基于轨迹数据的共享电动自行车逆行风险行为影响因素研究

为改善电动自行车带来的交通安全问题，研究逆行风险行为与其影响因素间的相关关系。基于长沙市芙蓉区共享电动自行车GPS轨迹数据，实现逆行行为的精准识别，采用机器学习CatBoost模型与SHAP可解释机器学习框架，从道路条件、交通状态、土地利用性质等方面开展逆行行为影响要素挖掘及作用解析。研究结果表明：CatBoost模型能够有效预测路段逆行频次并提取逆行行为的重要影响因素，主要包括出行时段、公共交通设施、土地利用性质、道路条件及交通状态等；从出行时段来看，工作日、早晚高峰时段更容易发生逆行；从公共交通设施与土地利用性质来看，道路周围公交站地铁站出口数量及餐饮、公司、购物等设施数量与逆行频次呈现非线性影响关系，在一定范围内设施数量与逆行行为存在正影响作用；从道路条件来看，过街通道间距在50~400 m时不易发生逆行，在非机动车道无物理隔离设施或过街通道间距在400~600 m时容易发生逆行，间距大于600 m时作用不稳定；从路段机非分隔形式来看，护栏分隔的逆行概率较低，绿化带分隔的逆行概率较高；从交通状态来看，当骑行速度、加速度较低或较高时与逆行行为负相关，当骑行速度在6~16 km·h^-1及加速度在0.3~1.0 m·s^-2时与逆行行为正相关。研究成果可为共享电动自行车风险骑行行为辨识、非机动车交通安全管理提供有效的技术支持。

Research on Influencing Factors of Reverse Riding Risk Behavior of Shared E-bike Based on Trajectory Data

In order to improve the traffic safety problems caused by electric bicycles, this paper studies the relationship between reverse riding risk behavior and its influencing factors. Based on the GPS trajectory data of shared E-Bike in Furong district, Changsha City, the accurate identification of reverse riding behavior is realized. The machine learning model CatBoost and interpretable machine learning framework SHAP are used to extract and analyze the influencing factors of reverse riding behavior from the aspects of road conditions, traffic conditions, land use attributes, etc. The results show that:① CatBoost model can effectively predict the reverse riding frequency of road sections and extract the important influencing factors of reverse riding behavior, mainly including travel time, public transport facilities, land use attributes, road conditions and traffic conditions. ② In terms of travel time, reverse riding is more likely to occur on weekdays and morning & evening peak hours; In terms of public transport facilities and land use attributes, the number of bus stops and subway station exits, and the number of restaurants, companies, shopping and other facilities around the roads present a non-linear influence relationship to the reverse riding frequency. In a certain range, the number of facilities has a positive effect on the reverse riding behavior. In terms of road conditions, reverse riding is less likely to happen with road crossing intervals of 50~400 m. But reverse riding is more likely to happen when there are no physical separation facilities in the bicycle lane or with road crossing intervals of 400~600 m. And the effect is unstable when the intervals is wider than 600 m. In terms of bicycle lane, the reverse riding probability with guardrail separation is lower, while the probability with green belt separation is higher. In terms of traffic conditions, when the riding speed and acceleration are too low or too high, it is negatively related to the reverse riding behavior. When the riding speed is between 6~16 km·h^-1 and the acceleration is between 0.3~1.0 m·s^-2, it is positively related to the reverse riding behavior. This research can provide an effective technical support for the identification of shared E-Bike risk riding behavior, as well as for the management of non-motor vehicle traffic safety.