scholarly journals Methodology for Microscopic Traffic Simulation Modelling of Land Port of Entries along the U.S.-Mexican Border: Ysleta – Zaragoza Land Port of Entry Case Study

2016 ◽  
Vol 83 ◽  
pp. 321-328 ◽  
Author(s):  
David Salgado ◽  
Dusan Jolovic ◽  
Rafael M. Aldrete ◽  
Peter T. Martin ◽  
Jeff Shelton
Author(s):  
Tomer Toledo ◽  
Haris N. Koutsopoulos ◽  
Angus Davol ◽  
Moshe E. Ben-Akiva ◽  
Wilco Burghout ◽  
...  

The calibration and validation approach and results from a case study applying the microscopic traffic simulation tool MITSIMLab to a mixed urban-freeway network in the Brunnsviken area in the north of Stockholm, Sweden, under congested traffic conditions are described. Two important components of the simulator were calibrated: driving behavior models and travel behavior components, including origin–destination flows and the route choice model. In the absence of detailed data, only aggregate data (i.e., speed and flow measurements at sensor locations) were available for calibration. Aggregate calibration uses simulation output, which is a result of the interaction among all components of the simulator. Therefore, it is, in general, impossible to identify the effect of individual models on traffic flow when using aggregate data. The calibration approach used takes these interactions into account by iteratively calibrating the different components to minimize the deviation between observed and simulated measurements. The calibrated MITSIMLab model was validated by comparing observed and simulated measurements: traffic flows at sensor locations, point-to-point travel times, and queue lengths. A second set of measurements, taken a year after the ones used for calibration, was used at this stage. Results of the validation are presented. Practical difficulties and limitations that may arise with application of the calibration and validation approach are discussed.


Author(s):  
Zenghao Hou ◽  
Joyoung Lee

This paper proposes an innovative multi-thread stochastic optimization approach for the calibration of microscopic traffic simulation models. Combining Quasi-Monte Carlo (QMC) sampling and the Particle Swarm Optimization (PSO) algorithm, the proposed approach, namely the Quasi-Monte Carlo Particle Swarm (QPS) calibration method, is designed to boost the searching process without prejudice to the calibration accuracy. Given the search space constructed by the combinations of simulation parameters, the QMC sampling technique filters the searching space, followed by the multi-thread optimization through the PSO algorithm. A systematic framework for the implementation of the QPS QMC-initialized PSO method is developed and applied for a case study dealing with a large-scale simulation model covering a 6-mile stretch of Interstate Highway 66 (I-66) in Fairfax, Virginia. The case study results prove that the proposed QPS method outperforms other methods utilizing Genetic Algorithm and Latin Hypercube Sampling in achieving faster convergence to obtain an optimal calibration parameter set.


1992 ◽  
Vol 17 (1) ◽  
Author(s):  
Stephen Block

Abstract: This paper attempts to unravel the very complex issue of balance first by addressing its historical and theoretical contexts. Then the coverage of the U.S.-Canada Free Trade Agreement (FTA) is used as a case study. Résumé: Dans cet article l'auteur s'applique à décortiquer la complexité de la controverse notion de "balance'' dans la couverture médiatique. Il la place d'abord dans son contexte historique et théorique. Il s'appuie, ensuite, comme exemple, sur le suivi que les médias ont fait autour des pourparlers et de l'entente du libre-échange entre le Canada et les États-Unis.


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