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We have developed a new computational traffic model in which security aspects are fundamental. In this paper we show that this model reproduces many known empirical aspects of vehicular traffic such as the three states of traffic flow and the backward speed of the downstream front of a traffic jam (C), without the aid of adjustable parameters. The model is studied for both open and closed single lane traffic systems. Also, we were able to analytically compute the value of C as 15.37 km/h from a relation that only includes the human reaction time, the mean vehicle length and the effective friction coefficient during the braking process of a vehicle as its main components.

Road traffic risk is currently one of the most concerned problems of drive tourists. The motivation of this research is to quantify the impact of driving tour risk factors on road traffic safety and efficiency. Based on the characteristics of tourists’ aberrant driving behaviors while traveling by cars, the most frequently observed driving tour risk factors were explored, the conditions for the occurrence of various dangerous situations during road trips were defined, and a two-lane CA model considering drivers’ lane-changing and car-following behaviors was established to quantify the impact of single factors as well as the superposition of multi-factors on road traffic safety and efficiency. The simulation results indicate that the driving tour risk factors have significant negative impact on road traffic risk by increasing the frequencies of dangerous situations, inducing a reduction in traffic flow, and an increase in traffic congestion. In addition, the superposition of multi-factors, in particular the superposition of “fatigue driving” and “environment,” further increases the risk, specially aggravates the problems of traffic accidents and congestions.

Driving behavior is the core of traffic simulation modeling. Despite the sizable efforts to investigate the influence of risky driving behavior on traffic flow, very little is known about its influence under car–truck heterogeneous traffic conditions. Therefore, this study aims to analyze the influence of several common risky driving behaviors on car–truck heterogeneous traffic flow. A two-lane cellular automata (CA) model for heterogeneous traffic flow consisting of cars and trucks was proposed, the conditions for the occurrence of dangerous situations (DSs) and traffic congestion were defined. Then the vehicle performance under the influence of risky driving behaviors prevalent in car–truck heterogeneous traffic was obtained and incorporated into the proposed CA model to measure their influence on crash probabilities and congestion rates. The simulation results indicate that risky driving behaviors have a significant negative influence on traffic safety and efficiency, increasing the frequencies of DSs, reducing the traffic flow and aggravating the traffic congestion. In addition, the combinations of risky driving behaviors in traffic flow further deteriorate the traffic safety and efficiency. For example, when two risky driving behaviors, such as distracted driving and aggressive lane-changing, affect the traffic flow simultaneously, the frequencies of DSs increase dramatically and in some density ranges, they are even higher than the sum of the frequencies caused by two single risky driving behaviors. These findings may have notable practical implications in developing policies and regulations that enable truck operations without compromising safety and efficiency.

Improper lane-changing behavior may have a great impact on the safety of the surrounding vehicles, and accurate risk assessment of lane-changing behavior can detect improper lane-changing behavior. Many existing risk assessment methods are based on the traditional minimum safe distance model as the theoretical foundation, not taking into account the effects of different weather and road conditions. Moreover, the initially calculated safe lane-changing distance through the model is generally the limit safety distance, which deviates to some extent from the initial distance maintained when the vehicle begins to change lanes during the actual driving process. The ratio of these two distances can reflect the degree of risk between the changing vehicle and its neighboring vehicles during lane-changing. For this reason, a lane-changing safety coefficient is defined and lane-changing risk assessment model based on the hyperbolic tangent function is constructed by combining the change characteristics of this coefficient. An environmental adjustment factor was introduced into the model to consider the influence of different driving weather and road conditions on the lane-changing risk assessment. Then, based on survey data from the survey questionnaire, the weights of the influencing factors were determined by using the hierarchical analysis method, and the environmental adjustment factor in the model was calibrated by using the fuzzy comprehensive assessment method. Finally, to verify the effectiveness of the model, the lane-changing trajectories of 373 vehicles were extracted from the HighD dataset. The risk assessment value of lane-changing was calculated by using the constructed model and was classified into high-risk, medium-risk and low-risk by using the K-means++ clustering algorithm. Various evaluation results were compared with the evaluation results from the expert evaluation method and the classical risk evaluation indexes, and the evaluation results are basically the same, which verifies the effectiveness of the lane-changing risk model established.

This paper presents a novel microscopic modeling framework to explore the impact of driver stochasticity and other related factors on heterogeneous traffic flows, consisting of both regular human-driven vehicles (RHDVs) and connected human-driven vehicles (CHDVs). We apply the stochastic optimal velocity car-following model for RHDVs, while an extended version of the stochastic continuous car-following model, taking into account optimal velocity guidance and driver compliance, is devised for CHDVs. The stability of CHDV car-following model is examined theoretically and numerically. Simulation experiments are conducted to analyze the stochastic heterogeneous traffic flow properties (i.e. stability and safety) with the proposed microscopic modeling framework under diverse parameter settings including CHDV penetration rate, driver compliance, and driver stochasticity. Results show that traffic flow stability and safety improve with the increase in CHDV penetration rate and driver compliance but deteriorate as the driver’s stochasticity strength increases. CHDVs significantly enhance traffic stability when the CHDV penetration rate or driver compliance exceeds a certain threshold, typically ranging between 0.5 and 0.7. Additionally, there is an interaction between driver stochasticity and CHDV penetration rate in their effect on traffic flow stability, where the contribution of stochasticity to traffic oscillations first increases and then decreases as the penetration rate rises. When the penetration rate is between 0 and 0.4, the traffic risk in emergency situations can be significantly reduced as the penetration rate increases. These findings may guide the management and control strategies of heterogeneous traffic flow.

Heavy fog weather can increase traffic accidents and lead to freeway closures which result in delays. This paper aims at exploring traffic accident and emission characteristics in heavy fog, as well as freeway intermittent release measures for heavy fog weather. A driving simulator experiment is conducted for obtaining driving behaviors in heavy fog. By proposing a multi-cell cellular automaton (CA) model based on the experimental data, the role of intermittent release measures on the reduction of traffic accidents and CO emissions is studied. The results show that, affected by heavy fog, when cellular occupancy $\rho <0.8$, the probability of traffic accidents is much higher; and CO emissions increase significantly when $\rho <0.2$. After an intermittent release measure is applied, the probability of traffic accidents and level of CO emissions become reasonable. Obviously, the measure can enhance traffic safety and reduce emissions.

Memory demand is associated with increased mental workload. The objective of the present study was to examine the effects of visuospatial memory secondary tasks on driving performance. Memory tasks for the unknown word-figure pairs and recognition tasks for word-figure pairs at two-level difficulties were employed separately to represent working memory’s process and long-term memory’s process. A simulator study was conducted based on the simulation of the standard environment of Lane change test (LCT). The performance of lane keeping, lane change, and secondary tasks was measured by statistical methods. The comprehensive appraisal model was constructed to quantify total driving performance. The results showed that the mean path deviation, steering angle, and lane excursion times increased, and the proportion of correct lane change decreased, with the perceived workload increasing and the total driving performance decreasing in dual-task driving condition. Compared with the simple working memory group, as the difficulty of tasks increased in difficult working memory group, lane change performance degraded and the perceived workload increased. In contrast to difficult working memory group, the performance of lane keeping and lane change increased, while the perceived workload decreased and the total performance increased by about 50% in difficult recognition group. There were few differences between the simple working memory group and simple recognition group. The difficult working memory group had the lowest total driving performance. The results indicate that as the secondary task’s difficulty increases, driving performance will degrade. Performance improves significantly when the working memory process is converted to the recognition process. This trend is more obvious when the memory task assumes to be more difficult.

For rail-cum-road bridges with single-level arrangement, there are not only dynamic interference, but also aerodynamic interference between trains and vehicles, which means that the train-induced winds generated by high-speed trains are non-negligible for vehicles. In this work, the test method of moving vehicle model is used to test the aerodynamic characteristics of vehicles under train-induced winds. Then, the model of coupled vibration of train–vehicle–bridge system considering train-induced winds is established. Finally, taking a rail-cum-road bridge with single-level arrangement as an example, the coupled vibration of train–vehicle–bridge system considering train-induced winds is analyzed, and the influence of train-induced winds, lateral spacing between trains and vehicles on train–vehicle–bridge system is discussed. The results show that train-induced winds decrease with the increase of the lateral spacing between trains and vehicles. Without considering train-induced winds, the moving train has a greater impact on the vertical response of the vehicle, and a smaller impact on the lateral response of the vehicle. When considering train-induced winds, the lateral response of the vehicle increases significantly, but decreases with the increase of the lateral spacing. In addition, road conditions and the setting of anti-dazzle plates can change traffic safety under train-induced winds.

This paper deals with the wavelet-based performance analysis of the safety barrier for use in a full-scale test. The test involves a vehicle, a Ford Fiesta, which strikes the safety barrier at a prescribed angle and speed. The vehicle speed before the collision was measured. Vehicle accelerations in three directions at the center of gravity were measured during the collision. The yaw rate was measured with a gyro meter. Using normal speed and high-speed video cameras, the behavior of the safety barrier and the test vehicle during the collision was recorded. Based upon the results obtained, the tested safety barrier has proved to satisfy the requirements for an impact severity level. By taking into account the Haar wavelets, the property of integral operational matrix is utilized to find an algebraic representation form for calculate of wavelet coefficients of acceleration signals. It is shown that Haar wavelets can construct the acceleration signals well.

The correlation between stress response ability of male drivers and number of trainings under different road conditions and sources of stress is analyzed. HED eye tracker was used to measure the physiological indicators of 12 objects. Stress response ability of male drivers under different road conditions and sources of stress was correlated with the number of trainings through regression analysis. Variation rate of pupil area of drivers decreased with the number of trainings under different road conditions and the correlation with number of trainings was best described by logistic regression model (Sig<0.05). Drivers demonstrated worst stress response on highway in foggy weather (sudden change of lane by vehicle in front without turn signal) and on rural highway in foggy weather (sudden stopping of vehicle in front). Most drivers had to go through recovery training to achieve the previous stress response level. Variation rate of pupil area is an effective measure of male drivers’ stress response ability. Drivers will exhibit lower level of stress response and reduced occurrence of stress through training.

Mountain highways are restricted by the comprehensive effect of road alignment, longitudinal slope and complex road conditions, which results in a larger accident rate. This article firstly studies the road alignment of mountain highways and attributes of random interference factors, and analyzes the laws of traffic flows in mountain highways. Then by incorporating the impacts of environment factors of mountain highways into the traditional car following model, it puts forward a nonlinear car-following model on the basis of nonlinear dynamics principles. The results conducted on VISSIM proved the correctness and practicability of the newly-established model. This study has provided theoretical support for traffic safety in mountain highways.