Gabriel E. Sánchez-Martínez

Event-Driven Holding Control for High-Frequency Transit

Transit operations are subject to disruptions caused by events. Some events cause unpredictable disruptions (e.g., signal failures in rail transit and traffic accidents along a bus route operating in mixed traffic). Other events, such as concerts or sport contests, cause foreseen disruptions. In some cases, dynamic functions of running times and demand can be used to predict and model disruptions. Past research has explored the use of operations control to respond to disruptions after service deteriorates, assuming constant running times and demand. The proposed framework enables anticipatory control strategies by dynamically modeling expected changes in running times and demand during events. A holding optimization model formulated to capture dynamics is applied to a simulated transit system experiencing disruptions induced by an unforeseen event and a foreseen event. Controlling operations with an awareness of events has little effect in the unforeseen event case but significantly improves performance in the foreseen event case. Erroneous estimates of the time an event will occur can lead to counterproductive control policies.

Inference of Public Transportation Trip Destinations by Using Fare Transaction and Vehicle Location Data: Dynamic Programming Approach

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.

Schedule-free high-frequency transit operations

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.

Improving High-Frequency Transit Performance through Headway-Based Dispatching: Development and Implementation of a Real-Time Decision-Support System on a Multi-Branch Light Rail Line

Service reliability is a major concern for public transportation agencies. Transit services experience natural variability in operations performance, due to factors such as congestion, changes in demand, and operator behavior. This variability leads to irregular headways, resulting in longer passenger waits and decreased effective capacity as gaps in service form. Real-time control strategies allow controllers to regulate service and improve performance. This research tested the effectiveness of a headway-based dispatching strategy at a terminal on the Massachusetts Bay Transportation Authority (MBTA) Green Line in Boston, a complex, four-branch light rail line. Terminal personnel were provided with tablet computers showing departure times optimized by an even-headway policy. When optimized departure times were adhered to, peak period headway variability was reduced by 40%. The average wait was shortened by 15% (30 sec), and the 90th percentile wait was shortened by 21% (90 sec). The results show that adopting headway-based dispatching at terminals of high-frequency lines promises significant benefits to service and passengers if operational changes are accompanied by improved supervision.

Estimation of Passengers Left Behind by Trains in High-Frequency Transit Service Operating Near Capacity

Measuring rail system crowding is important to transit agencies. Crowding data has implications for safety, operations control, service planning, performance measurement, and customer information. This paper proposes a bi-level regression model that transit agencies can use to estimate the number of passengers left behind on a platform by high-frequency trains operating at capacity. Inputs to the model include the number of passenger arrivals between trains and train departure times, which are derived from automatic fare collection and vehicle location data. The data are used to calculate the proposed measure of cumulative capacity shortage, which is shown to have high correlation with the number of passengers left behind. A bi-level regression approach is introduced and applied to calibrate the model parameters based on manual counts of passengers left behind. A case study using data from the Chicago Transit Authority’s Blue Line demonstrates promising results, with an adjusted coefficient of determination of 0.81. The model could be used for post-hoc analysis of crowding performance or, in the context of real-time operations monitoring, for near-term predictions of passengers left behind.

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.

Simulation-Based Comparison of Holding Strategies for a Multibranch Light Rail Service

Light rail transit services face many operational challenges, such as capacity constraints, mixed-traffic interference, and branch junctions. The service plans developed for these lines typically specify a precise schedule for each vehicle. Running time and demand variability, special events, and incidents make it challenging to adhere to schedules. Operators can enact real-time control actions to mitigate delays. This research compared the effectiveness of schedule- and headway-based holding strategies applied en route and at terminals (i.e., dispatching) on a simulation model of the Massachusetts Bay Transportation Authority Green Line, a four-branch light rail line. The effects of control point placement at terminals, along branches, along a central trunk, and in combinations of these three were studied, as were the effects of limiting holding at midroute stations. Holding strategies were compared on the basis of service and passenger-oriented performance. Headway-based holding was found to be a more effective method for ensuring that passengers experienced reasonable wait times within scheduled headways. Holding at terminals appeared to be the most beneficial to passengers; additional holding along the branches and limited holding along the trunk were shown to enhance these benefits. Holding only within the trunk of a multibranch service worsened service because of blockages from held trains.