Routing Autonomous Trucks on Dedicate Lanes- Phase 2
Trucks are known to have a significant impact on congestion during traffic peak hours due to their size and slower dynamics. Human operated trucks for freight transport are faced with two constraints: those imposed by the service demand and those imposed by the human driver. For long haul operations, for example, truck drivers must meet the constraints of hours of service. For short haul they must meet family and personal constraints which often do not allow them to operate during odd hours. With automation the human constraints are removed which opens the way to view truck routing and scheduling under different and more flexible constraints. The major problem faced by automated trucks operating with the rest of traffic, however, is safety as due to the different sizes involved the sensing problem is more challenging and potential accidents can be catastrophic. Moving trucks from times of high congestion to times of no congestion will bring considerable benefits to trucking companies as well as to all other users of the road network, as fewer trucks will be operating during peak traffic hours. In addition, trucking companies that are short of truck drivers will be able to operate without disruptions and without human imposed constraints, saving on labor costs. During the first phase of the project, the research team developed microscopic traffic simulation model which the team validated using real data from I-710. The network considered was part of I-710 and the team assumed as a first step single origin-destination (OD) flows. The team considered the scenario where trucks sharing the same road network as passenger cars become automated and operate on dedicated truck lanes at times that the traffic demand is very low, so that lanes can be switched dynamically to dedicated automated truck lanes without affecting traffic. By doing so we can keep the automated trucks separated from manually driven vehicles, thereby addressing the issue of safety. The ongoing phase 1 study shows that by removing a number of trucks which are about 0.4% of all vehicles during a high peak traffic and have them automated and operating on dynamically dedicated lanes during off peak traffic the travel time for trucks is reduced by 4.5% while the travel time of passenger vehicles during the high peak traffic decreases by about 3%. These preliminary findings suggest that temporal rescheduling of freight demand, combined with dynamic lane management, could improve both freight and overall network performance. In phase 1 the team simply used the traffic simulator to test their ad hoc approach of moving trucks from high peak to low peak traffic without any form of optimization. In phase 2 the team plans to extend the approach as follows: (1) The team will expand the road network to include some of the most popular truck routes covering short medium and long-haul scenarios. The issue of parking and refueling in the absence of driver will also be addressed. (2) The team will extend the results of phase 1 to multiple interacting OD pairs, allowing the framework to capture more realistic freight demand patterns and network-level coordination effects. (3) The team considers the case of truck platoons which will include fully automated truck platoons but also the more realistic case where the first truck in the platoon has a human driver. In other words, the lead truck will be driven by a human driving and following trucks will be electronically connected and fully automated. Truck platooning is an attractive concept as it has shown to have the potential of reducing aerodynamic drag and contribute to significant fuel savings. (4) The team plans to optimize their decisions of temporal rescheduling of freight demand, combined with dynamic lane management to achieve the best possible outcome. The team views the problem as assigning loads in 2 dimensions temporal and spatial in a way that reduces travel time and lowers fuel cost for both trucks and passenger vehicles.
Language
- English
Project
- Status: Active
- Funding: $80,000.00
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Contract Numbers:
69A3551747109
PSR-25-SP08
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Sponsor Organizations:
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Managing Organizations:
Pacific Southwest Region University Transportation Center
University of Southern California
Los Angeles, CA United States 90089METRANS Transportation Consortium
University of Southern California
Los Angeles, CA United States -
Project Managers:
Hong, Jennifer
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Performing Organizations:
University of Southern California, Los Angeles
University Park Campus
Los Angeles, CA United States 90089 -
Principal Investigators:
Ioannou, Petros
- Start Date: 20260515
- Expected Completion Date: 20270516
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers
Subject/Index Terms
- TRT Terms: Autonomous vehicles; Routes and routing; Scheduling; Traffic platooning; Traffic simulation; Truck lanes; Truck traffic; Vehicle mix
- Subject Areas: Freight Transportation; Highways; Motor Carriers; Operations and Traffic Management; Planning and Forecasting;
Filing Info
- Accession Number: 01996388
- Record Type: Research project
- Source Agency: Pacific Southwest Region University Transportation Center
- Contract Numbers: 69A3551747109, PSR-25-SP08
- Files: UTC, RIP
- Created Date: Jul 22 2026 5:28PM