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A new class of dynamic methods for the identification of origin-destination flows

  • Published:1987-04 Issue:2 Volume:21 Page:117-132
  • ISSN:0191-2615
  • Container-title:Transportation Research Part B: Methodological
  • language:en
  • Short-container-title:Transportation Research Part B: Methodological

Author:

Cremer M.,Keller H.

Publisher

Elsevier BV

Subject

Management Science and Operations Research,Transportation,Civil and Structural Engineering

Reference15 articles.

1. Stochastische Differentialgleichungen;Arnold,1972

2. Matrix-Modelle zur Ermittlung von Teilströmen an Querschnittsmessungen;Beil;Dissertation an der Universität Karlsruhe,1979

3. Log-linear models for the estimation of O-D matrices from traffic counts: an approximation;Bell,1984

4. Estimation of trip matrices from traffic counts and survey data: a generalized least squares estimator;Cascetta;Transp. Res.,1984

5. Determining the time dependent trip distribution in a complex intersection for traffic responsive control;Cremer,1983

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1. A hybrid modelling framework for the estimation of dynamic origin–destination flows;Transportation Research Part B: Methodological;2023-10

2. Transportation Origin-Destination Demand Estimation with Quasi-Sparsity;Transportation Science;2023-03

3. Ridership dynamics and characteristics of potential riders of a transit system: The SunRail of Central Florida;Transportation Research Interdisciplinary Perspectives;2022-12

4. Dynamic Origin-Destination Demand Prediction with Improved LSTM Model;2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE);2022-11-11

5. Bayesian origin-destination estimation in networked transit systems using nodal in- and outflow counts;Transportation Research Part B: Methodological;2022-07
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