Robert Corbally, Linhao Yang, Abdollah Malekjafarian
{"title":"Predicting the duration of motorway incidents using machine learning","authors":"Robert Corbally, Linhao Yang, Abdollah Malekjafarian","doi":"10.1186/s12544-024-00632-6","DOIUrl":null,"url":null,"abstract":"Motorway incidents are frequent & varied in nature. Incident management on motorways is critical for both driver safety & road network operation. The expected duration of an incident is a key parameter in the decision-making process for control room operators, however, the actual duration for which an incident will impact the network is never known with true certainty. This paper presents a study which compares the ability of different machine learning algorithms to estimate the duration of motorway incidents on Ireland’s M50 motorway, using an extensive historical incident database. Results show that the support vector machine has the best performance in most cases, but a different method may need to be used to improve accuracy in some situations. Results highlight the main challenges in accurately forecasting incident durations in real time & recommendations are made for improving prediction accuracy through systematic recording of various additional incident details.","PeriodicalId":12079,"journal":{"name":"European Transport Research Review","volume":null,"pages":null},"PeriodicalIF":5.1000,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Transport Research Review","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1186/s12544-024-00632-6","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"TRANSPORTATION","Score":null,"Total":0}
引用次数: 0
Abstract
Motorway incidents are frequent & varied in nature. Incident management on motorways is critical for both driver safety & road network operation. The expected duration of an incident is a key parameter in the decision-making process for control room operators, however, the actual duration for which an incident will impact the network is never known with true certainty. This paper presents a study which compares the ability of different machine learning algorithms to estimate the duration of motorway incidents on Ireland’s M50 motorway, using an extensive historical incident database. Results show that the support vector machine has the best performance in most cases, but a different method may need to be used to improve accuracy in some situations. Results highlight the main challenges in accurately forecasting incident durations in real time & recommendations are made for improving prediction accuracy through systematic recording of various additional incident details.
期刊介绍:
European Transport Research Review (ETRR) is a peer-reviewed open access journal publishing original high-quality scholarly research and developments in areas related to transportation science, technologies, policy and practice. Established in 2008 by the European Conference of Transport Research Institutes (ECTRI), the Journal provides researchers and practitioners around the world with an authoritative forum for the dissemination and critical discussion of new ideas and methodologies that originate in, or are of special interest to, the European transport research community. The journal is unique in its field, as it covers all modes of transport and addresses both the engineering and the social science perspective, offering a truly multidisciplinary platform for researchers, practitioners, engineers and policymakers. ETRR is aimed at a readership including researchers, practitioners in the design and operation of transportation systems, and policymakers at the international, national, regional and local levels.