Data Driven Analysis of Quality of Service and Demand for Demand Responsive Transportation Systems


Eric Gonzales

Publication Date



This project investigates quality of service measures, specifically on-time performance (OTP), and the potential effects on passenger demand of using ridesourcing to accommodate Americans with Disabilities Act of 1990 (ADA) paratransit trips for demand responsive transportation systems.

A data driven approach is used to analyze just over three years of trip data from the Pioneer Valley Transit Authority (PVTA) to study the effects on the overall system performance and the delays experienced by passengers resulting from a decision to split the demand responsive transit fleet into separate fleets for customers with disabilities and senior citizens who would not otherwise qualify for ADA service. Similar methods were then applied to ADA paratransit trip data from the Massachusetts Bay Transit Authority (MBTA). While there is a general trend between how busy a service is and how much delay there is, this research demonstrates that there are too many factors influencing delay day-to-day to create a reliable delay model. Additionally, the PVTA data demonstrates that efficiency is lost when vehicles within a fleet are restricted to only cover certain trips on the network.

The last part of the project involves analyzing the effect of ridesourcing on paratransit demand. The MBTA is experimenting with a program that allows ADA paratransit customers to choose ridesourcing services such as Uber and Lyft for a limited number of subsidized trips. Data from participants in this pilot program was studied to better understand how their travel demand patterns have changed as a result of the pilot program. Ridesourcing for some passengers does effect travel behaviors and it was observed in the data that a passenger’s need for a wheelchair accessible van can be used to predict how ridesourcing will affect their paratransit travel behavior.

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