Planning & Policy

Much of the Transit Lab research has been focused on developing methods and tools to support planning and decision-making within transit agencies by making better use of data from automated data collection systems, including automatic fare collection, automatic vehicle location and automatic passenger counting systems. The central tool developed as the foundation for these improvements, known as ODX, infers the origin-destination travel patterns for individual public transport passengers on the system, connecting their journey segments into “linked” journeys, which are aggregated to form a seed origin-destination matrix, which is then expanded to represent the aggregate travel of all public transport passengers. The resulting expanded origin-destination matrix forms the foundation for many planning and decision support methods, as well as facilitating measurement of system performance attuned to the passengers’ perception of service quality, including service reliability at the origin-destination level, which is a critical element in modal choice for many individuals.

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Specific methods building on ODX include bus network design (with applications in London and Boston), decision support for rail and bus service control both under disrupted and normal conditions (with applications in London, Boston and Hong Kong), and the design of integrated urban transport systems including both fixed route, scheduled bus and demand responsive, shared-ride and autonomous vehicle operations (with applications in London, and Chicago).

Featured Projects

Public Transportation

Evaluating Operating Strategies to Incorporate Rapid Transit Network Extension

Agency:

MBTA

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.

Public Transportation

Evaluation of TNC Interactions with Public Transit

Agency:

CTA

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.

Public Transportation

Tools for Evaluating Future Operations and Design of Demand Management

Agency:

MTR

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.

Featured Publications

Train

Schedule-free High-Frequency Transit Operations

Authors:

Gabriel E. Sanchez-Martinez, Nigel H.M. Wilson and Haris N. Koutsopoulos

Journal:

Public Transport

Date:

2017

High-frequency transit systems are essential for the socioeconomic and environmental well-being of large and dense cities. The planning and control of their operations are important determinants of service quality. Although headway and optimization-based control strategies generally outperform schedule-adherence strategies, high-frequency operations are mostly planned with schedules, in part because operators must observe resource constraints (neglected by most control strategies) while planning and delivering service. This research develops a schedule-free paradigm for high-frequency transit operations, in which trip sequences and departure times are optimized in real-time, employing stop-skipping strategies and utilizing real-time information to maximize service quality while satisfying operator resource constraints. Following a discussion of possible methodological approaches, a simple methodology is applied to operate a simulated transit service without schedules. Results demonstrate the feasibility of the new paradigm.

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Train

Optimal Design of Promotion Based Demand Management Strategies in Urban Rail Systems

Authors:

Zhenliang Ma and Haris N. Koutsopoulos

Journal:

Transportation Research Part C

Date:

2019

Travel demand management (TDM) is used for managing congestion in urban areas. While TDM is well studied for car traffic, its application in transit is still emerging. Well-structured transit TDM approaches can help agencies better manage the available system capacity when the opportunity and investment to expand are limited. However, transit systems are complex and the design of a TDM scheme, deciding when, where, and how much discount or surcharge is implemented, is not trivial. The paper proposes a general framework for the optimal design of promotion based TDM strategies in urban rail systems. The framework consists of two major components: network performance and optimization. The network performance model updates the origin-destination (OD) demand based on the response to the promotion strategy, assigns it to the network, and estimates performance metrics. The optimization model allocates resources to maximize promotion performance in a cost effective way by better targeting users whose behavioral response to the promotion improves system performance. The optimal design of promotion strategies is facilitated by the availability of smart card (automated fare collection, AFC) data. The proposed approach is demonstrated with data from a busy urban rail system. The results illustrate the value of the method, compare the effectiveness of different strategies, and highlight the limits of the effectiveness of such strategies.

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Train

How does Ridesourcing Substitute for Public Transit? A Geospatial Perspective in Chengdu, China

Authors:

Hui Kong, Xiaohu Zhang and Jinhua Zhao

Journal:

Journal of Transport Geography

Date:

2020

The explosive growth of ridesourcing services has stimulated a debate on whether they represent a net substitute for or a complement to public transit. Among the empirical evidence that supports discussion of the net effect at the city level, analysis at the disaggregated level from a geospatial perspective is lacking. It remains unexplored the spatiotemporal pattern of ridesourcing's effect on public transit, and the factors that impact the effect. Using DiDi Chuxing data in Chengdu, China, this paper develops a three-level structure to recognize the potential substitution or complementary effects of ridesourcing on public transit. Furthermore, this paper investigates the effects through exploratory spatiotemporal data analysis and examines the factors influencing the degree of substitution via linear, spatial autoregressive, and zero-inflated beta regression models. The results show that 33.1% of DiDi trips have the potential to substitute for public transit. The substitution rate is higher during the day (8:00-18:00), and the trend follows changes in public transit coverage. The substitution effect is more exhibited in the city center and the areas covered by the subway, while the complementary effect is more exhibited in suburban areas as public transit has poor coverage. Further examination of the factors impacting the relationship indicates that housing price is positively associated with the substitution rate, and distance to the nearest subway station has a negative association with it, while the effects of most built environment factors become insignificant in zero-inflated beta regression. Based on these findings, policy implications are drawn regarding the partnership between transit agencies and ridesourcing companies, the spatially differentiated policies in the central and suburban areas, and the potential problems in providing ridesourcing service to the economically disadvantaged population.

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