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.

Automated data collection systems are becoming increasingly common in urban public transport systems, both in the US and throughout the developed world. These systems, which include Automatic Vehicle Location (AVL), Automatic Passenger Counting (APC), and Automatic Fare Collection (AFC), are often designed to support specific and fairly narrow functions within the transport agency. However, it is clear that the data obtained from these systems can have wide-ranging applications within public transport, well beyond the design applications. Of particular interest in the planning of public transport is the opportunity to make use of these increasingly ubiquitous databases to develop a better picture of how public transport systems are performing and being used. In some cases, better estimates of certain performance measures and usage attributes may be made at lower cost than by using conventional data collection methods, even though there are important limitations on the detailed attributes typically available from these systems. In other cases it is possible for the first time to estimate important performance attributes, such as those related to reliability and its impacts, which have hitherto been virtually impossible to quantify because of paucity of data. This paper describes two applications, focusing on system usage and passenger behavior, which have been developed jointly between MIT and the Chicago Transit Authority (CTA), taking advantage of CTA's AFC and AVL systems. The specific applications are the estimation of passenger origin-destination matrices for the CTA rail system and the estimation of path choice models for CTA rail passengers. Next steps in the development of further applications for urban public transport systems are also discussed.

A system using farecard data at the Chicago Transit Authority in Chicago, Illinois, to estimate rail passenger flows within the rail rapid transit network has been validated and used for several planning and analysis purposes. The system, which derives destinations for entry-only rail farecard users, is an enhanced version of the New York City Transit farecard origin–destination model. Enhancements include algorithmic changes to improve destination inference, greater time period granularity, enhanced sampling techniques, and path assignment to individual scheduled trips. Applications of the model outputs, including its use in lieu of passenger counts, are illustrated.

This paper explores the weather-ridership relationship and its potential applications in transit operations and planning. Using the Chicago Transit Authority (CTA) as a case, the paper investigates the impact of five weather elements (temperature, rain, snow, wind, and fog) on daily bus and rail ridership, and its variation across modes, day types, and seasons. The resulting relationships are applied to the CTA ridership trend analysis, showing how preliminary findings may change after controlling for weather. The paper emphasizes the importance of having a theoretical framework encompassing weather and travel.

Abstract:  Automatic data collection (ADC) systems are becoming increasingly common in transit systems throughout the world. Although these ADC systems are often designed to support specific fairly narrow functions, the resulting data can have wide‐ranging application, well beyond their design purpose. This article illustrates the potential that ADC systems can provide transit agencies with new rich data sources at low marginal cost, as well as the critical gap between what ADC systems directly offer and what is needed in practice in transit agencies. To close this gap requires data processing and analysis methods with support of technologies such as database management systems (DBMS) and geographic information systems (GIS). This research presents a case study of the automatic fare collection (AFC) system of the Chicago Transit Authority (CTA) rail system and develops a method for inferring rail passenger trip origin‐destination (OD) matrices from an origin‐only AFC system to replace expensive passenger OD surveys. A software tool is developed to facilitate the method implementation and the results of the application in CTA are reported.

This paper explores the weather–ridership relationship and its potential applications in transit operations and planning. Using the Chicago Transit Authority (CTA) in Illinois as a case study, the paper investigates the impact of five weather elements (temperature, rain, snow, wind, and fog) on daily bus and rail ridership and variation across modes, day types, and seasons. The resulting relationships are applied to the CTA ridership trend analysis, showing how preliminary findings may change after controlling for weather. The paper emphasizes the importance of having a theoretical framework encompassing weather and travel.

New York City Transit’s automated fare collection system, known as MetroCard, is an entry-only system that records the serial number of the MetroCard and the time and location (subway turnstile or bus number) of each use. A methodology that estimates station-to-station origin and destination (O-D) trip tables by using this MetroCard information is described. The key is to determine the sequence of trips made throughout a day on each MetroCard. This is accomplished by sorting the MetroCard information by serial number and time and then extracting, for each MetroCard, the sequence of the trips and the station used at the origin of each trip. A set of straightforward algorithms is applied to each set of MetroCard trips to infer a destination station for each origin station. The algorithms are based on two primary assumptions. First, a high percentage of riders return to the destination station of their previous trip to begin their next trip. Second, a high percentage of riders end their last trip of the day at the station where they began their first trip of the day. These assumptions were tested by using travel diary information collected by the New York Metropolitan Transportation Council. This diary information confirmed that both assumptions are correct for a high percentage (90%) of subway users. The output was further validated by comparing inferred destination totals to station exit counts by time of day and by estimating peak load point passenger volumes by using a trip assignment model. The major applications of this project are to describe travel patterns for service planning and to create O-D trip tables as input to a trip assignment model. The trip assignment model is used to determine passenger volumes on trains at peak load points and other locations by using a subway network coded with existing or modified service. These passenger volumes are used for service planning and scheduling and to quantify travel patterns. This methodology eliminates the need for periodic systemwide O-D surveys that are costly and time-consuming. The new method requires no surveying and eliminates sources of response bias, such as low response rates for certain demographic groups. The MetroCard market share is currently 80% and increasing. MetroCard data are available continuously 365 days a year, which allows O-D data estimation to be repeated for multiple days to improve accuracy or to account for seasonality.