Map-based Algorithm Visualization with METAL Highway Data

Teresco, James D.; Fathi, Razieh; Ziarek, Lukasz; Bamundo, MariaRose; Pengu, Arjol; Tarbay, Clarice F.

doi:10.1145/3159450.3159583

Cited by 3 publications

(1 citation statement)

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“…Transportation network. We collect the highway network data from the Map-based Educational Tools for Algorithm Learning (METAL) project [18], the road speed data from HERE Maps, and the elevation data from the Shuttle Radar Topography Mission (SRTM) project. We consider a typical origin-destination pair from Atlanta to Nashville, representing 7.5 billion dollars of transported freight [19].…”

Section: A Experimental Setupmentioning

confidence: 99%

Follow the Sun and Go with the Wind: Carbon Footprint Optimized Timely E-Truck Transportation

Lin

Chen

2023

Proceedings of the 14th ACM International Conference on Future Energy Systems

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Carbon footprint optimization (CFO) is important for sustainable heavy-duty e-truck transportation. We consider the CFO problem for timely transportation of e-trucks, where the truck travels from an origin to a destination across a national highway network subject to a deadline. The goal is to minimize the carbon footprint by orchestrating path planning, speed planning, and intermediary charging planning. We first show that it is NP-hard even just to find a feasible CFO solution.We then develop a (1 + ϵF , 1 + ϵ β ) bi-criteria approximation algorithm that achieves a carbon footprint within a ratio of (1+ϵF ) to the minimum with no deadline violation and at most a ratio of (1 + ϵ β ) battery capacity violation (for any positive ϵF and ϵ β ). Its time complexity is polynomial in the size of the highway network, 1/ϵF , and 1/ϵ β . Such algorithmic results are among the best possible unless P=NP. Simulation results based on real-world traces show that our scheme reduces up to 11% carbon footprint as compared to baseline alternatives considering only energy consumption but not carbon footprint.

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Section: A Experimental Setupmentioning

confidence: 99%