MIT Transit Lab

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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.

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.

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.

Investigating the effects of climate change on rail rapid transit systems, researchers at Transit Lab created a web-based analysis tool for evaluating climate change resilience and vulnerability for the MBTA. The analysis tool utilizes AFC data, track geometry, lowest critical elevations, and the latest coastal flood projections to model the performance impacts of coastal flood events under sea level rise. The analysis tool will be utilized by the MBTA to evaluate the climate resilience of projects proposed for their annual Capital Investment Plan.

The Transit Lab is working in anticipation of the MBTA’s Green Line Extension (GLX) to analyze the ways in which different operational strategies and conventions intersect to create viable service plans. The inherent complexity of the MBTA Green Line — a hybrid subway/streetcar with multiple branches and termini — demands an approach to service planning that goes above and beyond traditional orthodoxy. Demand models, simulation, evaluation of future states of infrastructure are among the methods being used to assess the desirability and responsiveness of potential solutions.

The Transit Lab used mobile ticketing data for the MBTA commuter rail system (m-Ticket) to market monthly and weekend passes to riders. Using ridership information, the lab was able to identify riders that would be most likely to purchase a pass and sent an informational nudge. New technological developments in fare payments facilitates more direct marketing campaigns to riders who would most benefit from certain products.

Transit Lab students have looked for ways to improve the agency’s internal resource planning by leveraging data created by new tools. With the roll out of HASTUS Daily operator scheduling software at the MBTA, students helped the agency make use of the information from this new data source and have created models to forecast absences, overtime availability and used such models to aid in the optimization of extraboard scheduling.

The Transit Lab helped encourage MIT to run a pilot program on a Mobility Pass: a universal, fully subsidized transit pass for all benefit-eligible employees. MIT paid the MBTA (local transit agency) based on the transactions made by employees using AFC data. After analyzing the results of the pilot program, the Transit Lab estimated the costs and benefits of expanding the Mobility Pass to all Corporate Program organizations for the MBTA. The analysis has helped the T understand the risks and benefits of offering a new product before launching the product.

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.

With the future expansion of the network there is a need for better tools to answer what if operating questions and design and evaluate strategies to deal with disruptions, increases in demand, etc. This activity will build capabilities, based on commercial tools, to evaluate alternatives operating strategies, and strategies to mitigate and relieve system congestion, either recurrent or due to incidents.

Information has long been recognized as an important instrument for behavioral change. However, generic information provision often proves ineffective. This project aims to develop a framework toward individualized information provision to MTR users. Effective personalization includes three components: Sparse use of information; Deep Customization; Data Infrastructure and Predictive Analytics. One methodological component is the demand prediction at the individualized customer level. The research will develop the general requirements for provision of individualized customer information, evaluate technological alternatives for the communication of information, and design potential experiments to evaluate their effectiveness. Customer segmentation will be used as the means for better understanding different passenger groups and their information needs.

The research is looking into the problem of assigning individual passengers to train trips. A probabilistic model utilizing detailed AFC and train movement data is under development, incorporating capacity constraints of individual vehicles. The model estimates the probability that a given passenger boarded a specific train itinerary and the probability of being denied boarding. Such a model can be used for the assessment of the capacity utilization of the system, development of detailed performance metrics from the passengers’ point of view (for example, crowding), identification of individual journey time components, and estimation of the (expected) number of passengers denied boarding, as well information that can used by travel planners.

At a strategic level, JTL is developing simulation and analysis platforms to understand and communicate the impact of new infrastructure and transformative technologies such as autonomous vehicles. Some of these methodologies and tools have been featured at the UK Science Museum’s Our Lives in Data exhibit. The strong record and commitment of JTL and TfL make this an ideal research partnership for understanding and shaping the future of urban transport.

This research project created a dynamic passenger assignment model designed to accurately and efficiently model the impact on the Underground network of passenger behavior during unplanned disruptions. Taking into account the limited knowledge of system conditions that passengers have during disruptions allows the model to identify disruption impacts that existing models may omit.

Public transportation investment is made because of its potential benefits to the society. Properly identifying and measuring these benefits has been the central concern in rail transit decision making. In this research, we focus on the social dimension of rail transit benefits through the Tren Urbano case study. The objectives are: (1) to characterize the social benefits of public transportation, especially rail transit systems, in terms of job accessibility provision; (2) to analyze the distributional effects of the Tren Urbano benefits among geographical locations and social groups in the SJMR; and (3) to examine the effects of supplementary transportation and land use strategies in further enhancing job accessibility in the region.

Since Tren Urbano is the first privately contracted rail system,
which will operate in North America, this project represents the first attempt to analyze the
impact of such incentive and penalty clauses on service quality.
The results from the comparison between the optimal control strategies with the
Tren Urbano hypothetical solution showed that the optimal control strategies resulted in
greater passenger waiting time savings, ranging from 4%-33%. Proposals are developed
to modify the contract terms as well as the incentive/penalty structure in order to create a
set of new contract provisions where the contractor's interest are more perfectly aligned
with the owner's objectives and passengers' expectations.

This project presents procurement strategies for control, operation and maintenance of the Tren Urbano system beyond the first five years of O & M. Issues that will influence the procurement decision-making process are discussed and analyzed. Five possible O & M alternatives are examined. Their merits and demerits are weighed, and strategies for preparing each alternative are devised.

This study surveys 182 seniors age 60 and over at eleven transit-accessible senior centers in the
Boston Metropolitan Area. The centers were chosen for their diversity in terms of income, proximity to public transit, and level of transit use in the community. The objectives of the survey were to find out the frequency with which seniors take public transit; what they use it for; how they make mode choice decisions; the extent and cause of their public transit fear; and if
their fear impacts their ridership patterns. The data was analyzed by using various statistical
techniques: counts, averages, cross-tabulation, correlation coefficients, regression, and hypothesis testing.

The goal of this project was to provide a framework and a working model for automating workflow in
large-scale engineering projects. A web based workflow management system for construction
management was presented in the context of Tren Urbano (Urban Train) Project. The system is designed to recognize that the Project's workflow process often involves geographically distributed collaboration, and it requires an effective and efficient mechanism for easy and secure
information access and exchange. As a web
based application, the system also has such build-in features as intelligent workflow routing, automatic document control, and digital signature to provide a virtual space for workflow management.

The confluence of rail transit and real estate development opportunities in San Juan, Puerto Rico raises a number of questions related to the future built form of the city and surrounding region. As the construction of Tren Urbano nears completion many advocate transit supportive development that captures the social benefits that results for public investment in Tren Urbano. Private developers have been slow to react to the potential for transit supportive real housing.