Skip to main content
SpringerLink
Log in

A Big Data Demand Estimation Framework for Multimodal Modelling of Urban Congested Networks

  • Conference paper
  • First Online:
Data Analytics: Paving the Way to Sustainable Urban Mobility (CSUM 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 879))

Included in the following conference series:

  • 2718 Accesses

Abstract

This paper deals with the problem of estimating daily mobility flows using different sources of data, and in particular from mobile devices, such as mobile phones and floating car data. We show how mobile phone data can be used to better estimate the structure of the demand matrix, both temporally (i.e. the daily generated flows from each zone) and spatially (i.e. distributing the flows on the different OD pairs). Then, floating car data together with traffic counts can be used to further distribute the demand on the available modes and routes. During this phase, a behavioral modelling approach is used, according to traditional dynamic user equilibrium using a joint route and departure time choice model. Floating car data information is used to estimate speed profiles at all links where information is available, and for route travel times, which feed the utility-based models. A two-step approach is then proposed to solve the problem for large scale networks, in which the total demand is first generated, and then equilibrium is calculated through a dynamic traffic assignment model. The effectiveness and reliability of the proposed modelling framework is shown on a realistic case study involving the road network of Luxembourg City and its surroundings, and is compared to the traditional bi-level formulation solved using the Generalized Least Square (GLS) Estimation. The comparison shows how the two-step approach is more robust in generating realistic daily OD flows, and in exploiting the information collected from mobile sensors.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. McNally, M.G.: The four-step model. In: Handbook of Transport Modelling, vol. 1, pp. 35–53. Emerald Group Publishing Limited (2007)

    Google Scholar 

  2. Toole, J.L., Colak, S., Sturt, B., Alexander, L.P., Evsukoff, A., González, M.C.: The path most traveled: travel demand estimation using big data resources. Transp. Res. Part C Emerg. Technol. 58, 162–177 (2015). Part B

    Article  Google Scholar 

  3. Cascetta, E., Inaudi, D., Marquis, G.: Dynamic estimators of origin-destination matrices using traffic counts. Transp. Sci. 27(4), 363–373 (1993)

    Article  Google Scholar 

  4. Ashok, K., Ben-Akiva, M.E.: Estimation and prediction of time-dependent origin-destination flows with a stochastic mapping to path flows and link flows. Transp. Sci. 36(2), 184–198 (2002)

    Article  Google Scholar 

  5. Tavana, H.: Internally-consistent estimation of dynamic network origin-destination flows from intelligent transportation systems data using bi-level optimization. Ph.D. thesis (2001)

    Google Scholar 

  6. Balakrishna, R., Ben-Akiva, M., Koutsopoulos, H.: Offline calibration of dynamic traffic assignment: simultaneous demand-and-supply estimation. Transp. Res. Rec. 2003, 50–58 (2007)

    Article  Google Scholar 

  7. Frederix, R., Viti, F., Corthout, R., Tampère, C.M.J.: New gradient approximation method for dynamic origin-destination matrix estimation on congested networks. Transp. Res. Rec. 2263, 19–25 (2011)

    Article  Google Scholar 

  8. Cipriani, E., Florian, M., Mahut, M., Nigro, M.: A gradient approximation approach for adjusting temporal origin-destination matrices. Transp. Res. Part C Emerg. Technol. 19(2), 270–282 (2011)

    Article  Google Scholar 

  9. Antoniou, C., Lima Azevedo, C., Lu, L., Pereira, F., Ben-Akiva, M.: W-SPSA in practice: approximation of weight matrices and calibration of traffic simulation models. Transp. Res. Part C Emerg. Technol. 59, 129–146 (2015)

    Article  Google Scholar 

  10. Tympakianaki, A., Koutsopoulos, H.N., Jenelius, E.: c-SPSA: cluster-wise simultaneous perturbation stochastic approximation algorithm and its application to dynamic origin–destination matrix estimation. Transp. Res. Part C Emerg. Technol. 55, 231–245 (2015)

    Article  Google Scholar 

  11. Djukic, T., Van Lint, J., Hoogendoorn, S.: Application of principal component analysis to predict dynamic origin-destination matrices. Transp. Res. Rec. 2283, 81–89 (2012)

    Article  Google Scholar 

  12. Cascetta, E., Papola, A., Marzano, V., Simonelli, F., Vitiello, I.: Quasi-dynamic estimation of o–d flows from traffic counts: formulation, statistical validation and performance analysis on real data. Transp. Res. Part B Methodol. 55, 171–187 (2013)

    Article  Google Scholar 

  13. Cantelmo, G., Viti, F., Tampère, C.M.J., Cipriani, E., Nigro, M.: Two-step approach for the correction of seed matrix in dynamic demand estimation. Transp. Res. Rec. 2466, 125–133 (2014)

    Article  Google Scholar 

  14. Cantelmo, G., Viti, F., Cipriani, E., Marialisa, N.: 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 (2015)

    Google Scholar 

  15. Antoniou, C., et al.: Towards a generic benchmarking platform for origin–destination flows estimation/updating algorithms: design, demonstration and validation. Transp. Res. Part C Emerg. Technol. 66, 79–98 (2016)

    Article  Google Scholar 

  16. Marzano, V., Papola, A., Simonelli, F.: Limits and perspectives of effective O–D matrix correction using traffic counts. Transp. Res. Part C Emerg. Technol. 17(2), 120–132 (2009)

    Article  Google Scholar 

  17. Derrmann, T., Frank, R., Engel, T., Viti, F.: 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 (2017)

    Google Scholar 

  18. Di Donna, S.A., Cantelmo, G., Viti, F.: 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 (2015)

    Google Scholar 

  19. Cantelmo, G., Viti, F., Derrmann, T.: 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 (2017)

    Google Scholar 

  20. Small, K.A.: The bottleneck model: an assessment and interpretation. Econ. Transp. 4(1–2), 110–117 (2015)

    Article  Google Scholar 

  21. Cipriani, E., Del Giudice, A., Nigro, M., Viti, F., Cantelmo, G.: The impact of route choice modeling on dynamic OD estimation. In: Proceedings of the IEEE Conference on Intelligent Transportation Systems, ITSC 2015, pp. 1483–1488 (2015)

    Google Scholar 

Download references

Acknowledgements

The authors acknowledge the FNR for providing the financing grant: AFR-PhD grant 6947587 IDEAS. We also like to thank Motion-S Luxembourg for providing Speed data.

Author information

Authors and Affiliations

  1. University of Luxembourg, 4364, Esch-Sur-Alzette, Luxembourg

    Guido Cantelmo & Francesco Viti

Authors
  1. Guido Cantelmo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Francesco Viti
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Francesco Viti .

Editor information

Editors and Affiliations

  1. Department of Civil Engineering, Traffic, Transportation and Logistics Laboratory (TTLog), University of Thessaly, Volos, Greece

    Eftihia G. Nathanail

  2. Department of Civil Engineering, Traffic, Transportation and Logistics Laboratory (TTLog), University of Thessaly, Volos, Greece

    Ioannis D. Karakikes

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Cantelmo, G., Viti, F. (2019). A Big Data Demand Estimation Framework for Multimodal Modelling of Urban Congested Networks. In: Nathanail, E., Karakikes, I. (eds) Data Analytics: Paving the Way to Sustainable Urban Mobility. CSUM 2018. Advances in Intelligent Systems and Computing, vol 879. Springer, Cham. https://doi.org/10.1007/978-3-030-02305-8_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-02305-8_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02304-1

  • Online ISBN: 978-3-030-02305-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation