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Data mining the Kansas traffic-crash database : summary.
  • Published Date:
  • Language:
Filetype[PDF-327.76 KB]

  • Publication/ Report Number:
    K-TRAN: KSU-05-6
  • Resource Type:
  • Geographical Coverage:
  • Edition:
    Final report; Feb. 2005-July 2009
  • NTL Classification:
  • Format:
  • Abstract:
    Traffic crashes results from the interaction of different parameters which includes highway geometrics, traffic

    characteristics and human factors. Geometric variables include number of lanes, lane width, median width, shoulder

    width, roadway section length, and shoulder width while traffic characteristics include AADT, Percentage of Heavy

    Vehicles and Speed. The effect of these parameters can be correlated by crash prediction models that predict crash

    rates at particular roadway section.

    Transportation Agencies and State Departments of Transportation are continuously faced with decisions concerning

    the safety of highways. The evaluation and comparison of alternative long-range highway plans should include the

    safety implications of respective plans. The commonly available models for safety analysis are crash prediction

    models. By performing an in-depth analysis of crash databases and developing crash rate prediction models, better

    decisions can be taken in regard to future traffic planning operations.

    The main objective of this study is to utilize artificial neural network techniques and develop crash rate prediction

    models for Kansas road networks. Six networks have been studied and crash prediction models for each network

    have been developed.

    The models developed for each of the road networks are unique and show that geometric variables and traffic

    have a significant impact on the crash behavior. The models developed in this study would be utilized by Kansas

    Department of Transportation in evaluating roadway design features, reconstruction impacts and to make decisions

    in regard to future traffic planning operations. Sensitivity analysis was performed on all the geometric variables in

    the models. It has been found that all the continuous variables have different effects on different networks. It is very

    difficult to generalize the behavior of a particular variable. The same results were observed for categorical variables,


    Vehicle Type, Driver age and seat belt use by drivers have also been studied and it has been found that Driver Age

    Group (18-20) has the highest involvement in crashes on all road networks. Passenger cars have the highest crash

    involvement among vehicle types and among all vehicle types; bus drivers have the highest seat belt compliance for

    all networks.

    This research serves as a starting point to demonstrate the use of artificial neural networks to develop crash

    rate prediction models that could present useful insight to the potential corresponding safety and traffic operation


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