Simultaneous estimation of the origin-destination matrices and travel-cost coefficient for congested networks in a stochastic user equilibrium

Transportation Science

Volumn 35, Issue 2, 2001, Pages 107-123

  Yang, Hai ^b   Meng, Qiang ^b   Bell, Michael G H ^b  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

^b NONE

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; OPTIMIZATION; QUADRATIC PROGRAMMING; RANDOM PROCESSES;

CONGESTED NETWORKS; STOCHASTIC USER EQUILIBRIUM (SUE);

TRANSPORTATION;

EID: 0035326953     PISSN: 00411655     EISSN: None     Source Type: Journal    
DOI: 10.1287/trsc.35.2.107.10133     Document Type: Article

References (49)

1. Cyclic flow, Markov process and stochastic traffic assignment
2. Statistical properties of mathematical programming models of stochastic network equilibrium
3. Network loading versus equilibrium estimation of the stochastic route choice model: Maximum likelihood and least squares revisited
4. Estimation of an origin-destination table using a method on shortest augmenting paths
5. Variance and covariance for origin-destination flows when estimated by log-linear models
6. The estimation of origin-destination matrices by constrained generalized least squares
7. Alternatives of Dial's logit assignment algorithm
8. MEUSE: An origin-destination matrix estimator that exploits structure
9. Estimation of origin-destination matrices from traffic counts using multiobjective programming formulations
10. Estimation of trip matrices from traffic counts and survey data: A generalized least squares estimator
11. A unified framework for estimating or updating matrices from traffic counts
12. Some statistical problems in connection with traffic assignment
13. On stochastic models of traffic assignment
14. Exact local solution of the continuous network design problem via stochastic user equilibrium assignment
15. A probabilistic multipath traffic assignment model which obviates path enumeration
16. A note on the maximum likelihood calibration on Dial's assignment method
17. Some developments in equilibrium traffic assignment
18. A note on trip matrix estimation from traffic count data
19. On combining maximum entropy trip matrix estimation with user optimal assignment
20. Trip matrix estimation from link traffic counts: The congested network case
21. A coordinate descent method for the bi-level O-D matrix adjustment problem
22. A successive quadratic programming algorithm with global and superlinear convergence properties
23. A comparative performance evaluation of 27 nonlinear programming codes
24. Selection of a trip table which reproduces observed link flows
25. Estimation of a trip table and the θ parameter in a stochastic network
26. Inference on trip matrices from observations on link volumes: A Bayesian statistical approach
27. A regression formulation of the matrix estimation problem
28. Estimating origin-destination matrices from observed flows
29. Algorithms for nonlinear constraints that use Lagrangian functions
30. Variable metric methods for constrained optimization
31. A recursive quadratic programming algorithm that uses differentiable penalty functions
32. Calibration of Dial's assignment method
33. The convergence of equilibrium algorithms with predetermined step size
34. A linear programming approach for synthesizing origin-destination trip tables from link traffic volumes
35. A maximum likelihood model for estimating origin-destination matrices
36. Estimation of trip tables from observed link volumes
37. Selected node-pair analysis in Dial's assignment algorithm
38. The most likely trip matrix estimated from traffic counts
39. Estimating time-dependent trip matrices from traffic counts
40. Heuristic algorithms for the bilevel origin-destination matrix estimation problem
41. A sensitivity analysis based algorithm for the congested origin-destination matrix estimation problem
42. The equilibrium-based origin-destination matrix estimation problem
43. Estimation of origin-destination matrices from link traffic counts in congested networks