Otwarty dostęp

A Big Data Demand Estimation Model for Urban Congested Networks


Zacytuj

1. McNally, M. G. (2007) The Four-Step Model. In: Handbook of Transport Modelling, 1, Emerald Group Publishing Limited, pp. 35–53.10.1108/9780857245670-003Search 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. 198810.1016/0191-2615(88)90024-0Search 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. 1993.10.1287/trsc.27.4.363Search 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.2019.05.010Search 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. 2003.10.3141/2003-07Search 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.2010.5625220Search 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 2016.10.1016/j.trc.2015.08.009Search 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. 134–144.10.1016/j.trpro.2015.09.063Search 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/s12544-011-0058-1Search 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. 159-174.10.1080/15472450.2017.1421462Search 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.2015.7223263Search 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.2015.04.022Search 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 2019.10.1109/MTITS.2019.8883329Search 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. 2013.10.1080/18128602.2011.619587Search 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. 120–132.10.1016/j.trc.2008.09.001Search 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. 2283.10.3141/2283-09Search 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. 2013.10.1016/j.trb.2013.06.007Search 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, 125-133.10.3141/2466-14Search 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.2015.241Search 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. 184–198.10.1287/trsc.36.2.184.563Search 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. 173–201.10.1007/978-3-642-21551-3_7Search 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, 2011.10.3141/2263-03Search 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. 270–282.10.1016/j.trc.2010.05.013Search 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. 2015.10.1016/j.trc.2015.04.030Search 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.2015.01.016Search 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.2019.2915273Search 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.2017.8005721Search 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.2017.8005697Search 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.2018.01.039Search 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.2015.242Search in Google Scholar

eISSN:
1407-6179
Język:
Angielski
Częstotliwość wydawania:
4 razy w roku
Dziedziny czasopisma:
Engineering, Introductions and Overviews, other