Strategic Deployment of Electric Buses Through Replacement Factor Prediction: A Machine Learning Framework for Cost-Effective Electrification

IF 2.5 4区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Kareem Othman, Amer Shalaby, Baher Abdulhai
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Abstract

The transition to electric buses (e-buses) is essential for reducing greenhouse gas emissions in urban transit systems. However, successful e-bus deployment requires careful planning to ensure service reliability while minimising costs. A key challenge in this transition is determining the replacement factor, the ratio of e-buses needed to replace the current diesel-engine bus fleet for a certain route. This factor is essential for transit agencies as it directly influences fleet size, capital investment, and operational efficiency. Accurately estimating replacement factors allows agencies, to prioritise routes where electrification achieves the highest economic and environmental benefits while preventing unnecessary fleet expansion and idle capacity by selecting routes with low replacement factors. This study develops a framework for estimating e-bus replacement factors based on route characteristics, vehicle attributes, and external conditions. Multiple machine learning models are evaluated, with XGBoost achieving the highest accuracy (R2 = 0.93). Model interpretability using SHapley Additive exPlanations (SHAP) analysis identifies the average bus speed and ambient temperature as the main variables affecting the replacement factor. The proposed framework enables transit agencies to optimise fleet deployment by prioritising routes with lower replacement factors, maximising e-bus utilisation, and achieving cost efficiencies while aligning with environmental objectives.

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通过替代因子预测的电动公交车战略部署:一个具有成本效益的电气化机器学习框架
向电动公交车(e-bus)过渡对于减少城市交通系统的温室气体排放至关重要。然而,成功的电子巴士部署需要仔细规划,以确保服务可靠性,同时将成本降至最低。在这一转变过程中,一个关键的挑战是确定替代系数,即在某条路线上,电动公交车取代现有柴油发动机公交车的比例。这一因素对运输机构至关重要,因为它直接影响到车队规模、资本投资和运营效率。准确估计替代系数可以让代理商优先考虑电气化实现最高经济和环境效益的路线,同时通过选择替代系数低的路线来防止不必要的车队扩张和闲置容量。本研究建立了基于路线特征、车辆属性和外部条件的电动巴士替代因子估算框架。对多个机器学习模型进行了评估,XGBoost达到了最高的精度(R2 = 0.93)。使用SHapley加性解释(SHAP)分析的模型可解释性确定了平均总线速度和环境温度是影响替换因子的主要变量。拟议的框架使运输机构能够优化车队部署,优先考虑更换率较低的路线,最大限度地提高电动巴士的利用率,并在符合环境目标的同时实现成本效益。
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来源期刊
IET Intelligent Transport Systems
IET Intelligent Transport Systems 工程技术-运输科技
CiteScore
6.50
自引率
7.40%
发文量
159
审稿时长
3 months
期刊介绍: IET Intelligent Transport Systems is an interdisciplinary journal devoted to research into the practical applications of ITS and infrastructures. The scope of the journal includes the following: Sustainable traffic solutions Deployments with enabling technologies Pervasive monitoring Applications; demonstrations and evaluation Economic and behavioural analyses of ITS services and scenario Data Integration and analytics Information collection and processing; image processing applications in ITS ITS aspects of electric vehicles Autonomous vehicles; connected vehicle systems; In-vehicle ITS, safety and vulnerable road user aspects Mobility as a service systems Traffic management and control Public transport systems technologies Fleet and public transport logistics Emergency and incident management Demand management and electronic payment systems Traffic related air pollution management Policy and institutional issues Interoperability, standards and architectures Funding scenarios Enforcement Human machine interaction Education, training and outreach Current Special Issue Call for papers: Intelligent Transportation Systems in Smart Cities for Sustainable Environment - https://digital-library.theiet.org/files/IET_ITS_CFP_ITSSCSE.pdf Sustainably Intelligent Mobility (SIM) - https://digital-library.theiet.org/files/IET_ITS_CFP_SIM.pdf Traffic Theory and Modelling in the Era of Artificial Intelligence and Big Data (in collaboration with World Congress for Transport Research, WCTR 2019) - https://digital-library.theiet.org/files/IET_ITS_CFP_WCTR.pdf
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