Big Data Intelligence for Eco-Friendly Bus Routing: A Case Study

Proceedings of the 2015 Industrial and Systems Engineering Research Conference S. Cetinkaya and J. K. Ryan, eds.

Matthew Snead, David Holt, Michael Mullen, Michelle Londa, Tongdan Jin
Ingram School of Engineering
Texas State University
San Marcos, TX 78666, USA


Public bus transportation is a vital service to all populated areas, and much of this service relies on structured driving routes. As buses move they consume large amounts of fossil fuels, impacting the environment and incurring operating costs. The goal of this paper is to propose a sustainable bus routing solution that minimizes the annual fuel consumption with guaranteed quality of services. The study is carried out based on a bus fleet that currently serves more than 34,000 students at a public university in Texas. Utilizing a wealth of historical data from the provider’s webpage, the locations of specific buses are able to be determine as they run on a route. In addition to onsite observations, run-time data are also retrieved from the web site through JavaScript Object Notation and fed into advanced statistical tools for real-time decision making. The base model is constructed within SIMIO, validated, and optimized using add-in functions to identify the most fuel-efficient bus route. The study shows that by synthesizing big data analytics with passengers’ arrival pattern, the fuel efficiency can be improved by 10-20 percent, potentially reducing 15 percent of operating cost and 24 thousand tons of carbon dioxide emissions per year.

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