Wu, W, Jiang, S, Liu, R orcid.org/0000-0003-0627-3184 et al. (2 more authors) (2020) Economic development, demographic characteristics, road network and traffic accidents in Zhongshan, China: gradient boosting decision tree model. Transportmetica A: Transport Science, 16 (3). ISSN 2324-9935
Abstract
This paper explores the joint effects of economic development, demographic characteristics and road network on road safety. Although extensive efforts have been undertaken to model safety effects of various influential factors, little evidence is provided on the relative importance of explanatory variables by accounting for their mutual interactions and non-linear effects. We present an innovative gradient boosting decision tree (GBDT) model to explore joint effects of comprehensive factors on four traffic accident indicators (the number of traffic accidents, injuries, deaths, and the economic loss). A total of 27 elaborated influential factors in Zhongshan, China during 2000–2016 are collected. Results show that GBDT not only presents high prediction accuracy, but can also handle the multicollinearity between explanatory variables; more importantly, it can rank the influential factors on traffic accidents. We also investigate the partial effects of key influential factors. Based on key findings, we highlight the practical insights for planning practice.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Hong Kong Society for Transportation Studies Limited. This is an author produced version of an article published in Transportmetrica A: Transport Science. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Traffic accidents; socio-economic; demographics; relative importance |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Spatial Modelling and Dynamics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 11 Sep 2019 12:45 |
Last Modified: | 04 Jan 2021 01:38 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/23249935.2020.1711543 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:150710 |