Evaluating online surveys for public transit agencies using a prompted recall approach

Public transit agencies have traditionally relied on manually collected customer surveys to understand travel behavior and customer satisfaction. With formerly manually collected data such as ridership and running times now being automatically collected, there exists an opportunity to simplify surveys using this automatically collected information. This thesis evaluates an online approach to conduct customer surveys at a public transit agency by linking prior trip history into the survey. It also tests the prompted recall survey approach, where the personalized survey displays a prior trip segment and asks about the journey made by the respondent. The Massachusetts Bay Transportation Authority (MBTA), Boston's public transit agency, was used as a case study to develop a customer panel and test the online survey approach with prompted recall. The research showed that verifying a trip was made in the previous week significantly increased the chances of survey response having an associated trip record. Confirming that a recent trip was made by the respondent increased the rate of matching surveyed journeys to fare transaction data from 26.7% of individuals with no recent trip to 64.2% for individuals with a recent trip. Prompted recall had a slightly higher match rate of 67.3% of individuals, but the rate of partial matches using the prompted recall approach was significantly higher at 88%. Some missed matches may be due to inaccurate or incomplete records in fare transaction records, and solutions to these issues may increase the percentage of matches through the prompted recall approach. This result shows promise for transit agencies that may look to target surveys towards individuals using specific lines or routes. The success of this approach was primarily due to the construction of the survey, which allowed for previous trip records to be analyzed prior to subsequent survey distribution, and therefore should be used as one way to increase the quantity and quality of survey responses.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Civil and Environmental Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2014

Author dc:creator

• Chow, William, S.M. Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• John P. Attanucci.

• Civil and Environmental Engineering.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/89853

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/89853