Estimating an origin-destination matrix using smart cards. An application to the Megabús transport system in Pereira
Abstract
In the operational planning of transport systems, it is essential to have a characterization of the demand for the service by means of an Origin-estination (OD) Matrix of travels. This matrix is frequently estimated using statistical sampling techniques, which offer high-quality results but require complex design and high cost. It is therefore appropriate to take advantage of the information that is generated from the records of the smart cards used as means of payment in the systems that provide this technology. This source of information allows for obtaining estimates of high-quality Origin–Destination (OD) matrix with a low cost. In this work, it is implemented a methodology for the estimation of an Origin–Destination (OD) matrix between bus stations in the public transport system of MEGABÚS in the city of Pereira. The methodology allows for a debugging in the database taking into account the peculiarities of the system as the forms of income, type of card, and resale of tickets. In addition, it considers the possibility that a user can decide to step down at an intermediate station close to his destination station using the parameterization of a penalty factor that is associated with the walking-travel time in the objective function. The inclusion of the factor does not significantly influence the total percentage of estimated travels. Nevertheless, it recalculates some values of the Origin-Destination (OD) Matrix considering the alight in intermediate stations. The proposed method makes possible to estimate OD matrices for different time windows for any day of the week and different times of the year.
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Colciencias, Universidad Tecnológica de PereiraReferences
Buneman, K. (1984). Automated and passenger-based transit performance measures. Transportation Research Record, 992, pp. 23-28.
Furth, P. G., Hemily, B. J., Muller, T. H. J. & Strathman, J. G. (2006). Using Archived AVL-APC Data to Improve Transit Performance and Management. Transportation research board. doi: 10.17226/13907
Barry, J. J., Newhouser, R., Rahbee, A. & Sayeda, S. (2002). Origin and destination estimation in New York City with automated fare system data. Transportation Research Record, 1817, pp. 183-187. doi: 10.3141/1817-24
Zhao, J., Rahbee, A. & Wilson, N. (2007). Estimating a rail passenger trip origin-destination matrix using automatic data collection systems. Computer-Aided Civil and Infrastructure Engineering, 22(5), pp. 376-387. doi: 10.1111/j.1467-8667.2007.00494.x
Muller, T. H. J. & Furth, P. G. (2001). Trip time analyzes: key to transit service quality. Transportation Research Record, 1760, pp. 10-19. doi: 10.3141/1760-02
Lianfu, Z., Shuzhi, Z., Yonggang, Z. & Ziyin, Z. (2007). Study on the method of constructing bus stops OD matrix based on IC card data. En The International Conference on Wireless Communications, Networking and Mobile Computing, pp. 3147-3150. doi: 10.1109/WICOM.2007.780
Trépanier, M., Tranchant, N. & Chapleau, R. (2007). Individual trip destination estimation in a transit smart card automated fare collection system. Journal of Intelligent Transportation Systems, 11(1), pp.1-14. doi: 10.1080/15472450601122256
Chapleau, R. & Chu, K. K. (2007). Modeling transit travel patterns from location-stamped smart card data using a disaggregate approach. En The 11th World Conference on Transportation Research, pp. 1-29.
Chapleau, R., Trépanier, M. & Chu, K. K. (2008). The ultimate survey for transit planning: Complete information with smart card data and GIS. En The 8th International Conference on International Steering Committee for Travel Survey Conferences, Montreal, Canadá.
Zhao J. (1977). The Planning and Analysis Implications of Automated Data Collection System: Rail Transit OD Matrix Inference and Path Choice Modeling Examples. (Tesis doctoral). Department of Urban Studies and Planning and the Department of Civil and Environmental Engineering. Massachusetts Institute of Technology, Boston.
Hofmann, M. & O'Mahony, M. (2005). Transfer Journey Identification and Analyses form Electronic Fare Collection Data. En Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems. Viena, Austria, pp. 34-39. doi:10.1109/ITSC.2005.1520156
Farzin, J. (2008). Constructing an Automated Bus Origin-Destination Matrix Using Farecard and Global Positioning System Data in São Paulo, Brazil. Transportation Research Record, 2072, pp. 30-37. doi: 10.3141/2072-04
Munizaga, M., Palma, C. & Mora, P. (2010). Public Transport OD Matrix Estimation from Smart Card Payment System Data. En: Proceedings from 12th World Conference on Transport Research, Lisboa, Portugal, pp. 1-16.
Pelletier, M. P., Trépanier, M. & Morency, C. (2011). Smart card data use in public transit: A literature review. Transportation Research Part C, 19, pp. 557-568. doi: 10.1016/j.trc.2010.12.003
Devillaine, F., Munizaga, M. & Trépanier, M. (2012). Detection activities of public transport users by analyzing smart card data. Transportation Research Record, 2276, pp. 48-55. doi: 10.3141/2276-06
Munizaga, M. A. & Palma, C. (2012). Estimation of a disaggregate multimodal public transport origin-destination matrix from passive smartcard data from Santiago, Chile. Transportation Research Part C, 24, pp. 9-18. doi: 10.1016/j.trc.2012.01.007
Giahi, R. & Moghaddam, R. (2014). A Holding Strategy to Optimize the Bus Transit Service. En: International Journal of Industrial Engineering & Production Research, 25(1), pp. 33-40.
