Enabling Decision-Making in Battery Electric Bus Deployment through Interactive Visualization

Xiaoyue Cathy Liu, University of Utah



The transit industry is rapidly transitioning to battery-electric fleets because of the direct environmental and financial benefits they could offer such as zero emissions, less noise, lower maintenance costs. Yet the unique spatiotemporal characteristics associated with transit system, charging requirements, as well as various objectives when prioritizing the fleet electrification, requires the system operators and/or decision-makers to fully understand the status of transit system and energy/power system, in order to make informed deployment decisions. A recently completed NITC project No. 1222 titled An Electric Bus Deployment Framework for Improved Air Quality and Transit Operational Efficiency, developed a bi-objective spatio-temporal optimization model for the strategic deployment of Battery Electric Bus (BEB) to minimize the cost of purchasing BEBs, on-route and in-depot charging stations, and to maximize the environmental equity for disadvantaged populations. As agencies such as Utah Transit Authority (UTA) adopt the model and results, they desire to have a tool that could enable detailed spatiotemporal monitoring of components for the BEB system (e.g. locations of BEBs, the state-of-charge of batteries, charging station energy consumption at each specific timestamp), so that the integration of BEBs into the power/grid system as well as its operating condition could be better understood. To this end, this Translate Research to Practice grant will support the development of a visualization tool that allows transit operators/planners as well as decision-makers to explore the interdependency of the BEB transit system and energy infrastructure in both spatial and temporal dimensions with high resolution. The tool will be built on the scenario-based optimization modeling effort in NITC Project No. 1222, and allow agencies to make phase-wise (short-, mid-, or long-term) decisions based on investment resources and strategic goals. This project will also develop a guidebook to provide step-by-step guidance on data compilation for BEB analysis, model input, model implementation, and results interpretation. It will further detail how the developed visualization tool is structured and designed to ensure results exploration across transit operation and energy consumption. Both the guidebook and the tool will be directly useful to practitioners to easily implement our optimization model for their own transit network, and allow them to build interactive visualizations to assist with decision making.

Project Details

Project Type:
Technology Transfer
Project Status:
In Progress
End Date:
November 30,2022
UTC Grant Cycle:
NITC 16 Translate Research to Practice
UTC Funding: