Space-Time Graph Modeling of Ride Requests Based on Real-World Data
This work addresses ride-sharing optimization for urban mobility by providing a modeling tool, but it is incremental as it builds on known graph properties and generation methods.
The paper tackles the problem of modeling ride requests and their spatiotemporal variability using real-world ride-sharing data, resulting in a graph model that captures ride pooling potential and matches the densification factor of actual data with synthetic generation.
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.