Chicago Transit Authority – MIT Partnership

Funding: Chicago Transit Authority

Urban transportation is rapidly evolving with the introduction of new mobility services, including car sharing, bike sharing, and on-demand taxis and shuttles. These modes may both conflict and complement public transportation. Transit Lab works with Chicago Transit Authority to identify ways in which the new modes can be integrated with public transportation to improve user experience while achieving sustainability.

  • Impacts of congestion pricing on ride-hailing ridership: Evidence from Chicago

    Transportation Research Part A: Policy and Practice
    (
    2023
    )

    To combat congestion, promote sustainable forms of transportation, and support the public transit system, Chicago introduced a congestion pricing policy targeting transportation network company (TNC) services on January 6, 2020. This policy aimed to discourage single-occupant and peak-period TNC travel, particularly in high-congestion areas. Using TNC trip record data collected from the Chicago Data Portal, we quantify the impacts of the congestion pricing policy on TNC ridership in Chicago, differentiating between shared and single-occupant trips. Employing a Difference-in-Differences identification strategy, we find that the implementation of the congestion pricing policy led to an increase in shared TNC trip counts and a much larger decrease in single-occupant trip counts. Overall, the policy implementation is associated with a 7.1% reduction of total TNC pickup trips, a 16.4% increase of shared TNC pickup trips and a 11% reduction of single TNC pickup trips. Given the estimated policy effects, we find that the price elasticity of the TNC trip volume in the downtown areas is roughly -0.48. In terms of spatial variation, we find that the lost TNC trips were mainly trips that began and ended in the central business district. The south side of Chicago, which has a high proportion of African-American and low-income residents, shows evidence of single trip reduction for trips that began or ended in the downtown areas due to the policy implementation, but the policy did not seem to incentivize pooling to or from the downtown areas as effectively in the south side as in other regions of Chicago. Regarding the time-of-day variation, we find that the policy is more effective in encouraging trip sharing for off-peak travels than for peak-time travels. Our research provides local planners and policymakers with valuable insights into the impacts of the congestion pricing policy. The method and findings of this research can also be used for other cities that are considering adopting congestion pricing policies on TNCs in the future.

  • Induction_bus_and_car

    Customer Loyalty Differences Between Captive and Choice Transit Riders

    Journal of the Transportation Research Board
    2415
    ,
    (
    2014
    )

    Traditionally, efforts to increase the customer base of public transportation agencies have focused primarily on attracting first-time users. Customer retention, however, has many benefits not often realized. Loyal customers provide recommendations to others, increase and diversify their use of the service, and do not require acquisition costs associated with new customers. An earlier study identified key drivers of customer loyalty, with the Chicago Transit Authority (CTA) in Illinois as a case study. A customer loyalty model was created with service value, service quality, customer satisfaction, problem experience, and perception of CTA as constructs. The present study examined customer loyalty differences of captive and choice riders. Captive riders had no viable travel alternatives and might have continued to use transit even if unhappy with service. Choice riders chose to use transit after they compared travel options and might have switched to an alternative if service degraded. Captive riders reported experiencing more problems and were more sensitive to problems; each additional problem brought significant drops in service quality ratings. Captive riders tolerated problems and continued to use transit but showed discontent through their ratings of service quality. Service value was insignificant in captive riders’ loyalty decisions because cost–benefit analysis defined service value as irrelevant to them. The relationship between perceptions of CTA and of service quality was stronger for choice riders. If they began the service with high opinions of the transit agency, they were much more likely to have high ratings of service quality than were captive riders.

  • Induction_bus_and_car

    Estimating a Rail Passenger Trip Origin-Destination Matrix Using Automatic Data Collection Systems

    Computer-Aided Civil and Infrastructure Engineering
    22
    ,
    (
    2007
    )

    Automatic data collection (ADC) systems are becoming increasingly common in transit systems throughout the world. Although these ADC systems are often designed to support specific fairly narrow functions, the resulting data can have wide-ranging application, well beyond their design purpose. This article illustrates the potential that ADC systems can provide transit agencies with new rich data sources at low marginal cost, as well as the critical gap between what ADC systems directly offer and what is needed in practice in transit agencies. To close this gap requires data processing and analysis methods with support of technologies such as database management systems (DBMS) and geographic information systems (GIS). This research presents a case study of the automatic fare collection (AFC) system of the Chicago Transit Authority (CTA) rail system and develops a method for inferring rail passenger trip origin-destination (OD) matrices from an origin-only AFC system to replace expensive passenger OD surveys. A software tool is developed to facilitate the method implementation and the results of the application in CTA are reported.

Team Members