Citation

Das P, Park S, Bhavsar P. J. Adv. Transp. 2019; 2019: 1-13.

Copyright

(Copyright © 2019, Institute for Transportation, Publisher John Wiley and Sons)

DOI

10.1155/2019/1980363

PMID

unavailable

Abstract

The efficient movement of users and goods is the primary purpose of the surface transportation system. Roadway traffic crashes have devastating impacts on quality of life of the users as well as health of the system. While researchers are utilizing advanced computing and communication tools to reduce number of crashes on the roadways, there is still an absence of appropriate method to evaluate the safety performances of these advanced technologies in the planning stage. Development of crash modification factors (CMFs) is a standard method to evaluate the safety effect of proposed countermeasures. Though, the current practices of developing CMFs are not efficient and cost-effective in case of addressing impacts of Intelligent Transportation System (ITS) countermeasures. This study demonstrated a proof of concept of simulation-based framework for determining CMFs for ITS countermeasures. The proposed framework includes the application of traffic microsimulation model and Surrogate Safety Assessment Model (SSAM) developed by Federal Highway Administration (FHWA). The integration of these two models is suggested to estimate CMFs efficiently. However, the calibration of traffic microsimulation model and SSAM model is essential to portrait the real-world scenarios. A case study for estimating CMFs of ITS countermeasures was conducted to validate the proposed simulation-based approach. Four ITS countermeasures were considered: ramp metering, variable speed limit, junction control, and dynamic lane assignment. They were coded in traffic microsimulation environment and vehicle trajectory files were generated to import into SSAM model. After analyzing these trajectory files in SSAM tool, it was found that all proposed ITS countermeasures, except variable speed limit assignment, could reduce the number of crashes at crash prone locations.

Language: en