Abstract
Automatic fare collection system (AFC) has been widely used for public transport all over the world. However, in China, the most important information, the Origin-Destination (OD) matrix of each bus route, cannot be directly obtained from AFC since alighting information is not recorded at each bus stop. This paper presents an OD estimation model, which applies trajectory search algorithms to track passengers’ daily trip trajectory using pre-processed smart card data from all the passengers in one city of China. The results of a rigorous validation with on/off data from a real bus route reveal that the proposed model is quite effective and reliable in estimating the OD matrix for identification of the underlying demand pattern of a transit route. The algorithm is validated using one-day smart card data in Jinan city. The results have shown that the OD estimation from the proposed algorithm match more than 75% with the actual OD pairs. During the peak hours, the matching rate goes up to 85%. Hence, the proposed algorithm significantly improves the utilization of the smart card data. It is valuable to evaluate route network and optimize bus scheduling basing on estimated passenger trip OD matrix.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Yu, J., Liu, Y., Chang, G.L., Ma, W., Yang, X.: A Cluster-Based Hierarchical Model for Urban Transit Hub Location Planning: Formulation, Solution, and Case Study. Transportation Research Record 2112, 8–16 (2009)
Yu, J., Liu, Y., Yang, X.: Cluster-based Optimization of Urban Transit Hub Locations: Methodology and Case Study in China. Transportation Research Record 2042, 109–116 (2008)
Barry, J.J., Newhouser, R., Rahbee, A., Sayeda, S.: Origin and Destination Estimation in New York City with Automated Fare System Data. Transportation Research Record: Journal of the Transportation Research Board No. 1817, 183–187 (2002)
Trépanier, M., Chapleau, R.: Destination estimation from public transport smart card data. In: The 12th IFAC symposium on Information Control Problems in Manufacturing, Saint-Etienne, France (2006)
Trépanier, M., Tranchant, N., Chapleau, R.: Individual Trip Destination Estimation in a Transit Smart Card Automated Fare Collection System. Journal of Intelligent Transportation Systems: Technology, Planning and Operations 11(1), 1–14 (2007)
Zhao, J.: The Planning and Analysis Implications of Automated Data Collection Systems: Rail Transit OD Matrix Inference and Path Choice Modeling Examples. Master’s Thesis, Massachusetts Institute of Technology, Cambridge, MA (2004)
Zhao, J., Rahbee, A., Wilson, N.: Estimating a Rail Passenger Trip Origin-Destination Matrix Using Automatic Data Collection Systems. Computer-Aided Civil and Infrastructure Engineering 22, 376–387 (2007)
Cui, A.: Bus Passenger Origin-Destination Matrix Estimation Using Automatic Data Collection Systems. M.S. Thesis, Massachusetts Institute of Technology, MA (2006)
Farzin, J.: Constructing an Automated Bus Origin-Destination Matrix Using Fare card and GPS Data in São Paulo, Brazil. Presented at the 87th TRB Annual Conference, Washington, DC (2008)
Lin, Y.J., Jia, L., Zou, N.: Estimating Passenger Origin-Destination Matrix of Fixed-fare Bus Smart Card Usage Information. Presented at the 17th World Congress on Intelligent Transportation System, Busan, Korea (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, D., Lin, Y., Zhao, X., Song, H., Zou, N. (2011). Estimating a Transit Passenger Trip Origin-Destination Matrix Using Automatic Fare Collection System. In: Xu, J., Yu, G., Zhou, S., Unland, R. (eds) Database Systems for Adanced Applications. DASFAA 2011. Lecture Notes in Computer Science, vol 6637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20244-5_48
Download citation
DOI: https://doi.org/10.1007/978-3-642-20244-5_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20243-8
Online ISBN: 978-3-642-20244-5
eBook Packages: Computer ScienceComputer Science (R0)