Details:

Creators:
Najjar, Yacoub M. ; Mandavilli, Srinivas
Najjar, Yacoub M. ; Mandavilli, Srinivas Less -
Corporate Creators:
Kansas State University. Civil Engineering Dept.
Corporate Contributors:
Kansas. Dept. of Transportation. Bureau of Materials & Research
Subject/TRT Terms:
[+]
Crash Analysis Crashes Data Mining--Kansas Highway Safety Information Storage And Retrieval Systems--Traffic Accidents Traffic Accidents--Statistical Methods Traffic Safety--Statistical Methods
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.


Format:
PDF
Funding:
C1532
Collection(s):
US Transportation Collection
Main Document Checksum:
[+]
urn:sha256:ca8d3464441934afe1423bba23f543a2855c22cdaa1e3b736d058136ee00bc6d
Download URL:
https://rosap.ntl.bts.gov/view/dot/5667/dot_5667_DS1.pdf
File Type:

Supporting Files

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