Valuation of Urban Public Bus Electrification with Open Data and Physics-Informed Machine Learning

Abstract

This research provides a novel framework to estimate the economic, environmental, and social values of electrifying public transit buses, for cities across the world, based on open-source data. Electric buses are a compelling candidate to replace diesel buses for their environmental and social benefits. However, the state-of-art models to evaluate the value of bus electrification require granular and bespoke data on bus operation that can be difficult to procure. This strict requirement on data and modeling can hinder potential collaborators on bus electrification, such as electric vehicle manufacturers or electricity providers, from selecting the candidate public transit agencies in the initial phase of the market investigation. Our valuation tool uses General Transit Feed Specification, a standard data format used by transit agencies worldwide, to provide high-level guidance on developing a prioritization strategy for electrifying a bus fleet. We develop physics-informed machine learning models to evaluate energy consumption, carbon emissions, health impacts, and the total cost of ownership for each transit route. We demonstrate our tool with a case study of the bus lines in the Greater Boston and Milan metropolitan areas. While the capital cost of electric buses and the price of diesel fuel may impact the economic benefit of electrification, we expect significant improvements in health and environmental performances. We estimate about 50% health cost reduction by electrifying only 4 routes out of 15 to 19 routes in each city. For Boston and Milan, some routes are estimated to produce less than 5% of the diesel bus emissions and 14%, respectively, for which investment in electrification can be prioritized.