A Queueing-Theoretic Framework for Vehicle Dispatching in Dynamic Car-Hailing [technical report]

TL;DR

This paper introduces a queueing-theoretic framework for vehicle dispatching in dynamic car-hailing, combining demand prediction and driver idle time estimation to maximize platform revenue.

Contribution

It proposes a novel queueing-based dispatching framework that integrates machine learning demand prediction with queueing models for driver idle time estimation.

Findings

Framework outperforms existing methods in experiments

Effective in both real and synthetic datasets

Maximizes platform revenue during batch dispatching

Abstract

With the rapid development of smart mobile devices, the car-hailing platforms (e.g., Uber or Lyft) have attracted much attention from both the academia and the industry. In this paper, we consider an important dynamic car-hailing problem, namely \textit{maximum revenue vehicle dispatching} (MRVD), in which rider requests dynamically arrive and drivers need to serve as many riders as possible such that the entire revenue of the platform is maximized. We prove that the MRVD problem is NP-hard and intractable. In addition, the dynamic car-hailing platforms have no information of the future riders, which makes the problem even harder. To handle the MRVD problem, we propose a queueing-based vehicle dispatching framework, which first uses existing machine learning algorithms to predict the future vehicle demand of each region, then estimates the idle time periods of drivers through a queueing model for each region. With the information of the predicted vehicle demands and estimated idle time periods of drivers, we propose two batch-based vehicle dispatching algorithms to efficiently assign suitable drivers to riders such that the expected overall revenue of the platform is maximized during each batch processing. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed approaches over both real and synthetic datasets.