John Attanucci

Analyzing Multimodal Public Transport Journeys in London with Smart Card Fare Payment Data

This paper contributes to the emerging literature on the application of smart card fare payment data to public transportation planning. The research objective is to identify and assess complete, multimodal journeys using Oyster smart card fare payment data in London. Three transfer combinations (bus-to-Underground, Underground-to-bus, and bus-to-bus) are considered to formulate recommendations for maximum elapsed time thresholds to identify transfers between journey stages for each passenger on the London network. Recommended elapsed time thresholds for identifying transfers are 20 min for Underground-to-bus, 35 min for bus-to-Underground, and 45 min for bus-to-bus, but a range of values that account for variability across the network are also assessed. Key findings about bus and Underground travel in London include an average of 2.3 daily public transportation journeys per passenger, 1.3 journey stages per public transportation journey, and 23% of Underground journeys involving a transfer to or from a bus. The application of complete journey data to bus network planning is used to illustrate the value of new information that would be available to network planners through the use of smart card fare payment data.

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.

Automated Inference of Linked Transit Journeys in London Using Fare-Transaction and Vehicle Location Data

Urban public transit providers historically have planned and managed their networks and services with little knowledge of their customers’ travel patterns. Although ticket gates and bus fareboxes yield counts of passenger activity in specific stations or vehicles, the relationships between these transactions—the origins, transfers, and destinations of individual passengers—typically have been acquired only through small, costly, and infrequent rider surveys. New methods for inferring the journeys of all riders on a large public transit network have been built on recent work into the use of automated fare collection and vehicle location systems for analysis of passenger behavior. Complete daily sets of data from Londons Oyster farecard and the iBus vehicle location system were used to infer boarding and alighting times and locations for individual bus passengers and to infer transfers between passenger trips of various public modes, and origin–destination matrices of linked intermodal transit journeys that include the estimated flows of passengers not using farecards were constructed. The outputs were validated against surveys and traditional origin–destination matrices. The software implementation demonstrated that the procedure is efficient enough to be performed daily, allowing transit providers to observe travel behavior on all services at all times.

Real-Time Ridesharing: Opportunities and Challenges in Using Mobile Phone Technology to Improve Rideshare Services

In recent years, an innovative ridesharing service relying heavily on advanced mobile phone technologies known as real-time ridesharing or dynamic ridesharing, has gained popularity in some groups: providers, organizations, and employers. Traditionally, rideshare arrangements between two or more unrelated individuals for commuting purposes have been relatively inflexible, long-term arrangements. Real-time ridesharing attempts to add flexibility to rideshare arrangements by allowing drivers and passengers to arrange occasional shared rides ahead of time or on short notice. The addition of this service innovation presents opportunities to overcome existing rideshare challenges but also leads to new challenges. The overall goal of this study was to provide a foundation for further realtime ridesharing research. The aims of the study were to identify, highlight, and discuss the potential benefits of and obstacles to real-time ridesharing and to point to the next steps to understand better and possibly advance this mode of travel. A definition of real-time ridesharing was given, followed by a comprehensive categorization of challenges hindering greater rideshare participation. The information gathered suggested that rather than being a single challenge to be overcome, the rideshare challenge was a series of economic, behavioral, institutional, and technological obstacles to be addressed. Potential opportunities and obstacles created by real-time innovations were then highlighted. Several recommendations are provided toward next steps to understand further how rideshare participants use real-time services, focusing on the need for multiple, comprehensive trials of real-time rideshare.

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 incident-related 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.

Decision Factors in Service Control on High-Frequency Metro Line: Importance in Service Delivery

Service control—the task of implementing the timetable in daily operations on a metro line—plays a key role in service delivery, because it influences the quality of the service provided to passengers. Shortfalls of previous research on the role and importance of service control have been noted. A framework intended to remedy some of these shortfalls is proposed. An important element of this framework is the description of the full decision environment in which service control takes place. On the basis of insights gained from extended visits to a control center, the reliability of the system is found to depend on many endogenous factors. These factors were not previously recognized in a comprehensive manner by either researchers or practitioners. Aside from the objectives of maintaining adequate levels of service from an operations perspective and minimizing the impact of schedule deviations on passengers, the management of crew and rolling stock, safety, and infrastructure capacity are major considerations in service control decisions. Given the uncertain environment in which service control operates, a strong preference was observed among controllers for manageable and robust control strategies. An example is discussed in which service controllers react to two similar disruptions with different recovery strategies, mainly because of crew management considerations. This research demonstrates the importance of a comprehensive understanding of the objectives and constraints faced by service controllers in daily operations.

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.

Inferring Public Transport Access Distance from Smart Card Registration and Transaction Data

Access distance to public transport is an important metric for planning, modeling, and evaluating public transport networks and is often used in policy goals and statements. However, accurately measuring access (and egress) distance can be difficult. Estimates often rely either on aggregate inferences based on census data or on small samples of disaggregate data from travel diary surveys. When smart cards used for fare payment are also registered with home address information, they represent a new data source that can be used to infer access distances for a large sample of users, at a disaggregate level and at low cost, compared with travel diary surveys. This paper demonstrates the inference of access distance from smart card fare and transaction data for a large sample of London public transport journeys and compares the inferred access distributions to data from the London Travel Demand Survey, a travel diary survey. Possible instances of false inferences are considered and measures to eliminate false inferences are discussed. This access distance inference methodology allows for the analysis of variation in access distance across the network, and examples of this type of analysis are presented.

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.

User Behavior in Multiroute Bus Corridors: Analysis by a Web-Based Survey

Multiroute corridors are a common feature of bus networks. In these corridors, passengers select a route from a set of parallel routes that serve the desired destinations. Understanding how passengers make these decisions can help measure passenger experience and inform network and service planning. A web-based survey was used to collect information on users of a multiroute corridor in London that includes both local and limited-stop bus service. The survey was used both as a tool to understand behavior and as a demonstration case for the viability of web-based surveys, a relatively new methodology for data collection on public transport user behavior. The representativeness and the accuracy of the survey responses were analyzed. The results revealed that online surveys could collect detailed information from a large, fairly representative sample of bus passengers. The responses to questions in the survey were used to categorize passenger behavior by route choice strategy. Passengers could either wait for a bus of a specific route or take the first bus to arrive that serves their destination. The survey data showed that passengers’ route choice strategies were influenced by several factors, including trip length, trip purpose, passenger income, use of countdown next-bus information, passenger attitudes toward crowding, and levels of risk aversion.