时间：2024年6月28日14:00-15:30

地点：西小讲堂

1.Optimal Charging Schedule of a Large-Scale Electric Bus Fleet with Conventional and Renewable Electricity Sources

Electrification poses a promising approach to reducing carbon emissions and promoting carbon neutrality in the transportation sector. Along the transportation electrification pathway, carbon neutrality can be further accelerated with an increasing amount of electricity being generated from renewable energies. Many metropolises across the world began to employ electric buses to offer its transit service, especially in China. An important daily operations issue with urban electric buses is how to coordinate their charging activities in a cost-effective manner, considering various physical, financial, and managerial constraints. This talk discusses scheduling the daily charging activities of an electric bus fleet operated across multiple bus lines and charging depots and terminals, with the aim of finding an optimal set of charging location and time decisions given the available charging windows, with the electricity supplied by both the conventional power grid and emerging photovoltaic-storage-charging stations. A regional electric bus fleet of 122 buses in Shanghai, China is used as a case study for validating the effectiveness and practicability of the proposed charging scheduling models and algorithms.

2.Public transport Usage and Activity Participation: Fusing Data Sources to Understand Behavioural Adaptations and Resilience To Disruptions

COVID-19, natural disasters as well as large events disrupt daily life in cities. Impacts such as traffic congestion can be measured but the resulting overall resilience to such disruptions is more difficult to quantify as it requires an understanding of demand and supply impacts. This talk will discuss this as well as discuss data that quantify activity participation and public transport usage as two important aspects of urban and regional resilience. The usage of high quality mobile phone statistics as well as the feasibility to use opportunistic data such as social media data web-crawled data with the need for data fusion and machine learning techniques will be discussed. The talk will conclude with some general remarks on the effects of short-term unreliability for long-term resilience and well-being.

3.城轨突发事件影响范围识别及应急公交接驳优化研究

城市轨道交通凭借运量大、速度快、准时性高等优势，已成为大城市公共交通的重要组成部分，有效缓解了城市交通压力。然而，由于一些不可抗因素，城轨突发事件时有发生，给运营安全带来巨大挑战。一方面，城轨线网密度及客流规模不断增加，突发事件导致的列车延误在城轨网络上扩散速度快、影响范围大。另一方面，突发事件发生后大量乘客滞留在城轨站点，进一步增加了安全风险。基于以上背景，本研究基于深度学习技术构建模型对突发事件时空影响范围进行识别，并针对考虑滞留客流疏散的应急公交接驳问题、考虑动态客流接续的应急公交接驳问题、考虑网络换乘客流疏运的应急公交补充接驳问题分别构建数学优化模型进行求解，旨在为城轨突发事件应急组织与管理提供决策支撑，减少乘客出行延误，降低安全风险。