Taxi Dispatch with Real-Time Sensing Data in Metropolitan Areas: A Receding Horizon Control Approach

TL;DR

This paper introduces a receding horizon control framework for taxi dispatch in metropolitan areas, leveraging real-time sensing data to improve efficiency, reduce idle driving distance, and better match demand and supply.

Contribution

The study presents a novel RHC-based dispatch method that integrates real-time GPS and occupancy data with demand/supply models, enhancing urban taxi system performance.

Findings

Reduces average total idle distance by 52%

Decreases supply demand ratio error by 45%

Compatible with various predictive models and robust optimization

Abstract

Traditional taxi systems in metropolitan areas often suffer from inefficiencies due to uncoordinated actions as system capacity and customer demand change. With the pervasive deployment of networked sensors in modern vehicles, large amounts of information regarding customer demand and system status can be collected in real time. This information provides opportunities to perform various types of control and coordination for large-scale intelligent transportation systems. In this paper, we present a receding horizon control (RHC) framework to dispatch taxis, which incorporates highly spatiotemporally correlated demand/supply models and real-time GPS location and occupancy information. The objectives include matching spatiotemporal ratio between demand and supply for service quality with minimum current and anticipated future taxi idle driving distance. Extensive trace-driven analysis with a data set containing taxi operational records in San Francisco shows that our solution reduces the average total idle distance by 52%, and reduces the supply demand ratio error across the city during one experimental time slot by 45%. Moreover, our RHC framework is compatible with a wide variety of predictive models and optimization problem formulations. This compatibility property allows us to solve robust optimization problems with corresponding demand uncertainty models that provide disruptive event information.