Work

Machine learning for on-demand ride services: A survey

Year: 2025

Type: article

Abstract: On-demand ride services have become an integral part of urban transportation systems and have notably influenced mobility patterns. This study provides a comprehensive review of the applications of machine learning (ML) techniques, including supervised learning, reinforcement learning, explainable AI, and generative AI, in the context of on-demand ride services. We investigate the role of ML in addressing two core challenges: supply-demand management, including demand forecasting, passenger choice modeling, and driver behavior analysis; and platform operations, such as dynamic pricing, matching, route planning, and vehicle repositioning. To promote reproducible research in the field, we also summarize public datasets that are crucial for benchmarking and validating ML-based solutions. By identifying key challenges such as latent demand estimation and Human-AI collaboration, this study aims to inspire interdisciplinary innovations for building a more sustainable and equitable urban transportation system.

Source: Artificial Intelligence for Transportation

DOI: 10.1016/j.ait.2025.100008

Authors: Jian Liang, Jintao Ke, Siyuan Feng, Hai Yang

Institutions: University of Hong Kong, Hong Kong Polytechnic University, Hong Kong University of Science and Technology

Language: English

Topic: Transportation and Mobility Innovations

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,000