Evaluation of Effects from Sample-Size Origin-Destination Estimation Using Smart Card Fare Data
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 143, Issue 4
Abstract
Public transport planners are required to make decisions on transport infrastructure and services worth billions of dollars. The decision-making process for transport planning needs to be informed, accountable, and founded on comprehensive, current, and reliable data. One of the major issues affecting the accuracy of the estimated origin-destination (O-D) matrices is sample size. Cost, time, precision, and biases are some issues associated with sample size. Smart card data can potentially provide much information based on better understanding and assessment of the sample size impact on the estimated O-D matrices. This paper uses South East Queensland (SEQ) data to study the effect of different data sample sizes on the accuracy level of the generated public transport O-D matrices and to quantify the sample size required for a certain level of accuracy. As a result, the total number of O-D trips for the whole network can be accurately estimated at all levels of sample sizes. However, a wide distribution of O-D trips appeared at different sample sizes. The large difference from the actual distribution at 100% sample size was readily captured at small sample sizes where more O-D pairs were not representative. The wide distribution of O-D trips at different levels of sample sizes caused significant errors even at large sample sizes. The variation of the errors within the same sample was also captured as a result of the 80 iterations for each sample size. It is concluded that three major parameters (distribution, number, and sample size of selected stations) have a significant impact on the estimated O-D matrices. These results can be also reflected on the sample size of the traditional O-D estimation methods, such household travel surveys.
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Acknowledgments
The authors would like to acknowledge the support of the Australian Research Council (Grant DE130100205). The authors are grateful to TransLink (the public transport authority of South East Queensland, Australia) for providing the data for this research. We would also like to thank Professor Mark Hickman, Chair of the Academic Strategic Transport Research Alliance (ASTRA) at the University of Queensland, for his valuable support.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 143 • Issue 4 • April 2017
Copyright
©2017 American Society of Civil Engineers.
History
Received: Mar 3, 2016
Accepted: Sep 26, 2016
Published online: Jan 27, 2017
Published in print: Apr 1, 2017
Discussion open until: Jun 27, 2017
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