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報告題目：Optimal Taxi Rebalancing Recommender System

報告人：Prof. Rong Su

報告人單位：新加坡南洋理工大學(xué)

報告時間：2024年12月25日（周三）10:00-12:00

會議地點(diǎn)：電氣工程與控制科學(xué)學(xué)院416報告廳（崇德樓D座416）

舉辦單位：電氣工程與控制科學(xué)學(xué)院

報告人簡介：RongSu，教授，博導(dǎo)，新加坡南洋理工大學(xué)電氣與電子工程學(xué)院計算機(jī)控制與自動化碩士項(xiàng)目負(fù)責(zé)人、KTH-NTU 聯(lián)合博士項(xiàng)目管理委員會 NTU負(fù)責(zé)人。研究方向包括多智能體系統(tǒng)、離散事件系統(tǒng)理論、基于模型的故障診斷、網(wǎng)絡(luò)安全分析和綜合、復(fù)雜網(wǎng)絡(luò)的控制和優(yōu)化，以及在柔性制造、智能交通、人機(jī)界面、電源管理和綠色建筑中的應(yīng)用，擁有330多篇期刊和會議出版物、2部專著、18項(xiàng)已授予/申請的專利，曾獲得多項(xiàng)最佳論文獎，包括IEEE/CAA Journal of Automatica Sinica 2021年Hsue-shen Tsien論文獎等。 目前，他擔(dān)任IEEE Transactions on Cybernetics、Automatica (IFAC)、Journal of Discrete Event Dynamic Systems: Theory and Applications和Journal of Control and Decision的副主編。

報告摘要：Ride hailing systems suffer from spatial-temporal supply demand imbalance due to drivers operating in independent, and uncoordinated manner. Several fleet rebalancing models have been proposed that can provide repositioning recommendations to idle standing drivers with the objective of maximizing service rate or minimizing customer waiting time. Existing models assume complete adherence by the drivers which leads to limited practical implementation of these models. A novel taxi rebalancing model and a procedure to compute recommendations for repositioning of taxis are proposed which accounts for uncertainties in adherence arising from taxi driver’s preferences and the evolving confidence of the driver in the system due to outcomes of repositioning recommendations. Extensive simulations using NYC taxi dataset showed that the proposed model can lead to 6.3% higher allocation rates, 11.8% higher driver profits, 4.5% higher demand fulfillment, and 14% higher confidence of drivers’in the rebalancing system, as compared to a state-of-the-art rebalancing model that is agnostic to the taxi driver preferences and confidence in the system

審核人：薄翠梅