A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Urban hotspots detection of taxi stops with local maximum density
Abstract When getting on and off the taxis, people prefer to choose around landmarks, such as a shopping mall, the gate of a residential unit, or a road intersection. This preference leads to multiple local peaks in spatial density, existing in both highly popular and less popular regions. These multiple local peaks provide a natural form to describe small-scale hotspots where the pick-up and drop-off events actually happen. However, they have not been fully studied. In this paper, we propose the local maximum density (LMD) approach to identify the hotspots as a small area around a local maximum density of incidents. It is evaluated using a 6-month taxi dataset of Wuhan City, where 90 m × 90 m square is recommended as the size of a hotspot by LMD. Results show that LMD not only identifies multiple local hotspots in highly popular regions, but also detects potential hotspots in less popular regions. Moreover, a non-uniform spatial pattern is found between pick-up and drop-off local hotspots. Comparing with pick-up behaviors, drop-off behaviors diffuse more in less popular hotspots, but also concentrate more on highly popular hotspots. The driving factors of this phenomenon are further explored by analyzing the match mode of pick-up and drop-off local hotspots with different popularity.
Highlights Representing small-scale hotspots as an area around local maximum density. Algorithm is proposed to adaptatively detect small-scale hotspots. Small-scale hotspots are detected in highly popular and less popular regions. Comparing with pick-up stops, drop-off stops are more gathering in popular hotspots but more dispersive in less popular hotspots.
Urban hotspots detection of taxi stops with local maximum density
Abstract When getting on and off the taxis, people prefer to choose around landmarks, such as a shopping mall, the gate of a residential unit, or a road intersection. This preference leads to multiple local peaks in spatial density, existing in both highly popular and less popular regions. These multiple local peaks provide a natural form to describe small-scale hotspots where the pick-up and drop-off events actually happen. However, they have not been fully studied. In this paper, we propose the local maximum density (LMD) approach to identify the hotspots as a small area around a local maximum density of incidents. It is evaluated using a 6-month taxi dataset of Wuhan City, where 90 m × 90 m square is recommended as the size of a hotspot by LMD. Results show that LMD not only identifies multiple local hotspots in highly popular regions, but also detects potential hotspots in less popular regions. Moreover, a non-uniform spatial pattern is found between pick-up and drop-off local hotspots. Comparing with pick-up behaviors, drop-off behaviors diffuse more in less popular hotspots, but also concentrate more on highly popular hotspots. The driving factors of this phenomenon are further explored by analyzing the match mode of pick-up and drop-off local hotspots with different popularity.
Highlights Representing small-scale hotspots as an area around local maximum density. Algorithm is proposed to adaptatively detect small-scale hotspots. Small-scale hotspots are detected in highly popular and less popular regions. Comparing with pick-up stops, drop-off stops are more gathering in popular hotspots but more dispersive in less popular hotspots.
Urban hotspots detection of taxi stops with local maximum density
Chen, Xiao-Jian (author) / Wang, Ying (author) / Xie, Jiayi (author) / Zhu, Xinyan (author) / Shan, Jie (author)
2021-05-22
Article (Journal)
Electronic Resource
English
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