Chicago Transit Authority
The Chicago Transit Authority (CTA) operates the nation's second largest public transportation system and covers the City of Chicago and 35 surrounding suburbs. On an average weekday, approximately 1.6 million rides are taken on the CTA.
The CTA is an independent governmental agency created by state legislation. It began operating on October 1, 1947, after acquiring the properties of the Chicago Rapid Transit Company and the Chicago Surface Lines. On October 1, 1952, CTA became the sole operator of Chicago transit when it purchased the Chicago Motor Coach system.
Unplanned Incident Management for Urban Rail Systems
This project aims to evaluate the impact of unplanned rail disruptions (incidents) on system performance, including passengers response to incidents and how demand and operations of transit services may change. Machine learning techniques are applied to provide decision support for central office and terminal dispatchers to mitigate the congestion caused by incidents.
Customer Segmentation (Mary Rose)
Leveraging data from the Chicago Transit Authority's account-based fare payment system, Transit Lab students have used clustering algorithms to identify dominant transit ridership behaviors in the city and used this as a framework for understanding the impacts of the COVID-19 pandemic on transit. This analysis reveals that even among riders who rode frequently before the pandemic, there were distinct behavioral responses to the pandemic among bus and rail riders, and among peak and off-peak riders, with off-peak bus riders continuing to use the system in much higher numbers than peak rail riders.
Evaluation of TNC Interactions with Public Transit
Given the rapid rise of TNCs such as Uber and Lyft, transit agencies are trying to understand how these services complement and compete with transit trips. The MIT Transit Lab is undertaking a comprehensive review of transit and TNC trips in Chicago to quantify the relationship between the two travel modes and inform public policy going forward.
Bus Performance Visualization Dashboard
Transit Lab students have built a custom dashboard using automatically collected data (APC, AVL, AFC) to visualize the performance of the entire MBTA and CTA bus networks across dozen different performance metrics. Agency staff can filter by route, time period, direction and metrics for quick, detailed queries. In the future, this project will include an identification system to prioritize infrastructure upgrade locations based on performance and ridership.
Customer Loyalty Differences Between Captive and Choice Transit Riders
Jinhua Zhao, Valerie Webb and Punit Shah
Transportation Research Record
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.
The Potential Impact of Automated Data Collection Systems on Urban Public Transport Planning
Nigel H.M. Wilson, Jinhua Zhao and Adam Rahbee
Schedule-Based Modeling of Transportation Networks (Book)
Automated data collection systems are becoming increasingly common in urban public transport systems, both in the US and throughout the developed world. These systems, which include Automatic Vehicle Location (AVL), Automatic Passenger Counting (APC), and Automatic Fare Collection (AFC), are often designed to support specific and fairly narrow functions within the transport agency. However, it is clear that the data obtained from these systems can have wide-ranging applications within public transport, well beyond the design applications. Of particular interest in the planning of public transport is the opportunity to make use of these increasingly ubiquitous databases to develop a better picture of how public transport systems are performing and being used. In some cases, better estimates of certain performance measures and usage attributes may be made at lower cost than by using conventional data collection methods, even though there are important limitations on the detailed attributes typically available from these systems. In other cases it is possible for the first time to estimate important performance attributes, such as those related to reliability and its impacts, which have hitherto been virtually impossible to quantify because of paucity of data. This paper describes two applications, focusing on system usage and passenger behavior, which have been developed jointly between MIT and the Chicago Transit Authority (CTA), taking advantage of CTA's AFC and AVL systems. The specific applications are the estimation of passenger origin-destination matrices for the CTA rail system and the estimation of path choice models for CTA rail passengers. Next steps in the development of further applications for urban public transport systems are also discussed.
Bus Supervision Deployment Strategies and Use of Real-Time Automatic Vehicle Location for Improved Bus Service Reliability
Christopher Pangilinan, Nigel H.M. Wilson and Angela Moore
Transportation Research Record
Bus service reliability has long been a top concern for transit agencies and their customers. Improving service reliability, however, has not been easy to accomplish. The use of appropriate recovery times, improved operator training, and better street supervision has produced limited results. Supervision deployment strategies and the use of real-time automatic vehicle location (AVL) information are investigated to improve current supervision practices and enhance bus service reliability. The Chicago (Illinois) Transit Authority's real-time AVL pilot project for Route 20 Madison is the case study for evaluation of the effectiveness of real-time AVL to improve reliability. A simulation model of the route was developed on the basis of archived AVL data and was used to predict the effects on service reliability when real-time AVL information is used in bus supervision. A week-long experiment was carried out both to verify the model and to address the feasibility and scalability of the system. The main conclusion is that real-time AVL does indeed have great potential to improve service reliability. Service restoration strategies previously impossible to execute are now feasible because of this new information stream. However, many obstacles remain to networkwide implementation, including the supervision communications structure and manpower deployment questions. The flood of information into a central control center must also be addressed. Automation techniques and exception-based reporting are strategies to deal with the problem of information overload.
Mary Rose Fissinger
Mary Rose is a student in the Interdepartmental Doctoral Program in Transportation at MIT. She holds a Bachelor of Science degree in Mathematics from Boston College and a Master of Science degree in Civil Systems Engineering from the University of California, Berkeley. After graduating from Berkeley she worked for the Boston-based microtransit startup Bridj. This experience sparked her interest in how individuals evaluate new or innovative transportation options. In conjunction with both the Urban Mobility Lab and the Transit Lab at MIT, Mary Rose works on the partnership with the Chicago Transport Authority. In her spare time, she enjoys running, doing crossword puzzles, and participating in the feminist bookclub she started with a group of friends.
Xiaotong is a student in the Interdepartmental Ph.D. in Transportation program at MIT. He holds a Bachelor of Engineering degree in Traffic Engineering from Tongji University and a Master of Science degree in Transportation Systems Engineering from Cornell University. He is interested in modeling and algorithms design for solving problems in complex urban mobility systems. His current research focuses on incorporating time flexibility in the ridesharing system and public transit network design considering shared mobility service for passengers. Outside of school, Xiaotong loves playing basketball and traveling.
Patrick is a first-year student pursuing a dual Master of Science degree in the Interdepartmental Program in Transportation and the Technology & Policy Program at MIT. Patrick holds a Bachelor of Applied Science in Engineering Science degree from the University of Toronto, and has internship experience as a transit technology consultant with IBI Group. Patrick has focused previous research on improving transit assignment for travel behaviour models, modelling transit fare integration, and developing methods for heterogenous delivery fleet vehicle routing. Outside of school, Patrick enjoys playing ultimate frisbee, canoe tripping, and spending time outdoors.
Baichuan is a graduate student in the Interdepartmental Ph.D. in Transportation program at MIT. Prior to join MIT, he got a B.S. degree from Dept. of Civil Engineering, Tsinghua University in Beijing, awarded with the Tsinghua supreme scholarship (10 out of 3000+ undergraduates). Baichuan’s main research interest is data-driven transportation modeling and demand modeling. His current research focuses on Network Performance Modelling and Bayesian Individual Mobility Prediction base for MTR (Hong Kong). Outside of school, Baichuan enjoys jogging and cooking.