A framework for evaluating operations control on a metro line: integrating multiple perspectives and automatically collected train and passenger movement data

Transit operations control, the task of implementing the operations plan in daily operations on a metro line, plays a key role in service delivery because it determines the quality of the service experienced by passengers. Yet, it is one of the most poorly understood aspects of rail transit operations. Faced with a disruption or infeasibility, dispatchers typically choose between several response strategies. However, to date, it has been very difficult to evaluate the positive and negative effects of individual control strategies with respect to operations and passenger travel times under real-world conditions. This paper proposes a framework for the study of rail operations control decisions that integrates automatically collected service and passenger demand data, which are increasingly available and accessible to transit agencies. The framework supports a multiperspective analysis methodology that can inform operational policies and plans, and help operations control decision-makers choose the most appropriate strategies to manage service. By using automatically collected data, taking into consideration the operations control decision environment, and acknowledging that the reliability of the resulting service depends on many factors endogenous to it, this paper takes a distinctly different approach from previous studies, which have relied heavily on modeling, assumed simple operating contexts, and did not consider the full range of available data. Two real-world applications of the framework, where control decisions are evaluated in terms of their operational and passenger impacts, are presented. The methodology is found to be versatile and valuable in providing insights that could not have been gained otherwise. Although the framework is applied to the London Underground, its logic, structure, and procedures are applicable and transferable to other metro systems recognizing that certain specifics would need to be tailored to the available data.Transport for London (Organization

Service Reliability Measurement Using Automated Fare Card Data Application to the London Underground

This paper explores the potential of using automated fare card data to quantify the reliability of service as experienced by passengers of rail transit systems. The distribution of individual passenger journey times can be accurately estimated for those systems requiring both entry and exit fare card validation. With the use of this information, a set of service reliability measures is developed that can be used to routinely monitor performance, gain insights into the causes of unreliability, and serve as an input into the evaluation of transit service. An estimation methodology is proposed that classifies performance into typical and nonrecurring conditions, which allows analysts to estimate the level of unreliability attributable to incidents. The proposed measures are used to characterize the reliability of one line in the London Underground under typical and incident-affected conditions with the use of data from the Oyster smartcard system for the morning peak period. A validation of the methodology with the use of incident-log data confirms that a large proportion of the unreliability experienced by passengers can be attributed to incidentrelated disruptions. In addition, the study revealed that the perceived reliability component of the typical Underground trip exceeds its platform wait time component and equals about half of its on-train travel time as well as its station access and egress time components, suggesting that sizable improvements in overall service quality can be attained through reliability improvements

Applications of Inferred Origins, Destinations, and Interchanges in Bus Service Planning

A growing number of researchers and transit agencies are using fare card and vehicle location data to infer passengers’ origins, destinations, and transfers. A number of researchers have suggested that these new data sets provide valuable information for transit network design, but few concrete applications have been developed to address bus network design and service planning problems. This paper proposes new service planning procedures to aggregate these automated data to examine travel patterns to specific locations of interest to propose needed improvements. The data from existing passengers’ trips are then analyzed to assess the benefits of the proposed service changes. In particular, the number of existing passengers who would likely experience shorter travel times with the service changes is calculated according to the geometry of how a proposed new or extended route intersects with the existing transit network. The results of this analysis provide planners with better information than is currently available to support decisions on how to allocate the scarce resources typically available for service changes. Several case studies from the Massachusetts Bay Transportation Authority are presented to illustrate these analytical techniques

Incorporating Product Choice into Transit Fare Policy Scenario Models

Customer fare product choices can affect both ridership and revenue, so they are strategically important for transit agencies. Nearly all major agencies offer choices between pay-per-use and pass products, and with each potential fare change, agencies face decisions about whether to modify pass “multiples”—the number of rides needed to “break even” on a pass purchase. However, the simple elasticity spreadsheet models often used to analyze the potential ridership and revenue impacts of fare changes make little or no adjustment for shifts in fare product choices. This paper reviews different options for incorporating product choice into fare policy scenario models, and it presents a ridership and revenue prediction procedure that combines a multinomial logit fare product choice model with the logic of an elasticity spreadsheet model. This combination facilitates evaluation of complex fare changes that are likely to alter fare product market shares while maintaining much of the flexibility and simplicity of a traditional spreadsheet model. Additionally, the proposed model uses only preexisting, revealed-preference automated fare collection data rather than requiring customer surveys. The proposed model is demonstrated using examples at the Chicago Transit Authority (CTA). The CTA experienced a large shift from passes to pay-per-use following a fare change in 2013, illustrating the potential value of accounting for fare product choices in fare scenario evaluation

A randomized controlled trial in travel demand management

© 2019, Springer Science+Business Media, LLC, part of Springer Nature. This paper presents a trial aimed at reducing parking demand at a large urban employer through an informational campaign and monetary incentives. A 6-week randomized controlled trial was conducted with (N = 2000) employee commuters at the Massachusetts Institute of Technology, all of whom frequently drove to campus. Split into four arms of five hundred each, one group received weekly informational emails highlighting MIT’s various new transportation benefits; a second group received monetary rewards for reducing their frequency of parking; a third group received both interventions, while a control group was monitored with no intervention. The paper aims to examine how behavioral incentives, namely targeted information provision and monetary rewards, can be used independently or in combination to encourage alternatives to drive-alone commuting. Success was measured as the extent to which drivers decreased their frequency of parking and increased their use of alternative modes during and after the campaign. While the combined treatment group contained the highest number of top-performing participants, no statistically significant differences-in-differences were observed amongst the treatment arms compared to the control. A post-experiment survey indicated a widespread increase in awareness of employer transportation benefits, and a much larger stated shift from driving towards transit than was supported by passively-collected data. Survey results suggested that while intent to reduce car use existed, complaints of insufficient quality of transit service and relative convenience of driving suppressed modal shifts. Most importantly, the discrepancy between self-reported and actual behavior change highlights important limitations and biases of survey-based travel behavior research

Ridership Response to Incremental Bus Rapid Transit Upgrades in North America Demographic and Network Effects

This paper explores ridership increases in response to incrementally upgraded bus services in U.S. and Canadian cities. Current guidelines for developing bus rapid transit (BRT) corridors reveal a tension between comprehensive implementation of full-fledged corridors on the one hand and incremental, flexible development on the other. A review of the literature discusses this tension, various BRT elements, and the impact of these elements on performance and ridership. A methodology for comparing high-productivity bus corridors in different contexts using general transit feed specification (GTFS) data and a spatial database framework is described. Longitudinal and cross-sectional sketch models, with corridors as the unit of analysis, offer some insights into the relative impact of BRT features and external factors. Current data limitations allow for suggestive, if not definitive, results. Dedicated lanes and signal priority were positively correlated with increased ridership in some models tested, even when decreased travel time was controlled for and, suggesting that they may have had important perception and reliability benefits beyond improved speeds. While BRT can be a promising mode for a range of contexts, this analysis suggests that service frequency and reliability improvements are the common foundation for successful projects. Building political momentum for sustained improvements in bus networks is a challenge; the use of emerging data sources, such as GTFS, to compare incremental BRT projects allows for a better understanding of projects that can help meet this challenge

Bus Passenger Origin-Destination Estimation and Related Analyses Using Automated Data Collection Systems

This research explores the application of archived data from Automated Data Collection Systems (ADCS) to transport planning with a focus on bus passenger travel behavior, including Origin-Destination (OD) inference, using London as a case study. It demonstrates the feasibility and ease of applying trip-chaining to infer bus passenger OD from smart card transactions and Automatic Vehicle Location (AVL) data and is the first known attempt to validate the results by comparing them with manual passenger survey data. With the inferred OD matrices, the variations of weekday and weekend bus route OD patterns are examined for planning purposes. Moreover, based on the inferred OD matrices and the AVL data, alighting times for bus passengers also can be estimated. Bus journey stages, therefore, can easily be linked. By comparing the interchange time and the connecting bus route’s headway, it provides a way to evaluate bus connections

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

A framework for evaluating operations control on a metro line: integrating multiple perspectives and automatically collected train and passenger movement data

Transit operations control, the task of implementing the operations plan in daily operations on a metro line, plays a key role in service delivery because it determines the quality of the service experienced by passengers. Yet, it is one of the most poorly understood aspects of rail transit operations. Faced with a disruption or infeasibility, dispatchers typically choose between several response strategies. However, to date, it has been very difficult to evaluate the positive and negative effects of individual control strategies with respect to operations and passenger travel times under real-world conditions. This paper proposes a framework for the study of rail operations control decisions that integrates automatically collected service and passenger demand data, which are increasingly available and accessible to transit agencies. The framework supports a multiperspective analysis methodology that can inform operational policies and plans, and help operations control decision-makers choose the most appropriate strategies to manage service. By using automatically collected data, taking into consideration the operations control decision environment, and acknowledging that the reliability of the resulting service depends on many factors endogenous to it, this paper takes a distinctly different approach from previous studies, which have relied heavily on modeling, assumed simple operating contexts, and did not consider the full range of available data. Two real-world applications of the framework, where control decisions are evaluated in terms of their operational and passenger impacts, are presented. The methodology is found to be versatile and valuable in providing insights that could not have been gained otherwise. Although the framework is applied to the London Underground, its logic, structure, and procedures are applicable and transferable to other metro systems recognizing that certain specifics would need to be tailored to the available data