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Digital Library

of the European Council for Modelling and Simulation

 

Title:

Solving Location Problem For Vehicle Identification Sensors To Observe And Estimate Path Flows In Large-Scale Networks

Authors:

Pegah T. Yazdi, Yousef Shafahi

Published in:

 

 

 

(2018). ECMS 2018 Proceedings Edited by: Lars Nolle, Alexandra Burger, Christoph Tholen, Jens Werner, Jens Wellhausen European Council for Modeling and Simulation. doi: 10.7148/2018-0005

 

ISSN: 2522-2422 (ONLINE)

ISSN: 2522-2414 (PRINT)

ISSN: 2522-2430 (CD-ROM)

 

32nd European Conference on Modelling and Simulation,

Wilhelmshaven, Germany, May 22nd – May 265h, 2018

 

 

Citation format:

Pegah T. Yazdi, Yousef Shafahi (2018). Solving Location Problem For Vehicle Identification Sensors To Observe And Estimate Path Flows In Large-Scale Networks, ECMS 2018 Proceedings Edited by: Lars Nolle, Alexandra Burger, Christoph Tholen, Jens Werner, Jens Wellhausen European Council for Modeling and Simulation. doi: 10.7148/2018-0323

DOI:

https://doi.org/10.7148/2018-0323

Abstract:

Origin-Destination (OD) demand is one of the important requirements in transportation planning. Estimating OD demand could be an expensive and time consuming procedure. These days using vehicle identification sensors for OD estimation has become very common because of its low cost and high accuracy. In this paper, we focus on solving two location problems of these sensors: one to observe and one to estimate path flows. These problems have only been solved for small-scale networks until recently due to being computationally expensive. Therefore, we try to present a method to solve these models for large-scale networks. Due to resemblance of these models and set covering problem, we used heuristic and meta-heuristic methods based on set covering problem. For this purpose, we defined our new set covering matrix based on prime matrix. In order to determine which method is more appropriate, we chose a large-scale and six medium-scale networks. The results represent that through heuristic methods and meta-heuristic methods a greedy algorithm and a Tabu search are more appropriate respectively.

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