Optimal use of plate-scanning resources for route flow estimation in traffic networks

Authors:
Enrique Castillo;Inmaculada Gallego;José María Menéndez;Ana Rivas
Affiliations:
Department of Applied Mathematics and Computational Sciences, University of Cantabria, Santander, Spain;Department of Civil Engineering, University of Castilla La Mancha, Ciudad Real, Spain;Department of Civil Engineering, University of Castilla La Mancha, Ciudad Real, Spain;Department of Civil Engineering, University of Castilla La Mancha, Ciudad Real, Spain
Venue:
IEEE Transactions on Intelligent Transportation Systems
Year:
2010

Citing 9
Cited 0

An Orthogonally Based Pivoting Transformation of Matrices and Some Applications

SIAM Journal on Matrix Analysis and Applications
A Network Based Model for Traffic Sensor Location with Implications on O/D Matrix Estimates

Transportation Science
Dynamic origin-destination demand estimation using automatic vehicle identification data

IEEE Transactions on Intelligent Transportation Systems
A License Plate-Recognition Algorithm for Intelligent Transportation System Applications

IEEE Transactions on Intelligent Transportation Systems
Vehicle and Guard Rail Detection Using Radar and Vision Data Fusion

IEEE Transactions on Intelligent Transportation Systems
Superresolution of License Plates in Real Traffic Videos

IEEE Transactions on Intelligent Transportation Systems
The Observability Problem in Traffic Models: Algebraic and Topological Methods

IEEE Transactions on Intelligent Transportation Systems
Vehicle Detector Deployment Strategies for the Estimation of Network Origin–Destination Demands Using Partial Link Traffic Counts

IEEE Transactions on Intelligent Transportation Systems
License Plate Recognition From Still Images and Video Sequences: A Survey

IEEE Transactions on Intelligent Transportation Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper discusses the problem of optimizing the use of scanning resources for route flow estimation in traffic networks. Three problems are considered: 1) Minimize the number of cameras to be used for estimating a given subset of route flows; 2) identify the subsets of links to be scanned for a given number of available cameras, assuming that several runs can be done; and 3) solve the previous problems with consideration of scanning errors and error recovery. In addition to the main objective, which consists of minimizing the number of cameras, minimizing the total number of links to be scanned in all runs and maximizing the number of determined route flows are also considered as secondary and tertiary objectives, respectively. A simple and a medium-size real network are used to illustrate the proposed methods and prove the feasibility of the proposed methodology.