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A resampling approach to disaggregate analysis of bus-involved crashes using panel data with excessive zeros
Chen, Tiantian1,2; Lu, Yuhuan3; Fu, Xiaowen1,4; Sze, N. N.2; Ding, Hongliang2
2022-01
Source PublicationAccident Analysis and Prevention
ABS Journal Level3
ISSN0001-4575
Volume164
Abstract

Public bus constitutes more than 70% of the overall road-based public transport patronage in Hong Kong, and its crash involvement rate has been the highest among all public transport modes. Though previous studies had identified explanatory factors that affect the crash risk of buses, use of considerably imbalanced crash data with excessive zero observations could lead to inaccurate parameter estimation. This study aims to resolve the excess zero problem of disaggregate analysis of bus-involved crashes based on synthetic data using a Synthetic Minority Over-Sampling Technique for panel data (SMOTE-P). Dataset comprising crash, traffic, and road inventory data of 88 road segments in Hong Kong during the period from 2014 to 2017 is used. To assess the data balancing performance, other common data generation approaches such as Random Under-sampling of the Majority Class (RUMC) technique, Cluster-Based Under-Sampling (CBUS), and mixed resampling, are also considered. Random effect Poisson (REP) models based on synthetic data and random effect zero-inflated Poisson (REZIP) model based on original data are estimated. Results indicate that REP model based on synthetic data using SMOTE-P outperforms REZIP model based on original data and REP models based on synthetic data using RUMC, CBUS and mixed approaches, in terms of statistical fit, prediction error, and explanatory factors identified. Results of model estimation based on SMOTE-P suggest that factors including morning peak, evening peak, hourly traffic flow, average lane width, road length, bus stop density, percentage of bus in the traffic stream, and presence of bus priority lane all affect the bus-involved crash frequency. More importantly, this study provides a feasible solution for disaggregate crash analysis with imbalanced panel data.

KeywordBus Safety Crash Frequency Model Excessive Zeros Resampling Approach
DOI10.1016/j.aap.2021.106496
URLView the original
Indexed BySSCI
Language英語English
WOS Research AreaEngineering ; Public, Environmental & Occupational Health ; Social Sciences - Other Topics ; Transportation
WOS SubjectErgonomics ; Public, Environmental & Occupational Health ; Social Sciences, Interdisciplinary ; Transportation
WOS IDWOS:000744274300005
Scopus ID2-s2.0-85119255694
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorSze, N. N.
Affiliation1.Department of Industrial and System Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
2.Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
3.Department of Computer and Information Science, State Key Laboratory of Internet of Things for Smart City, University of Macau, Taipa, Macao
4.Knowledge Management and Innovation Research Centre, Hong Kong Polytechnic University, Hung Hom, Hong Kong
Recommended Citation
GB/T 7714
Chen, Tiantian,Lu, Yuhuan,Fu, Xiaowen,et al. A resampling approach to disaggregate analysis of bus-involved crashes using panel data with excessive zeros[J]. Accident Analysis and Prevention,2022,164.
APA Chen, Tiantian,Lu, Yuhuan,Fu, Xiaowen,Sze, N. N.,&Ding, Hongliang.(2022).A resampling approach to disaggregate analysis of bus-involved crashes using panel data with excessive zeros.Accident Analysis and Prevention,164.
MLA Chen, Tiantian,et al."A resampling approach to disaggregate analysis of bus-involved crashes using panel data with excessive zeros".Accident Analysis and Prevention 164(2022).
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