Nigel H. M. Wilson

BUS NETWORK DESIGN

This paper describes the bus network design problem, summarizes the different approaches that have been proposed for its solution and proposes a new approach incorporating some of the positive aspects of prior work. The proposed approach is intended to be easier to implement and less demanding in terms of both data requirements and analytical sophistication than previous methods. An algorithm is presented that can be used to design new bus routes taking account of both passenger and operator interests; however, this algorithm focuses on only a single component of the overall bus operations planning process described in this paper.

The Holding Problem with Real-Time Information Available

Holding is one of the most commonly used real-time control strategies in transit operations. Given a transit network and its operations plan, the holding problem is to decide at a given time at a control station, which vehicle is to be held and for how long, such that the total passenger cost along the route is minimized over a time period. Previous research on the holding problem has always assumed no real-time information available. Such an assumption not only poses great difficulties in solving the problem, but also limits practical applications in a real-time, dynamic operations environment. In this paper we formulate the holding problem as a deterministic quadratic program in a rolling horizon scheme, and develop an efficient solution algorithm to solve it. Using headway data collected by an automated system, we tested the algorithm and evaluated the impact of the resulting holding policies. Important and interesting properties of the holding solution, obtained from both theoretical and computational analyses, are presented.

Estimating a Rail Passenger Trip Origin‐Destination Matrix Using Automatic Data Collection Systems

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 applications, well beyond their design purpose. This paper 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/analysis methods with support of technologies such as database management systems and geographic information systems. This work 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 created to facilitate the method implementation. Results of the application in CTA are given.

PASSENGER TRAVEL TIME AND PATH CHOICE IMPLICATIONS OF REAL-TIME TRANSIT INFORMATION

Abstract This paper considers information systems in public transit in which the passenger receives information in real time regarding projected vehicle travel times. Such information systems may have value to passengers in situations where they may choose among different origin-to-destination paths. To provide a preliminary assessment of these systems, an analytic framework is presented to evaluate path choices and travel time benefits resulting from real-time information. A behavioral model of transit path choice is presented that frames the choice in terms of a decision whether to board a departing vehicle. Furthermore, this path choice model accommodates network travel times that are both stochastic and time-dependent, two elements that have been neglected in previous studies but are critical to evaluating real-time information systems. The path choice model is extended to demonstrate how real-time information may be incorporated by the passenger in making a path choice decision. This analytic framework is applied to a case study corridor at the Massachusetts Bay Transportation Authority (MBTA), using a computer simulation to model vehicle movements and passenger path choices in the corridor. The results suggest that real-time information yields only very modest improvements in passenger service measures such as the origin-to-destination travel times and the variability of trip times. Based on this analysis, the quantitative benefits of real-time information for transit passenger path choices appear to be questionable.

The real-time deadheading problem in transit operations control

In high frequency transit operations, randomness and incidents often result in highly irregular headways which can significantly decrease service quality. Deadheading is one commonly used real-time operations control strategy that can improve service quality in such situations. When a vehicle is deadheaded, it runs empty from a terminal skipping a number of stations, typically in order to reduce expected large headways at later stations. The real-time deadheading problem is to determine at dispatching time which vehicles to deadhead and how many stations to skip in order to minimize the total passenger cost in the system. This paper formulates this problem, optimally solves a simplified version of the general formulation, and demonstrates that the solutions of the simpler problem are good approximations to the solutions of the more general problem.

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.

Impact of Weather on Transit Ridership in Chicago, Illinois

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.

The allocation of buses in heavily utilized networks with overlapping routes

Many transit systems outside North America are characterized by networks with extensively overlapping routes and buses frequently operating at, or close to, capacity. This paper addresses the problem of allocating a fleet of buses between routes in this type of system; a problem that must be solved recurrently by transit planners. A formulation of the problem is developed which recognizes passenger route choice behavior, and seeks to minimize a function of passenger wait time and bus crowding subject to constraints on the number of buses available and the provision of enough capacity on each route to carry all passengers who would select it. An algorithm is developed based on the decomposition of the problem into base allocation and surplus allocation components. The base allocation identifies a feasible solution using an (approx.) minimum number of buses. The surplus allocation is illustrated for the simple objective of minimizing the maximum crowding level on any route. The bus allocation procedure developed in this paper has been applied to part of the Cairo bus system in a completely manual procedure, and is proposed to be the central element of a short-range bus service planning process for that city.

ASSESSMENT OF THE TRANSFER PENALTY FOR TRANSIT TRIPS: GEOGRAPHIC INFORMATION SYSTEM-BASED DISAGGREGATE MODELING APPROACH

Transit riders negatively perceive transfers because of their inconvenience, often referred to as a transfer penalty. Understanding what affects the transfer penalty can have significant implications for a transit authority and also lead to potential improvements in ridership forecasting models. A new method was developed to assess the transfer penalty on the basis of onboard survey data, a partial path choice model, and geographic information system techniques. This approach was applied to the Massachusetts Bay Transportation Authority subway system in downtown Boston. The new method improves the estimates of the transfer penalty, reduces the complexity of data processing, and improves the overall understanding of the perception of transfers.