DocumentCode :
597494
Title :
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
Link To Document :
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