How do the recognizability and driving styles of automated vehicles affect human drivers’ gap acceptance at T- Intersections?

Future traffic will be composed of both human-driven vehicles (HDVs) and automated vehicles (AVs). To accurately predict the performance of mixed traffic, an important aspect is describing HDV behavior when interacting with AVs. A few exploratory studies show that HDVs change their behavior when interacting with AVs, being influenced by factors such as recognizability and driving style of AVs. Unsignalized priority intersections can significantly affect traffic flow efficiency and safety of the road network. To understand HDV behavior in mixed traffic at unsignalized priority T-intersections, a driving simulator experiment was set up in which 95 drivers took part in it. The route in the driving simulator included three T-intersections where the drivers had to give priority to traffic on the major road. The participants drove different scenarios which varied in whether the AVs were recognizable or not, and in their driving style (Aggressive or Defensive). The results showed that in mixed traffic having recognizable aggressive AVs, drivers accepted significantly larger gaps (and had larger critical gaps) when merging in front of AVs as compared to mixed traffic having either recognizable defensive AVs or recognizable mixed AVs (composed of both aggressive and defensive). This was not the case when merging in front of an HDV in the same scenarios. Drivers had significantly smaller critical gaps when driving in traffic having non-recognizable aggressive AVs compared to non-recognizable defensive AVs. The findings suggest that human drivers change their gap acceptance behavior in mixed traffic depending on the combined effect of recognizability and driving style of AVs, including accepting shorter gaps in front of non-recognizable aggressive AVs and changing their original driving behavior. This could have implications for traffic efficiency and safety at such priority intersections. Decision makers must carefully consider such behavioral adaptations before implementing any policy changes related to AVs and the infrastructure.     

The effects of traffic wardens on the red-light infringement behavior of vulnerable road users

In some Chinese cities, traffic wardens are employed to maintain traffic order at the intersections with a high mixed traffic flow in peak hours. The main work of a traffic warden is to advise pedestrians and riders to wait at the appropriate area during red light periods. In many other countries, there are also traffic wardens at the areas with dense crowds or vehicles, such as at some large parking lots near parks and shopping malls. This paper investigated the effects of traffic wardens on the crossing behavior of pedestrians, cyclists and electric bike riders at signalized intersections. A total of 795 samples with traffic wardens and 773 samples without traffic wardens at intersections in Beijing, China were observed. Logistic regression and multivariate analysis of variance were used to test the effect of traffic wardens on the red-light crossing behavior. The results indicated that the presence of traffic wardens would significantly reduce by 21% red-light infringement behavior of vulnerable road users. However, the effects of traffic wardens were different among different groups. The effect of traffic wardens on reducing the violation rate for pedestrians was smaller compared to cyclists and electric bike riders (8% vs. 23% and 27%, respectively). The effect of traffic wardens was significant for the individuals approaching straight ahead the intersection, while it was not significant for the individuals approaching from the left and right sides of the observed direction. The possible reasons for the different effects and some practical countermeasures to reduce red light infringement were discussed. This study provides insights into the effects of traffic wardens on enhancing the safety of vulnerable road users under mixed traffic conditions. It is useful for the administrators to evaluate the supervision performance of traffic wardens and make informed decisions to employ traffic wardens at locations with dense crowds or vehicles.     

Safety at first sight? – Manual drivers’ experience and driving behavior at first contact with Level 3 vehicles in mixed traffic on the highway

Soon, manual drivers will interact with conditionally automated vehicles (CAVs; SAE Level 3) in a mixed traffic on highways. As of yet, it is largely unclear how manual drivers will perceive and react to this new type of vehicle. In a driving simulator study with N = 51 participants aged 20 to 71 years (22 female), we examined the experience and driving behavior of manual drivers at first contact with Level 3 vehicles in four realistic driving scenarios (highway entry, overtaking, merging, introduction of a speed limit) that Level 3 vehicles may handle alone once their operational domain extends beyond driving in congested traffic. We also investigated the effect of an external marking via a visual external human–machine interface (eHMI), with participants being randomly assigned to one of three experimental groups (none, correct, incorrect marking). Participants experienced each driving scenario four times, twice with a human-driven vehicle (HDV), and twice with a CAV. After each interaction, participants rated perceived driving mode of the target vehicle as well as perceived safety and comfort. Minimum time headways between participants and target vehicles served as an indicator of safety criticality in the interactions. Results showed manual driver can distinguish CAVs from HDVs based on behavioral differences. In all driving scenarios, participants rated interactions with CAVs at least as safe as interactions with HDVs. The driving data analysis showed that manual driver interactions with CAVs were largely uncritical. However, the CAVs’ strict rule-compliance led to short time headways of following manual drivers in some cases. The eHMI used in this study neither affected the subjective ratings of the manual drivers nor their driving behavior in mixed traffic. Thus, the results do not support the use of eHMIs on the highway, at least not for the eHMI design used in this study.     

Behavior model and guidance strategies of the crossing behavior at unsignalized intersections in the connected vehicle environment

The connected vehicle environment is considered to be a disruptive technology that reconstructs the way people travel and road transport, and more importantly, ensures traffic safety. This study aims on investigating drivers’ interactive behavior at an unsignalized intersection in the connected vehicle environment. Specifically, a simplified iterative behavior model was established to predict the potential conflicting vehicles’ behavior. Furthermore, based on principles of safety, efficiency, and comfort, the guidance strategies were proposed to help the subject vehicles cross intersections. A multi-user driving simulator experiment was carried out and 48 participants were divided into 24 pairs to complete the test. The simplified iterative behavior model was constructed based on the dynamic interaction of each participant pair as they approached the intersection from straight-crossing directions. The comparison results showed the behavior model was an effective microscopic simulation tool for vehicles at unsignalized intersections with high accuracy and good applicability. Then, the guidance strategies under different compliance rates (baseline, 50 % compliance, 100% compliance) were evaluated based on three indexes i.e. standard deviation of speed (SDS), duration of crossing the intersection (DCI), and time exposed post-encroachment-time (TEP). The numerical simulation results showed that the guidance strategies could effectively improve the safety and efficiency of drivers crossing the intersection under both 50% and 100% compliance rates. Besides, with the increase of compliance rate, the comfort level also increased evidently. This study can provide theoretical and algorithmic references for microscopic simulation and guidance strategy at unsignalized intersections in the connected vehicle environment.     

Analysis of overtaking patterns of Indian drivers with data collected using a LiDAR

Vehicle overtaking is a complex maneuver on highways and high-speed arterials. When overtaking occurs in heterogeneous traffic conditions, it becomes even more challenging due to the variety of different vehicle sizes and operating characteristics. This study analyzes overtaking behavior on Indian highways under high vehicle heterogeneity. Data were collected using LiDAR and video cameras from an instrumented vehicle. Overtaking times for both sides of overtaking and for different types of vehicles were analyzed. This study introduces a new variable, “excess distance,” to understand overtaking. The results show that on divided four-lane roads, the side of overtaking plays an important role in determining overtaking behavior. On undivided two-lane roads, the type of overtaken vehicle is found to be significant. It is observed, as expected, that while overtaking at higher speeds, overtaking vehicles maintained greater excess distances with the overtaken vehicle. However, data shows that the rate of change of excess distance depends upon the type of road and the type of overtaking vehicle. The analysis also shows that overtaking behavior on undivided roads is affected by the presence of oncoming vehicles and the type of overtaken vehicle.     

A field investigation of red-light-running in Shanghai,China

Red-Light-Running (RLR) is the major cause of severe injury crashes at signalized intersections for both China and the US. As several studies have been conducted to identify the influencing factors of RLR behavior in the US, no similar studies exist in China. To fill this gap, this study was conducted to identify the key factors that affect RLR and compare the contributing factors between US and China. Data were collected through field observations and video recordings; four intersections in Shanghai were selected as the study sites. Both RLR drivers and comparison drivers, who had the opportunity to run the light but did not, were identified. Based on the collected data, preliminary analyses were firstly conducted to identify the features of the RLR and comparison groups. It was determined that: around 57% of RLR crossed the stop line during the 0–0.4 second time interval after red-light onset, and the numbers of red light violators decreased as the time increased; among the RLR vehicles, 38% turned left and 62% went straight; and at the onset of red, about 88% of RLR vehicles were in the middle of a vehicle platoon. Furthermore, in order to compare the RLR group and non-RLR group, two types of logistic regression models were developed. The ordinary logistic regression model was developed to identify the significant variables from the aspects of driver characteristics, driving conditions, and vehicle types. It was concluded that RLR drivers are more likely to be male, have local license plates, and are driving passenger vehicles but without passengers. Large traffic volume also increased the likelihood of RLR. However, the ordinary logistic regression model only considers influencing factors at the vehicle level: different intersection design and signal settings may also have impact on RLR behaviors. Therefore, in order to account for unobserved heterogeneity among different types of intersections, a random effects logistic regression model was adopted. Through the model comparisons, it has been identified that the model goodness-of-fit was substantially improved through considering the heterogeneity effects at intersections. Finally, benefits of this study and the analysis results were discussed.     

Analyzing drivers’ crossing decisions at unsignalized intersections in China

In China, when two vehicle drivers encounter at an unsignalized intersection, almost neither of them completely stops the vehicle. Instead, one gradually approaches and dynamically makes a decision to either yield or preempt by gaming with the other vehicle. This process generates traffic conflicts and increases the probability of accidents. In this study, we aimed to study how straight-moving drivers made preemptive/yielding decisions when they encountered other drivers straight-moving across at unsignalized intersections. A total of 150 crossing cases were collected at an unsignalized intersection in Kunming City, China. By using detection program we made, motion parameters of the vehicles were extracted. Classification tree analysis was used to identify the decision moment of drivers and the major motion parameters that affected their decisions. Results showed that for crossing processes at unsignalized intersections in China, straight-moving drivers from the right side made preemptive/yielding decisions from 0.9 s to 1.3 s before reaching the crossing point. However, straight-moving drivers from the left side made decisions from 0.9 s to 1.2 s before reaching the crossing point. The speed difference between the two vehicles was the most important factor that affected a driver’s decision-making. If the vehicle driver from the right side drove significantly slower than that from the left, then most drivers from the right side would yield to those from the left. On the contrary, if the vehicle driver from the right side drove significantly faster than that from the left, then most drivers from the right side would preempt those from the left. The findings of this study will help understand the decision-making patterns of drivers under crossing conditions, and thus provide suggestions to improve drivers’ behavior at unsignalized intersections in China.     

Pedestrian temporal gap acceptance behavior at unsignalized intersections in Kanpur,India

Vehicle heterogeneity, lack of lane discipline, lack of infrastructure facilities, and weak enforcement of traffic rules are all characteristics of Indian traffic. These characteristics make Indian roads difficult for non-motorized road users, especially pedestrians. Most intersections in urban areas in India are unsignalized, which results in complex interactions between pedestrians and motorists. This study aims to understand pedestrian gap acceptance behavior at unsignalized intersections in India under heterogeneous traffic conditions. Pedestrian data were collected from six unsignalized intersections in the city of Kanpur. The critical gap was estimated using Wu’s (2006) macroscopic model, and then the effects of several variables on the critical gap were studied. A discrete choice model (binary logit model) was developed to understand the choice of accepted and rejected vehicular gaps depending on traffic, road, and peer pedestrian characteristics. The mean critical headway was 3.76 s using Wu’s model. The behavioral analysis revealed that pedestrians accept rolling gaps to cross unsignalized intersections, resulting in a reduction of the size of the critical gap. The adoption of rolling gap behavior can be attributed to the unyielding behavior of motorists, which forces pedestrians to accept short vehicular gaps in the traffic stream.     

Influence of traffic conditions on driver behavior before making a right turn at an intersection: Analysis of driver behavior based on measured data on an actual road

This paper describes an investigation of the influence of the position of a forward vehicle and following vehicle on the onset of driver preparatory behavior before making a right turn at an intersection. Four experimental vehicles with various sensors and a driving recorder system were developed to measure driver behavior before making a right turn at a specific intersection on a public road. The experimental term was eight weeks to collect data on natural driving maneuvers. The relationships between the remaining distances to the center of the intersection when releasing the accelerator pedal, moving the right foot to cover the brake pedal, and activating the turn signal and the relative distances from the forward and following vehicles were analyzed based on the measured data. The time it took to reach the center of the turn and the driving speed when each behavioral event occurred were also evaluated from the viewpoint of the relative position between the driver’s vehicle and the leading or following vehicles. The results suggest that the drivers approached the target intersection in a car-following condition, and that the positions of the front and rear vehicles and the vehicle velocity influence the onset location and timing of releasing the accelerator pedal and covering the brake pedal. Drivers began to decelerate closer to the center of the intersection when they approached the intersection close to a leading or following vehicle at a reduced driving speed. However, these influences were not reflected in the turn signal operation, indicating that drivers intend to make a right turn at a constant location while approaching a target intersection and that intention appears in the turn signal activation. The findings of this observational study imply that the method of providing route guidance instruction, in which the traffic conditions surrounding the driver’s vehicle are taken into consideration, is effective in reducing driver errors in receiving instruction and following the correct route. The results also indicate that measuring and accumulating different behavioral indices based on traffic conditions contribute to determining the criteria for the presentation timing just before reaching the intersection, which can assist drivers in preparing to make a right turn at a usual location. Driver decelerating maneuvers are used while driving without leading or following vehicles and while driving with a lead and/or following vehicle at long range, and driver turn signal operations are used when approaching an intersection under close car-following conditions.     

The role of drivers’ social interactions in their driving behavior: Empirical evidence and implications for car-following and traffic flow

Models for describing the microscopic driving behavior rarely consider the “social effects” on drivers’ driving decisions. However, social effect can be generated due to interactions with surrounding vehicles and affect drivers’ driving behavior, e.g., the interactions result in imitating the behavior of peer drivers. Therefore, social environment and peer influence can impact the drivers’ instantaneous behavior and shift the individuals’ driving state. This study aims to explore empirical evidence for existence of a social effect, i.e., when a fast-moving vehicle passes a subject vehicle, does the driver mimic the behavior of passing vehicle? High-resolution Basic Safety Message data set (N = 151,380,578) from the Safety Pilot Model Deployment program in Ann Arbor, Michigan, is used to explore the issue. The data relates to positions, speeds, and accelerations of 63 host vehicles traveling in connected vehicles with detailed information on surrounding environment at a frequency of 10 Hz. Rigorous random parameter logit models are estimated to capture the heterogeneity among the observations and to explore if the correlates of social effect can vary both positively and negatively. Results show that subject drivers do mimic the behavior of passing vehicles –in 16 percent of passing events (N = 18,099 total passings occurred in freeways), subject vehicle drivers are observed to follow the passing vehicles accelerating. We found that only 1.2 percent of drivers normally sped up (10 km/hr in 10 s) during their trips, when they were not passed by other vehicles. However, if passed by a high speed vehicle the percentage of drivers who sped up is 16.0 percent. The speed change of at least 10 km/hr within 10 s duration is considered as accelerating threshold. Furthermore, the acceleration of subject vehicle is more likely if the speed of subject driver is higher and more surrounding vehicles are present. Interestingly, if the difference with passing vehicle speed is high, the likelihood of subject driver’s acceleration is lower, consistent with expectation that if such differences are too high, the subject driver may be minimally affected. The study provides new evidence that drivers’ social interactions can change traffic flow and implications of the study results are discussed.     

Increasing motorist compliance and caution at stop signs.

This study evaluated strategies to improve motorist compliance and caution at three stop‐sign‐controlled intersections with a history of motor vehicle crashes. The primary intervention was a light‐emitting diode (LED) sign that featured animated eyes scanning left and right to prompt drivers to look left and right for approaching traffic. Data were scored from videotape on the percentage of drivers coming to a complete stop and the percentage of drivers looking right before entering the intersection. Observational data were collected on the percentage of right‐angle conflicts (defined as braking suddenly or swerving from the path to avoid an intersection crash). The introduction of the LED sign according to a multiple baseline across the three intersections was associated with an increase in the percentage of vehicles coming to a complete stop at all three intersections and a small increase in the percentage of drivers looking right before entering the intersections. Conflicts between vehicles on the major and minor road were also reduced following the introduction of the animated eyes prompt.     

Investigating the impact of a novel active gap metering signalization strategy on driver behavior at highway merging sections

A safe headway to the lead vehicle is important to reduce conflicts with merging vehicles from highway on-ramps. Previous research has outlined the advantage of gap metering strategies to yield sufficient space to merging vehicles and improve highway capacity during peak hours. However, prevailing gap metering systems fail to indicate the minimum required gap and leave it to the drivers’ judgment to adjust their headway. This paper proposes a new Active Gap Metering (AGM) signalization that helps outer lane drivers to adjust their headway to the lead vehicle when approaching highway ramps with incoming vehicles. This AGM signalization represents a combination of pavement markings and an innovative Variable Message Sign (VMS). The AGM system was tested alone and in combination with additional variable speed limits (VSL) in distinct environments of the Doha Expressway in the State of Qatar using a driving simulator. The driving behavior of 64 drivers was analyzed using repeated-measures ANOVA. The results showed that the AGM effectively influenced the drivers’ behavior on the right stream lane. Drivers did gradually increase the distance to the lead vehicle, which resulted in optimal headways to merging on-ramp vehicles. Most importantly, the minimum time-to-collision (TTC_min) to the merging vehicle was increased by an additional 1–1.5 s as compared to no treatment. The proposed AGM signalization can, therefore, be considered by policymakers to influence drivers’ headways at critical merging sections.     

Field study on the behavior of right-turning vehicles in Malaysia and their contribution on the safety of unsignalized intersections

Behavior of right turning vehicles in the context of safety is characterized by their use of turning indicators and compliance with the stop rule. They are influence by the width of the carriageway and the variation in volume on the major road with respect to the traffic moving in the near and far side direction. Other factors affecting the behavior are the speed and spacing between vehicles moving on the major road. Lack of adequate past knowledge on the effect of geometric variation in terms of road width and directional variation in volume on the safety of unsignalized intersections have provided the motivation for this study. This paper focuses on the many factors that affect the behavior of right-turning vehicles resulting into conflicts. A brief account of the unique indigenous maneuver termed as the “Weaving Merging Right Turn” (WMRT) is provided and its effectiveness with respect to conventional right turn is evaluated. Data of 39,016 vehicles collected on 10 sites between January and June 2014 was analyzed. Multiple accidents were observed only on sites which had near side traffic volume greater than far side traffic volume. This result remains consistent with sites having single as well as multiple lanes per direction on the major roads. The number of conflicts for vehicles performing the WMRT was 2.5 times less as compared to the conventional right turn. Moreover WMRT was found to be the maneuver of choice for right turning motorcyclists with 60% of them opting for it over the conventional right turn on intersections having major road width less than 9 m. None of the motorcyclists, which were involved in a traffic conflict, were observed to use their turning indicator. Moreover none of the motorcyclists, which experienced a traffic conflict, were found to comply with the stopping rule at sites with major road width less than 9 m. On sites with major road width greater than 9 m, 45% of motorcyclists, involved in a traffic conflict, complied with the stopping rule as compared to 79% by vehicles other than motorcycles.     

International study on the importance of communication between automated vehicles and pedestrians

The market launch of automated vehicles will take place in complex mixed traffic containing pedestrians and other non-automated drivers. Hand gestures, eye contact or similar informal communication strategies to human road users will not be available without a human car driver. Road crossing studies show that people are feeling confused and unsafe without such feedback. Additional external signaling devices have the aim to increase the perception of safety by providing auditory or visual feedback for road users. Due to the international relevance of this topic, we surveyed participants from six countries on the importance of communication. Our results reveal that intention messages are more important than the status signal. The importance of communication is independent of the time of day, traffic density and number of pedestrians. Cross-group analysis indicates a match of 72.97% between the tested nationalities on which kind of message types an automated vehicle should be able to communicate.     

自动驾驶汽车与行人交互中的沟通界面设计：基于行人过街决策模型的评估

自动驾驶汽车要进入人车混行的普通道路, 需确保与过街行人之间的交互安全和效率。为解决这一问题, 高等级自动驾驶汽车往往在车辆外部装置显示设备, 即外部人机界面(eHMIs)以和行人沟通信息。在具体设计上, 已有研究主要采用文字、图形、投影等视觉沟通形式, 传达车辆状态(是否在自动驾驶模式)、意图和对行人的过街建议等沟通信息, 并在真实路段实验、虚拟场景及实验室实验等情境中评估了界面的使用对行人过街意向、速度和准确性等指标的影响。然而, 以行人为中心的外部界面设计需系统地支持行人过街决策前各阶段的信息加工需求。因此, 我们结合行人过街决策过程和情境意识理论, 提出行人与自动驾驶汽车交互中的动态过街决策模型, 从行人认知加工视角评估各种界面的沟通效果。评估的结果启示, eHMIs应促进行人对车辆信息的感知、理解和预测。在感知阶段, 应采用多种类型界面、多呈现载体相结合, 增强信息的可识别性。在理解阶段, 需结合文字说明、合理选择沟通视角、信号标准化和培训提高可理解性。在预测阶段, 应结合车辆内隐运动信息, 帮助行人快速准确获取车辆未来行动意图。更重要的是, 未来研究应关注在多行人、多车辆混行情境下的信息沟通设计及其对行人的影响。理论方面, 未来研究也需要关注外部界面如何通过自下而上的通路影响情境意识和心智模型的形成。     

Investigating driver reactions to movements of autonomous vehicle in permitted right turn through driving simulator experiments

Traffic safety has always been a hot topic for human-driven (HDV) and autonomous vehicles (AV) mixed flow. The conflict between permitted right-turn vehicles (PRT) and opposing through vehicles (TH) at signalized intersections (left-handed traffic) is extraordinarily critical. AVs with aggressive behaviors are able to accept short gap time without losing safety. However, such a turning maneuver may lead to dangerous feelings and cause unexpected reactions of approaching drivers. This study aims to investigate and model drivers’ reactions in TH movements to PRT AVs considering the trust degree of drivers to AVs. Questionnaire surveys and driving simulator experiments were conducted for 41 participants. Results reveal that the right turn timing of PRT AV will significantly influence drivers’ reactions. Basically, TH drivers will brake with a high probability under the situation of small expected post encroachment time (PET). It is also found that female drivers and drivers with low trust in AVs are more vigilant to PRT AVs than other drivers. Based on this finding a two-layer model for reproducing TH drivers’ reactions to PRT AVs is proposed. The first layer is to determine the braking decision and the second layer is to calculate the parameters of braking behaviors (brake lag, braking time, and speed drop). The significance and coefficients variables in these models proved that the trust in AV will influence drivers’ decisions and braking behaviors (brake lag and braking time). The more the drivers trust AVs, the smaller the expected PET to AVs they can accept for passing without braking, and the more gently they will brake (longer brake lag and shorter braking time) due to the cutting in of PRT AVs. This effect will become significant after drivers have experienced several interactions with AVs.     

A simulator study of factors influencing drivers’ behavior at traffic lights

Drivers’ reactions to changing traffic lights have an impact on safety at intersections. We examined the influence of transient factors – more specifically time pressure and social context, both conducive to traffic-light violation – on behavior behind the wheel when a traffic light changes. We carried out an experiment on a driving simulator. The participants were 94 car drivers (53 males) with a mean age of 21.7 years. They drove under time pressure vs. no time pressure. At several intersections the participants were alone (no other drivers present), whereas at several other intersections they were behind a line-up of vehicles, the last of which ran the yellow light (other drivers present). As expected, time pressure and social context (presence of other drivers) increased participants’ risky behaviors while approaching, and going through traffic lights, as well as undesirable rapid accelerations when the signal changes to green. The effect of time pressure on yellow-light running was not mediated by approach speed, which showed that participants in a hurry were likely to run lights intentionally. The results are interpreted in view of proposing effective measures for reducing yellow-light running and rapid accelerations at traffic lights.     

Gap acceptance at stop-controlled T-intersections in a simulated rural environment

A high proportion of road crashes occur at intersections: in Victoria, Australia, 15% of fatal crashes and 25% of serious injury crashes occur at T-intersections, with similar proportions occurring at cross intersections. Many of these crashes can be attributed to drivers’ inappropriate gap selection. The current study used a driving simulator to examine the influence of both the driver’s intended manoeuvre and the gap duration on gap acceptance behaviour at stop-controlled T-intersections. Drivers completed 18 gap acceptance trials, with manoeuvre (turning across traffic, merging with traffic) and gap duration (3–11 s) manipulated within-subjects. There was a trend whereby drivers accepted shorter gaps when turning across traffic compared to merging with traffic, which was significant at longer gaps (⩾9 s) but not at shorter, safety–critical gaps (⩽8 s). In addition, accepted lag times varied with manoeuvre. When merging with traffic drivers demonstrated longer accepted lag times, suggesting that turn strategies differ depending on traffic direction and intended manoeuvre. Overall the results suggest that the drivers’ intended manoeuvre influences gap acceptance, although gap duration remains the most influential factor. Implications of these findings for the development and design of intersection decision support systems are discussed.     

Can prosocial attitude reduce the risk behavior in simulated driving?

High traffic density may lead to more traffic accidents because of more frequent lane change and overtaking behaviors, but drivers with different characteristics may exhibit different driving behaviors. The present study explored the difference in driving behaviors between drivers with a high/low prosocial attitude under high/low traffic density. In this study, a 2 (high/low prosocial attitude) *2 (high/low traffic density) mixed design was used to investigate the interaction between prosocial attitude and traffic density on lane change and overtaking behavior. The implicit association test paradigm was used to measure prosocial attitude, and drivers were divided into two groups. Forty subjects were asked to complete simulated driving tasks under the two conditions of high and low traffic density, and driving behaviors were recorded by driving simulators. The results show that high traffic density leads to more lane change and overtaking behavior. Drivers with a high prosocial attitude have better driving performance under both high and low traffic density, but drivers with a low prosocial attitude maintain a smaller transverse distance from adjacent vehicles in high traffic density, which may increase risk. This study provides support for the selection, training and intervention of professional drivers.