Abstract
Abstract. City hotspots refer to the areas where residents visit frequently, and large traffic flow exist, which reflect the people travel patterns and distribution of urban function area. Taxi trajectory data contain abundant information about urban functions and citizen activities, and extracting interesting city hotspots from them can be of importance in urban planning, traffic command, public travel services etc. To detect city hotspots and discover a variety of changing patterns among them, we introduce a data field-based cluster analysis technique to the pick-up and drop-off points of taxi trajectory data and improve the method by introducing the time weight, which has been normalized to estimate the potential value in data field. Thus, in the light of the new potential function in data field, short distance and short time difference play a powerful role. So the region full of trajectory points, which is regarded as hotspots area, has a higher potential value, while the region with thin trajectory points has a lower potential value. The taxi trajectory data of Wuhan city in China on May 1, 6 and 9, 2015, are taken as the experimental data. From the result, we find the sustaining hotspots area and inconstant hotspots area in Wuhan city based on the spatiotemporal data field method. Further study will focus on optimizing parameter and the interaction among hotspots area.
Highlights
Hotspots refer to the area where some events happen frequently
Each point in taxi trajectory data indicates the spatiotemporal event of passengers travel behaviour, and the areas where those points cluster can be considered as city hotspots
We introduce a data field-based cluster analysis technique to the pick-up and drop-off points of taxi trajectory data, and present an improved method for spatiotemporal clustering
Summary
Hotspots refer to the area where some events happen frequently. There are variety of meanings of hotspots, such as hotspots of crime (Malleson and Andrese., 2015; Newton and Felson 2015; Steil and Parrish., 2009), hotspots of incident (Anderson., 2006; Vemulapalli et al, 2016), hotspots of disease (Wanjala et al 2011; Hu et al, 2013) and hotspots of business (Chen et al, 2016; Turner., 2013). The pick-up and drop-off points in the taxi trajectory data depict the spatiotemporal event of passenger or driver behaviour patterns. Each point in taxi trajectory data indicates the spatiotemporal event of passengers travel behaviour, and the areas where those points cluster can be considered as city hotspots. Clustering such spatiotemporal trajectory points should be beneficial for us to describe the attraction of city area, and discover the behaviour patterns of the passengers or drivers. We introduce a data field-based cluster analysis technique to the pick-up and drop-off points of taxi trajectory data, and present an improved method for spatiotemporal clustering. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W7, 2017 ISPRS Geospatial Week 2017, 18–22 September 2017, Wuhan, China the traffic management, like congestion warning and traffic dispersion so that we can prevent serious traffic congestion
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