Driver behavior in traffic.

Abbas, Montasir; Medina, Alejandra; Chong, Linsen; Higgs, Bryan; McGhee, Catherine; Fontain, Mike; Goodall, Noah; Kautzsch, Lukas; Leonhardt, Axel; Ova, Kiel; Virginia Polytechnic Institute and State University

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Driver behavior in traffic.

2012-02-01
By Abbas, Montasir ; Medina, Alejandra ; Chong, Linsen ; ...

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English

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Details:

Creators:

Abbas, Montasir ; Medina, Alejandra ; Chong, Linsen ; Higgs, Bryan ; McGhee, Catherine ; ... More +

Abbas, Montasir ; Medina, Alejandra ; Chong, Linsen ; Higgs, Bryan ; McGhee, Catherine ; Fontain, Mike ; Goodall, Noah ; Kautzsch, Lukas ; Leonhardt, Axel ; Ova, Kiel Less -
Corporate Creators:

Virginia Polytechnic Institute and State University
Subject/TRT Terms:

[+]

Artificial Intelligence Automatic Data Collection Systems Automobile Drivers--Psychology Behavior Drivers Fuzzy Algorithms Learning (Artificial Intelligence) Motor Vehicle Driving--Computer Simulation Traffic Simulation
Publication/ Report Number:

FHWA-HRT -12 -036
Resource Type:

Manuscript
Geographical Coverage:

United States
Edition:

Final report.
Corporate Publisher:

United States. Federal Highway Administration ; Turner-Fairbank Highway Research Center

United States. Federal Highway Administration ; Turner-Fairbank Highway Research Center Less -
NTL Classification:

NTL-SAFETY AND SECURITY-Highway Safety;NTL-SAFETY AND SECURITY-Human Factors;
Abstract:

Existing traffic analysis and management tools do not model the ability of drivers to recognize their environment and respond to it with behaviors that vary according to the encountered driving situation. The small body of literature on characterizing drivers’ behavior is typically limited to specific locations (i.e., by collecting data on specific intersections or freeway sections) and is very narrow in scope. This report documented the research performed to model driver behavior in traffic under naturalistic driving data. Of special interest to this research was the modeling of both normal and safety-critical driving. The research resulted in the development of a hybrid car-following model. In addition, neuro-fuzzy reinforcement learning, an agent-based artificial intelligence machine-learning technique, was used to model driving behavior. The naturalistic driving database was used to train and validate driver agents. The proposed methodology simulated events from different drivers and proved behavior heterogeneities. Robust agent activation techniques were also developed using discriminant analysis. The developed agents were implemented in the VISSIM simulation platform and were evaluated by comparing the behavior of vehicles with and without agent activation. The results showed very close resemblance of the behavior of agents to driver data. Prototype agents (spreadsheets and codes) were developed. Future research recommendations include training agents using more data to cover a wider region in the Wiedemann regime space, and sensitivity analysis of agent training parameters. The goal of this effort is to provide the industry with methods for developing more accurate and more sensitive traffic simulation models. This could also enable future research to develop new generations of traffic simulation models that accurately model driver behavior during incidents and other complex traffic situations.
Format:

PDF
Funding:

DTFH61-09-H-00007
Collection(s):

Federal Highway Administration US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:d5a8cce0d887b0a03bc1e87b9e41ccb5c5e0af6ab7ce8810c0cdc27cf72d6078
Download URL:

https://rosap.ntl.bts.gov/view/dot/25687/dot_25687_DS1.pdf
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Driver behavior in traffic.

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