IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v92y1996i3p550-572.html
   My bibliography  Save this article

New operations research and artificial intelligence approaches to traffic engineering problems

Author

Listed:
  • Bielli, Maurizio
  • Reverberi, Pierfrancesco

Abstract

No abstract is available for this item.

Suggested Citation

  • Bielli, Maurizio & Reverberi, Pierfrancesco, 1996. "New operations research and artificial intelligence approaches to traffic engineering problems," European Journal of Operational Research, Elsevier, vol. 92(3), pages 550-572, August.
  • Handle: RePEc:eee:ejores:v:92:y:1996:i:3:p:550-572
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/0377-2217(96)00010-0
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bielli, Maurizio, 1992. "A DSS approach to urban traffic management," European Journal of Operational Research, Elsevier, vol. 61(1-2), pages 106-113, August.
    2. Cantarella, G. E. & Improta, G., 1988. "Capacity factor or cycle time optimization for signalized junctions: A graph theory approach," Transportation Research Part B: Methodological, Elsevier, vol. 22(1), pages 1-23, February.
    3. John D. C. Little, 1966. "The Synchronization of Traffic Signals by Mixed-Integer Linear Programming," Operations Research, INFORMS, vol. 14(4), pages 568-594, August.
    4. Cascetta, Ennio & Nguyen, Sang, 1988. "A unified framework for estimating or updating origin/destination matrices from traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 22(6), pages 437-455, December.
    5. Ritchie, Stephen G., 1990. "A Knowledge- Based Decision Support Architecture for Advanced Traffic Management," University of California Transportation Center, Working Papers qt9818b161, University of California Transportation Center.
    6. Little, John D. C. & Kelson, Mark D. & Gartner, Nathan H., 1981. "MAXBAND : a versatile program for setting signals on arteries and triangular networks," Working papers 1185-81., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    7. Yang, Hai & Sasaki, Tsuna & Iida, Yasunori & Asakura, Yasuo, 1992. "Estimation of origin-destination matrices from link traffic counts on congested networks," Transportation Research Part B: Methodological, Elsevier, vol. 26(6), pages 417-434, December.
    8. Van Zuylen, Henk J. & Willumsen, Luis G., 1980. "The most likely trip matrix estimated from traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 14(3), pages 281-293, September.
    9. Dickson, Thomas J., 1981. "A note on traffic assignment and signal timings in a signal-controlled road network," Transportation Research Part B: Methodological, Elsevier, vol. 15(4), pages 267-271, August.
    10. Bell, Michael G. H., 1992. "Future directions in traffic signal control," Transportation Research Part A: Policy and Practice, Elsevier, vol. 26(4), pages 303-313, July.
    11. Gartner, Nathan H. & Assman, Susan F. & Lasaga, Fernando & Hou, Dennis L., 1991. "A multi-band approach to arterial traffic signal optimization," Transportation Research Part B: Methodological, Elsevier, vol. 25(1), pages 55-74, February.
    12. Maher, M. J., 1983. "Inferences on trip matrices from observations on link volumes: A Bayesian statistical approach," Transportation Research Part B: Methodological, Elsevier, vol. 17(6), pages 435-447, December.
    13. Cascetta, Ennio, 1984. "Estimation of trip matrices from traffic counts and survey data: A generalized least squares estimator," Transportation Research Part B: Methodological, Elsevier, vol. 18(4-5), pages 289-299.
    14. Ritchie, Stephen G., 1990. "A Knowledge-Based Decision Support Architecture for Advanced Traffic Management," University of California Transportation Center, Working Papers qt7qv4w8kj, University of California Transportation Center.
    15. Spiess, Heinz, 1987. "A maximum likelihood model for estimating origin-destination matrices," Transportation Research Part B: Methodological, Elsevier, vol. 21(5), pages 395-412, October.
    16. Gartner, Nathan H. & Gershwin, Stanley B. & Little, John D. C. & Ross, Paul, 1980. "Pilot study of computer-based urban traffic management," Transportation Research Part B: Methodological, Elsevier, vol. 14(1-2), pages 203-217.
    17. Mahmassani, Hani S. & Chang, Gang-Len, 1986. "Experiments with departure time choice dynamics of urban commuters," Transportation Research Part B: Methodological, Elsevier, vol. 20(4), pages 297-320, August.
    18. Fisk, C. S., 1984. "Game theory and transportation systems modelling," Transportation Research Part B: Methodological, Elsevier, vol. 18(4-5), pages 301-313.
    19. Improta, G. & Cantarella, G. E., 1984. "Control system design for an individual signalized junction," Transportation Research Part B: Methodological, Elsevier, vol. 18(2), pages 147-167, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shivam Gupta & Sachin Modgil & Samadrita Bhattacharyya & Indranil Bose, 2022. "Artificial intelligence for decision support systems in the field of operations research: review and future scope of research," Annals of Operations Research, Springer, vol. 308(1), pages 215-274, January.
    2. Celikoglu, Hilmi Berk, 2013. "Reconstructing freeway travel times with a simplified network flow model alternating the adopted fundamental diagram," European Journal of Operational Research, Elsevier, vol. 228(2), pages 457-466.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Menon, Aditya Krishna & Cai, Chen & Wang, Weihong & Wen, Tao & Chen, Fang, 2015. "Fine-grained OD estimation with automated zoning and sparsity regularisation," Transportation Research Part B: Methodological, Elsevier, vol. 80(C), pages 150-172.
    2. Hai Yang & Qiang Meng & Michael G. H. Bell, 2001. "Simultaneous Estimation of the Origin-Destination Matrices and Travel-Cost Coefficient for Congested Networks in a Stochastic User Equilibrium," Transportation Science, INFORMS, vol. 35(2), pages 107-123, May.
    3. Doblas, Javier & Benitez, Francisco G., 2005. "An approach to estimating and updating origin-destination matrices based upon traffic counts preserving the prior structure of a survey matrix," Transportation Research Part B: Methodological, Elsevier, vol. 39(7), pages 565-591, August.
    4. Juha-Matti Kuusinen & Janne Sorsa & Marja-Liisa Siikonen, 2015. "The Elevator Trip Origin-Destination Matrix Estimation Problem," Transportation Science, INFORMS, vol. 49(3), pages 559-576, August.
    5. Fu, Hao & Lam, William H.K. & Shao, Hu & Ma, Wei & Chen, Bi Yu & Ho, H.W., 2022. "Optimization of multi-type sensor locations for simultaneous estimation of origin-destination demands and link travel times with covariance effects," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 19-47.
    6. Louis Grange & Felipe González & Shlomo Bekhor, 2017. "Path Flow and Trip Matrix Estimation Using Link Flow Density," Networks and Spatial Economics, Springer, vol. 17(1), pages 173-195, March.
    7. Hazelton, Martin L., 2000. "Estimation of origin-destination matrices from link flows on uncongested networks," Transportation Research Part B: Methodological, Elsevier, vol. 34(7), pages 549-566, September.
    8. Yang, Yudi & Fan, Yueyue & Wets, Roger J.B., 2018. "Stochastic travel demand estimation: Improving network identifiability using multi-day observation sets," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 192-211.
    9. Shao, Hu & Lam, William H.K. & Sumalee, Agachai & Chen, Anthony & Hazelton, Martin L., 2014. "Estimation of mean and covariance of peak hour origin–destination demands from day-to-day traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 52-75.
    10. Bierlaire, M. & Toint, Ph. L., 1995. "Meuse: An origin-destination matrix estimator that exploits structure," Transportation Research Part B: Methodological, Elsevier, vol. 29(1), pages 47-60, February.
    11. Sherali, Hanif D. & Narayanan, Arvind & Sivanandan, R., 2003. "Estimation of origin-destination trip-tables based on a partial set of traffic link volumes," Transportation Research Part B: Methodological, Elsevier, vol. 37(9), pages 815-836, November.
    12. Lo, H. P. & Zhang, N. & Lam, W. H. K., 1999. "Decomposition algorithm for statistical estimation of OD matrix with random link choice proportions from traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 33(5), pages 369-385, June.
    13. Seungkyu Ryu, 2020. "A Bicycle Origin–Destination Matrix Estimation Based on a Two-Stage Procedure," Sustainability, MDPI, vol. 12(7), pages 1-14, April.
    14. Xie, Chi & Kockelman, Kara M. & Waller, S. Travis, 2011. "A maximum entropy-least squares estimator for elastic origin–destination trip matrix estimation," Transportation Research Part B: Methodological, Elsevier, vol. 45(9), pages 1465-1482.
    15. Dimitris Bertsimas & Julia Yan, 2018. "From Physical Properties of Transportation Flows to Demand Estimation: An Optimization Approach," Transportation Science, INFORMS, vol. 52(4), pages 1002-1011, August.
    16. S. Travis Waller & Sai Chand & Aleksa Zlojutro & Divya Nair & Chence Niu & Jason Wang & Xiang Zhang & Vinayak V. Dixit, 2021. "Rapidex: A Novel Tool to Estimate Origin–Destination Trips Using Pervasive Traffic Data," Sustainability, MDPI, vol. 13(20), pages 1-27, October.
    17. Yang, Yudi & Fan, Yueyue, 2015. "Data dependent input control for origin–destination demand estimation using observability analysis," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 385-403.
    18. Lo, Hing-Po & Chan, Chi-Pak, 2003. "Simultaneous estimation of an origin-destination matrix and link choice proportions using traffic counts," Transportation Research Part A: Policy and Practice, Elsevier, vol. 37(9), pages 771-788, November.
    19. Gunnar Flötteröd & Michel Bierlaire & Kai Nagel, 2011. "Bayesian Demand Calibration for Dynamic Traffic Simulations," Transportation Science, INFORMS, vol. 45(4), pages 541-561, November.
    20. T. Abrahamsson, 1998. "Estimation of Origin-Destination Matrices Using Traffic Counts- A Literature Survey," Working Papers ir98021, International Institute for Applied Systems Analysis.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:92:y:1996:i:3:p:550-572. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.