TU, Meiting (Tongji University), Chen, Xinran (the Key Laboratory of Road and Traffic Engineering, Ministry of ), Ji, Ang (Southwest Jiaotong University), Shi, Tongtong (Tongji University)

Predicting Ride-Hailing Demand with Consideration of Social Equity: A Case Study of Chengdu

Scheduled for presentation during the Invited Session "Cooperative Driving Technology for Connected Automated Vehicles" (WeAT10), Wednesday, September 25, 2024, 11:50−12:10, Salon 18

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

In the realm of shared autonomous vehicle ride-sharing, the precise prediction is vital for optimizing resource allocation and improving travel efficiency. However, existing studies tend to overlook social attributes and demographic characteristics across various regions, resulting in disparities in prediction fairness between areas with plentiful and limited transportation resources. An innovative framework is proposed incorporating demographic, spatial, and transportation accessibility information into multiple functional graphs, including functional similarity, population structure, and historical demand graphs. Furthermore, we develop a social graph convolution long short-term memory model and employ fairness indicators into the loss function to balance prediction accuracy and fairness. The findings indicate that there is an enhancement in both prediction accuracy and fairness by at least 8.9% and 11.1% respectively compared to base models. Furthermore, the predictions for rush hours in both privileged and underprivileged regions exhibit greater precision and rationality. The proposed framework could effectively capture the demands of diverse social groups, thereby contributing to the advancement of social equity.