Selection of a trip table which reproduces observed link flows

LeBlanc, Larry J.; Farhangian, Keyvan

doi:10.1016/0191-2615(82)90029-7

Cited by 54 publications

(11 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This assumption causes an inconsistency issue in congested networks, because the predetermined assignment matrix may not be replicated when the estimated O-D table is assigned onto the network according to some route choice assumptions. A conventional way to resolve this inconsistency is to introduce a traffic assignment component into the estimation problem (see, e.g., Nguyen 1977;Gur et al 1980;LeBlanc and Farhangian 1982;Nguyen 1984;Fisk 1984Fisk , 1988Yang et al 1992;Yang 1995).…”

Section: Introductionmentioning

confidence: 99%

A Relaxation Approach for Estimating Origin–Destination Trip Tables

Nie

Zhang

2008

Netw Spat Econ

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

A Relaxation Approach for Estimating Origin–Destination Trip Tables

Nie

Zhang

2008

Netw Spat Econ

View full text Add to dashboard Cite

show abstract

“…Unfortunately, the model did not provide a unique solution. This method was subsequently extended (Turnquist et al, 1979;LeBlanc et al, 1982;Sheffi et al, 1987), however, the extended method requires accurate knowledge of the link travel times and of the relationship between link volume and link travel time.…”

Section: 43mentioning

confidence: 99%

Estimating dynamic origin-destination demands from link and probe counts

1997

Transportation Research Part A: Policy and Practice

View full text Add to dashboard Cite

Many traffic engineering problems consist of evaluating a number of alternate schemes in terms of some measures of performance, such as total travel time, volume to capacity ratio, average speed, total fuel consumption, total emissions, etc. These evaluations generally require prior knowledge of the temporal trip making behaviour of drivers by origin and destination. This thesis presents the development, application, and evaluation of two models capable of inferring these temporal origin -destination (O-D) traffic demands on the basis of observed link traffic flows and assumptions regarding drivers' route choices. In particular, this thesis presents the development and evaluation of a Least Squared Error (LSE) and a Least Relative Error (LRE) model, each of which is capable of estimating either static demands, a time series of static demands, or dynamic demands. Furthermore, the potential of using probe data, from route guidance system (RGS) equipped vehicles, to enhance these estimated dynamic O-D demands is examined.Both of the LSE and LRE models' mathematical formulations are presented. The LSE model formulation parallels that of a least squared regression as the error function is composed of the sum of the squared absolute difference between the observed and estimated link flows. In contrast, the LRE model is formulated on the basis that the link flow error, when measured relative to the observed flow, is to be minimized instead. Iterative solution algorithms, that are modifications of the Jacobi and Gauss-Seidel techniques, are proposed to solve each of the model formulations. It is shown, by way of the application of these iterative algorithms to several example networks, that the estimated O-D demands, which result from these iterative solution techniques, are consistent with the model formulations and with the analytical solutions. Furthermore, it is shown for several examples that, when multiple solutions exist which each exactly replicate the observed link flows, and no prior O-D demand information is specified, both the LSE and LRE models estimate demands that closely approximate the maximum likelihood solution.The proposed iterative solution algorithms have been incorporated into a computer model called QUEENSOD. This model can be practically applied to real networks using current computer memory constraints. This thesis describes the application of the LSE and LRE models to a 35 km section of multilane urban freeway in Toronto, Canada, in which alternate parallel routes exist. Dynamic 15 minute O-D demands were estimated for the eastbound direction for the period from 5 am to 11 am. Despite FTMS detector data being available for only 45% of the network, a correlation coefficient of approximately 98% was obtained for both models. This value reflects the high linear correlation between estimated and observed link traffic flow data for this network. The statistical analysis of the expected quality of O-D demands, which are estimated solely on the basis of RGS probe vehicle data, indicated that ...

show abstract

“…Such strategies are appealing when evaluation of the regional network 23 requires specialized expertise and/or is very computationally demanding, thus prohibiting 24 evaluation of one or more scenarios in a limited time frame. These simplified networks provide 25 planners with a less complex platform to facilitate quick-response and relatively informed 26 decision making early on. 27 While a number of studies addressed important theoretical and practical extraction/aggregation 28 issues for network abstraction (see [1][2][3][4][5][6][7][8], subnetwork analysis is still quite limited (see 9, 10).…”

mentioning

confidence: 99%

“…Step 1.4 (Basis matrix updating): Update the basic feasible path flow variables by 26 and update the basis matrix by inserting the entering path's link-path incidence 27 vector and removing the leaving path's link-path incidence vector. comparing it to that generated by the full-network traffic assignment.…”

mentioning

confidence: 99%