基于手机导航轨迹数据的城市大规模人群出行模式分析

Analysis of Urban Mass Crowd Traveling Patterns Based on Mobile Phone Navigation Trajectory Data

摘要:

手机导航轨迹数据具有多种交通方式，反映大规模人群的活动情况，适合开展不同交通方式下的出行模式研究。基于手机导航数据，利用LightGBM模型实现出行交通方式分类，得到步行、机动车和非机动车3种交通方式下的人群轨迹。基于这3类交通方式，给出人群出行在周末和工作日下时间、空间和距离的分析指标，并对上海4天数百万条手机导航数据开展了实验分析。结果表明：在时间分布上，上海居民的周末出行高峰比工作日更晚并且持续时间更短，出行方式主要以机动车和步行为主；在空间分布上，机动车主要集中在高架区域，步行主要集中在地铁站附近，高架路和地铁站的引导标志不充足，周末交通枢纽和商圈类热点区域比工作日多；在距离分布上，导航出行距离符合截断幂律分布，人群导航出行以中短距离为主，并随距离增长迅速衰减。研究结果可以为城市规划、城市交通管理政策的制定提供理论依据和技术支撑。

关键词:手机导航轨迹数据;交通方式识别;出行模式

Abstract:

Mobile phone navigation trajectory data has a variety of traffic modes， reflecting the activities of mass crowd， which is suitable for the study of traveling patterns in different traffic modes. Based on mobile phone navigation data， the LightGBM model for traffic mode classification is first proposed to obtain the trajectories of the population in three transportation modes： walking， motorized， and non-motorized mode. Based on these three types of modes， the analysis indexes of time， space， and distance traveling patterns on weekends and weekdays are given， and an experimental analysis is conducted with the mobile phone navigation data of millions of people in Shanghai for four days. The results show that in terms of time distribution， the weekend traveling peak of the residents in Shanghai is later and shorter than that of weekdays， and traveling modes are mainly motor vehicles and walking. In terms of spatial distribution， motor vehicles are mainly concentrated in elevated areas， walking is mainly concentrated near subway stations， the guidance signs of elevated roads and subway stations are not sufficient， and there are more traffic hubs and shopping hotspots on weekends than on weekdays. In terms of distance distribution， navigation traveling distance conforms to the power-law distribution of intercepted segments， and the navigation traveling of the crowd is dominated by short and medium distances， and decays rapidly with distance growth. The research results can provide theoretical basis and technical support for urban planning and urban traffic management policy formulation.

Key words:mobile phone navigation trajectory data;transportation mode recognition;traveling patterns