Predicting crash risk and identifying crash precursors on Korean expressways using loop detector data

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@Article{Kwak:2016:AAP,
  author =       "Ho-Chan Kwak and Seungyoung Kho",
  title =        "Predicting crash risk and identifying crash precursors
                 on Korean expressways using loop detector data",
  journal =      "Accident Analysi \& Prevention",
  volume =       "88",
  pages =        "9--19",
  year =         "2016",
  ISSN =         "0001-4575",
  DOI =          "doi:10.1016/j.aap.2015.12.004",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0001457515301561",
  abstract =     "In order to improve traffic safety on expressways, it
                 is important to develop proactive safety management
                 strategies with consideration for segment types and
                 traffic flow states because crash mechanisms have some
                 differences by each condition. The primary objective of
                 this study is to develop real-time crash risk
                 prediction models for different segment types and
                 traffic flow states on expressways. The mainline of
                 expressways is divided into basic segment and ramp
                 vicinity, and the traffic flow states are classified
                 into uncongested and congested conditions. Also, Korean
                 expressways have irregular intervals between loop
                 detector stations. Therefore, we investigated on the
                 effect and application of the detector stations at
                 irregular intervals for the crash risk prediction on
                 expressways. The most significant traffic variables
                 were selected by conditional logistic regression
                 analysis which could control confounding factors. Based
                 on the selected traffic variables, separate models to
                 predict crash risk were developed using genetic
                 programming technique. The model estimation results
                 showed that the traffic flow characteristics leading to
                 crashes are differed by segment type and traffic flow
                 state. Especially, the variables related to the
                 intervals between detector stations had a significant
                 influence on crash risk prediction under the
                 uncongested condition. Finally, compared with the
                 single model for all crashes and the logistic models
                 used in previous studies, the proposed models showed
                 higher prediction performance. The results of this
                 study can be applied to develop more effective
                 proactive safety management strategies for different
                 segment types and traffic flow states on expressways
                 with loop detector stations at irregular intervals.",
  keywords =     "genetic algorithms, genetic programming, Crash risk
                 prediction, Segment type, Traffic flow state,
                 Conditional logistic regression analysis, Loop
                 detector",
}

Genetic Programming entries for Ho-Chan Kwak Seungyoung Kho

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