Crash Severity Modeling in Urban Highways Using Backward Regression Method
Identifying and classifying intersections according to
severity is very important for implementation of safety related
counter measures and effective models are needed to compare and
assess the severity. Highway safety organizations have considered
intersection safety among their priorities. In spite of significant
advances in highways safety, the large numbers of crashes with high
severities still occur in the highways. Investigation of influential
factors on crashes enables engineers to carry out calculations in order
to reduce crash severity. Previous studies lacked a model capable of
simultaneous illustration of the influence of human factors, road,
vehicle, weather conditions and traffic features including traffic
volume and flow speed on the crash severity. Thus, this paper is
aimed at developing the models to illustrate the simultaneous
influence of these variables on the crash severity in urban highways.
The models represented in this study have been developed using
binary Logit Models. SPSS software has been used to calibrate the
models. It must be mentioned that backward regression method in
SPSS was used to identify the significant variables in the model.
Consider to obtained results it can be concluded that the main
factor in increasing of crash severity in urban highways are driver
age, movement with reverse gear, technical defect of the vehicle,
vehicle collision with motorcycle and bicycle, bridge, frontal impact
collisions, frontal-lateral collisions and multi-vehicle crashes in
urban highways which always increase the crash severity in urban
highways.
[1] L. Chang and F. Mannering, "Analysis of Vehicle Occupancy and the
Severity Of Rruck and Non-Truck-Involved Accidents," Department of
Civil Engineering, 121 More Hall, Box 352700 University of
Washington Seattle, Wa 98195, July 17, 1998.
[2] F. F. Saccomanno, S. A. Nassar, and J. H. Shortreed, "Reliability of
Statistical Road Accident Injury Severity Models," Transportation
Research Record, Issue 1542, pp. 14-23, 1996.
[3] W. Chen and P. P. Jovanis, "Method for Identifying Factors
Contributing to Driver-Injury Severity in Traffic Crashes,"
Transportation Research Record, Issue 1717, pp.1-9, 2000.
[4] K. M. Koekelman and Y. Kweon, "Driver Injury Severityi an
Application of Ordered Probit Models," Paper Submitted to Accident
Analysis and Preventation, Jan. 2001.
[5] A. Voget and J. Bared, "Accident Models for Two Lane Rural Segments
and Intersection," Transportation Research Record, Issue 1635, pp. 18-
29, 1999.
[6] K. Kim, L. Nitz and J. L. L. Richardson, "Analyzing The Relationship
Between Crash Type and Injuries In Motor Vehicle Collisions In
Hawaii," Transportation Research Record, Issue 1467, pp. 9-13, 1994.
[7] A. J. Khttak, P. Kantor and F. M. Council, "Role of Advers Weather In
Key Crash Type On Limited: Access Roadways Implications For
Advanced Weather Systems," Transportation Research Record, Issue
1621, pp. 15-19, 1999.
[8] H. T. Abdelwahab and M. A. Abdel-Aty, "Development of Artificial
Neural Network Models to Predict Driver Injury Severity in Traffic
Accidents at Signalizes Intersection," Transportation Research Record
issue 1746, Paper No.01-2234, pp. 6-13, 2001.
[9] M. A. Abdel-Aty and H. T. Abdelwahab, "Predicting injury severity
levels in traffic crashes: a modeling comparison," J. Transp. Eng. vol.
130, no. 2, pp. 204-210, 2004.
[10] D. Delen, R. Sharda and M. Bessonov, "Identifying significant
predictors of injury severity in traffic accidents using a series of artificial
neural networks," Accident Analysis and Prevention, vol. 38, pp. 434-
444, 2006.
[11] N. Ivan, E. P. Garder and S. Z. Sylvia, "Finding Strategies To Improve
Pedestrian Safety in Rural Areas," University of Connecticut and
University of Maine, 2001.
[12] Y. J. Kweon and K. M. Kochelman, "The Safety Effects of Speed Limit
Changes: Use of Panel Models, Including Speed, Use and Design
Variables," The 84th Annual Meeting of Transportation Research Board,
Washington D. C., 2005.
[13] Z. Sawallha and T. Sayed, "Statistical Issues in Traffic Accident
Modeling," TRB 2003 Annual Meeting, 2003.
[1] L. Chang and F. Mannering, "Analysis of Vehicle Occupancy and the
Severity Of Rruck and Non-Truck-Involved Accidents," Department of
Civil Engineering, 121 More Hall, Box 352700 University of
Washington Seattle, Wa 98195, July 17, 1998.
[2] F. F. Saccomanno, S. A. Nassar, and J. H. Shortreed, "Reliability of
Statistical Road Accident Injury Severity Models," Transportation
Research Record, Issue 1542, pp. 14-23, 1996.
[3] W. Chen and P. P. Jovanis, "Method for Identifying Factors
Contributing to Driver-Injury Severity in Traffic Crashes,"
Transportation Research Record, Issue 1717, pp.1-9, 2000.
[4] K. M. Koekelman and Y. Kweon, "Driver Injury Severityi an
Application of Ordered Probit Models," Paper Submitted to Accident
Analysis and Preventation, Jan. 2001.
[5] A. Voget and J. Bared, "Accident Models for Two Lane Rural Segments
and Intersection," Transportation Research Record, Issue 1635, pp. 18-
29, 1999.
[6] K. Kim, L. Nitz and J. L. L. Richardson, "Analyzing The Relationship
Between Crash Type and Injuries In Motor Vehicle Collisions In
Hawaii," Transportation Research Record, Issue 1467, pp. 9-13, 1994.
[7] A. J. Khttak, P. Kantor and F. M. Council, "Role of Advers Weather In
Key Crash Type On Limited: Access Roadways Implications For
Advanced Weather Systems," Transportation Research Record, Issue
1621, pp. 15-19, 1999.
[8] H. T. Abdelwahab and M. A. Abdel-Aty, "Development of Artificial
Neural Network Models to Predict Driver Injury Severity in Traffic
Accidents at Signalizes Intersection," Transportation Research Record
issue 1746, Paper No.01-2234, pp. 6-13, 2001.
[9] M. A. Abdel-Aty and H. T. Abdelwahab, "Predicting injury severity
levels in traffic crashes: a modeling comparison," J. Transp. Eng. vol.
130, no. 2, pp. 204-210, 2004.
[10] D. Delen, R. Sharda and M. Bessonov, "Identifying significant
predictors of injury severity in traffic accidents using a series of artificial
neural networks," Accident Analysis and Prevention, vol. 38, pp. 434-
444, 2006.
[11] N. Ivan, E. P. Garder and S. Z. Sylvia, "Finding Strategies To Improve
Pedestrian Safety in Rural Areas," University of Connecticut and
University of Maine, 2001.
[12] Y. J. Kweon and K. M. Kochelman, "The Safety Effects of Speed Limit
Changes: Use of Panel Models, Including Speed, Use and Design
Variables," The 84th Annual Meeting of Transportation Research Board,
Washington D. C., 2005.
[13] Z. Sawallha and T. Sayed, "Statistical Issues in Traffic Accident
Modeling," TRB 2003 Annual Meeting, 2003.
@article{"International Journal of Information, Control and Computer Sciences:52353", author = "F. Rezaie Moghaddam and T. Rezaie Moghaddam and M. Pasbani Khiavi and M. Ali Ghorbani", title = "Crash Severity Modeling in Urban Highways Using Backward Regression Method", abstract = "Identifying and classifying intersections according to
severity is very important for implementation of safety related
counter measures and effective models are needed to compare and
assess the severity. Highway safety organizations have considered
intersection safety among their priorities. In spite of significant
advances in highways safety, the large numbers of crashes with high
severities still occur in the highways. Investigation of influential
factors on crashes enables engineers to carry out calculations in order
to reduce crash severity. Previous studies lacked a model capable of
simultaneous illustration of the influence of human factors, road,
vehicle, weather conditions and traffic features including traffic
volume and flow speed on the crash severity. Thus, this paper is
aimed at developing the models to illustrate the simultaneous
influence of these variables on the crash severity in urban highways.
The models represented in this study have been developed using
binary Logit Models. SPSS software has been used to calibrate the
models. It must be mentioned that backward regression method in
SPSS was used to identify the significant variables in the model.
Consider to obtained results it can be concluded that the main
factor in increasing of crash severity in urban highways are driver
age, movement with reverse gear, technical defect of the vehicle,
vehicle collision with motorcycle and bicycle, bridge, frontal impact
collisions, frontal-lateral collisions and multi-vehicle crashes in
urban highways which always increase the crash severity in urban
highways.", keywords = "Backward regression, crash severity, speed, urbanhighways.", volume = "3", number = "12", pages = "2753-6", }