Abstract
Taxi mobility data plays an important role in understanding urban mobility in the context of urban traffic. Specifically, the taxi is an important part of urban transportation, and taxi trips reflect human behaviors and mobility patterns, allowing us to identify the spatial variety of such patterns. Although taxi trips are generated in the form of network flows, previous works have rarely considered network flow patterns in the analysis of taxi mobility data; Instead, most works focused on point patterns or trip patterns, which may provide an incomplete snapshot. In this work, we propose a novel approach to explore the spatial-temporal patterns of taxi travel by considering point, trip and network flow patterns in a simultaneous fashion. Within this approach, an improved network kernel density estimation (imNKDE) method is first developed to estimate the density of taxi trip pick-up and drop-off points (ODs). Next, the correlation between taxi service activities (i.e., ODs) and land-use is examined. Then, the trip patterns of taxi trips and its corresponding routes are analyzed to reveal the correlation between trips and road structure. Finally, network flow analysis for taxi trip among areas of varying land-use types at different times are performed to discover spatial and temporal taxi trip ODs from a new perspective. A case study in the city of Shenzhen, China, is thoroughly presented and discussed for illustrative purposes.
Highlights
Urban mobility data are of significant importance to urban development and play an important role in understanding urban traffic system [1,2], typically include taxi trajectories [3,4], bus trajectories [5], smart card records for transportation [6], bike sharing trajectories [7,8] and other public transport systems
The taxi is an important part of urban transportation, and the taxi trips reflect human behaviors and mobility patterns, allowing us to identify the spatial variety of mobility patterns
Transportation theory demonstrates that drivers minimize travel time for route choice behavior [32], and some studies found that taxi trajectories can give relevant insights into passengers’ behavior [10,11], trip purposes [12,13], and spatial patterns [14,15,16], drawing plenty of attention for research
Summary
Urban mobility data are of significant importance to urban development and play an important role in understanding urban traffic system [1,2], typically include taxi trajectories [3,4], bus trajectories [5], smart card records for transportation [6], bike sharing trajectories [7,8] and other public transport systems (underground, tramway, railway, etc). Transportation theory demonstrates that drivers minimize travel time for route choice behavior [32], and some studies found that taxi trajectories can give relevant insights into passengers’ behavior [10,11], trip purposes [12,13], and spatial patterns [14,15,16], drawing plenty of attention for research. We pursue the integration of point patterns, trip patterns, and network flow patterns to provide a better identification and understanding of the spatial variety of taxi mobility data. We propose to identify the spatial variety of travel patterns from taxi mobility data by considering the point, trip and ISPRS Int. J. We propose to use network flow pattern analytics for modeling the differences between taxi trip ODs and land-use data.
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