Stop or go decisions at the onset of yellow light in a connected environment: A hybrid approach of decision tree and panel mixed logit model

, , Zheng, Zuduo, & Bliemer, Michiel C.J. (2021) Stop or go decisions at the onset of yellow light in a connected environment: A hybrid approach of decision tree and panel mixed logit model. Analytic Methods in Accident Research, 31, Article number: 100165.

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Description

Driver decisions at the onset of yellow traffic lights are often critical, as inaccurate decisions may result in traffic conflicts and collisions. A future connected environment where vehicles can communicate with traffic lights is expected to minimize the uncertainty associated with a driver's decision-making at signalized intersections by providing advance information related to traffic light changes. The effectiveness of such a connected environment, however, remains unexplored due to the paucity of relevant data. This study examines driver decisions at the onset of yellow traffic lights when they are assisted with advance information about traffic light changes. Seventy-eight participants with diverse backgrounds performed driving experiments on an urban route with a signalized intersection simulated in the CARRS-Q Advanced Driving Simulator. The experiment consisted of two randomized driving conditions: baseline (without advance information aids) and connected environment (with advance information aids). Contrary to the existing literature, this study employs a hybrid approach, leveraging the combined benefits of data mining to identify a priori relationships and a panel mixed logit model (more specifically, correlated grouped random parameters logit with heterogeneity-in-means approach) to account for unobserved heterogeneity as well as the correlation among random parameters. Our analysis shows that drivers in the connected environment are less likely to proceed through intersections at the onset of yellow light compared to the baseline condition. However, at the individual driver-level, the connected environment's impact on driver decisions is mixed. Female drivers have been found to have a higher propensity for yellow light running in the connected environment than that of male drivers. Overall, the connected environment assists drivers in making safer decisions at the onset of yellow light.

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45 citations in Scopus
17 citations in Web of Science®
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ID Code: 211395
Item Type: Contribution to Journal (Journal Article)
Refereed: Yes
ORCID iD:
Ali, Yasirorcid.org/0000-0002-5770-0062
Haque, Md Mazharulorcid.org/0000-0003-1016-110X
Additional Information: Funding Information: The authors would like to acknowledge Mr. Andrew Haines for programming the simulator experiment, and the help of Dr. Anshuman Sharma and Dr. Mohammad Saifuzzaman in the experiment design and data collection. This research is partly funded by Australian Research Council, Australia grants DE160100449 and LP160101021.
Measurements or Duration: 20 pages
Additional URLs:
Keywords: Connected environment, Correlated grouped random parameters model, Driving behavior, Machine learning, Road safety, Yellow light
DOI: 10.1016/j.amar.2021.100165
ISSN: 2213-6657
Pure ID: 86990111
Divisions: Current > Research Centres > Centre for Future Mobility/CARRSQ
Current > QUT Faculties and Divisions > Faculty of Engineering
Current > Schools > School of Civil & Environmental Engineering
Current > QUT Faculties and Divisions > Faculty of Health
Funding Information: The authors would like to acknowledge Mr. Andrew Haines for programming the simulator experiment, and the help of Dr. Anshuman Sharma and Dr. Mohammad Saifuzzaman in the experiment design and data collection. This research is partly funded by Australian Research Council, Australia grants DE160100449 and LP160101021 .
Funding:
Copyright Owner: 2021 Elsevier Ltd.
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Deposited On: 29 Jun 2021 00:26
Last Modified: 16 Jul 2024 17:58