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

Determining Segment and Network Traffic Volumes from Video Imagery Obtained from Transit Buses in Regular Service: Developments and Evaluation of Approaches for Ongoing Use across Urban Networks

69A3551747111Transit agencies around the world are increasingly mounting video cameras inside and outside their buses for liability, safety, and security reasons. Some of the cameras provide fields of view that allow observation of vehicles traveling on the surrounding roadways. Such video imagery could conceivably be used to estimate traffic volumes on roadway segments traversed by the transit buses. Transit buses are attractive platforms for acquiring the information that leads to traffic volume estimates, since a fleet of transit buses collectively covers most major surface streets in an urban area and the buses regularly and repeatedly cover the same roadway segments, which would allow for multiple, independent estimates of roadway segment flows across days and by time of day. Since the video cameras are already installed for other purposes, the costs of estimating traffic flows from video obtained from transit buses in regular service would be minimal. Therefore, traffic flows could be estimated with much greater geographic coverage, with much greater frequency, and with much lower cost than is presently available from existing traffic volume observation methods

Optimal infrastructure condition sampling over space and time for maintenance decision-making under uncertainty

Infrastructure management is the process through which inspection, maintenance, and rehabilitation (IM&R) decisions are made to minimize the total life-cycle cost. Measurement, forecasting, and spatial sampling are three main sources of errors introducing uncertainty into the process. The first two uncertainties are captured in the infrastructure management literature. However, the third one has not been recognized and quantified. This paper presents a methodology where the spatial sampling uncertainty in question is captured and the sample size is incorporated as a decision variable in an optimization framework. An illustrative realistic example is presented to demonstrate an application of the developed framework. The results indicate that by not addressing the sampling uncertainty and decisions, the optimum IM&R decisions would not be achieved, and consequently, marked unnecessary overspending could take place.Infrastructure management Inspection, maintenance, and rehabilitation decision-making Optimization under uncertainty Condition assessment Spatial sampling

Grouping of Bus Stops for Aggregation of Route-Level Passenger Origin-Destination Flow Matrices

Route-level, bus passenger origin-destination (O-D) matrices summarize useful information on travel patterns for use in route planning, design, and operations. However, the size of stop-to-stop O-D matrices makes it difficult to synthesize important flow patterns and to estimate stop-to-stop O-D passenger flows accurately. To reduce the size of the O-D matrix for improved estimation, analysis, and communication, stops can be grouped so that passenger flows between far fewer stop group pairs can be estimated and represented. The problem of grouping of bus stops for aggregation of passenger O-D flows is presented with the explicit objective of reducing the passenger O-D flow matrix while capturing important O-D flow characteristics. Two computationally efficient heuristic algorithms for solution of this problem are proposed. A set of empirical studies—conducted with automatic passenger counter data collected on major bus routes operated by the Ohio State University\u27s Campus Area Bus System, the Central Ohio Transit Authority, and the Los Angeles County, California, Metropolitan Transit Authority—shows that the stop group configurations identified with the proposed heuristic algorithms are close to optimal and capture pertinent flow patterns and dominant clusters of stops much better than stop group configurations produced from land use characteristics. The heuristic algorithms can identify solutions for all possible numbers of stops with a reasonable computational time, whereas optimal configurations can be found for only a few groups on long routes

Effect of Real-Time Passenger Information Systems on Perceptions of Transit’s Favorable Environmental and Traffic Reduction Roles

A two-wave survey of faculty, staff, and students at a large university was conducted to study the perceptions of and attitudes toward several dimensions of the university bus service before and after the implementation of a real-time passenger information system. In this study, community perceptions of the bus service’s role in enhancing the environment and reducing traffic were investigated. Results showed that both users and nonusers of the bus service had positive perceptions of the bus service’s environmental and traffic reduction roles, that those who noticed the recently implemented real-time information system had more positive attitudes, and that the effect of the information system on the perceptions was as great or greater for those who did not use the bus service as it was for those who used the service. It is hypothesized that these results, especially if confirmed in different communities, could motivate transit agencies to promote environmental and traffic reduction benefits of transit to gain public support of nonusers for transit subsidies and to market high-tech and progressive investments to increase support among nonusers

Passenger Wait Time Perceptions at Bus Stops: Empirical Results and Impact on Evaluating Real - Time Bus Arrival Information

This study quantifies the relationship between perceived and actual waiting times experienced by passengers awaiting the arrival of a bus at a bus stop. Understanding such a relationship would be useful in quantifying the value of providing realtime information to passengers on the time until the next bus is expected to arrive at a bus stop. Data on perceived and actual passenger waiting times, along with socioeconomic characteristics, were collected at bus stops where no real-time bus arrival information is provided, and relationships between perceived and actual waiting times are estimated. The results indicate that passengers do perceive time to be greater than the actual amount of time waited. However, the hypothesis that the rate of change of perceived time does not vary with respect to the actual waiting time could not be rejected (over a range of 3 to 15 minutes). Assuming that a passenger’s perceived waiting time is equal to the actual time when presented with accurate real-time bus arrival information, the value of the eliminated additional time is assessed in the form of reduced vehicle hours per day resulting from a longer headway that produces the same mean passenger waiting time. The eliminated additional time is also assessed in the form of uncertainty in the headway resulting in the same extra waiting time. Naturally, such benefits of passenger information can only be confirmed when the actual effect of information on the perception of waiting time is quantified

Estimating Transit Route OD Flow Matrices from APC Data on Multiple Bus Trips Using the IPF Method with an Iteratively Improved Base: Method and Empirical Evaluation

An iterative method is proposed to estimate bus route origin-destination (OD) passenger flow matrices from boarding and alighting data for time-of-day periods in the absence of good a priori estimates of the flows. The algorithm is based on the widely used iterative proportional fitting (IPF) method and takes advantage of the large quantities of boarding and alighting data that are routinely collected by transit agencies using automatic passenger count (APC) technologies. An arbitrarily chosen OD matrix can be used as the base matrix required to initialize the algorithm, and the IPF method is applied with bus trip-level boarding and alighting data and the base matrix to produce an estimate of the OD flow matrix for each bus trip. The trip-level OD flow matrices are then aggregated to produce an estimate of the period-level OD flow matrix, which in turn is used as the base matrix for the following iteration. The process is repeated until convergence. Empirical results are conducted on operational bus routes using APC data collected during multiple season-years, where directly observed OD passenger flows are available to represent the ground truth. In all cases in which APC data are available for even a reasonably small number of bus trips, the iteratively improved base method produces better estimates than the application of the traditional IPF method when using a null base matrix, which is commonly adopted in the absence of a priori information without updating. Moreover, the algorithm converges in minimal computational time to the same estimates regardless of the initializing matrices used. Read More: http://ascelibrary.org/doi/abs/10.1061/%28ASCE%29TE.1943-5436.000064