Data mining the Kansas traffic-crash database : summary.

Details

• Creators:
  Najjar, Yacoub M. ; Mandavilli, Srinivas
• Corporate Creators:
  Kansas State University. Civil Engineering Dept.
• Corporate Contributors:
  Kansas. Dept. of Transportation. Bureau of Materials & Research
• Subject/TRT Terms:
  • Crash analysis
  • Highway safety
  • Crashes
  • Traffic accidents--Statistical methods
  • Traffic safety--Statistical methods
  • Data mining--Kansas
  • Information storage and retrieval systems--Traffic accidents
• Publication/ Report Number:
  K-TRAN: KSU-05-6
• Resource Type:
  Tech Report
• Geographical Coverage:
  Kansas
• Edition:
  Final report; Feb. 2005-July 2009
• Corporate Publisher:
  Kansas. Dept. of Transportation. Bureau of Materials & Research
• 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, ; too. ; 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 ; performance.
  More +
• Format:
  PDF
• Funding:
  C1532
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/5668/dot_5668_DS1.pdf
• File Type:
  [PDF - 327.76 KB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha256:0a190e8a1bfe83e3b6aa5eadd5e5b44280e00217836dccdfccbc3f88cf2f0836