A Two-Phase Clustering Approach for Urban Hotspot Detection With Spatiotemporal and Network Constraints

Urban hotspots are regions with intensive passenger flow, sound infrastructure, and thriving business during a certain period of time, which mirror the travel behavior of residents. Taxi trajectory is one of the important data sources for urban hotspot detection. Unfortunately, it should be pointed out that quite a few of the relevant studies have ignored the temporal dynamics or network-constrained characteristics of urban hotspots, making the detecting results less reasonable and reliable. In this study, a two-phase clustering approach is proposed to detect urban hotspot with taxi trajectory. Concretely, in the first phase, spatiotemporal hierarchical density-based spatial clustering of applications with noise is utilized to cluster the trajectory points with spatial and temporal attributes, which is essential for understanding the evolution of urban hotspots over time. In the second phase, the idea of region growing is introduced to further filter noise, in which the spatial similarity between data points is measured by the route distance, considering that the trajectory data are constrained by the road network. A case study is carried out by the proposed method. Meanwhile, in combination with the Luojia1-01 night-time light remote sensing data and POI data, the reliability of the clustering results is verified and the semantic meaning of the discovered clusters is enriched. Furthermore, not only the spatiotemporal distribution but also the trip lengths and directions of the detected hotspots are explored. These findings can serve as a scientific basis for policymakers in traffic control, public facilities planning, as well as location-based service.

Automated summary

This study proposes a two-phase clustering approach to detect urban hotspots using taxi trajectory data. The reliability of the clustering results is verified through a case study and enriched by combining with night-time light remote sensing data and POI data. The study also explores the spatiotemporal distribution, trip lengths, and directions of the detected hotspots.