Validating and improving public transport origin–destination estimation algorithm using smart card fare data |
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| Institution: | 1. Tongji University, The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Caoan Road 4800, Shanghai 201804, China;2. The Ohio State University, Department of Civil, Environmental and Geodetic Engineering, 2070 Neil Ave., Rm 470, Columbus, OH 43210, USA;1. Centre for Advanced Spatial Analysis, University College London, W1T 4TJ, 90 Tottenham Court Road, London, UK;2. School of Planning and Geography, Cardiff University, CF10 3WA Cardiff, Wales, UK;3. Nijmegen School of Management, Radboud University, Thomas van Aquinostraat 5, 6525 GD Nijmegen, The Netherlands;4. Faculty of Architecture and Town Planning, Technion - Israel Institute of Technology, Amado Building, Technion City, Haifa 32000, Israel;5. School of Architecture, Tsinghua University, Beijing 100084, PR China;1. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States;2. Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, United States;3. Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA 02139, United States;1. Massachusetts Institute of Technology, Cambridge, MA, USA;2. Northeastern University, Boston, MA, USA |
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| Abstract: | Smart card data are increasingly used for transit network planning, passengers’ behaviour analysis and network demand forecasting. Public transport origin–destination (O–D) estimation is a significant product of processing smart card data. In recent years, various O–D estimation methods using the trip-chaining approach have attracted much attention from both researchers and practitioners. However, the validity of these estimation methods has not been extensively investigated. This is mainly because these datasets usually lack data about passengers’ alighting, as passengers are often required to tap their smart cards only when boarding a public transport service. Thus, this paper has two main objectives. First, the paper reports on the implementation and validation of the existing O–D estimation method using the unique smart card dataset of the South-East Queensland public transport network which includes data on both boarding stops and alighting stops. Second, the paper improves the O–D estimation algorithm and empirically examines these improvements, relying on this unique dataset. The evaluation of the last destination assumption of the trip-chaining method shows a significant negative impact on the matching results of the differences between actual boarding/alighting times and the public transport schedules. The proposed changes to the algorithm improve the average distance between the actual and estimated alighting stops, as this distance is reduced from 806 m using the original algorithm to 530 m after applying the suggested improvements. |
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| Keywords: | O–D estimation Validation Public transport smart card fare data Trip-chaining method |
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