Empirical Analysis of Drivers’ Merging and Diverging Responses to Autonomous Truck Platooning on Freeway Weaving Segments
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 150, Issue 10
Abstract
Autonomous truck platoons hold the potential for substantial economic and environmental advantages. However, there is a lack of comprehensive research on the interactions between drivers and different configurations of truck platoons at waving segments. This study aims to contribute to the literature by achieving three primary objectives: (1) examine the effects of various truck platoon configurations on merging and diverging behaviors [time to merge (TTM) and time to diverge (TTD)]; (2) explore the impact of individual characteristics such as age, gender, education level, and driving experience on TTM and TTD; and (3) investigate the decision-making associated with these maneuvers (merging or diverging in front of the platoon, behind the platoon or cut in through the platoon). A driving simulator study was conducted with 85 participants across 12 distinct scenarios, considering variations in platoon size, intraplatoon spacing, and platoon lane-change behavior. Several statistical methods were employed, including ANOVA, the Cox proportional hazards model, and machine-learning techniques, to analyze the factors impacting TTM and TTD. The results revealed that increasing the headway distances between trucks in a platoon to 13.72 m (45 ft) or 18.29 m (60 ft) substantially decreased TTM, enhancing traffic flow. Furthermore, splitting the truck platoon between two lanes of the freeway before the merging point significantly influenced other drivers’ merging decisions. When half of the trucks in a platoon switched to the left lane before the merging point, a larger proportion of participants chose to merge ahead of the platoon. Age, gender, education level, and self-assessment of driving skills were all found to significantly influence merging and diverging behaviors. Drivers with higher degrees took longer to merge, whereas older, male, and experienced drivers merged faster. The lowest average TTD was observed when half the platoon switched to the left lane before the diverging point.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors would like to thank the Louisiana Board of Regents for supporting this project (Award No. AM220469).
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Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 150 • Issue 10 • October 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Nov 15, 2023
Accepted: Apr 10, 2024
Published online: Aug 14, 2024
Published in print: Oct 1, 2024
Discussion open until: Jan 14, 2025
ASCE Technical Topics:
- Business management
- Driver behavior
- Economic factors
- Education
- Engineering fundamentals
- Highway and road management
- Highway transportation
- Highways and roads
- Infrastructure
- Models (by type)
- Practice and Profession
- Traffic engineering
- Traffic flow
- Traffic management
- Traffic models
- Transportation engineering
- Trucks
- Vehicles
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