Valuation of Public Bus Electrification with Open Data

TL;DR

This paper introduces a scalable, open-data-based framework for evaluating the economic, environmental, and social benefits of electrifying public bus fleets worldwide, using machine learning models and standard transit data.

Contribution

It presents a novel valuation tool that leverages open-source transit data and physics-informed machine learning to assess bus electrification impacts without requiring detailed operational data.

Findings

Effective evaluation of bus electrification benefits using open data

Scalable approach demonstrated in Boston and Milan case studies

Provides high-level guidance for prioritizing bus fleet electrification

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 the environmental and social benefits. However, the state-of-art models to evaluate the value of bus electrification are limited in applicability because they require granular and bespoke data on bus operation that can be difficult to procure. 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 the energy consumption, the carbon emissions, the health impacts, and the total cost of ownership for each transit route. We demonstrate the scalability of our tool with a case study of the bus lines in the Greater Boston and Milan metropolitan areas.