Reasonable setting of tunnel traffic signs can alleviate the "spatial-temporal tunnel effect" caused by the special environment of tunnels to a certain extent. This paper studied the influence of traffic signs (speed limit signs and exit warning signs) on driving behavior in highway tunnels. Firstly, it selected long tunnels as experimental sections, obtained fine-grained microscopic behavioral data of drivers through driving simulator, and constructed a linear mixed effect model considering the influence of random effects to reveal the influence characteristics and mechanism of traffic signs on driving behavior in tunnels. The results show that: there are differences in the effects of traffic signs on driving behavior in different influence zones; installation of speed limit signs inside the tunnel can reduce the probability of accident risk to a certain extent and the repeated setting of speed limit signs can improve drivers' maneuvering ability to a certain extent; the setting of speed limit signs and exit distance warning signs inside the tunnel can help improve drivers' spatial control ability and driving comfort. Among them, the installation of tunnel exit distance warning signs in the impact zone of less than 1km from the tunnel exit helps to improve the driving comfort of drivers; the installation of tunnel exit distance warning signs in the impact zone of 2 km and 1.5 km from the tunnel exit can improve the vehicle control ability and at the same time make drivers have a higher driving speed. In addition, driving behavior under the effect of tunnel traffic signs is influenced by driver's gender, age, driving experience and other characteristic attributes, with male drivers having a lower driving risk.
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With the increasing number of elderly drivers and their growing driving demand, China has abolished the age limit for applying for small car driver’s license, and the travel safety of elderly drivers has become a hot research issue. Due to the biological and psychological decline related to driving behavior with aging, the elderly need to consider the impact of decreased physical function while maintaining mobility through self-regulation. Self-regulation is an effective compensation strategy for the elderly to prolong driving life and reduce driving risk. Based on the theory of planned behavior (TPB), this study introduced four extended variables, including physical condition, risk perception, driving skills and alternative traffic quality, to establish an extended TPB model and analyzed elderly drivers’ selfe-regulation behavior and its influencing factors. Cronbach’s α coefficient and confirmatory factor were used to verify the reliability and validity of the self-regulation behavior questionnaires collected from 849 elderly drivers, and path analysis was used to explore the factors influencing elderly drivers’ self-regulation behavior. The results show that the relationship among basic TPB variables is consistent with the assumptions of the basic model. Physical condition, alternative traffic quality, driving ability and risk perception all indirectly affect the self-regulation behavior of the elderly. The results are helpful to understand elderly drivers’ self-regulation behavior and influencing factors, and provide the theoretical basis for further research on elderly drivers’ driving behavior training and safety education.
In order to demonstrate the effectiveness of the warning system, this study explored the influence of the head up display warning system on driving behavior under different weather conditions in the connected environment for pedestrian crossing events. Firstly, this paper designed three warning systems: baseline, head down display (HDD), and head up display (HUD). Based on the driving simulation platform, it developed connected scenarios of urban roads under clear and foggy weather conditions and extracted driving behavior parameters through experimental testing. Then, from the micro level, the paper described the whole spatial change process of driving behavior under the action of connected HUD warning system for pedestrian crossing events, analyzed the influence rules of the warning systems and weather conditions on driving behavior, and constructed the influence model of driving behavior including risk avoidance stage and recovery stage. Next, a comprehensive index system was proposed to reflect the safety level at the risk avoidance stage, the recovery level at the recovery stage and the stability level of the whole process, including the first braking distance, the minimum conflict distance, the recovery stabilization time, the recovery amount of velocity, the maximum deceleration and the maximum acceleration. Finally, it quantified the effects of different warning systems (Baseline/ HDD/ HUD), weather conditions (clear weather/ foggy weather) and the interaction between the two factors on driving behavior. Research shows that the connected HUD warning system for pedestrian crossing events has a significant effect on drivers’driving behavior in the risk avoidance and recovery stage, and it significantly improves the safety level of driving behavior in the risk avoidance stage. At the same time, it has a significant improvement effect on the recovery efficiency during the recovery stage, and also significantly affects the stability level of the operational characteristics throughout the whole process. The connected HUD warning system for pedestrian crossing events shows more obvious advantages under foggy conditions.
Designing an effective road network is essential for constructing a comprehensive training platform. The proposed design integrates various traffic elements, enhances the authenticity of road network simulations, saves construction space, maximizes the integrated role of the sand table, and improves the comprehensiveness of the platform. Based on a multitype road network structure, different types of traffic facilities can be laid, which can replicate intelligent transportation application scenes. Then, the road elements can be enriched according to the requirements of the sand table and the actual road scene. Diversified road network design of training platforms can better facilitate platform construction and intelligent transportation personnel training.
A diversified road network design scheme is proposed to address the current and future requirements for intelligent transportation and personnel training. This involves comparing and analyzing different design schemes using multidimensional characteristic evaluation indices. This paper evaluates existing sand table applications and proposes five road network design schemes to meet different application requirements: ① customization- based road network design, ② modularization-based road network design, ③ projection-based road network design, ④ electronic screen-based road network design, and ⑤ print-based road network design. Each scheme supports diverse application scenarios, such as teaching demonstrations, scientific research experiments, popular science exhibitions, and professional competitions.
This study analyzes the functional differences among the five proposed road network design schemes, evaluating them based on multidimensional characteristic evaluation indices. These indexes include function display degree, cost advantage, deployment convenience, display effect, intelligent vehicle applicability, secondary expansion, occupied area, practical operation, and service life. Each scheme is compared from technical perspectives, typical application scenarios, advantages and disadvantages, improvement directions, and development trends.
To adapt to the development of intelligent transportation and meet personnel training needs, this research proposes five road network design schemes. These schemes provide guidance and reference for designing and selecting road networks for practical teaching, scientific research, exhibitions, and other purposes. They enhance the simulation effect between simulation road networks and real-world road conditions, improve the application effectiveness of scientific research, practical training, and exhibitions, and facilitate the training of intelligent transportation talents. The results provide guidance and reference for cost control and functional design of road network design throughout the platform’s life cycle. By guiding the construction of high-quality practical training platforms, this study can support high-quality construction projects and efficient personnel training. It also has far-reaching implications for actively serving the current needs of intelligent and connected road traffic systems.
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Connected vehicle-based variable speed limit (CV-VSL) systems in fog area use multi-source detection data to indicate drivers to make uniform change in speed when low visibility conditions suddenly occur. The purpose of the speed limit is to make the driver's driving behavior more consistent, so as to improve traffic safety and relieve traffic congestion. The on-road dynamic message sign (DMS) and on-board human–machine interface (HMI) are two types of warning technologies for CV-VSL systems. This study aims to analyze drivers’ acceptance of the two types of warning technologies in fog area and its influencing factors.
This study developed DMS and on-board HMI for the CV-VSL system in fog area on a driving simulator. The DMS and on-board HMI provided the driver with weather and speed limit information. In all, 38 participants participated in the experiment and completed questionnaires on drivers’ basic information, perceived usefulness and ease of use of the CV-VSL systems. Technology acceptance model (TAM) was developed to evaluate the drivers’ acceptance of CV-VSL systems. A variance analysis method was used to study the influencing factors of drivers’ acceptance including drivers’ characteristics, technology types and fog density.
The results showed that drivers’ acceptance of on-road DMS was significantly higher than that of on-board HMI. The fog density had no significant effect on drivers’ acceptance of on-road DMS or on-board HMI. Drivers’ gender, age, driving year and driving personality were associated with the acceptance of the two CV-VSL technologies differently. This study is beneficial to the functional improvement of on-road DMS, on-board HMI and their market prospects.
Previous studies have been conducted to evaluate the effectiveness of CV-VSL systems. However, there were rare studies focused on the drivers’ attitude toward using which was also called as acceptance of the CV-VSL systems. Therefore, this research calculated the drivers’ acceptance of two normally used CV-VSL systems including on-road DMS and on-board HMI using TAM. Furthermore, variance analysis was conducted to explore whether the factors such as drivers’ characteristics (gender, age, driving year and driving personality), technology types and fog density affected the drivers’ acceptance of the CV-VSL systems.
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