Massachusetts Bay Transportation Authority
The Massachusetts Bay Transportation Authority (abbreviated MBTA and known colloquially as "the T") is the public agency responsible for operating most public transportation services in Greater Boston, Massachusetts. Earlier modes of public transportation in Boston were independently owned and operated; many were first folded into a single agency with the formation of the Metropolitan Transit Authority (MTA) in 1947. The MTA was replaced in 1964 with the present-day MBTA, which was established as an individual department within the Commonwealth of Massachusetts before becoming a division of the Massachusetts Department of Transportation (MassDOT) in 2009.
Climate Change Vulnerability Dashboard
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.
Improving Efficiency of Crew Scheduling
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.
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.
Inference of Public Transportation Trip Destinations by Using Fare Transaction and Vehicle Location Data: Dynamic Programming Approach
Gabriel E. Sanchez-Martinez
Transportation Research Record
Origin-destination matrices provide vital information for service planning, operations planning, and performance measurement of public transportation systems. In recent years, methodological advances have been made in the estimation of origin-destination matrices from disaggregate fare transaction and vehicle location data. Unlike manual origin-destination surveys, these methods provide nearly complete spatial and temporal coverage at minimal marginal cost. Early models inferred destinations on the basis of the proximity of possible destinations to the next origin and disregarded the effect of waiting time, in-vehicle time, and the number of transfers on path choice. The research reported here formulated a dynamic programming model that inferred destinations of public transportation trips on the basis of a generalized disutility minimization objective. The model inferred paths and transfers on multileg journeys and worked on systems that served a mix of gated stations and ungated stops. The model is being used to infer destinations of public transportation trips in Boston, Massachusetts, and is producing better results than could be obtained with earlier models.
Shuttle Planning for Link Closures in Urban Public Transport Networks
Evelien van der Hurk, Haris N. Koutsopoulos, Nigel H.M. Wilson, Leo G. Kroon and Gabor Maroti
Urban public transport systems must periodically close certain links for maintenance, which can have significant effects on the service provided to passengers. In practice, the effects of closures are mitigated by replacing the closed links with a simple shuttle service. However, alternative shuttle services could reduce inconvenience at a lower operating cost. This paper proposes a model to select shuttle lines and frequencies under budget constraints. We propose a new formulation that allows a minimal frequency restriction on any line that is operated and minimizes passenger inconvenience cost, which includes transfers and frequency-dependent waiting time costs. This model is applied to a shuttle design problem based on a real-world case study of the Massachusetts Bay Transportation Authority network of Boston, Massachusetts. The results show that additional shuttle routes can reduce passenger delay compared to the standard industry practice, while also distributing delay more equally over passengers, at the same operating budget. The results are robust under different assumptions about passenger route choice behavior. Computational experiments show that the proposed formulation, coupled with a preprocessing step, can be solved faster than prior formulations.
Uncertainty in Bus Arrival Time Predictions: Treating Heteroscedasticity with a Meta-Model Approach
Aidan O'Sullivan, Francisco C. Pereira, Jinhua Zhao, and Haris N. Koutsopoulos
Transactions on Intelligent Transportation Systems
Arrival time predictions for the next available bus or train are a key component of modern Traveller Information Systems (TIS). A great deal of research has been conducted within the ITS community developing an assortment of different algorithms that seek to increase the accuracy of these predictions. However, the inherent stochastic and non-linear nature of these systems, particularly in the case of bus transport, means that these predictions suffer from variable sources of error, stemming from variations in weather conditions, bus bunching and numerous other sources. In this paper we tackle the issue of uncertainty in bus arrival time predictions using an alternative approach. Rather than endeavour to develop a superior method for prediction we take existing predictions from a TIS and treat the algorithm generating them as a black box. The presence of heteroscedasticity in the predictions is demonstrated and then a meta-model approach deployed that augments existing predictive systems using quantile regression to place bounds on the associated error. As a case study this approach is applied to data from a real-world TIS in Boston. This method allows bounds on the predicted arrival time to be estimated, which give a measure of the uncertainty associated with the individual predictions. This represents to the best of our knowledge the first application of methods to handle the uncertainty in bus arrival times that explicitly takes into account the inherent heteroscedasticity. The meta-model approach is agnostic to the process generating the predictions which ensures the methodology is implementable in any system.
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.
Nick Caros is a Ph.D. candidate at MIT interested in how urban mobility providers and public transit agencies can adapt to the future of work. He is a graduate of the University of British Columbia and New York University, where his research focused on travel behavior and the simulation of modular autonomous vehicle mobility services. Prior to joining MIT, Nick worked as a Transportation Planner for Stantec in New York City, modeling proposed toll facilities across the US and developing strategies for traffic calming on local streets. Outside of school, he can be found cooking Greek food, reading science fiction and exploring New England.
Ehab comes from a small town in Giza governorate, Egypt and is a second-year student in the Master in City Planning and Master of Science in Transportation programs at MIT. He holds a BS in Urban Studies and a BA in History from Cornell University, where his transit-related research explored the use of automatic vehicle location data to estimate bus transit delay. He has interned at the District Department of Transportation and the Washington Metropolitan Area Transit Authority in Washington, D.C., where he worked on a variety of transit performance and travel behaviour research projects. In his free time and when procrastinating, Ehab enjoys biking, making Middle Eastern desserts, and reading history.
Rubén is a second year in the dual Master of City Planning and Masters of Science in Transportation degree program. He is an alumnus from the Ohio State University, having a B.S. in Civil Engineering. In his undergraduate career, he researched Origin-Destination algorithms and interned in Traffic Engineering. His current research includes using clustering algorithms to better understand the customer base for the MBTA. He is also researching corporate fare policies that will accommodate the new fare structure for the MBTA. Rubén spends his free time playing squash and exploring new places.
John is a first-year student in the Master of Science in Transportation program. Prior to arriving at MIT, he attended the Ohio State University in Columbus, OH and graduated with a B.S. in Civil Engineering. While there, he researched the simulation of integrated bus rapid transit and freight/logistics corridors as well as the origin-destination flows of campus- and city-wide bus networks in Columbus. His internship experience includes transportation consulting and the construction of the TEXRail commuter rail line in Fort Worth, TX. In his free time, John enjoys exploring places by rail and writing songs on the guitar.”
Michael V. Martello
Mike is a second year Masters of Science student primarily studying geotechnical engineering. He is a recent graduate of Manhattan College, where he earned a Bachelor of Science in Civil Engineering. During his undergraduate studies, Mike conducted research investigating the correlation between tunnel boring machine (TBM) operational parameters and surface settlements caused by tunneling. He also has a wide range of internship experience, and has industry exposure to construction management, structural design, traffic engineering, and geotechnical engineering. Mike’s current research is focused on providing a flood resilience assessment framework and climate change adaptation strategies for the MBTA and their assets. Apart from engineering, Mike enjoys biking through Boston, making espresso, and exploring other academic disciplines, particularly sociology and philosophy.
Qingyi is a student in the Interdepartmental Ph.D. in Transportation program. Her current research involves optimizing the scheduling process of bus operators at the MBTA to reduce overtime requested and increase service reliability. Prior to MIT, she graduated with a Bachelor’s of Applied Science in Engineering Science from the University of Toronto and interned with the Big Data Team at the City of Toronto Transportation Services for a year. Her undergraduate research and work experience includes demand modeling, trajectory mining and traffic volume predictions.