Department of Civil Engineering, University of British Columbia, Canada.
Accid Anal Prev. 2024 Dec;208:107789. doi: 10.1016/j.aap.2024.107789. Epub 2024 Sep 18.
Several studies have developed pedestrian-vehicle interaction models. However, these studies failed to consider pedestrian distraction, which considerably influences the safety of these interactions. Utilizing data from two intersections in Vancouver, Canada, this research uses the Multi-agent Adversarial Inverse Reinforcement Learning (MA-AIRL) framework to make inferences about the behavioral dynamics of distracted and non-distracted pedestrians while interacting with vehicles. Results showed that distracted pedestrians maintained closer proximity to vehicles, moved at reduced speeds, and rarely yielded to oncoming vehicles. In addition, they rarely changed their interaction angles regardless of lateral proximity to vehicles, indicating that they mostly remain unaware of the surrounding environment and have decreased navigational efficiency. Conversely, non-distracted pedestrians executed safer maneuvers, kept greater distances from vehicles, yielded more frequently, and adjusted their speeds accordingly. For example, non-distracted pedestrian-vehicle interactions showed a 46.5% decrease in traffic conflicts severity (as measured by the average Time-to-Collision (TTC) values) and an average 30.2% increase in minimum distances when compared to distracted pedestrian-vehicle interactions. Vehicle drivers also demonstrated different behaviors in response to distracted pedestrians. They often opted to decelerate around distracted pedestrians, indicating recognition of potential risks. Furthermore, the MA-AIRL framework provided different results depending on the type of interactions. The performance of the distracted vehicle-pedestrian model was lower than the non-distracted model, suggesting that predicting non-distracted behavior might be relatively easier. These findings emphasize the importance of refining pedestrian simulation models to include the unique behavioral patterns from pedestrian distractions. This should assist in further examining the safety impacts of pedestrian distraction on the road environment.
已有多项研究开发了行人和车辆相互作用的模型。然而，这些研究都未能考虑行人分神这一因素，而这一因素对这些相互作用的安全性有重要影响。本研究利用加拿大温哥华两个路口的数据，采用多智能体对抗式逆向强化学习（MA-AIRL）框架，推断分心和不分心的行人与车辆相互作用时的行为动态。结果表明，分心行人与车辆保持更近的距离，速度更慢，很少让行迎面而来的车辆。此外，无论与车辆的横向距离如何，他们很少改变相互作用的角度，这表明他们大多对周围环境没有意识，导航效率降低。相反，不分心的行人则采取更安全的动作，与车辆保持更大的距离，更频繁地让行，并相应地调整速度。例如，与分心行人-车辆相互作用相比，非分心行人-车辆相互作用的交通冲突严重程度（以平均碰撞时间（TTC）值衡量）降低了 46.5%，最小距离平均增加了 30.2%。车辆驾驶员对分心行人的行为也有所不同。他们经常选择在分心行人周围减速，这表明他们认识到潜在的风险。此外，MA-AIRL 框架的表现因相互作用的类型而异。分心行人-车辆模型的性能低于非分心模型，这表明预测非分心行为可能相对更容易。这些发现强调了细化行人模拟模型以纳入行人分神独特行为模式的重要性。这有助于进一步研究行人分神对道路环境安全的影响。
