Combined car-following and unsafe event trajectory simulation using agent based modeling techniques

Author

Abbas, Muhammad Muddassir ; Higgs, Bryan ; Linsen Chong ; Medina, Aurelio

Author_Institution

Virginia Tech Blacksburg, Blacksburg, VA, USA

fYear

2012

fDate

9-12 Dec. 2012

Firstpage

1

Lastpage

10

Abstract

This paper presents a research effort aimed at modeling normal and safety-critical driving behavior in traffic under naturalistic driving data using agent based modeling techniques. Neuro-fuzzy reinforcement learning was used to train the agents. The developed agents were implemented in the VISSIM simulation platform and were evaluated by comparing the behavior of vehicles with and without agent behavior activation. The results showed very close resemblance of the behavior of agents to driver data.

Keywords

automobiles; behavioural sciences; digital simulation; fuzzy reasoning; learning (artificial intelligence); neural nets; road traffic; software agents; traffic engineering computing; VISSIM simulation platform; agent behavior activation; agent training; agent-based modeling technique; car-following trajectory simulation; naturalistic driving data; neuro-fuzzy reinforcement learning; normal driving behavior modeling; road traffic; safety-critical driving behavior modeling; unsafe event trajectory simulation; vehicle behavior; Acceleration; Data models; Learning; Mathematical model; Trajectory; USA Councils; Vehicles;

fLanguage

English

Publisher

ieee

Conference_Titel

Simulation Conference (WSC), Proceedings of the 2012 Winter

Conference_Location

Berlin

ISSN

0891-7736

Print_ISBN

978-1-4673-4779-2

Electronic_ISBN

0891-7736

Type

conf

DOI

10.1109/WSC.2012.6465329

Filename

6465329