Zhao Shuo, Qi Geqi, Li Peihao, Guan Wei
Beijing Jiaotong University, China.
Beijing Jiaotong University, China.
J Safety Res. 2024 Dec;91:381-392. doi: 10.1016/j.jsr.2024.10.004. Epub 2024 Oct 17.
Traffic congestion is closely related to traffic accidents, as prolonged traffic congestion often results in frustration and aggressive behavior. Moreover, in daily commuting, drivers often have to pass through multiple congested road sections, and aggressive driving performance due to exiting or re-entering traffic jams has rarely been analyzed.
To fill this research gap, we designed an intermittent traffic congestion scenario using a driving simulator and employed unsupervised learning algorithms to extract high-level driving patterns gathered with EEG data to investigate the continuous effects of traffic jams, particularly when drivers exit and re-enter traffic jam conditions.
We discovered that drivers, upon exiting congested areas, engage in abrupt braking with a decrease in braking time of approximately 0.47 s and smooth lane changes with an increase in lane change time of approximately 0.5 s to maintain high-speed driving conditions. When drivers re-enter a traffic jam, they exhibit more abrupt stop-and-go behaviors to escape the traffic jam. The results of the risk assessment of driving behavior indicated that after leaving congested areas, free-flow segments have greater risk factors than other segments. Electroencephalogram (EEG) data were analyzed to identify instances of mind-wandering when a driver transitions into free-flowing segments, followed by a substantial increase in brain activity upon re-entry into congested traffic conditions.
The research outcomes suggest that optimizing the road segments after congestion, using appropriate entertainment systems to reduce driver stress, and implementing adaptive traffic signals to achieve smooth transitions during intermittent congestion can reduce aggressive driving behavior and enhance traffic safety.
交通拥堵与交通事故密切相关，因为长时间的交通拥堵常常导致司机沮丧和攻击性驾驶行为。此外，在日常通勤中，司机经常要经过多个拥堵路段，而因驶出或重新进入拥堵路段导致的攻击性驾驶行为却很少被分析。
为填补这一研究空白，我们使用驾驶模拟器设计了一种间歇性交通拥堵场景，并采用无监督学习算法提取通过脑电图（EEG）数据收集的高级驾驶模式，以研究交通拥堵的持续影响，特别是当司机驶出和重新进入拥堵状态时的影响。
我们发现，司机在驶出拥堵区域时，会突然刹车，刹车时间减少约0.47秒，并平稳变道，变道时间增加约0.5秒，以维持高速行驶状态。当司机重新进入交通拥堵时，他们会表现出更多突然的启停行为以逃离拥堵。驾驶行为风险评估结果表明，离开拥堵区域后，自由流路段的风险因素比其他路段更大。对脑电图（EEG）数据进行分析，以识别司机进入自由流路段时走神的情况，随后在重新进入拥堵交通状况时大脑活动大幅增加。
研究结果表明，优化拥堵后的路段、使用适当的娱乐系统减轻司机压力以及实施自适应交通信号以在间歇性拥堵期间实现平稳过渡，可以减少攻击性驾驶行为并提高交通安全。
