Hit-and-Run Accidents Missing Data Interpolation Using Random Forest

Wei Bai; Jinzhao Liu; Jushang Ou; Huahua Liu; Chuanyun Fu

doi:10.1007/978-981-95-8620-2_32

Hit-and-Run Accidents Missing Data Interpolation Using Random Forest

Wei Bai^*
, Jinzhao Liu
, Jushang Ou
, Huahua Liu
, Chuanyun Fu

^*Corresponding author for this work

Sichuan Police College
School of Transportation Science and Engineering, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Investigating the characteristics and regularities of historical accident data is a crucial pathway in researching road traffic accidents and traffic safety. However, the prevalence of missing data is an inescapable flaw in research on road traffic accidents. Multiple imputation theories and methodologies offer a potential solution for utilizing samples with missing data more effectively. This paper focuses on escapement accidents as the subject of study and establishes the FCS (Flexible Combination Strategy) multiple imputation strategy and process based on a Random Forest method model. It determines the number of multiple imputations for variables with different types and scales of missingness. On this basis, the paper compares the conclusions of the analysis of the full sample without missing data to derive the characteristics of factors influencing the occurrence of escapement accidents under the multiple imputation model. It also analyzes and discusses the similarities and differences of the analytical conclusions. This research provides guidance and reference for improving the road traffic accident data system, preventing and reducing the occurrence of road traffic accidents, and reducing the severity of injuries caused by accidents.

Original language	English
Title of host publication	Resilience Transportation and Mobility Safety
Editors	Wuhong Wang, Yusheng Ci, Xiaowei Hu, Haiqiu Tan, Min Li
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	451-471
Number of pages	21
ISBN (Print)	9789819586196
DOIs	https://doi.org/10.1007/978-981-95-8620-2_32
State	Published - 2026
Externally published	Yes
Event	16th International Conference on Green Intelligent Transportation System and Safety, GITSS 2025 - Shanghai, China Duration: 9 May 2025 → 11 May 2025

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	1607 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	16th International Conference on Green Intelligent Transportation System and Safety, GITSS 2025
Country/Territory	China
City	Shanghai
Period	9/05/25 → 11/05/25

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

hit-and-run accidents
influential factor identification
missing data
multiple imputation
random forest
traffic safety

Access to Document

10.1007/978-981-95-8620-2_32

Cite this

Bai, W., Liu, J., Ou, J., Liu, H., & Fu, C. (2026). Hit-and-Run Accidents Missing Data Interpolation Using Random Forest. In W. Wang, Y. Ci, X. Hu, H. Tan, & M. Li (Eds.), Resilience Transportation and Mobility Safety (pp. 451-471). (Lecture Notes in Electrical Engineering; Vol. 1607 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-95-8620-2_32

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title = "Hit-and-Run Accidents Missing Data Interpolation Using Random Forest",

abstract = "Investigating the characteristics and regularities of historical accident data is a crucial pathway in researching road traffic accidents and traffic safety. However, the prevalence of missing data is an inescapable flaw in research on road traffic accidents. Multiple imputation theories and methodologies offer a potential solution for utilizing samples with missing data more effectively. This paper focuses on escapement accidents as the subject of study and establishes the FCS (Flexible Combination Strategy) multiple imputation strategy and process based on a Random Forest method model. It determines the number of multiple imputations for variables with different types and scales of missingness. On this basis, the paper compares the conclusions of the analysis of the full sample without missing data to derive the characteristics of factors influencing the occurrence of escapement accidents under the multiple imputation model. It also analyzes and discusses the similarities and differences of the analytical conclusions. This research provides guidance and reference for improving the road traffic accident data system, preventing and reducing the occurrence of road traffic accidents, and reducing the severity of injuries caused by accidents.",

keywords = "hit-and-run accidents, influential factor identification, missing data, multiple imputation, random forest, traffic safety",

author = "Wei Bai and Jinzhao Liu and Jushang Ou and Huahua Liu and Chuanyun Fu",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.; 16th International Conference on Green Intelligent Transportation System and Safety, GITSS 2025 ; Conference date: 09-05-2025 Through 11-05-2025",

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doi = "10.1007/978-981-95-8620-2\_32",

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Bai, W, Liu, J, Ou, J, Liu, H & Fu, C 2026, Hit-and-Run Accidents Missing Data Interpolation Using Random Forest. in W Wang, Y Ci, X Hu, H Tan & M Li (eds), Resilience Transportation and Mobility Safety. Lecture Notes in Electrical Engineering, vol. 1607 LNEE, Springer Science and Business Media Deutschland GmbH, pp. 451-471, 16th International Conference on Green Intelligent Transportation System and Safety, GITSS 2025, Shanghai, China, 9/05/25. https://doi.org/10.1007/978-981-95-8620-2_32

Hit-and-Run Accidents Missing Data Interpolation Using Random Forest. / Bai, Wei; Liu, Jinzhao; Ou, Jushang et al.
Resilience Transportation and Mobility Safety. ed. / Wuhong Wang; Yusheng Ci; Xiaowei Hu; Haiqiu Tan; Min Li. Springer Science and Business Media Deutschland GmbH, 2026. p. 451-471 (Lecture Notes in Electrical Engineering; Vol. 1607 LNEE).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Hit-and-Run Accidents Missing Data Interpolation Using Random Forest

AU - Bai, Wei

AU - Liu, Jinzhao

AU - Ou, Jushang

AU - Liu, Huahua

AU - Fu, Chuanyun

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.

PY - 2026

Y1 - 2026

N2 - Investigating the characteristics and regularities of historical accident data is a crucial pathway in researching road traffic accidents and traffic safety. However, the prevalence of missing data is an inescapable flaw in research on road traffic accidents. Multiple imputation theories and methodologies offer a potential solution for utilizing samples with missing data more effectively. This paper focuses on escapement accidents as the subject of study and establishes the FCS (Flexible Combination Strategy) multiple imputation strategy and process based on a Random Forest method model. It determines the number of multiple imputations for variables with different types and scales of missingness. On this basis, the paper compares the conclusions of the analysis of the full sample without missing data to derive the characteristics of factors influencing the occurrence of escapement accidents under the multiple imputation model. It also analyzes and discusses the similarities and differences of the analytical conclusions. This research provides guidance and reference for improving the road traffic accident data system, preventing and reducing the occurrence of road traffic accidents, and reducing the severity of injuries caused by accidents.

AB - Investigating the characteristics and regularities of historical accident data is a crucial pathway in researching road traffic accidents and traffic safety. However, the prevalence of missing data is an inescapable flaw in research on road traffic accidents. Multiple imputation theories and methodologies offer a potential solution for utilizing samples with missing data more effectively. This paper focuses on escapement accidents as the subject of study and establishes the FCS (Flexible Combination Strategy) multiple imputation strategy and process based on a Random Forest method model. It determines the number of multiple imputations for variables with different types and scales of missingness. On this basis, the paper compares the conclusions of the analysis of the full sample without missing data to derive the characteristics of factors influencing the occurrence of escapement accidents under the multiple imputation model. It also analyzes and discusses the similarities and differences of the analytical conclusions. This research provides guidance and reference for improving the road traffic accident data system, preventing and reducing the occurrence of road traffic accidents, and reducing the severity of injuries caused by accidents.

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KW - influential factor identification

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KW - multiple imputation

KW - random forest

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SN - 9789819586196

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SP - 451

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BT - Resilience Transportation and Mobility Safety

A2 - Wang, Wuhong

A2 - Ci, Yusheng

A2 - Hu, Xiaowei

A2 - Tan, Haiqiu

A2 - Li, Min

PB - Springer Science and Business Media Deutschland GmbH

T2 - 16th International Conference on Green Intelligent Transportation System and Safety, GITSS 2025

Y2 - 9 May 2025 through 11 May 2025

ER -

Hit-and-Run Accidents Missing Data Interpolation Using Random Forest

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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Cite this