Abstract
Understanding the urban mobility patterns is essential for the planning and management of public infrastructure and transportation services. In this paper we focus on taxicab moving trajectory records and present a new approach to modeling and analyzing urban mobility dynamics. The proposed method comprises two phases. First, discrete space partition based on flexible grid is developed to divide urban environment into finite nonoverlapping subregions. By integrating mobility origin-destination points with covered region, the partitioned discrete subregions have better spatial semantics scalability. Then, we study mobility activity and its distribution randomness during given time periods among discrete subregions. Moreover, we also carry out the analysis of mobility linkage of mobility trips between different regions by O-D matrix. We present a case study with real dataset of taxicab mobility logs in Shenzhen, China, to demonstrate and evaluate the methodology. The experimental results show that the proposed method outperforms the clustering partition and regular partition methods.
Highlights
The widespread deployment of location-aware technologies in urban area has led to a massive increase in the volume of movement trace records
GPS-equipped taxicabs can be viewed as ubiquitous mobile sensors probing a city’s rhythm and pulse. These taxicab trace records allow for the development of novel way to uncover the underlying human behavior and urban mobility dynamics
As we focus on moving behavior of citizens, the roaming trip without passengers is unvalued to reveal the actual urban mobility patterns
Summary
The widespread deployment of location-aware technologies in urban area has led to a massive increase in the volume of movement trace records By means of these movement trajectories, we can advance our method of urban computing and human behavior analysis. GPS-equipped taxicabs can be viewed as ubiquitous mobile sensors probing a city’s rhythm and pulse These taxicab trace records allow for the development of novel way to uncover the underlying human behavior and urban mobility dynamics. We explore the challenges of modeling urban mobility by taxicab moving trajectories. Based on the proposed flexible discrete region partition method, we calculate urban mobility activity by origin-destination points in taxicabs moving dataset and analyze the activity randomness across different time scales.
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