Laurel Paget-Seekins

Examining regulatory reform for bus operations in Latin America

Governments in Latin American cities are pursuing regulatory reforms to address the negative externalities of informal public transportation service providers. This is achieved by regulatory actions that work to consolidate many small operators competing “in the market” into fewer larger companies competing “for the market.” This reform addresses problems in the previous phase of the regulatory cycle, but requires a larger role for public authorities. The cities of Bogotá, Santiago and Mexico City took different approaches and have achieved different levels of formalization. Under these cities’ new regulatory regimes, bus companies have consolidated and public authorities now rely on renegotiation of contracts instead of open rebidding. However, industry formalization increases costs, requiring public subsidy or higher fares, and puts financial pressure on the public sector. These results imply a continued instability in the regulatory cycle; without increased public sector capacity, it is possible that large, entrenched operators and increasing costs will create a new market opening for informal service.

Estimation of Short-Term Bus Travel Time by Using Low-Resolution Automated Vehicle Location Data

Estimation of short-term bus travel time is an essential component of effective intelligent transportation systems (ITS), including traveler information systems and transit signal priority (TSP) strategies. Several technologies, such as automated vehicle location (AVL) systems, can provide real-time information for estimation of bus travel time. However, low resolution of data from such technologies presents a challenge to accurate estimation of travel time. Several models for estimation of bus travel time at signalized urban arterials were developed and tested. These models used low-frequency AVL data and required only knowledge of network specifications such as locations of bus stops and intersections. First, a linear regression model was developed; it decomposed total travel time into its components, including running travel time, dwell time at bus stops, and delay at signalized intersections. Second, various machine learning models, including support vector regression (SVR) with nonlinear kernel, ridge, Lasso, decision tree, and Bayesian ridge were trained by using Python libraries such as scikit-learn and evaluated. A segment of Washington Street in Boston, Massachusetts, was selected as the study site. The results indicate that the SVR model outperformed other regression models in generalized error measures, in particular those of mean absolute error and root mean square error. The findings of this study can lead to improved traveler information systems and more-efficient TSP strategies and, overall, can contribute to better transit quality of service.

Atlanta: Scarcity and Abundance

The Atlanta, Georgia metropolitan region exists solely as the result of the intersection of transport infrastructure—railways, interstate freeways, and the world’s busiest airport. However, neither vehicle drivers nor public transport passengers are happy with the state of their mobility. Racism, poor planning, and rapid economic growth have worked together to create a low-density urban fabric and a transport network that limits accessibility. Atlanta’s development cannot be separated from its history of racial and class segregation. Mobility in Atlanta suffers from an abundance of vehicles on the roads and a scarcity of public transport services. Atlanta built roads to serve its low-density suburbs, and refused to invest in public transport. Drivers are inconvenienced, but the public transport passengers, mostly poor and Black, have limited access to the majority of the region.

Bus Load Inference and Crowding Performance Evaluation through Disaggregate Analysis of Fare Transaction, Vehicle Location, and Passenger Count Data

Comfort is an important aspect of the transit passenger experience. Crowding can significantly decrease passenger comfort and disrupt service delivery, causing passenger travel times to increase and even resulting in passengers being unable to board an arriving vehicle. This research explores the use of automatically collected vehicle location data, fare transaction data, and passenger origin–destination inference to measure crowding on buses. Three model components are involved: scaling vehicle trip-level origin–destination transfer data, measuring crowding as perceived by passengers through performance measures defined for this purpose, and determining the sources of crowding. The latter is important to identify the most effective means of addressing crowding in each case. The models are tested on data from the Massachusetts Bay Transportation Authority, and examples of graphical applications already being used by planners are presented.

The Youth Pass: A Study of the Conflicts Between Ideal and Practical Research Design in a Pilot Social Program

Public transportation agencies provide reduced fares to seniors, students, and disabled people, but only infrequently provide discounts to low-income members of the general population. A major reason for this is that it is difficult and labor-intensive for transit agencies to determine who is of low income. To address societal need and pilot the feasibility of such a program, the Massachusetts Bay Transportation Authority (MBTA) piloted a program for young people who were unable to receive reduced fares in another way. The MBTA partnered with local municipalities, and applicants proved their eligibility for the program through partner offices. The research requirements in the program provided adequate data to evaluate the effects of the program, but the requirements themselves negatively affected participation and attrition in the pilot. The ways the research affected participation are explored in detail, particularly the attrition rate of subjects throughout the study. It was found that the research requirements disproportionately affected those of very low income as well as African-American and Hispanic participants. Using the data from the pilot, the MBTA decided to implement a full youth pass program benefiting those populations without the barriers of the pilot.

Campaign for the 2012 Transportation Referendum in Atlanta, Georgia: A Region Divided

Following the national trend toward funding transportation with local sales taxes, the Atlanta, Georgia, metropolitan region voted on an

Bus rapid transit as a neoliberal contradiction

8.5 billion referendum in July 2012. Despite bipartisan and biracial support from the political elites and an

The publicness of public transport: The changing nature of public transport in Latin American cities

8 million campaign by the business community, the referendum failed with less than 40% of the vote. Although the majority of residents in the metropolitan Atlanta region agrees that there is a transportation problem, not everyone agrees on how to define the problem or the solutions. An examination of three competing discourses—congestion, choice, and equity—framing transportation in Atlanta explained why the referendum failed. Polling data, participant observation, and examination of campaign materials were used to describe the interplay of the discourses and their public acceptance. Recommendations are offered for building consensus around transportation moving forward in Atlanta and other regions, and what this consensus will mean for the growing trend of using sales tax referendums to fund transportation projects is addressed.