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Mehmet Baran Ulak, Eren Erman Ozguven, Ren Moses, Thobias Sando, Walter Boot, Yassir AbdelRazig, John Olusegun Sobanjo. 2019: Assessment of traffic performance measures and safety based on driver age and experience: A microsimulation based analysis for an unsignalized T-intersection. Journal of Traffic and Transportation Engineering (English Edition), 6(5): 455-469. DOI: 10.1016/j.jtte.2018.05.004
Citation: Mehmet Baran Ulak, Eren Erman Ozguven, Ren Moses, Thobias Sando, Walter Boot, Yassir AbdelRazig, John Olusegun Sobanjo. 2019: Assessment of traffic performance measures and safety based on driver age and experience: A microsimulation based analysis for an unsignalized T-intersection. Journal of Traffic and Transportation Engineering (English Edition), 6(5): 455-469. DOI: 10.1016/j.jtte.2018.05.004

Assessment of traffic performance measures and safety based on driver age and experience: A microsimulation based analysis for an unsignalized T-intersection

  • Traffic safety and performance measures such as crash risk and queue lengths or travel times are influenced by several important factors including those related to environment, human, and roadway design, especially at intersections. Previous research has studied different aspects related to these factors, yet these characteristics are not fully investigated with a focus on age and experience of drivers. In this paper, we investigate this issue by using a two-phase approach via a case study application on a critical T-intersection in the City of Tallahassee, Florida. The first phase includes a scenario-based microsimulation analysis through the use of a microscopic simulation software, namely VISSIM, to illustrate the variations in traffic performance measures with respect to driver compositions of different age groups in the traffic stream. A variety of scenarios is created where the driving characteristics are provided as inputs to these scenarios in terms of decision making and risk taking. This is also supported by a sensitivity analysis conducted based on the driver composition in the traffic. The second phase includes the analysis of microsimulation outputs via a tool developed by Federal Highway Administration tool, namely the Surrogate Safety Assessment Model (SSAM), in order to determine the traffic conflicts that occur in each scenario. These conflicts are also compared with real-life crash data for validation purposes. Results show that (a) the differences in risk perception that affect driving behavior might be significant in influencing traffic safety and performance measures, and (b) the proposed approach is considerably successful in simulating the actual crash conflict points.
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