关键词: 城市交通, 职住地, 出行活动, 轨迹数据, 聚类算法

Abstract:

Vehicle trajectory data contains rich information about travel behavior. By analyzing the origins and destinations within these trajectories-such as places of residence, work, and travel-related points of interest (POIs)-researchers can deeply examine travel activity characteristics and patterns. This study identified potential residential locations based on the first origin and the final destination of each day’s travel activities, while other origins and destinations form the candidate sets for workplaces and interest points. Building on these candidate sets, the paper proposed a method for identifying places of residence and employment using mean shift clustering with spatiotemporal constraints. The identification is based on three criteria: cluster density, average dwell time within the cluster, and the time range of travel activities. This approach enables the extraction of residential, work, and interest point coordinates. The KD-Tree algorithm was then used to match each identified coordinate with nearby POIs, providing specific names and locations for residences and workplaces. Based on the identification of workplace and travel interests, the study quantified travel activity levels using metrics such as travel frequency, distance, and time. It also characterized travel regularity using stability and variability indicators. A K-means++ clustering algorithm was employed to classify types of travel activity patterns. Taking 1 708 vehicles with travel activities for 34 days in Beijing as an example, the empirical research was conducted based on the driving trajectory data. The research results indicate that the distribution characteristics of workplace and residential areas determined by the proposed method is consistent with practical laws, with high accuracy and reliability. The classification of travel activity characteristics based on the K-means++ algorithm reveals that active travel patterns dominate in megacities, accounting for 59.84% of total activities. Among these, frequent travelers constitute the largest subgroup at 31.09%. Active travel behaviors primarily manifest as regular trips on weekdays and irregular trips on non-working days. This research provides theoretical support for optimizing transportation infrastructure planning.

Key words: urban transportation, workplace and residence, travel activities, trajectory data, clustering algorithm