In November 2025, Dr. Xiaoling Luo of our university, as the lead author, has published a research paper entitled “A continuum approximation approach for designing corridor-based heterogeneous transit service using modular autonomous vehicles” in Transportation Research Part E, a leading international journal in the transportation field classified as a Chinese Academy of Sciences Q1 TOP journal.

The study addresses the innovative application of Modular Autonomous Vehicles (MAVs) within public transport corridors, proposing an optimized design framework for modular transit systems aimed at substantially enhancing operational efficiency. The research team developed an integrated optimization model incorporating multiple decision variables, including stop configuration, service headways, grouping station locations, fleet capacity, and vehicle allocation across stations. On this basis, an efficient solution algorithm integrating analytical derivation and linearization techniques was innovatively formulated. Findings indicate that the MAV-based transit system reduces the average travel cost by 10.6 minutes per passenger compared to conventional public transportation systems.

A key contribution of this work lies in its novel integration of conventional transit services with modular operational requirements into a unified optimization framework. By exploiting the structural properties of the formulated model, an efficient decomposition algorithm was designed, offering a new methodological perspective for optimizing complex public transportation systems.

Dr. Luo has long been engaged in research on intelligent transportation system optimization and innovative public transport management. To date, he has authored more than 20 first-authored papers, including two published in Transportation Research Part C and two in Transportation Research Part E. In recent years, he has maintained sustained collaborations with a number of renowned institutions, including The Hong Kong Polytechnic University, Southwest Jiaotong University, University of Arizona, University of Hawaii, and Roma Tre University, contributing actively to the advancement and real-world application of intelligent transportation research.