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Truck platooning enabled by connected automated vehicle (CAV) technology has been demonstrated to effectively reduce fuel consumption for trucks in a platoon. However, given the limited number of trucks in the traffic stream, it remains questionable how great an energy saving it may yield for a practical freight system if we only rely on ad-hoc platooning. Assuming the presence of a central platooning coordinator, this paper is offered to substantiate truck platooning benefits in fuel economy produced by exploiting platooning opportunities arising from the United States’ domestic truck demands on its highway freight network. An integer programming model is utilized to schedule trucks’ itineraries to facilitate the formation of platoons at platoonable locations to maximize energy savings. A simplification of the real freight network and an approximation algorithm are used to solve the model efficiently. By analyzing the numerical results obtained, this study quantifies the importance of scheduled platooning in improving trucks’ fuel economy. Furthermore, the allowable platoon size, schedule flexibility, and fuel efficiency all play a crucial role in energy savings. Specifically, by assuming that following vehicles in a platoon obtain a 10% energy reduction, an average energy reduction of 8.48% per truck can be achieved for the overall network if the maximum platoon size is seven, and the schedule flexibility is 30 min. The cost–benefit analysis provided at the end suggests that the energy-saving benefits can offset the investment cost in truck platooning technology.
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Understanding the Workload of Remote Truck Operators with Discrete Event Simulation
This study employs a discrete event simulation (DES) model to understand the dynamic workload of remote truck operators managing partially-automated trucks. The DES model uses operator queues and event generators simulating automated truck events and leverages data from the California DMV’s disengagement database and driving simulation experiments. Disengagement data were partitioned into three groups by disengagement frequency: low, moderate, and high and separate arrival time distributions were developed for each group. Simulations from the model suggest that for companies with low disengagement rates, operator utilization will likely remain below minimal thresholds to prevent boredom. In contrast, companies with moderate or high disengagement rates both exceed operator utilization capacity and generate prolonged wait times as more trucks are controlled. These findings suggest that calibrating remote truck control to human capabilities will be challenging. A sensitivity analysis suggests that accurately estimating disengagement rates will be crucial for model accuracy and predictive performance.
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- PAR ID:
- 10532560
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Proceedings of the Human Factors and Ergonomics Society Annual Meeting
- Volume:
- 68
- Issue:
- 1
- ISSN:
- 1071-1813
- Format(s):
- Medium: X Size: p. 947-948
- Size(s):
- p. 947-948
- Sponsoring Org:
- National Science Foundation
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