Paper Info
Title
Unfolding Spatial-Temporal Patterns Of Taxi Trip Based On An Improved Network Kernel Density Estimation
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.
Year
DOI
Venue
2020
10.3390/ijgi9110683
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
Keywords
DocType
Volume
pick-up and drop-off points (ODs), network kernel density estimation (NKDE), land-use data, map-matching
Journal
9
Issue
Citations 
PageRank 
11
0
0.34
References 
Authors
24
5
Name
Order
Citations
PageRank
Boxi Shen100.34
Xiang Xu211.39
Jun Li3136097.59
Antonio Plaza43475262.63
Qunying Huang542029.69