1,110
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Revealing representative day-types in transport networks using traffic data clustering

ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Received 01 Nov 2021, Accepted 17 Apr 2023, Published online: 18 May 2023

References

  • Adolfsson, A., Ackerman, M. and Brownstein, N. C. (2019). To cluster, or not to cluster: An analysis of clusterability methods. Pattern Recognition, 88, 13–26. https://doi.org/10.1016/j.patcog.2018.10.026
  • Buzna, L., Koháni, M. and Janáček, J. (2014). An approximation algorithm for the facility location problem with lexicographic minimax objective. Journal of Applied Mathematics, 2014, 1–12. https://doi.org/10.1155/2014/562373
  • Cebecauer, M., Gundlegård, D., Jenelius, E. and Burghout, W. (2019). 3d speed maps and mean observations vectors for short-term urban traffic prediction. In Transportation Research Board Annual Meeting (TRB) (pp. 1–20).
  • Cebecauer, M., Jenelius, E. and Burghout, W. (2017). Integrated framework for real-time urban network travel time prediction on sparse probe data. IET Intelligent Transport Systems, 12(1), 66–74. https://doi.org/10.1049/iet-its.2017.0113
  • Cebecauer, M., Jenelius, E. and Burghout, W. (2018). Spatio-temporal partitioning of large urban networks for travel time prediction. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC) (pp. 1390–1395). https://doi.org/10.1109/ITSC.2018.8569648
  • Chiabaut, N. and Faitout, R. (2021). Traffic congestion and travel time prediction based on historical congestion maps and identification of consensual days. Transportation Research Part C: Emerging Technologies, 124, 102920. https://doi.org/10.1016/j.trc.2020.102920
  • Chrobok, R., Kaumann, O., Wahle, J. and Schreckenberg, M. (2004). Different methods of traffic forecast based on real data. European Journal of Operational Research, 155(3), 558–568. https://doi.org/10.1016/j.ejor.2003.08.005
  • Clark, S. (2003). Traffic prediction using multivariate nonparametric regression. Journal of transportation engineering, 129(2), 161–168. https://doi.org/10.1061/(ASCE)0733-947X(2003)129:2(161)
  • Davies, D. L. and Bouldin, D. W. (1979). A cluster separation measure. IEEE transactions on pattern analysis and machine intelligence 2 224–227. https://doi.org/10.1109/TPAMI.1979.4766909
  • Djukic, T., Van Lint, J. and Hoogendoorn, S. (2012). Application of principal component analysis to predict dynamic origin–destination matrices. Transportation research record, 2283(1), 81–89. https://doi.org/10.3141/2283-09
  • Ester, M., Kriegel, H.-P., Sander, J. and Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In KDD-96 Proceedings (pp. 226–231).
  • Estivill-Castro, V. (2002). Why so many clustering algorithms: A position paper. ACM SIGKDD Explorations Newsletter, 4(1), 65–75. https://doi.org/10.1145/568574.568575
  • Feldman, R., Sanger, J. (2007). The text mining handbook: Advanced approaches in analyzing unstructured data. Cambridge University Press.
  • Ferranti, F. (2020). Public transport origin-destination matrices: Pattern recognition and short-term prediction. KTH Royal institute of technology.
  • Frey, B. J. and Dueck, D. (2007). Clustering by passing messages between data points. Science, 315(5814), 972–976. https://doi.org/10.1126/science.1136800
  • García, S., Labbé, M. and Marín, A. (2011). Solving large p-median problems with a radius formulation. INFORMS Journal on Computing, 23(4), 546–556. https://doi.org/10.1287/ijoc.1100.0418
  • Jenelius, E. and Koutsopoulos, H. N. (2018). Urban network travel time prediction based on a probabilistic principal component analysis model of probe data. IEEE Transactions on Intelligent Transportation Systems, 19(2), 436–445. https://doi.org/10.1109/TITS.2017.2703652
  • Ji, Y. and Geroliminis, N. (2012). On the spatial partitioning of urban transportation networks. Transportation Research Part B: Methodological, 46(10), 1639–1656. https://doi.org/10.1016/j.trb.2012.08.005
  • Krishnakumari, P., Cats, O. and van Lint, H. (2020). A compact and scalable representation of network traffic dynamics using shapes and its applications. Transportation Research Part C: Emerging Technologies, 121, 102850. https://doi.org/10.1016/j.trc.2020.102850
  • Kumar, S. V. and Vanajakshi, L. (2015). Short-term traffic flow prediction using seasonal arima model with limited input data. European Transport Research Review, 7(3), 1–9. https://doi.org/10.1007/s12544-015-0170-8
  • Laña, I., Sanchez-Medina, J. J., Vlahogianni, E. I. and Del Ser, J. (2021). From data to actions in intelligent transportation systems: A prescription of functional requirements for model actionability. Sensors, 21(4), 1121. https://doi.org/10.3390/s21041121
  • Lloyd, S. (1982). Least squares quantization in pcm. IEEE Transactions on Information Theory, 28(2), 129–137. https://doi.org/10.1109/TIT.1982.1056489
  • Lopez, C., Leclercq, L., Krishnakumari, P., Chiabaut, N. and Lint, H. (2017). Revealing the day-to-day regularity of urban congestion patterns with 3d speed maps. Scientific Reports, 7(1), 14029. https://doi.org/10.1038/s41598-017-14237-8
  • Luo, D., Cats, O. and Van Lint, H. (2017). Analysis of network-wide transit passenger flows based on principal component analysis. In 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (pp. 744–749). https://doi.org/10.1109/MTITS.2017.8005611
  • MacQueen, J., et al. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (Vol. 1, pp. 281–297).
  • Manibardo, E. L., Laña, I. and Del Ser, J. (2021). Deep learning for road traffic forecasting: Does it make a difference? IEEE Transactions on Intelligent Transportation Systems, 23(7), 6164–6188. https://doi.org/10.1109/TITS.2021.3083957
  • Murtagh, F. (1983). A survey of recent advances in hierarchical clustering algorithms. The Computer Journal, 26(4), 354–359. https://doi.org/10.1093/comjnl/26.4.354
  • Patel, M., Valderrama, C. and Yadav, A. (2022). Metaheuristic enabled deep convolutional neural network for traffic flow prediction: Impact of improved lion algorithm. Journal of Intelligent Transportation Systems, 26(6), 730–745. https://doi.org/10.1080/15472450.2021.1974857
  • Pfitzner, D., Leibbrandt, R. and Powers, D. (2009). Characterization and evaluation of similarity measures for pairs of clusterings. Knowledge and Information Systems, 19(3), 361–394. https://doi.org/10.1007/s10115-008-0150-6
  • Rendón, E., Abundez, I., Arizmendi, A. and Quiroz, E. M. (2011). Internal versus external cluster validation indexes. International Journal of computers and communications, 5(1), 27–34.
  • Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65. https://doi.org/10.1016/0377-0427(87)90125-7
  • Saeedmanesh, M. and Geroliminis, N. (2016). Clustering of heterogeneous networks with directional flows based on “snake” similarities. Transportation Research Part B: Methodological, 91, 250–269. https://doi.org/10.1016/j.trb.2016.05.008
  • Shi, J. and Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 888–905. https://doi.org/10.1109/34.868688
  • Toqué, F., Côme, E., El Mahrsi, M. K. and Oukhellou, L. (2016). Forecasting dynamic public transport origin-destination matrices with long-short term memory recurrent neural networks. In 2016 IEEE 19th international conference on intelligent transportation systems (ITSC) (pp. 1071–1076). https://doi.org/10.1109/ITSC.2016.7795689
  • Toqué, F., Khouadjia, M., Come, E., Trepanier, M. and Oukhellou, L. (2017). Short & long term forecasting of multimodal transport passenger flows with machine learning methods. In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) (pp. 560–566). https://doi.org/10.1109/ITSC.2017.8317939
  • Van Hinsbergen, C., Van Lint, J. and Sanders, F. (2007). Short term traffic prediction models. In Proceedings of the 14th World Congress on Intelligent Transport Systems (ITS), Held Beijing, October 2007.
  • van Lint, J. W. C. and van Hinsbergen, C. P. I. J. (2012). Short-term traffic and travel time prediction models. Artificial Intelligence Applications to Critical Transportation Issues, 22, 22–41.
  • Vlahogianni, E. I., Karlaftis, M. G. and Golias, J. C. (2014). Short-term traffic forecasting: Where we are and where we’re going. Transportation Research Part C: Emerging Technologies, 43, 3–19. https://doi.org/10.1016/j.trc.2014.01.005
  • Ward Jr, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American statistical association, 58(301), 236–244. https://doi.org/10.1080/01621459.1963.10500845
  • Weijermars, W. and Van Berkum, E. (2005). Analyzing highway flow patterns using cluster analysis. In Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005 (pp. 308–313). https://doi.org/10.1109/ITSC.2005.1520157
  • Weijermars, W. A. M. (2007). Analysis of urban traffic patterns using clustering Analysis of urban traffic patterns using clustering (Vol. 41). Netherlands TRAIL Research School.
  • Wild, D. (1997). Short-term forecasting based on a transformation and classification of traffic volume time series. International Journal of Forecasting, 13(1), 63–72. https://doi.org/10.1016/S0169-2070(96)00701-7
  • Yang, C., Yan, F. and Xu, X. (2017). Daily metro origin-destination pattern recognition using dimensionality reduction and clustering methods. In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) (pp. 548–553). https://doi.org/10.1109/ITSC.2017.8317899
  • Zhang, J., Che, H., Chen, F., Ma, W. and He, Z. (2021). Short-term origin-destination demand prediction in urban rail transit systems: A channel-wise attentive split-convolutional neural network method. Transportation Research Part C: Emerging Technologies, 124, 102928. https://doi.org/10.1016/j.trc.2020.102928
  • Zhang, J., Wang, F.-Y., Wang, K., Lin, W.-H., Xu, X. and Chen, C. (2011). Data-driven intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems, 12(4), 1624–1639. https://doi.org/10.1109/TITS.2011.2158001
  • Zhu, L., Yu, F. R., Wang, Y., Ning, B. and Tang, T. (2018). Big data analytics in intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems, 20(1), 383–398. https://doi.org/10.1109/TITS.2018.2815678