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.

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

[2]  M. Maher.  INFERENCES ON TRIP MATRICES FROM OBSERVATIONS ON LINK VOLUMES: A BAYESIAN STATISTICAL APPROACH , 1983 .

[3]  Martin L. Hazelton,et al.  Statistical Inference for Transit System Origin-Destination Matrices , 2010, Technometrics.

[4]  Moshe Ben-Akiva,et al.  ALTERNATIVE METHODS TO ESTIMATE ROUTE-LEVEL TRIP TABLES AND EXPAND ON-BOARD SURVEYS , 1985 .

[5]  Wei Wang,et al.  Bus Passenger Origin-Destination Estimation and Related Analyses , 2011 .

[6]  T. N. Sriram.  Asymptotics in Statistics–Some Basic Concepts , 2002 .

[7]  Alex Cui.  Bus passenger Origin-Destination Matrix estimation using Automated Data Collection systems , 2006 .

[8]  Stephen E. Fienberg,et al.  Discrete Multivariate Analysis: Theory and Practice , 1976 .

[9]  Alejandro Tirachini,et al.  Integrating short turning and deadheading in the optimization of transit services , 2011 .

[10]  Yuwei Li.  A generalized and efficient algorithm for estimating transit route ODs from passenger counts , 2007 .

[11]  Baibing Li.  Markov models for Bayesian analysis about transit route origin-destination matrices , 2009 .

[12]  Michael A. West,et al.  Bayesian Inference on Network Traffic Using Link Count Data , 1998 .

[13]  Martin L. Hazelton,et al.  Estimation of origin-destination matrices from link flows on uncongested networks , 2000 .

[14]  Yuxiong Ji,et al.  Distribution-based Approach to Take Advantage of Automatic Passenger Counter Data in Estimating Period Route-level Transit Passenger Origin-Destination Flows:Methodology Development, Numerical Analyses and Empirical Investigations , 2011 .

[15]  Baibing Li,et al.  Bayesian Inference for Origin-Destination Matrices of Transport Networks Using the EM Algorithm , 2005, Technometrics.

[16]  Dawei Lu.  Route Level Bus Transit Passenger Origin-Destination Flow Estimation Using Apc Data: Numerical And Empirical Investigations , 2008 .

[17]  Shinya Kikuchi,et al.  MODEL TO ESTIMATE PASSENGER ORIGIN-DESTINATION PATTERN ON A RAIL TRANSIT LINE , 1992 .

[18]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[19]  Xiao-Li Meng,et al.  The EM Algorithm—an Old Folk‐song Sung to a Fast New Tune , 1997 .

[20]  Mark R. McCord,et al.  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 , 2014 .

[21]  Mark R. McCord,et al.  Identifying Homogeneous Periods in Bus Route Origin-Destination Passenger Flow Patterns from Automatic Passenger Counter Data , 2011 .

[22]  Peter G Furth,et al.  BUS ROUTE O-D MATRIX GENERATION: RELATIONSHIP BETWEEN BIPROPORTIONAL AND RECURSIVE METHODS , 1992 .

[23]  Mark R. McCord,et al.  Effect of Onboard Survey Sample Size on Estimation of Transit Bus Route Passenger Origin–Destination Flow Matrix Using Automatic Passenger Counter Data , 2011 .

[24]  Peter G Furth,et al.  Part 4: Marketing and Fare Policy: Making Automatic Passenger Counts Mainstream: Accuracy, Balancing Algorithms, and Data Structures , 2005 .

[25]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[26]  Mark R. McCord,et al.  Estimating Transit Route-level OD Flow Matrices from APC Data on Multiple Bus Trips Using the IPF Method with an Iteratively Improved Base , 2013 .

[27]  Alejandro Tirachini,et al.  Optimal design and benefits of a short turning strategy for a bus corridor , 2011 .

[28]  E. Cascetta.  Estimation of trip matrices from traffic counts and survey data: A generalized least squares estimator , 1984 .

[29]  Y. Vardi,et al.  Network Tomography: Estimating Source-Destination Traffic Intensities from Link Data , 1996 .

[30]  Mark R. McCord,et al.  Iterative Proportional Fitting Procedure to Determine Bus Route Passenger Origin–Destination Flows , 2010 .

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