[1. McNally, M. G. (2007) The Four-Step Model. In: Handbook of Transport Modelling, 1, Emerald Group Publishing Limited, pp. 35–53.10.1108/9780857245670-003]Search in Google Scholar
[2. Cascetta, E. and S. Nguyen. (1988) A unified framework for estimating or updating origin/destination matrices from traffic counts, Transportation Research Part B: Methodological, vol. 22, no. 6, pp. 437–455, Dec. 1988]Search in Google Scholar
[3. Cascetta, E., Inaudi, D. and G. Marquis. (1993) Dynamic Estimators of Origin-Destination Matrices Using Traffic Counts, Transportation Science, 27(4), pp. 363–373, Nov. 1993.]Search in Google Scholar
[4. Cantelmo, G., Qurashi, M., Prakash, A.A., Antoniou, C., Viti, F. (2020) Incorporating trip chaining within online demand estimation. Transportation Research Part B: Methodological, 132, pp. 171-187.10.1016/j.trb.2019.05.010]Search in Google Scholar
[5. Tavana, H. (2001) Internally-consistent estimation of dynamic network origin-destination flows from intelligent transportation systems data using bi-level optimization, PhD Thesis, 2001.]Search in Google Scholar
[6. Balakrishna, R., Ben-Akiva, M., Koutsopoulos, H. (2007) Offline Calibration of Dynamic Traffic Assignment: Simultaneous Demand-and-Supply Estimation. Transportation Research Record, vol. 2003.10.3141/2003-07]Search in Google Scholar
[7. Frederix, R., Viti, F. and C. M. J. Tampère (2010) A density-based dynamic OD estimation method that reproduces within-day congestion dynamics. In: 13th International IEEE Conference on Intelligent Transportation Systems, 2010, pp. 694–699.10.1109/ITSC.2010.5625220]Search in Google Scholar
[8. Antoniou, C. et al. (2016) Towards a generic benchmarking platform for origin–destination flows estimation/updating algorithms: Design, demonstration and validation, Transportation Research Part C: Emerging Technologies, vol. 66, pp. 79–98, May 2016.]Search in Google Scholar
[9. Barceló, J. and L. Montero (2015) A Robust Framework for the Estimation of Dynamic OD Trip Matrices for Reliable Traffic Management, Transportation Research Procedia, vol. 10, pp. 134–144.]Search in Google Scholar
[10. Mitsakis, E., Grau, J.-M. S., Chrysohoou, E. and G. Aifadopoulou (2013) A Robust Method for Real Time Estimation of Travel Times for Dense Urban Road Networks Using Point-to-Point Detectors, Transportation Research Board 92nd Annual Meeting, 2013.]Search in Google Scholar
[11. Antoniou, C., Balakrishna, R., ---amp--- Koutsopoulos, H. N. (2011). A Synthesis of emerging data collection technologies and their impact on traffic management applications. European Transport Research Review, 3(3), 139–148.10.1007/s12544-011-0058-1]Search in Google Scholar
[12. Nigro, M., Cipriani, E. and A. Del Giudice (2017) Exploiting floating car data for time-dependent O-D matrices estimation, Journal of Intelligent Transportation Systems: Technology, Planning ---amp--- Operations, 22(2), pp. 159-174.]Search in Google Scholar
[13. Di Donna, S.A., Cantelmo, G., Viti, F. (2015) A Markov Chain dynamic model for trip generation and distribution based on CDR. In: 4th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2015, pp. 243-250, June 2015.10.1109/MTITS.2015.7223263]Search in Google Scholar
[14. Toole, J. L., Colak, S., Sturt, B., Alexander, L. P., Evsukoff, A. and M. C. González. (2015) The path most traveled: Travel demand estimation using big data resources, Transp. Res. Part C Emerg. Technol., vol. 58, Part B, pp. 162–177, Sep. 2015.10.1016/j.trc.2015.04.022]Search in Google Scholar
[15. Carrese, F., Cantelmo, G., Fusco, G., Viti, F. (2019) Leveraging GIS data and topological information to infer trip chaining behavior at macroscopic level. In: 6th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2019, June 2019.]Search in Google Scholar
[16. Frederix, R., Viti, F. and M. J. Tampère. (2013) Dynamic origin–destination estimation in congested networks: theoretical findings and implications in practice, Transportmetrica: Transport Science, 9(6), pp. 494–513, Jul. 2013.]Search in Google Scholar
[17. Marzano, V., Papola, A. and F. Simonelli. (2009) Limits and perspectives of effective O–D matrix correction using traffic counts, Transportation Research Part C: Emerging Technologies, 17(2), pp. 120–132.]Search in Google Scholar
[18. Ben-Akiva, M.E., Bowman, J.L. (1988) Activity Based Travel Demand Model Systems. In: Marcotte P., Nguyen S. (eds) Equilibrium and Advanced Transportation Modelling. Centre for Research on Transportation. Springer, Boston, MA.]Search in Google Scholar
[19. Djukic, T., Van Lint, J. and S. Hoogendoorn. (2012) Application of principal component analysis to predict dynamic origin-destination matrices. Transportation Research Record, vol. 2283.10.3141/2283-09]Search in Google Scholar
[20. Cascetta, E., Papola, A., Marzano, V., Simonelli, F. and I. Vitiello. (2013) Quasi-dynamic estimation of o–d flows from traffic counts: Formulation, statistical validation and performance analysis on real data, Transportation Research Part B: Methodological, vol. 55, pp. 171–187, Sep. 2013.]Search in Google Scholar
[21. Cantelmo, G., Viti, F., Tampère, C.M.J., Cipriani, E., Nigro, M. (2014) Two-step approach for the correction of seed matrix in dynamic demand estimation. Transportation Research Record 2466, 125-133.10.3141/2466-14]Search in Google Scholar
[22. Cantelmo, G., Viti, F., Cipriani, E. and M. Nigro (2015) A Two-Steps Dynamic Demand Estimation Approach Sequentially Adjusting Generations and Distributions. In: 2015 IEEE 18th International Conference on Intelligent Transportation Systems, pp. 1477–1482.10.1109/ITSC.2015.241]Search in Google Scholar
[23. Ashok, K. and M. E. Ben-Akiva (2002) Estimation and prediction of time-dependent origin-destination flows with a stochastic mapping to path flows and link flows, Transp. Sci., 36(2), pp. 184–198.]Search in Google Scholar
[24. Spall, J. C. (2012) Stochastic Optimization. In: Handbook of Computational Statistics, J. E. Gentle, W. K. Härdle, and Y. Mori, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 173–201.]Search in Google Scholar
[25. Frederix, R., Viti, F., Corthout, R. and C. M. J. Tampère (2011) New gradient approximation method for dynamic origin-destination matrix estimation on congested networks, Transp. Res. Rec., no. 2263, pp. 19–25, 2011.]Search in Google Scholar
[26. Cipriani, E., Florian, M., Mahut, M. and M. Nigro (2011) A gradient approximation approach for adjusting temporal origin-destination matrices, Transportation Research Part C: Emerging Technologies, 19(2), pp. 270–282.]Search in Google Scholar
[27. Antoniou, C., Lima Azevedo, C., Lu, L., Pereira, F. and M. Ben-Akiva (2015) W-SPSA in practice: Approximation of weight matrices and calibration of traffic simulation models, Transp. Res. Part C Emerg. Technol., vol. 59, pp. 129–146, Oct. 2015.]Search in Google Scholar
[28. Tympakianaki, A., Koutsopoulos, H. N. and E. Jenelius. (2015) c-SPSA: Cluster-wise simultaneous perturbation stochastic approximation algorithm and its application to dynamic origin–destination matrix estimation, Transp. Res. Part C Emerg. Technol., vol. 55, pp. 231–245.10.1016/j.trc.2015.01.016]Search in Google Scholar
[29. Qurashi, M., Ma, T., Chaniotakis, E., Antoniou, C. (2020) PC–SPSA: Employing Dimensionality Reduction to Limit SPSA Search Noise in DTA Model Calibration. IEEE Transactions on ITS, 21(4), pp. 1635-1645.10.1109/TITS.2019.2915273]Search in Google Scholar
[30. Derrmann, T., Frank, R., Engel, T., Viti, F. (2017). How mobile handovers reflect urban mobility: a simulation study. In: 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2017, pp. 486-491.10.1109/MTITS.2017.8005721]Search in Google Scholar
[31. Cantelmo, G., Viti, F., Derrmann, T. (2017). Effectiveness of the two-step dynamic demand estimation model on large networks. In: 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2017, pp. 356-361.10.1109/MTITS.2017.8005697]Search in Google Scholar
[32. Cantelmo, G., Viti, F., Cipriani, E., Nigro, M. (2018) A utility-based dynamic demand estimation model that explicitly accounts for activity scheduling and duration”. Transportation Research Part A: Policy and Practice, Vol. 114, pp. 303-320.10.1016/j.tra.2018.01.039]Search in Google Scholar
[33. Cipriani, E., Del Giudice, A., Nigro, M., Viti, F., Cantelmo, G. (2015). The impact of route choice modeling on dynamic OD estimation. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC2015, pp. 1483-1488.10.1109/ITSC.2015.242]Search in Google Scholar