| Title | Driver behavior in traffic. |
|---|---|
| Record ID | 47078 |
| Personal Name Creator |
Abbas, Montasir; Medina, Alejandra; Chong, Linsen; Higgs, Bryan; McGhee, Catherine; Fontain, Mike; Goodall, Noah; Kautzsch, Lukas; Leonhardt, Axel; Ova, Kiel |
| Corporate Creator | Virginia Polytechnic Institute and State University |
| Publisher | United States. Federal Highway Administration; Turner-Fairbank Highway Research Center |
| Publication Date | 20120200 |
| Language | English |
| 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. |
| Rosap ID | dot:25687 |
| Rosap URL | https://rosap.ntl.bts.gov/view/dot/25687 |
| TRT Terms | Drivers; Behavior; Machine learning; Fuzzy algorithms; Traffic simulation; Automatic data collection systems |
| General Subjects | Automobile drivers--Psychology; Motor vehicle driving--Computer simulation; Automatic data collection systems; Artificial intelligence; Fuzzy algorithms |
| Classification | NTL - SAFETY AND SECURITY - Highway Safety; NTL - SAFETY AND SECURITY - Human Factors |
| Geographical Coverage |
United States |
| Contract Number | DTFH61-09-H-00007 |
| Report Number | FHWA-HRT -12 -036 |
| Resource type | Manuscript |
| URL | https://ntlrepository.blob.core.windows.net/lib/47000/47000/47078/FHWA-HRT-12-036_Driver_behavior_in_traffic.pdf |
| Format | |
| Database | NTL Digital Repository |