High-Dimensional Data-Driven Energy Optimization for MultiModal Transit Agencies

Pugliese, Philip; Dubey, Abhishek; Laszka, Aron; Chen, Yuche

doi:10.2172/1824219

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DOE OSTI · 1824219

High-Dimensional Data-Driven Energy Optimization for MultiModal Transit Agencies

Pugliese, Philip·Dubey, Abhishek·Laszka, Aron·Chen, Yuche

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Abstract

Transportation accounts for 28% of the total energy use in the United States and as such, it is responsible for immense environmental impact, including urban air pollution and greenhouse gas emissions, and may pose a severe threat to energy security. As we encourage mode shift from personal vehicles to public transit, it is important to consider that public transit systems still require substantial amounts of energy; for example, public bus transit services in the U.S. are responsible for at least 19.7 million metric tons of CO 2 emission annually. As such it is absolutely crucial that we study the bottlenecks to energy efficiency in public transit and develop new algorithms that can help the public transit agencies, especially those that are still operating mixed fleets, which may consist of Electric vehicles (EVs), hybrids (HEVs), and internal combustion engine vehicles (ICEVs), optimize the operations by deciding which vehicles are assigned to serving which transit trips.

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29 ENERGY PLANNING, POLICY, AND ECONOMY 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION

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Pugliese, Philip, Dubey, Abhishek, Laszka, Aron, Chen, Yuche. 2020-11-01. High-Dimensional Data-Driven Energy Optimization for MultiModal Transit Agencies. https://doi.org/10.2172/1824219

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