621
Views
0
CrossRef citations to date
0
Altmetric
COMPUTER SCIENCE

Reconstruction of incomplete public transportation check-out records by heuristic approaching

, &
Article: 2203800 | Received 10 Mar 2022, Accepted 12 Apr 2023, Published online: 04 May 2023

References

  • Agard, B., Morency, C., & Trepanier, M. (2006). Mining public transport user behaviour from smart card data. IFAC Proceedings, Heidelberg, Germany, 39, 3, 399–20.
  • Allen, J., Muñoz, J. C., & de Dios Ortúzar, J. (2019). Understanding public transport satisfaction: Using Maslow’s hierarchy of (transit) needs. Transport Policy, 81, 75–94. https://doi.org/10.1016/j.tranpol.2019.06.005
  • Alsger, A., Assemi, B., Mesbah, M., & Ferreira, L. (2016). Validating and improving public transport origin–destination estimation algorithm using smart card fare data. Transportation Research Part C: Emerging Technologies, 68, 490–506. https://doi.org/10.1016/j.trc.2016.05.004
  • Bagchi, M., & White, P. R. (2005). The potential of public transport smart card data. Transport Policy, 12(5), 464–474. https://doi.org/10.1016/j.tranpol.2005.06.008
  • Barry, J. J., Freimer, R., & Slavin, H. (2009). Use of entry-only automatic fare collection data to estimate linked transit trips in New York City. Transportation Research Record. 2112(1), 53–61.
  • 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, 1, 183–187. https://doi.org/10.3141/1817-24
  • Chang, Y., & Zhao, C. (2016). Travel pattern recognition using smart card data in public transit. Int J Emerg Eng Res Technol, 4(7), 6–13.
  • Chen, Z., & Fan, W. (2018). Extracting bus transit boarding stop information using smart card transaction data. Journal of Modern Transportation, 26(3), 209–219. https://doi.org/10.1007/s40534-018-0165-y
  • Dokmanic, I., Parhizkar, R., Ranieri, J., & Vetterli, M. (2015). Euclidean distance matrices: Essential theory, algorithms, and applications. IEEE Signal Processing Magazine, 32(6), 12–30. https://doi.org/10.1109/MSP.2015.2398954
  • Fan, L., Mumford, C. L., & Evans, D. (2009 May). A simple multi-objective optimization algorithm for the urban transit routing problem. In 2009 IEEE Congress on Evolutionary Computation, Trondheim, Norway (pp. 1–7). IEEE.
  • Farzin, J. M. (2008). Constructing an automated bus origin–destination matrix using farecard and global positioning system data in são paulo, Brazil. 1 (1), 30–37. https://doi.org/10.3141/2072-04.
  • Gallaire, H. (1998). Faster, connected, smarter. In 21st century technologies : promises and perils of a dynamic future. OECD Publishing.
  • Haversine formula [Website] (Oct 20. 2021). https://en.wikipedia.org/wiki/Haversine_formula
  • Henrickson, K., Rodrigues, F., & Pereira, F. C. (2019). Data preparation. In C. Antoniou, L. Dimitriou, & F. Pereira (Eds.), Mobility Patterns, Big Data and Transport Analytics (pp. 73–106). Elsevier.
  • He, L., & Trepanier, M. (2015). Estimating the destination of unlinked trips in transit smart card fare data. Transportation Research Record, 2535(1), 97–104. https://doi.org/10.3141/2535-11
  • Huang, Z., Qingquan, L., Fan, L., & Xia, J. (2019). A novel bus-dispatching model based on passenger flow and arrival time prediction. IEEE Access, 7, 106453–106465. https://doi.org/10.1109/ACCESS.2019.2932801
  • Ingvardson, J. B., & Nielsen, O. A. 2019. The relationship between norms, satisfaction and public transport use: A comparison across six European cities using structural equation modelling. Transportation research part A: Policy and practice, 126, 37–57. https://doi.org/10.1016/j.tra.2019.05.016
  • Jo, J. B., Li, Y., & Gen, M. (2007). Nonlinear fixed charge transportation problem by spanning tree-based genetic algorithm. Computers & Industrial Engineering, 53(2), 290–298. https://doi.org/10.1016/j.cie.2007.06.022
  • Jung, J., & Sohn, K. (2017). Deep-learning architecture to forecast destinations of bus passengers from entry-only smart-card data. IET Intelligent Transport Systems, 11(6), 334–339. https://doi.org/10.1049/iet-its.2016.0276
  • Kim, J., Corcoran, J., & Papamanolis, M. (2017). Route choice stickiness of public transport passengers: Measuring habitual bus ridership behaviour using smart card data. Transportation Research Part C: Emerging Technologies, 83, 146–164. https://doi.org/10.1016/j.trc.2017.08.005
  • Kim, K., Hong Min, J., & Lee, I. (2014). The analysis of passenger’s tag behavior using transportation card data. Journal of the Korean Society for Railway, 5, 635–638.
  • Kim, K., & Lee, I. (2017). Public Transportation alighting estimation method using smart card data. Journal of the Korean Society for Railway, 20(5), 692–702. https://doi.org/10.7782/JKSR.2017.20.5.692
  • Lu, X., Li, J., Wu, C., Wu, J., & Daneshmand, M. (2021). Measuring similarity between any pair of passengers using smart card usage data. IEEE Internet of Things Journal, 9(2), 1458–1468. https://doi.org/10.1109/JIOT.2021.3089624
  • Minh Kieu, L., Bhaskar, A., & Chung, E. 2013. Mining temporal and spatial travel regularity for transit planning. In Australasian Transport Research Forum 2013 Proceedings, Brisbane, QLD. Australasian Transport Research Forum, 1–12.
  • Mohamed, K., Come, E., Oukhellou, L., & Verleysen, M. (2016). Clustering smart card data for urban mobility analysis. IEEE Transactions on Intelligent Transportation Systems, 18(3), 712–728. https://doi.org/10.1109/TITS.2016.2600515
  • Munizaga, M., Devillaine, F., Navarrete, C., & Silva, D. (2014). Validating travel behavior estimated from smartcard data. Transportation Research Part C: Emerging Technologies, 44, 70–79. https://doi.org/10.1016/j.trc.2014.03.008
  • 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: Emerging Technologies, 24, 9–18. https://doi.org/10.1016/j.trc.2012.01.007
  • Nicholas Maeda, T., Shiode, N., Zhong, C., Mori, J., & Sakimoto, T. (2019). Detecting and understanding urban changes through decomposing the numbers of visitors’ arrivals using human mobility data. Journal of Big Data, 6(1), 1–25. https://doi.org/10.1186/s40537-019-0168-5
  • Nunes, A. A., Dias, T. G., & Cunha, J. F. (2016). Passenger journey destination estimation from automated fare collection system data using spatial validation. IEEE Transactions on Intelligent Transportation Systems, Vol. 17(1), pp. 133–142
  • Nunes, A. A., Galvao Dias, T., & Falcao, J. (2015). Passenger journey destination estimation from automated fare collection system data using spatial validation. IEEE Transactions on Intelligent Transportation Systems, 17(1), 133–142. https://doi.org/10.1109/TITS.2015.2464335
  • Panchal, G., & Panchal, D. (2015). Solving np hard problems using genetic algorithm. Transportation, 106, 2–6.
  • Rui, X., & Wunsch, D. (2005). Survey of clustering algorithms. IEEE Transactions on Neural Networks, 16(3), 645–678. https://doi.org/10.1109/TNN.2005.845141
  • Sadeghi-Moghaddam, S., Hajiaghaei-Keshteli, M., & Mahmoodjanloo, M. (2019). New approaches in metaheuristics to solve the fixed charge transportation problem in a fuzzy environment. Neural Computing & Applications, 31(1), 477–497. https://doi.org/10.1007/s00521-017-3027-3
  • Shin, Y.S. (2020). Research of sparse dataset analysis for estimating missing alighting information of public transportation records. Journal of Transport Research, 27(4), 19–21.
  • Trepanier, 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), 1–14. https://doi.org/10.1080/15472450601122256
  • Wu, J., Guo, S., Huang, H., Liu, W., & Xiang, Y. (2018). Information and communications technologies for sustainable development goals: State-of-the-art, needs and perspectives. IEEE Communications Surveys & Tutorials, 20(3), 2389–2406. https://doi.org/10.1109/COMST.2018.2812301
  • Wu, J., Guo, S., Li, J., & Zeng, D. (2016). Big data meet green challenges: Big data toward green applications. IEEE Systems Journal, 10(3), 888–900. https://doi.org/10.1109/JSYST.2016.2550530
  • Xiao Lei, M., Wang, Y.H., Chen, F., & Liu, J.F. (2012). Transit smart card data mining for passenger origin information extraction. Journal of Zhejiang University Science C, 13(10), 750–760. https://doi.org/10.1631/jzus.C12a0049
  • Xiaolei, M., Yao-JanWu, Y., Chen, F., Liu, J., & Liu, J. (2013). Mining smart card data for transit riders’ travel patterns. Transportation Research Part C: Emerging Technologies, 36, 1–12. https://doi.org/10.1016/j.trc.2013.07.010
  • Zhang, L. 2007. Study on the method of constructing bus stops OD matrix based on IC card data. In 2007 International Conference on Wireless Communications, Networking and Mobile Computing. IEEE, 3147–3150.
  • Zhang, D., Zhao, J., Zhang, F., & Tian, H. 2015. coMobile: Realtime human mobility modeling at urban scale using multi-view learning. In Proceedings of the 23rd SIGSPATIAL international conference on advances in geographic information systems. 1–10.
  • Zhao, Z., Koutsopoulos, H. N., & Zhao, J. 2018. Individual mobility prediction using transit smart card data. Transportation research part C: Emerging technologies. 89, 19–34. https://doi.org/10.1016/j.trc.2018.01.022
  • Zhao, J., Qiang, Q., Zhang, F., Chengzhong, X., & Liu, S. (2017). Spatio-temporal analysis of passenger travel patterns in massive smart card data. IEEE Transactions on Intelligent Transportation Systems, 18(11), 11. https://doi.org/10.1109/TITS.2017.2679179