{"title":"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","authors":"Kareem Othman, Amer Shalaby, Baher Abdulhai","doi":"10.1049/itr2.70084","DOIUrl":null,"url":null,"abstract":"<p>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 (R<sup>2</sup> = 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.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70084","citationCount":"0","resultStr":"{\"title\":\"Strategic Deployment of Electric Buses Through Replacement Factor Prediction: A Machine Learning Framework for Cost-Effective Electrification\",\"authors\":\"Kareem Othman, Amer Shalaby, Baher Abdulhai\",\"doi\":\"10.1049/itr2.70084\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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 (R<sup>2</sup> = 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.</p>\",\"PeriodicalId\":50381,\"journal\":{\"name\":\"IET Intelligent Transport Systems\",\"volume\":\"19 1\",\"pages\":\"\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2025-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70084\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Intelligent Transport Systems\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/itr2.70084\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Intelligent Transport Systems","FirstCategoryId":"5","ListUrlMain":"https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/itr2.70084","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Strategic Deployment of Electric Buses Through Replacement Factor Prediction: A Machine Learning Framework for Cost-Effective Electrification
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.
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