Thesis Defense - Mohammad Bagheri (MSCE)

Mohammad Bagheri – M.Sc. Civil Engineering

Associate Prof. Bekir Bartin – Advisor

Date: 05.08.2022

Time: 13:30

Location: AB1 232

“Implementing Artificial Neural Network Based Gap Acceptance Models

in The Simulation Model of a Traffic Circle in SUMO”

Assoc. Prof. Bekir Bartin, Özyeğin University

Prof. İsmail Şahin, Yildiz Technical University

Asst.Prof. Taner Yilmaz, Özyeğin University

Abstract:

The impact of various operational and design alternatives at roundabouts and traffic circles can be evaluated using microscopic simulation tools. Most microscopic simulation software utilizes default underlying models for this purpose, which may not be generalized to specific facilities. Since the effectiveness of traffic operations at traffic circles and roundabouts is highly affected by the gap rejection–acceptance behavior of drivers, it is essential to accurately model driver’s gap acceptance behavior using location-specific data. The objective of this paper was to evaluate the feasibility of implementing an Artificial Neural Network (ANN)-based gap acceptance model in SUMO, using its application programming interface. A traffic circle in New Jersey was chosen as a case study. Separate ANN models for one stop-controlled and two yield-controlled intersections were trained based on the collected ground truth data. The output of the ANN-based model was then compared with the SUMO model, calibrated by modifying the default gap acceptance parameters to match the field data. Based on the analysis results it was concluded that the advantage of the ANN-based model lies not only in the accuracy of the selected output variables in comparison to the observed field values but also in the realistic vehicle crossings at the uncontrolled intersections in the simulation model.

Bio:

Mohammad Bagheri finished his first Master’s degree in Geotechnical Engineering at Azarbaijan Shahid Madani University, Tabriz, Iran. In his first Master’s Program, he conducted an experimental study to investigate the durability and strength of problematic soils improved with a novel polymeric stabilizer. His work was accompanied by two published journal articles. In his current Master’s program, his research interests lie in the field of transportation engineering, integrating machine learning techniques and traffic simulation programming. He modeled driver’s gap acceptance decisions using the Artificial Neural Network models and tested the models’ feasibility in microscopic traffic simulation software. As the results of this study, one conference paper is published in Elsevier, and one is submitted to the Transportation Research Board.