Conference proceeding
Dedicated Lane Planning for Autonomous Truck Fleets under Hours of Service Regulations
2024 IEEE Intelligent Vehicles Symposium (IV), pp.2890-2895
06/02/2024
DOI: 10.1109/IV55156.2024.10588454
Abstract
The introduction of automated trucks into the ground freight sector will yield significant transformations, such as the alleviation of labor shortages, the reduction of energy consumption, and the improvement of road safety. Given the current mixed-traffic environment where autonomous vehicles share roads with their human-driven counterparts, support from intelligent transportation infrastructure becomes imperative for ensuring the safe and efficient operations of autonomous trucks. This study proposes an integrated framework for designing dedicated lanes for automated trucks (DLAT) operating under Hours of Service (HOS) Regulations for total cost minimization. This framework considers the truck fleets' routing and scheduling behavioral changes brought by the implementation of DLAT. We formulate a mixed-integer program (MIP) model and propose an origin-destination(OD)-clustering-based iterative algorithm to tackle it. The algorithm contains two parts: the spectral clustering method for OD pairs partitions and DLAT design within each cluster, and the iterative algorithm addressing the dedicated lanes design problem on overlapping links across clusters. Three numerical experiments on the U.S. freight highway network and algorithm demonstrate the effectiveness of time-varying planning of DLAT, corroborating its benefits to stakeholders and providing managerial insights for future implementations.
Details
- Title: Subtitle
- Dedicated Lane Planning for Autonomous Truck Fleets under Hours of Service Regulations
- Creators
- Zhaoming Zeng - University of Michigan–Ann ArborXiaotong Sun - Hong Kong University of Science and TechnologyQi Luo - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- 2024 IEEE Intelligent Vehicles Symposium (IV), pp.2890-2895
- Publisher
- IEEE
- DOI
- 10.1109/IV55156.2024.10588454
- eISSN
- 2642-7214
- Grant note
- National Science Foundation (10.13039/100000001)
- Language
- English
- Date published
- 06/02/2024
- Academic Unit
- Business Analytics
- Record Identifier
- 9984696841102771
Metrics
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