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

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

EVALUATING REAL-TIME BUS ARRIVAL INFORMATION SYSTEMS

Real-time transit passenger information systems are intended to improve the level of service provided by transit agencies. For example, passengers are given real-time information on the expected arrival times of buses on various routes at bus stops. These real-time systems reflect emerging applications in public transit, and methods to evaluate their benefits are limited. An evaluation method is presented that focuses on the potential benefits of bus arrival information systems to passengers waiting at bus stops. Passenger arrivals and transit bus operations are modeled as a stochastic system in which the operator uses real-time bus location data to provide bus arrival-time information that maximizes passengers\u27 utilities. Simulation results reveal the nature of the dependence of system benefits on the type of real-time data available to the operator and the bus operations characteristics. An application to an existing bus transit system demonstrates the feasibility of the developed method and its role in assessing the value of real-time bus arrival information systems to passengers

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

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

Transit passenger origin–destination flow estimation: Efficiently combining onboard survey and large automatic passenger count datasets

As transit agencies increasingly adopt the use of Automatic Passenger Count (APC) technologies, a large amount of boarding and alighting data are being amassed on an ongoing basis. These datasets offer opportunities to infer good estimates of passenger origin–destination (OD) flows. In this study, a method is proposed to estimate transit route passenger OD flow matrices for time-of-day periods based on OD flow information derived from labor-intensive onboard surveys and the large quantities of APC data that are becoming available. The computational feasibility of the proposed method is established and its accuracy is empirically evaluated using differences between the estimated OD flows and ground-truth observations on an operational bus route. To interpret the empirical differences from the ground-truth estimates, differences are also computed when using the state-of-the-practice Iterative Proportional Fitting (IPF) method to estimate the OD flows. The empirical results show that when using sufficient quantities of boarding and alighting data that can be readily obtained from APC-equipped buses, the estimates determined by the proposed method are better than those determined by the IPF method when no or a small sample sized onboard OD flow survey dataset is available and of similar quality to those determined by the IPF method when a large sample sized onboard OD flow survey dataset is available. Therefore, the proposed method offers the opportunity to forgo conducting costly onboard surveys for the purpose of OD flow estimation

Iterative proportional fitting procedure to determine bus route passenger origin-destination flows

The advent of automatic passenger counter (APC) technologies is resulting in the collection of comprehensive boarding and alighting data on an ongoing basis across transit networks. The availability of APC data offers a new opportunity to determine origin-destination (O-D) flows on a frequent and comprehensive basis. In this paper, the performance of a simple procedure for route-level O-D flow determination requiring only boarding and alighting data is investigated. Specifically, the performance of the iterative proportional fitting (IPF) procedure used with a null base matrix is examined on the basis of a field experiment in which true O-D flows are observed. Because of the noninformative nature of the null matrix, using the IPF procedure with the null matrix as its input base may not be expected to produce good results. In a comparison of empirical results with those produced by other benchmark procedures, the IPF-null procedure is found to perform surprisingly well. The quality of the resulting matrices appears to be roughly similar to that of matrices derived from an onboard survey, the benchmark for what has been achieved in practice, but at much higher cost. The results indicate that much can be gained from using readily available APC data, even when the simple IPF-null procedure is applied. Moreover, using the better base obtained from an onboard survey with the IPF procedure improved performance, but less markedly compared with use of the null base; this difference indicates that combining onboard survey information with APC data provides a better O-D matrix than what can be derived from an onboard survey alone, even when the simple IPF procedure is used

Identifying Homogeneous Periods in Bus Route Origin-Destination Passenger Flow Patterns from Automatic Passenger Counter Data

Bus passenger origin-destination (O-D) flow matrices portray information on travel patterns that can be used for route planning, design, and operations functions. Because travel patterns are known to vary throughout the day, O-D flow matrices can be expected to vary throughout the day as well. A method identifies time-of-day periods of homogeneous normalized bus route O-D passenger flow matrices in which a normalized matrix depicts the probabilities that a random passenger in the homogeneous period will travel from various origin stops to various destination stops on the route. The method uses bus trip automatic passenger counter data to estimate trip-level O-D matrices, aggregates the trip-level O-D matrices into elemental matrices for relatively short time periods, and then considers these elemental matrices as inputs to a traditional clustering procedure that is modified to ensure that a cluster indicating a period of homogeneous normalized O-D flow spans a continuous time period during the day. The homogeneous O-D flow period method is applied to empirical automatic passenger counter data collected on a bus route for which temporal travel patterns are understood. The time periods identified correspond well to the a priori understanding of travel patterns. A parallel method that uses passenger volume, rather than estimated normalized O-D flow matrices, is applied to the same data. The periods identified by this volume-based approach are not responsive to the changes in the normalized O-D flow patterns determined by the homogeneous O-D flow period identification method