Effects of Individual Differences on Driving Behavior and Traffic Flow Characteristics
Abstract
Studying driving behavior has been considered an important approach to identifying solutions to increase roadway level of service, reduce roadway traffic crashes, improve vehicle designs, and develop in-vehicle safety devices. The purpose of this research was to get insight into the effect of individual differences on driving behavior and traffic flow characteristics. A method of integrating driving simulators and traffic simulation was developed to investigate the effects of driving behavior on traffic flow. First, a cluster analysis was performed to classify drivers into three categories: aggressive, conservative, and moderate. Second, the driving behavior parameters of the three types of drivers were calibrated for traffic simulation models using the experimental data collected in a driving simulator. Then the effects of driving behavior on traffic flow were analyzed by traffic simulation techniques. The results show that the roadways with aggressive drivers have the highest flow rate but least traffic flow stability. Hence, to maintain roadway level of service and improve traffic safety, it is necessary to develop public training and education campaigns to reach out to aggressive drivers.
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References
1. Lownes N. E., and Machemehl R. B. Sensitivity of Simulated Capacity to Modification of VISSIM Driver Behavior Parameters. In Transportation Research Record: Journal of the Transportation Research Board, No. 1988, Transportation Research Board of the National Academies, Washington, D.C., 2006, pp. 102–110.
2. Yannes C. D., and Lownes N. E. Identifying Capacity Interactions in Response to Simulated Driver Behavior. Presented at 88th Annual Meeting of the Transportation Research Board, Washington, D.C., 2009.
3. Pueboobpaphan R., and van Arem B. Driver and Vehicle Characteristics and Platoon and Traffic Flow Stability: Understanding the Relationship for Design and Assessment of Cooperative Adaptive Cruise Control. In Transportation Research Record: Journal of the Transportation Research Board, No. 2189, Transportation Research Board of the National Academies, Washington, D.C., 2010, pp. 88–97.
4. Gomes G., May A., and Horowitz R. Congested Freeway Micro-simulation Model Using VISSIM. In Transportation Research Record: Journal of the Transportation Research Board, No. 1876, Transportation Research Board of the National Academies, Washington D.C., 2004, pp. 71–81.
5. Brockfeld E., Kühne R. D., and Wagner P. Calibration and Validation of Microscopic Models of Traffic Flow. In Transportation Research Record: Journal of the Transportation Research Board, No. 1934, Transportation Research Board of the National Academies, Washington, D.C., 2005, pp. 179–187.
6. Knipling R. R., Olsen E. C. B., and Prailey T. D. Individual Differences and the “High-Risk” Commercial Driver. Commercial Truck and Bus Safety Synthesis Program, Washington, D.C. www.national-academies.org/trb/bookstore. Accessed June 18, 2010.
7. Vladisavljevic I., Cooper J. M., Martin P. T., and Strayer D. L. Importance of Integrating Driving and Traffic Simulations: Case Study of Impact of Cell Phone Drivers on Traffic Flow. Presented at 88th Annual Meeting of the Transportation Research Board, Washington, D.C., 2009.
8. Greenwood M., and Woods H. M. The Incidence of Industrial Accidents upon Individuals with Special Reference to Multiple Accidents. Report 4. Industrial Fatigue Research Board, Medical Research Committee, Her Majesty's Stationery Office, London, 1919.
9. Motor Vehicle Driver Physical Condition and Evaluation Requirements. GB 18463-2001. China Standard Press, Beijing, 2001.
10. Liu D. H., and Li N. SPSS 15.0 Statistical Analysis. Tsinghua University Press, Beijing, 2009.
11. Zhang Z., Rong J., and Ren F. Review of Car-Following Model Research. Journal of Highway and Transportation Research and Development, Vol. 21, No. 18, 2004, pp. 108–113.
12. Li D. H. Research on Drivers’ Difference and Simulation in Traffic Flow. PhD dissertation. Beijing University of Technology, 2008.
13. Rong J. Capacity of Basic Expressway Sections. PhD dissertation. Beijing University of Technology, 1999.
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Article first published online: January 1, 2011
Issue published: January 2011
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