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Long-term Fairness in Ride-Hailing Platform

Autor: Kang, Yufan, Chan, Jeffrey, Shao, Wei, Salim, Flora D., Leckie, Christopher

Matching in two-sided markets such as ride-hailing has recently received significant attention. However, existing studies on ride-hailing mainly focus on optimising efficiency, and fairness issues in ride-hailing have been neglected. Fairness issues

Externí odkaz: http://arxiv.org/abs/2407.17839

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STEMO: Early Spatio-temporal Forecasting with Multi-Objective Reinforcement Learning

Autor: Shao, Wei, Kang, Yufan, Peng, Ziyan, Xiao, Xiao, Wang, Lei, Yang, Yuhui, Salim, Flora D

Accuracy and timeliness are indeed often conflicting goals in prediction tasks. Premature predictions may yield a higher rate of false alarms, whereas delaying predictions to gather more information can render them too late to be useful. In applicati

Externí odkaz: http://arxiv.org/abs/2406.04035

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Promoting Two-sided Fairness in Dynamic Vehicle Routing Problem

Autor: Kang, Yufan, Zhang, Rongsheng, Shao, Wei, Salim, Flora D., Chan, Jeffrey

Dynamic Vehicle Routing Problem (DVRP), is an extension of the classic Vehicle Routing Problem (VRP), which is a fundamental problem in logistics and transportation. Typically, DVRPs involve two stakeholders: service providers that deliver services t

Externí odkaz: http://arxiv.org/abs/2405.19184

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Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attention

Autor: Shao, Wei, Jin, Zhiling, Wang, Shuo, Kang, Yufan, Xiao, Xiao, Menouar, Hamid, Zhang, Zhaofeng, Zhang, Junshan, Salim, Flora

Many real-world ubiquitous applications, such as parking recommendations and air pollution monitoring, benefit significantly from accurate long-term spatio-temporal forecasting (LSTF). LSTF makes use of long-term dependency between spatial and tempor

Externí odkaz: http://arxiv.org/abs/2204.11008

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