Journal of the Korean Society of Safety (한국안전학회지)

The Korean Society of Safety (한국안전학회)
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Macro-Level Accident Prediction Model using Mobile Phone Data

이동통신 자료를 활용한 거시적 교통사고 예측 모형 개발

  • Kwak, Ho-Chan (Future Transport Policy Research Division, Korea Railroad Research Institute) ;
  • Song, Ji Young (Future Transport Policy Research Division, Korea Railroad Research Institute) ;
  • Lee, In Mook (Future Transport Policy Research Division, Korea Railroad Research Institute) ;
  • Lee, Jun (Future Transport Policy Research Division, Korea Railroad Research Institute)
  • 곽호찬 (한국철도기술연구원 미래교통정책본부) ;
  • 송지영 (한국철도기술연구원 미래교통정책본부) ;
  • 이인묵 (한국철도기술연구원 미래교통정책본부) ;
  • 이준 (한국철도기술연구원 미래교통정책본부)
  • Received : 2018.04.16
  • Accepted : 2018.08.17
  • Published : 2018.08.31
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Abstract

Macroscopic accident analyses have been conducted to incorporate transportation safety into long-term transportation planning. In macro-level accident prediction model, exposure variable(e.g. a settled population) have been used as fundamental explanatory variable under the concept that each trip will be subjected to a probable risk of accident. However, a settled population may be embedded error by exclusion of active population concept. The objective of this research study is to develop macro-level accident prediction model using floating population variable(concept of including a settled population and active population) collected from mobile phone data. The concept of accident prediction models is introduced utilizing exposure variable as explanatory variable in a generalized linear regression with assumption of a negative binomial error structure. The goodness of fit of model using floating population variable is compared with that of the each models using population and the number of household variables. Also, log transformation models are additionally developed to improve the goodness of fit. The results show that the log transformation model using floating population variable is useful for capturing the relationships between accident and exposure variable and generally perform better than the models using other existing exposure variables. The developed model using floating population variable can be used to guide transportation safety policy decision makers to allocate resources more efficiently for the regions(or zones) with higher risk and improve urban transportation safety in transportation planning step.

Keywords

References

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