Using Data Analytics to Optimize Public Transportation on a College Campus

Kurt Zimmer · Hasan Kurban · Mark Jenne · Logan P. Keating · Perry J Maull · Mehmet M Dalkilic

9 citations0/0 files storedIEEE International Conference on Data Science and Advanced Analytics (DSAA)

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Transportation Planning and Optimization Traffic Prediction and Management Techniques Urban Transport Systems Analysis

Abstract

Using a large volume of bus data in the form of GPS coordinates (over 100 million data points) and automated passenger count data (over 1 million data points) we have developed (1) a system of analysis and prediction of future public transportation demand (2) a new model that uses concepts specific to college campuses that maximizes passenger satisfaction. Using these concepts we improve service of a model college public transportation service and more specifically the Indiana University Campus Bus Service (IUCBS).

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References

2010Behavioral intentions of public transit passengers—The roles of service quality, perceived value, satisfaction and involvement 2013A review of urban transportation network design problems 2008Transit network design and scheduling: A global review 1986Bus network design 2008Public transit user satisfaction: Variability and policy implications 2006Assessing tourist behavioral intentions through perceived service quality and customer satisfaction 2005A time–space network based exact optimization model for multi-depot bus scheduling 1991An AI‐based approach for transit route system planning and design

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