This paper focuses on modeling ride requests and their variations over location and time, based on analyzing extensive real-world data from a ride-sharing service. We introduce a graph model that captures the spatial and temporal variability of ride requests and the potentials for ride pooling. We discover these ride request graphs exhibit a well known property called densification power law often found in real graphs modelling human behaviors. We show the pattern of ride requests and the potential of ride pooling for a city can be characterized by the densification factor of the ride request graphs. Previous works have shown that it is possible to automatically generate synthetic versions of these graphs that exhibit a given densification factor. We present an algorithm for automatic generation of synthetic ride request graphs that match quite well the densification factor of ride request graphs from actual ride request data.

本论文基于详细分析真实世界来自共享乘车服务的数据，专注于对乘车请求和其时空变化进行建模。作者提出了一个图形模型来捕捉乘车请求的时空变异性和共乘的潜力，并发现这些乘车请求图形展现了人类行为建模的常见特征 - 稠密化功率法则。通过使用自动合成算法，本研究提供了一种自动生成满足实际数据的图形的方法。

基于现实世界数据的乘车请求时空图建模