Improving Analytical Travel Time Estimation for Transportation Planning Models

Lu, Chenxi

doi:10.25148/etd.fi10080410

Cited by 1 publication

(2 citation statements)

References 24 publications

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“…The accuracy of the travel time estimates produced by the developed model was evaluated using field data from the Minnesota Department of Transportation (MDOT) [15], as well as CORSIM simulation data for US-1 in Miami-Dade County, Florida. The mean absolute percentage error (MAPE) of travel time was used in these comparisons and the details of the comparison and results were presented in [13]. The results from the comparison indicate that the model is capable of producing better or comparable estimates of existing travel time estimation models, without requiring the input of signal timing parameters.…”

Section: Resultsmentioning

confidence: 99%

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Implementation of a New Travel Time Estimation Method for Demand Forecasting Models

Lü

Zhao

Hadi

et al. 2011

AMM

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This paper discusses the implementation of a new travel time estimation method in a regional demand forecasting model. The developed model considers implicitly the influence of signal timing as a function of main street and cross street traffic demands, although signal timing setting is not required as input. The application presented in this paper demonstrates that the developed model is applicable to a large network without the burden of signal timing input requirement. The results indicate that the application of the model can improve the performance of traffic assignment as part of the demand forecasting process. The model is promising to support dynamic traffic assignment (DTA) model applications in the future.

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Section: Resultsmentioning

confidence: 99%

“…Lu et al (13,14) developed an analytical travel time estimation method, considering the influence of signal timing plans for a variety of traffic combinations implicitly without the need of signal timing setting as input. The model shows promise of improved prediction accuracy of travel time estimation compared to existing models.…”

Section: Introductionmentioning

confidence: 99%