Towards a transformation in urban commuting analysis with high-precision mobile phone signaling data: Identifying commuting characteristics based on individual scale

Due to the widespread use of navigational satellites, the ubiquity of mobile phones, and the rapid advancement of mobile communication technologies, high-precision mobile phone signaling data (HMPSD) holds exceptional promise for discerning fine-grained characteristics of residents' travel behaviors, owing to its superior spatial and temporal resolution. This study focuses on identifying the most consistent commuting patterns of residents in the Qiaoxi District of Shijiazhuang, China, over the course of a month, using these patterns as the basis for transport mode identification. Leveraging the high-precise geographical coordinates of individuals' workplaces and homes, along with actual commuting durations derived from the high-frequency positioning of HMPSD, and comparing these with the predicted commuting durations for four transport modes from a navigational map, we have developed a novel approach for identifying individual transport modes, incorporating time matching, frequency ranking, and speed threshold assessments. This approach swiftly and effectively identifies the commuting modes for each resident—namely, driving, public transportation, walking, bicycling, and electric biking—along with their respective commuting distances and durations. Furthermore, to support urban planning and transportation management efforts, we aggregated individual commuting data—including flows, modes, distances, and durations—at a parcel level. This aggregation method effectively reveals favorable commuting characteristics within the central area of Qiaoxi District, highlights the commuting needs and irrational commuting conditions in peripheral parcels, and informs tailored strategies for adjusting planning layouts and optimizing facility configurations. This study facilitates an in-depth exploration of fine-grained travel patterns through integrated air-land transportation resources, providing new insights and methodologies for refined urban transportation planning and travel management through advanced data applications and identification methods.