Origin-destination (OD) matrices provide transportation experts with comprehensive information on the number and distribution of trips. For comparing two OD matrices, it is vital to consider not only the numerical but also the structural differences, including trip distribution priorities and travel patterns in the study region. The mean structural similarity (MSSIM) index, geographical window-based structural similarity index (GSSI), and socioeconomic, land-use, and population structural similarity index (SLPSSI) have been developed for the structural comparison of OD matrices. These measures have undeniable drawbacks that fail to correctly detect differences in travel patterns, therefore, a novel measure is developed in this paper in which geographical, socioeconomic, land-use, and population characteristics are simultaneously considered in a structural similarity index named GSLPSSI for comparison of OD matrices. The proposed measure was evaluated using OD matrices of smartphone Global Positioning System (GPS) data in Tehran metropolitan. Also, the robustness of the proposed measure was verified using sensitivity analysis. GSLPSSI was found to have up to 21%, 15%, and 9% higher accuracy than MSSIM, GSSI, and SLPSSI, respectively, regarding structural similarity calculation. Furthermore, the proposed measure showed 7% higher accuracy than SLPSSI in the structural similarity index of two sparse OD matrices.
Cipriani E, Florian M, Mahut M, Nigro M. A gradient approximation approach for adjusting temporal origin–destination matrices. Transportation Research Part C: Emerging Technologies. 2011;19(2): 270-82. doi: 10.1016/j.trc.2010.05.013.
Djukic T, Flötteröd G, Van Lint H, Hoogendoorn S. Efficient real time OD matrix estimation based on Principal Component Analysis. 2012 15th International IEEE Conference on Intelligent Transportation Systems. IEEE; 2012. p. 115-121. doi: 10.1109/ITSC.2012.6338720.
Michau G, et al. A primal-dual algorithm for link dependent origin destination matrix estimation. IEEE Transactions on Signal and Information Processing over Networks. 2016;3(1): 104-13. doi: 10.1109/TSIPN.2016.2623094.
Moreira-Matias L, et al. Time-evolving OD matrix estimation using high-speed GPS data streams. Expert Systems with Applications. 2016;44: 275-88. doi: 10.1016/j.eswa.2015.08.048.
Osorio C. Dynamic origin-destination matrix calibration for large-scale network simulators. Transportation Research Part C: Emerging Technologies. 2019;98: 186-206. doi: 10.1016/j.trc.2018.09.023.
Xiong X, Ozbay K, Jin L, Feng C. Dynamic origin–destination matrix prediction with line graph neural networks and kalman filter. Transportation Research Record. 2020;2674(8): 491-503. doi: 10.1177/0361198120919399.
Novačko L, Šimunović L, Krasić D. Estimation of origin-destination trip matrices for small cities. Promet – Traffic&Transportation. 2014;26(5): 419-28. doi: 10.7307/ptt.v26i5.1501.
Bierlaire M. The total demand scale: A new measure of quality for static and dynamic origin–destination trip tables. Transportation Research Part B: Methodological. 2002;36(9): 837-50. doi: 10.1016/S0191-2615(01)00036-4.
Djukic T, Hoogendoorn S, Van Lint H. Reliability assessment of dynamic OD estimation methods based on structural similarity index. Proceedings of the 92rd Annual Meeting of the Transportation Research Board; 2013. doi: 10.13140/RG.2.1.4174.1929.
Jin C, Nara A, Yang JA, Tsou MH. Similarity measurement on human mobility data with spatially weighted structural similarity index (SpSSIM). Transactions in GIS. 2020;24(1): 104-22. doi: 10.1016/S0191-2615(01)00036-4.
Tavassoli A, Alsger A, Hickman M, Mesbah M. How close the models are to the reality? Comparison of transit origin-destination estimates with automatic fare collection data. In: Australasian Transport Research Forum 2016 Proceedings, 1 January 2016, Melbourne, Australia; 2016. p. 1-15.
Behara KN, Bhaskar A, Chung E. A novel approach for the structural comparison of origin-destination matrices: Levenshtein distance. Transportation Research Part C: Emerging Technologies. 2020;111: 513-30. doi: 10.1016/j.trc.2020.01.005.
Andrienko G, Andrienko N, Fuchs G, Wood J. Revealing patterns and trends of mass mobility through spatial and temporal abstraction of origin-destination movement data. IEEE transactions on visualization and computer graphics. 2016;23(9): 2120-36. doi: 10.1109/TVCG.2016.2616404.
Bierlaire M, Toint PL. Meuse: An origin-destination matrix estimator that exploits structure. Transportation Research Part B: Methodological. 1995;29(1): 47-60. doi: 10.1016/0191-2615(94)00025-U.
Behara KN, Bhaskar A, Chung E. Geographical window based structural similarity index for origin-destination matrices comparison. Journal of Intelligent Transportation Systems. 2020;26(1): 46–67. doi: 10.1080/15472450.2020.1795651.
Barceló Bugeda J, Montero Mercadé L, Marqués L, Carmona Bautista C. A Kalman-filter approach for dynamic OD estimation in corridors based on bluetooth and Wi-Fi data collection. 12th World Conference on Transportation Research WCTR; 2010.
Frederix R, Viti F, Himpe WWE, Tampère CMJ. Dynamic Origin–Destination Matrix Estimation on Large-Scale Congested Networks Using a Hierarchical Decomposition Scheme. Journal of Intelligent Transportation Systems. 2014;18(1): 51-66. doi: 10.1080/15472450.2013.773249.
Hellinga B, Aerde MV. A statistical analysis of the reliability of using RGS vehicle probes as estimators of dynamic O-D departures rates. IVHS Journal. 1994;2(1): 21-44. doi: 10.1080/10248079408903813.
Antoniou C, Ben-Akiva M, Koutsopoulos HN. Incorporating automated vehicle identification data into origin-destination estimation. Transportation Research Record. 2004;1882(1): 37-44. doi: 10.3141/1882-05.
Barceló J, et al. Robustness and computational efficiency of Kalman filter estimator of time-dependent origin–destination matrices: Exploiting traffic measurements from information and communications technologies. Transportation Research Record. 2013;2344(1): 31-9. doi: 10.3141/2344-04.
Afandizadeh Zargari S, Memarnejad A, Mirzahossein H. A structural comparison between the origin-destination matrices based on local windows with socioeconomic, land-use, and population characteristics. Journal of Advanced Transportation. 2021 Jun 14;2021. doi: 10.1155/2021/9968698.
Van Vuren T, Day-Pollard T, editors. 256 shades of grey-comparing OD matrices using image quality assessment techniques. 2015 Scottish Transport Applications Research Conference Proceedings; 2015.
Ruiz de Villa A, Casas J, Breen M. OD matrix structural similarity: Wasserstein metric. 2014. Proceedings of the 93rd Annual Meeting of the Transportation Research Board; 2014.
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing. 2004;13(4): 600-12. DOI: 10.1109/TIP.2003.819861.
Djukic T. Dynamic OD demand estimation and prediction for dynamic traffic management. PhD thesis. Delft University of Technology; 2014.
Afandizadeh Zargari S, Safari F. Using clustering methods in multinomial logit model for departure time choice. Journal of Advanced Transportation. 2020 Jan 15;2020. doi: 10.1155/2020/7382569.
Zheng Y, Zhao G, Liu J. A novel grid based k-means cluster method for traffic zone division. Second International Conference on Cloud Computing and Big Data in Asia; 2015. p. 165-178. doi: 10.1007/978-3-319-28430-9_13.
Pollard T, Taylor N, van Vuren T, MacDonald M. Comparing the quality of OD matrices in time and between data sources. Proceedings of the European Transport Conference; 2013.
United Nations PD. The World’s Cities in 2016. Retrieved 2017-08-02.; 2017. https://www.un.org/en/development/desa/population/publications/pdf/urbanization/the_worlds_cities_in_2016_data_booklet.pdf [Accessed 14th June 2020].
Ge Q, Fukuda D. Updating origin–destination matrices with aggregated data of GPS traces. Transportation Research Part C: Emerging Technologies. 2016;69: 291-312. doi: 10.1016/j.trc.2016.06.002.
Dumbliauskas V, Grigonis V, Barauskas A. Application of Google-based data for travel time analysis: Kaunas city case study. Promet – Traffic&Transportation. 2017;29(6): 613-21. doi: 10.7307/ptt.v29i6.2369.
Iooss B, Lemaître P. A review on global sensitivity analysis methods. In: Dellino G, Meloni C. (eds) Uncertainty Management in Simulation-Optimization of Complex Systems. Operations Research/Computer Science Interfaces Series. Vol 59. Boston, MA: Springer; 2015. p. 101-22. doi: 10.1007/978-1-4899-7547-8_5.
Djukic T, et al. Advanced traffic data for dynamic OD demand estimation: The state of the art and benchmark study. TRB 94th Annual Meeting Compendium of Papers; 2015. p. 1-16.
Hasnine MS, Nurul Habib K. Tour-based mode choice modelling as the core of an activity-based travel demand modelling framework: A review of state-of-the-art. Transport Reviews. 2021;41(1): 5-26. doi: 10.1080/01441647.2020.1780648.
Behara KN, Bhaskar A, Chung E. A novel methodology to assimilate sub-path flows in bi-level OD matrix estimation process. IEEE Transactions on Intelligent Transportation Systems; 2020. doi: 10.1109/TITS.2020.2998475.
Behara KN, Bhaskar A, Chung E. Single-level approach to estimate origin-destination matrix: Exploiting turning proportions and partial OD flows. Transportation Letters. 2021: 1-12. doi: 10.1080/19427867.2021.1932182.
Guest Editor: Eleonora Papadimitriou, PhD
Editors: Dario Babić, PhD; Marko Matulin, PhD; Marko Ševrović, PhD.
The mission of our journal is to provide high quality communication of research results, which is crucial for the effectiveness of scientific research, the promotion of scientific information, new ideas, and original research in the field of traffic and transportation engineering.
1848-4069
0353-5320
Proud publisher
Accelerating Discoveries in Traffic Science |
2024 © Promet - Traffic&Transportation journal
