Simulation of vehicles’ gap acceptance decision at unsignalized intersections using SUMO

Name: Simulation of vehicles’ gap acceptance decision at unsignalized intersections using SUMO
Author: Bagheri, Mohammad, Bartın, Bekir Oğuz, Ozbay, K.

İsim	Simulation of vehicles’ gap acceptance decision at unsignalized intersections using SUMO
Yazar	Bagheri, Mohammad, Bartın, Bekir Oğuz, Ozbay, K.
Basım Tarihi:	2022
Basım Yeri	- Elsevier
Konu	Artificial neural network, Calibration, Gap acceptance, Machine learning, Microscopic simulation, Validation
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1877-0509
Kayıt Numarası	96c45ab0-5649-4389-8ed5-d5575c4ee058
Lokasyon	Civil Engineering
Tarih	2022
Örnek Metin	In this paper, an artificial neural network (ANN)-based gap acceptance behavior model was proposed. The feasibility of implementing this model in a microscopic simulation tool was tested using the application programming interface of Simulation of Urban Mobility (SUMO) simulation package. A stop-controlled intersection in New Jersey was selected as a case study. The simulation model of this intersection was calibrated using ground truth data extracted during the afternoon peak hours. The ANN-based SUMO model was compared to SUMO model with default gap acceptance parameters and the SUMO model with calibrated gap acceptance parameters. The comparison was based on wait time and accepted gap values at the minor approach of the intersection. The results showed that the ANN-based model produced superior results based on the selected outputs. The analysis results also indicated that the ANN-based model leads to significantly more realistic driving behavior of vehicles on the major approach of the intersection.
DOI	10.1016/j.procs.2022.03.043
Cilt	201

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Simulation of vehicles’ gap acceptance decision at unsignalized intersections using SUMO

Yazar Bagheri, Mohammad, Bartın, Bekir Oğuz, Ozbay, K.

Basım Tarihi 2022

Basım Yeri - Elsevier

Konu Artificial neural network, Calibration, Gap acceptance, Machine learning, Microscopic simulation, Validation

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1877-0509

Kayıt Numarası 96c45ab0-5649-4389-8ed5-d5575c4ee058

Lokasyon Civil Engineering

Tarih 2022

Örnek Metin In this paper, an artificial neural network (ANN)-based gap acceptance behavior model was proposed. The feasibility of implementing this model in a microscopic simulation tool was tested using the application programming interface of Simulation of Urban Mobility (SUMO) simulation package. A stop-controlled intersection in New Jersey was selected as a case study. The simulation model of this intersection was calibrated using ground truth data extracted during the afternoon peak hours. The ANN-based SUMO model was compared to SUMO model with default gap acceptance parameters and the SUMO model with calibrated gap acceptance parameters. The comparison was based on wait time and accepted gap values at the minor approach of the intersection. The results showed that the ANN-based model produced superior results based on the selected outputs. The analysis results also indicated that the ANN-based model leads to significantly more realistic driving behavior of vehicles on the major approach of the intersection.

DOI 10.1016/j.procs.2022.03.043

Cilt 201