Constructing Transit Origin–Destination Tables from Fragmented Data

This study proposes an approach that constructs the origin–destination table (O-D table) for urban bus or light rail lines from fragmented data about the number of boarding and alighting passengers at stops (B-A data) and the analyst's spot knowledge about the trip pattern for selected O-D pairs. The B-A data of transit lines in the city center are often incomplete, yet they may be the only data available to characterize the passenger travel pattern. The proposed approach constructs the O-D table by using data that contain different levels of uncertainties and incompleteness. The model is based on two basic principles, maximum uncertainty and minimum uncertainty. The former is implemented by maximizing the entropy of the O-D table to derive the least-biased values. The latter refers to the maximum consistency with the available data including language-based knowledge about some of the O-D table elements. These principles are implemented by the multiobjective optimization structure. The model is found to be robust if it can incorporate various types of available data as well as the analyst's knowledge. It was tested by using O-D data and B-A data from an actual transit operation. The quality of the derived O-D table is clearly related to the availability and the quality of the data; however, it can be improved significantly with the analyst's spot knowledge about the values of selected O-D pairs. This method will open the way for transit planners to quickly develop a reasonable O-D table from the available incomplete data.

[1]  Dragana Miljkovic,et al.  Examination of Methods That Adjust Observed Traffic Volumes on a Network , 2000 .

[2]  Shinya Kikuchi,et al.  Constructing a Transit Origin–Destination Table Using the Uncertainty Maximization Concept , 2009 .

[3]  Rainville.  BUS SCHEDULING MANUAL. TRAFFIC CHECKING AND SCHEDULE PREPARATION , 1982 .

[4]  G. Klir,et al.  Uncertainty-based information: Elements of generalized information theory (studies in fuzziness and soft computing). , 1998 .

[5]  Peter G Furth,et al.  Making Automatic Passenger Counts Mainstream , 2005 .

[6]  Leo Egghe,et al.  Uncertainty and information: Foundations of generalized information theory , 2007, J. Assoc. Inf. Sci. Technol..

[7]  Sergio A. Lugo-Serrato,et al.  Transit Capacity and Quality-of-Service Manual: Applications to Mexican Transit Systems , 2008 .

[8]  Adam Rahbee,et al.  Origin and Destination Estimation in New York City with Automated Fare System Data , 2002 .

[9]  George J. Klir,et al.  Uncertainty-Based Information , 1999 .

[10]  Daniel K Boyle,et al.  Appendixes to TCRP Report 135: Controlling System Costs: Basic and Advanced Scheduling Manuals and Contemporary Issues in Transit Scheduling , 2009 .

[11]  Shinya Kikuchi,et al.  Method for Balancing Observed Boarding and Alighting Counts on a Transit Line , 2006 .

[12]  Jinhua Zhao,et al.  Estimating a Rail Passenger Trip Origin‐Destination Matrix Using Automatic Data Collection Systems , 2007, Comput. Aided Civ. Infrastructure Eng..

[13]  Janine M Farzin.  Constructing an Automated Bus Origin–Destination Matrix Using Farecard and Global Positioning System Data in São Paulo, Brazil , 2008 .