Affiliation:
1. Department of Geo-Informatics, Central South University, Changsha 410006, China
2. Information and Network Center, Central South University, Changsha 410006, China
Abstract
The travel source–sink phenomenon is a typical urban traffic anomaly that reflects the imbalanced dissipation and aggregation of human mobility activities. It is useful for pertinently balancing urban facilities and optimizing urban structures to accurately sense the spatiotemporal ranges of travel source–sinks, such as for public transportation station optimization, sharing resource configurations, or stampede precautions among moving crowds. Unlike remote sensing using visual features, it is challenging to sense imbalanced and arbitrarily shaped source–sink areas using human mobility trajectories. This paper proposes a density-based adaptive clustering method to identify the spatiotemporal ranges of travel source–sink patterns. Firstly, a spatiotemporal field is utilized to construct a stable neighborhood of origin and destination points. Then, binary spatiotemporal statistical hypothesis tests are proposed to identify the source and sink core points. Finally, a density-based expansion strategy is employed to detect the spatial areas and temporal durations of sources and sinks. The experiments conducted using bicycle trajectory data in Shanghai show that the proposed method can accurately extract significantly imbalanced dissipation and aggregation events. The travel source–sink patterns detected by the proposed method have practical reference, meaning that they can provide useful insights into the redistribution of bike-sharing and station resources.
Funder
National Key R&D Program of China
National Nature Science Foundation of China
Hunan Provincial Natural Science Foundation of China
Central South University Innovation-Driven Research Program
Subject
General Earth and Planetary Sciences
Reference48 articles.
1. Urban Morphology and Traffic Congestion: Longitudinal Evidence from US Cities;Wang;Comput. Environ. Urban Syst.,2021
2. Cao, C., Zhen, F., and Huang, X. (2022). How Does Perceived Neighborhood Environment Affect Commuting Mode Choice and Commuting CO2 Emissions? An Empirical Study of Nanjing, China. Int. J. Environ. Res. Public Health, 19.
3. On the Simulation of Shared Autonomous Micro-Mobility;Martinez;Commun. Transp. Res.,2022
4. Data-Driven Interpretation on Interactive and Nonlinear Effects of the Correlated Built Environment on Shared Mobility;Gao;J. Transp. Geogr.,2023
5. Urban and Transport Planning Pathways to Carbon Neutral, Liveable and Healthy Cities; A Review of the Current Evidence;Nieuwenhuijsen;Environ. Int.,2020
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献