediction. First, considering the small sample size of traffic accidents and the incomplete information description, the traffic flow and traffic speed change rate are defined to represent traffic accident grey information, and a grey fixed-weight clustering method for extracting the characteristics of traffic accident grey information is established. Second, the spatiotemporal characteristics of traffic flow are analysed, and a third-order tensor is developed to fuse them with the accident characteristics and serve as the input of the G-CNN. Then the G-CNN model is built to predict traffic flow in a traffic accident environment. The Canadian Whitemud Drive experiment on traffic flow without traffic accident information reveals that the developed G-CNN model has a better ability to predict future traffic flow with better accuracy and stability. Meanwhile, it is verified that the fluctuation of the traffic change rate will cause traffic accidents, and the probability of traffic accidents at a certain time point will be obtained through feature extraction of traffic accidents. Finally, the model is verified for the traffic flow of Hangzhou Viaduct in China, which demonstrates that the G-CNN model outperforms all of the compared models. [ABSTRACT FROM AUTHOR]

• QN-ACTR could model effect of limited sight distance on car-following performance. • Cognition modeling provides support for adverse fog traffic safety assessments. • Frequent eye movement led to increasing of cognitive time when driving in fog. • Driver's perception, decision-making and control behavior were more complex in fog. • Reduction of cognitive capacity led to an impairment of the overall driver behavior. This study explored human error risk factors in car-following behaviors, cognitive resource bottlenecks and performance impairment mechanisms in the context of cognitive capacity while driving on foggy roads. Firstly, a hierarchical driving behavior assessment was used to observe risky driving behavior in a foggy area. A driver's dynamic speed adjustment analog experiment was carried out in foggy conditions. Unique parameters were substituted into the traditional QN-ACTR driver behavior model. The visual far-time of the traditional model and the latitude-longitude control sensitivity parameters were estimated. Foggy conditions were modeled based on the QN-ACTR cognitive architecture to observe the effect of limited sight distance through fog on car-following performance. Jsim in the Eclipse environment and TORCS were applied in co-simulation and verified. The results show that (1) the proposed cognitive modeling approach effectively simulates a foggy driving environment and allows researchers to study affected and related driver behavior; and (2) the car-following performance in low visibility is significantly worse than in high visibility. These findings provide theoretical support and a scientific basis for the study of speed, safe distance, standard of limited speed and design methods for engineering safety mechanisms to counter the negative effects of driving on foggy roads. This study can inform actions to increase road safety during fog. [ABSTRACT FROM AUTHOR]

Existing fatigued driving analysis methods mainly focus on lateral driving performance by using the measurements related to the steering wheel or lane position. There is a lack of research on longitudinal car following behavior. In this study, 40 professional drivers are invited to participate in field expressway driving experiment, lasting at least for 6 h. During the test, their performance is measured in terms of their self-reported fatigued driving level according to the Karolinska Sleepiness Scale (KSS), the PERcentage of eye CLOSures (PERCLOS) and the Time Headway (THW). Then the effects of the fatigued driving level on car following behavior are evaluated. The results indicate that the fatigue level (for both KSS and PERCLOS) has significantly impact on THW parameters, including the mean, standard deviation and minimum THW. An increase in KSS and PERCLOS leads to a lower mean and minimum THW. Meanwhile, the standard deviation of THW increases with the increase of KSS and PERCLOS. In conclusion, this study found that a higher fatigue level leads to the driver keeping a smaller THW when following another vehicle and choosing shorter THW to make lane change. More deviation of car following performance was also found with the increase of fatigue level. Therefore, the findings of this study can be used to explain fatigue as one of the major reasons for rear-end collisions. Also, the research findings demonstrate the impact of fatigue on driving behavior in terms of car following performance, which can be used as a measurement for monitoring fatigued drivers. [ABSTRACT FROM AUTHOR]

Pedestrian crossing safety has attracted increased attention in recent years. However, little research has been conducted for examining the interaction between vehicles and pedestrians at uncontrolled mid-block crosswalks. In this paper, both a decision model and a motion model are developed for simulating this interaction process. Cumulative prospect theory is embedded in the evolutionary game framework for modeling the decision behaviors of drivers and pedestrians during the interactions, which can capture the phenomenon of disagreement among a pedestrian crossing group. Cellular automata-based moving rules are used to depict the motion of vehicles with consideration of the three-second rule, and a modified bidirectional pedestrian model is developed in order to consider the right-moving preference and resolve the deadlock among mixed flows. Results of calibration and validation of the proposed model are also presented. An application is designed for the purpose of illustrating the model’s capabilities. The results demonstrate that the proposed model can well replicate actual observed traffic. [ABSTRACT FROM AUTHOR]

Drowsy/distracted driving has become one of the leading causes of traffic crash. Only certain particular drowsy/distracted driving behaviors have been studied by previous studies, which are mainly based on dedicated sensor devices such as bio and visual sensors. The objective of this study is to extract the common features for identifying drowsy/distracted driving through a set of common vehicle motion parameters. An intelligent vehicle was used to collect vehicle motion parameters. Fifty licensed drivers (37 males and 13 females, M = 32.5 years, SD = 6.2) were recruited to carry out road experiments in Wuhan, China and collecting vehicle motion data under four driving scenarios including talking, watching roadside, drinking and under the influence of drowsiness. For the first scenario, the drivers were exposed to a set of questions and asked to repeat a few sentences that had been proved valid in inducing driving distraction. Watching roadside, drinking and driving under drowsiness were assessed by an observer and self-reporting from the drivers. The common features of vehicle motions under four types of drowsy/distracted driving were analyzed using descriptive statistics and then Wilcoxon rank sum test. The results indicated that there was a significant difference of lateral acceleration rates and yaw rate acceleration between “normal driving” and drowsy/distracted driving. Study results also shown that, under drowsy/distracted driving, the lateral acceleration rates and yaw rate acceleration were significantly larger from the normal driving. The lateral acceleration rates were shown to suddenly increase or decrease by more than 2.0 m/s 3 and the yaw rate acceleration by more than 2.5°/s 2 . The standard deviation of acceleration rate (SDA) and standard deviation of yaw rate acceleration (SDY) were identified to as the common features of vehicle motion for distinguishing the drowsy/distracted driving from the normal driving. In order to identify a time window for effectively extracting the two common features, a double-window method was used and the optimized “Parent Window” and “Child Window” were found to be 55 s and 6 s, respectively. The study results can be used to develop a driving assistant system, which can warn drivers when any one of the four types of drowsy/distracted driving is detected. [ABSTRACT FROM AUTHOR]

Vehicular cyber physical system (VCPS) can comprehensively acquire road traffic safety related information, and provide drivers with early warning or driving assistance in emergency, in order to assist them avoid vehicle crash in the driving process. Literature review shows that previous studies mainly rely on observed vehicle motion/location data for assessing vehicle collision risk, where predicted vehicle motion/location, driver behavior and road geometry (e.g., curvature) are rarely considered. In this study, based on the simulated VCPS, a collision avoidance system that can explicitly consider the above issues is designed and presented in detail. Within the proposed collision avoidance system, an assessment method, which can predict collision risk by comprehensively considering vehicles motion/location, driver behavior and road geometry information from the VCPS, is developed. Firstly, the short-term motion of the objective vehicle and surrounding vehicles are predicted based on the Kalman Filter (KF) algorithm and the vehicle motion model. Furthermore, the proposed method that can explicitly take driver behavior and road curvature into account is used to predict vehicle location and calculate the traveled distance among vehicles in real-time. Then, the predicted vehicle gaps are compared with a safe distance threshold and the vehicle collision risk is predicted. Finally, the accuracy of the proposed collision risk assessment method is examined with a receiver operating characteristic (ROC) curve analysis over a section of curved road. Simulation results show that the proposed method is effective for detecting collision risk and providing accurate warnings in a timely fashion. [ABSTRACT FROM AUTHOR]

Studying the rear-end early warning methods of connected automated vehicles (CAVs) is useful for issuing early warnings and reducing traffic accidents. Establishing a corresponding driving model according to CAV characteristics is necessary when designing intelligent decision and control systems, especially for the safety speed threshold. However, since traffic systems are stochastic, there are random factors that influence car-following behavior. Therefore, this study proposes a rear-end collision warning method for CAVs based on a stochastic local multivehicle optimal speed (SLMOV) car-following model. First, the SLMOV model is proposed to characterize the car-following behavior of CAVs. Simultaneously, a stability analysis and parameter estimation method are discussed. Second, the safety distance between the CAVs changes with time because the speed of the rear vehicles satisfies the SLMOV model, which is used to calculate the safety probability of rear-end CAV collisions through an analysis of the driving process. The speed threshold is assessed by controlling the rear-end collision probability. Third, next-generation simulation (NGSIM) data are used in an empirical analysis of a rear-end collision warning method on the basis of a parameter estimation of the SLMOV model. The results present the speed thresholds of vehicles under different braking deceleration levels. Finally, the merits and demerits of fixed-speed and variable-speed adjustment time intervals are compared by considering driving safety and comfort as evaluation indexes. A reasonable CAV adjustment time interval of 0.4 s is determined. This result can be used to help develop a vehicle loading rear-end collision warning system. [ABSTRACT FROM AUTHOR]

• Three ICV environment HMI groups: control, warning, and guidance, were designed to study their impact on driver's performance. • Warning group exhibited cautious and smooth acceleration before merging, while the Guidance group demonstrated better speed control after merging. • Mainline vehicle gaps had no significant impact on merging positions across HMI groups. • Guidance group merged closest to taper, Control group farthest. • Abundant gaps led to smaller initial merging gaps and shorter adjustment times for Guidance group. Intelligent Connected Vehicle (ICV) is considered one of the most promising active safety technologies to address current transportation challenges. Human-Machine Interface (HMI) plays a vital role in enhancing user driving experience with ICV technology. However, in an ICV environment, drivers may exhibit excessive reliance on HMI, resulting in diminished proactive observation and analysis of the road environment, and subsequently leading to a potential decrease in drivers' situational awareness. This reduced situational awareness may consequently lead to a decline in their overall engagement in driving tasks. Therefore, to comprehensively investigate the impact of HMI on driver performance in various ICV environments, this study incorporates three distinct HMI systems: Control group, Warning group, and Guidance group. The Control group provides basic information, the Warning group adds front vehicle icon and real-time headway information, while the Guidance group further includes speed and voice guidance features. Additionally, the study considers three types of mainline vehicle gaps, namely, 30 m, 20 m, and 15 m. Through our self-developed ICV testing platform, we conducted driving simulation experiments on 43 participants in a freeway interchange merging area. The findings reveal that, drivers in the Guidance group exhibited explicit acceleration while driving on the ramp. Drivers in the Guidance and Warning groups demonstrated smoother speed change trends and lower mean longitudinal acceleration upon entering the acceleration lane compared to the Control group, indicating a preference for more cautious driving strategies. During the pre-merging section, drivers in the Warning group demonstrated a more cautious and smooth longitudinal acceleration. The Guidance group's HMI system assisted drivers in better speed control during the post-merging section. Differences in mainline vehicle gaps did not significantly impact the merging positions of participants across the three HMI groups. Drivers in the Guidance group merged closest to the left side of the taper section, while the Control group merged farthest. The research findings offer valuable insights for developing dynamic human–machine interfaces tailored to specific driving scenarios in the environment of ICVs. Future research should investigate the effects of various HMIs on driver safety, workload, energy efficiency, and overall driving experience. Conducting real-world tests will further validate the findings obtained from driving simulators. [ABSTRACT FROM AUTHOR]

Risky driving behavior (RDB) can cause panic and anger among other drivers. Under the influence of irrational emotions, RDB can reoccur, resulting in a phenomenon known as risky driving behavior propagation (RDBP). RDBP is a dangerous phenomenon, which can easily lead to traffic conflicts and accidents. It is significant to determine the propagation mechanism of RDBP to road safety. Firstly, RDBP can be considered a type of disease because three essential components of RDBP share similarities with an epidemic. A stochastic SIR model for risky driving behavior propagation (SSIR-RDBP) is constructed to quantify the mechanism of RDBP. Additionally, a Lyapunov function is adopted to prove the existence and uniqueness of the model solution. Secondly, based on current moment risk and cumulative risk, a two-stage risk approach is adopted to quantify the risk of RDBP. The Conditional Value at Risk (CVaR) is used to quantify the current moment risk, and the probability of the Markov state transition matrix is introduced to describe the cumulative risk. Finally, sensitivity analysis of the model parameters is carried out to explore the intrinsic variables of the parameters. The results suggest that the risk of RDBP increases rapidly to its peak value within the first 5 s. As traffic saturation increases, the decline rate of sustainable drivers and the rise rate of recovered individuals grows cubically, and the rate of reaching peak infection increases linearly. Furthermore, the fluctuation range of risk and the maximum risk both increase quadratically. The driver's emotional control ability is strongest when the infection rate is set to 4, and the recovery rate increases linearly with road safety education. Improving the driver's emotional control ability, and increasing awareness of road safety education, can help to control the risk in RDBP. • A stochastic SIR model for risky driving behavior propagation is presented. • Two-stage risk quantification approach based CVaR and Markov chain is proposed. • Relationship between model parameters and traffic measures are analyzed. [ABSTRACT FROM AUTHOR]

• The moderating effect of driving skills on the traffic climate-driver behaviors relationship was investigated. • In Turkey, both perceptual motor skills and safety skills moderated the majority of the relationships. • In China, only safety skills moderated some of the relationships. • There are both cultural differences and similarities in the moderating effect of driving skills. Introduction: While road traffic accidents and fatalities are a worldwide problem, the rates of road traffic accidents and fatalities show differences among countries. Similarly, driver behaviors, traffic climate, and their relationships also show differences among countries. The aim of the current study is to investigate the moderating effect of driving skills on the relationship between traffic climate and driver behaviors by country. (Turkey and China). Method: There were 294 Turkish drivers and 292 Chinese drivers, and they completed the Traffic Climate Scale, the Driving Skills Inventory, and the Driver Behavior Questionnaire. The moderated moderation analyses were conducted with Hayes PROCESS tool on SPSS. Results: The results showed that safety skills moderated the relationship between internal requirements and violations both in Turkey and China. Safety skills also moderated the relationship between internal requirements and errors only in China and the relationship between functionality and violations in Turkey. Perceptual-motor skills moderated the relationships between external affective demands and errors, and also the relationship between internal requirements and positive driver behaviors in Turkey. It can be inferred that driving skills has different influences on traffic climate-driver behaviors relationship in different cultures and there might be cultural differences in the evaluation of drivers' own driving skills. Practical Applications: Among driving skills, safety skills have a more critical role to increase road safety by decreasing number of violations. Interventions to increase safety skills of drivers might be promising for road safety. [ABSTRACT FROM AUTHOR]

• The relationships between traffic climate and driver behaviors were examined cross-culturally. • External affective demands and aberrant driver behaviors were positively related in Turkey and China. • External affective demands and positive driver behaviors were negatively related in Turkey and China. • Violations were negatively related to functionality in Turkey and negatively related to internal requirements in China. • The patterns of relationships might show both similarities and differences between countries. Road traffic accidents/fatalities and driver behaviors show regional differences. It is assumed that the perceived traffic climate in a given context is closely related to driver behaviors. In the current study, this assumption was tested cross-culturally for the first time. The aim was to compare the perceived traffic climate and driver behaviors between Turkey and China. Also, the relationships between traffic climate and driver behaviors in Turkey and China were investigated. In the study, there were 292 drivers aged between 21 and 64 years from China and 294 drivers aged between 19 and 61 from Turkey. The results revealed that Turkish drivers perceived their traffic climate higher in internal requirements and lower in external affective demands and functionality than Chinese drivers. Among driver behaviors, Turkish drivers reported higher numbers of violation and lower numbers of error than Chinese drivers. Perceiving traffic climate as externally demanding was positively related to aberrant driver behaviors (i.e., violations and errors) and negatively related to positive driver behaviors in both Turkey and China. Functionality was negatively related to violations in Turkey, and internal requirements were negatively related to violations in China. In both Turkey and China, external affective demands and functionality were closely related to driver behaviors and might be critical dimensions in road traffic safety. The interventions to improve road traffic safety should be planned based on the differences among cultures. Based on the differences, it might be plausible to suggest that more functional traffic is desired to increase road safety in Turkey, whereas higher internal requirements are important to increase road safety in China. [ABSTRACT FROM AUTHOR]

Highlights • The FOT data from 46 participants was used to analyze the effectiveness of drivers' characteristics, including gender, occupation and experience, on driving behaviors on a freeway deceleration lane. • Male drivers recognize risk more than female drivers, and also have more aggressive driving tendencies. • Professional drivers and experienced drivers made the last lane-change as early as possible to enter the deceleration lane. • The speed of the vehicles entering the exit ramp was significantly higher than the design speed. • The minimum TTC and the maximum deceleration show that the certain driving behaviors are related to high traffic risk. Abstract Deceleration lanes improve traffic flow by reducing interference, increasing capacity and enhancing safety. However, accident rates are higher on these interchange segments than on other freeway segments. It is important to attempt to reduce traffic accidents on these interchange segments by further exploring the behavior of different types of drivers on a highway deceleration lane. In this study, with field operational test (FOT) data from 89 driving instances (derived from 46 participants driving the test road twice) on a typical freeway deceleration lane, section speed profiles, vehicle trajectories, lane position and other key parameters were obtained. The lane-change characteristics and speed profiles of drivers with different genders, occupations and experiences were analyzed. The significant disparities between them reflects the risk associated with different groups of drivers. The study shows that male drivers changed to the outside lane earlier; professional drivers and experienced drivers made the last lane change as early as possible to enter the deceleration lane; and the speed of the vehicles entering the exit ramp was significantly higher than the speed limit. This research work provides ground truth data for deceleration lane design, driver ability training and off-ramp traffic safety management. [ABSTRACT FROM AUTHOR]

• A novel improved GRA and fuzzy theories based prioritizing model is proposed. • Comprehensive consideration for varying weight of each influencing factor and expert. • Rational fuzzy numbers of domain experts are utilized to the newly proposed technique. • A new framework for influencing factors analysis system of autonomous ship maneuvering. • A feasibility basis for autonomous ship maneuvering decision-making analysis system. Ship maneuvering decisions are influenced by several factors, and it is essential to prioritize the main influencing factors for efficient selection of the corresponding maneuvering decisions. Meanwhile, the autonomous ship maneuvering decision-making influencing factors constitute a typical grey system, which is suitable for research by grey relational analysis. Furthermore, in the fuzzy approach, linguistic assessment of factors is evaluated to obtain priorities numbers. Therefore, this study mainly focuses on the concept of human-like maneuvering for autonomous ships. Based on experimental data of experienced seafarers and using a simulation platform under the scenario of the Shanghai Waigaoqiao wharf, an inference model utilizing grey and fuzzy theories is proposed. The proposed model combined with expert linguistic terms in order to select the ship maneuvering decision-making main influencing factors from multi-source influencing factors (in overall and separated categories of natural environment, ship motion, force parameters, draft, and position), and to study the decision-making prioritization for maritime traffic safety for specific ship maneuvering scenarios. This method can prioritize the main factors which affect maneuvering decisions as well as guide an autonomous ship-assisted or automatic maneuvering evaluation system for the research of human-like maneuvering behavior. This study provides a new perspective on the identification of main ship maneuvering decision-making influencing factors in theory and in practice. It can be utilized for better decision-making concerning maritime traffic safety of autonomous ship maneuvering, which in turn makes shipping safer and promote the application and spreading of autonomous ships. [ABSTRACT FROM AUTHOR]

Abstract: This research presents a comprehensive analysis of motor vehicle–bicycle crashes using 4 years of reported crash data (2004–2007) in Beijing. The interrelationship of irregular maneuvers, crash patterns and bicyclist injury severity are investigated by controlling for a variety of risk factors related to bicyclist demographics, roadway geometric design, road environment, etc. Results show that different irregular maneuvers are correlated with a number of risk factors at different roadway locations such as the bicyclist age and gender, weather and traffic condition. Furthermore, angle collisions are the leading pattern of motor vehicle–bicycle crashes, and different irregular maneuvers may lead to some specific crash patterns such as head-on or rear-end crashes. Orthokinetic scrape is more likely to result in running over bicyclists, which may lead to more severe injury. Moreover, bicyclist injury severity level could be elevated by specific crash patterns and risk factors including head-on and angle collisions, occurrence of running over bicyclists, night without streetlight, roads without median/division, higher speed limit, heavy vehicle involvement and older bicyclists. This study suggests installation of median, division between roadway and bikeway, and improvement of illumination on road segments. Reduced speed limit is also recommended at roadway locations with high bicycle traffic volume. Furthermore, it may be necessary to develop safety campaigns aimed at male, teenage and older bicyclists. [Copyright &y& Elsevier]