01478030

Component

https://doi.org/10.3141/2333-08

http://scholar.google.com...kawa&publication_year=2013

Methods of estimating dynamic origin–destination (O-D) matrices for urban networks from probe vehicle data are explored. A speed–density function is derived and fitted for different types of roads with the use of the maximum likelihood method. Both a Bayesian method that carefully incorporates prior information and an ordinary method are used to estimate link flows from probe vehicle speed. A dynamic traffic assignment–based bilevel generalized least squares (GLS) estimator considering the distance between the estimated and target O-D matrices as well as the distance between the calculated and observed link flows is formulated to estimate dynamic O-D matrices from estimated link flows. In the iterative solution procedure, the upper level is solved with the extended Bell algorithm, and the microscopic dynamic traffic assignment system VISSIM is applied to produce the assignment matrix in the lower level. A medium-sized signalized network in Tokyo is modeled in a case study in which Bayesian and ordinary methods are compared both in link flow estimation and O-D matrix estimation. Further, the bilevel GLS estimator and bilevel ordinary least squares estimator are implemented and then compared in O-D estimation. The results validate the proposed bilevel GLS estimator.

01490598

13-2466

English

pp 66–73

2013

Transportation Research Record: Journal of the Transportation Research Board

9780309263382

Print

Figures (5) ; References (17) ; Tables (1)

Dynamic traffic assignment; Origin and destination; Probe vehicles; Traffic flow; Traffic models; Traffic speed

Bayesian analysis; Least square method

Tokyo (Japan)

Highways; Planning and Forecasting; Security and Emergencies; I72: Traffic and Transport Planning

PRP, TRIS, TRB, ATRI

Feb 5 2013 12:32PM

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