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Implementing artificial neural network-based gap acceptance models in the simulation model of a traffic circle in SUMO

Title	Implementing artificial neural network-based gap acceptance models in the simulation model of a traffic circle in SUMO
Author	Bagheri, Mohammad, Bartın, Bekir Oğuz, Ozbay, K.
Publication Date:	2023-12
Publication Place	- Sage
Subject	Operations, Calibration, Microscopic traffic simulation, Multi-agent simulation, Simulation
Type	Periodical
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	2-s2.0-85177236733
Record ID	dc03a40b-91fe-4239-9bd8-bb48e6d83d2d
Library Location	Civil Engineering
Date	2023-12
Notes	Ozyegin University ; C2SMART, a Tier 1 UTC at New York University - USDOT
Sample Text	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 drivers' 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 that of the SUMO model, which was calibrated by modifying the default gap acceptance parameters to match the field data. Based on the results of the analyses 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.
Cilt	2677

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Özyeğin University

Implementing artificial neural network-based gap acceptance models in the simulation model of a traffic circle in SUMO

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

Publication Date 2023-12

Publication Place - Sage

Subject Operations, Calibration, Microscopic traffic simulation, Multi-agent simulation, Simulation

Type Periodical

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 2-s2.0-85177236733

Record ID dc03a40b-91fe-4239-9bd8-bb48e6d83d2d

Library Location Civil Engineering

Date 2023-12

Notes Ozyegin University ; C2SMART, a Tier 1 UTC at New York University - USDOT

Sample Text 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 drivers' 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 that of the SUMO model, which was calibrated by modifying the default gap acceptance parameters to match the field data. Based on the results of the analyses 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.

Cilt 2677