Abstract
This paper proposes a matching degree to study dynamic spatiotemporal characteristics of urban taxi and offers a novel understanding of self-organization taxi dispatch in hotspots on top of the Fermi learning model. The proposed matching degree can not only reflect the overall spatiotemporal characteristics of urban taxi supply and demand but also show that the density of distribution and the distance between the taxis supply and the city center will affect the satisfaction of demand. Besides, it is interesting to note that supply always exceeds demand and they will self-organize into an equilibrium state in hotspots. To understand the phenomenon, we develop the Fermi learning model based on the prospect theory and compared the results with the popular reinforcement learning model. The results demonstrate that both models can account for self-organization behavior under different scenarios. We believe our work is crucial to explore taxis data and our indicator can provide a significant suggestion for urban taxis development.
Highlights
Because of the flexibility and convenience, taxis become one of the most important ways to travel
We propose a new Fermi learning model based on the prospect theory and Fermi rule
We study the dynamic spatiotemporal characteristics of the urban taxis
Summary
Because of the flexibility and convenience, taxis become one of the most important ways to travel. In order to understand the characteristics and operation of urban taxis, three general measurements of taxis supply and demand indicators—the number of ownership, full-load ratios, and mileage utilization rate—have been widely used [2,3,4]. Ese indicators can only reflect the urban taxis operations in a macroscope. Christoforou et al find that user’s residence area which has high population density is related to longer journey durations and off-peak and nighttime traveling last longer than others [7]. Those distribution characteristics can only present the static feature
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