NDLI: An Integrated Computational Framework for the Estimation of Dynamic OD Trip Matrices

Content Provider	IEEE Xplore Digital Library
Author	Jaume, B. Montero, L.
Copyright Year	2015
Description	Author affiliation: Dept. Stat. & Oper. Res., Barcelona Tech - UPC, Barcelona, Spain (Jaume, B.; Montero, L.)
Abstract	Origin-Destination (OD) trip matrices describe the patterns of traffic behavior across the network and play a key role as primary data input to many traffic models. OD matrices are a critical requirement, either in static or dynamic models for traffic assignment. However, OD matrices are not yet directly observable, thus, the current practice consists of adjusting an initial or a priori matrix from link flow counts, speeds, travel times and other aggregate demand data. This information is provided by an existing layout of traffic counting stations, as the traditional loop detectors. The availability of new traffic measurements provided by ICT applications offers the possibility to formulate and develop more efficient algorithms, especially suited for real-time applications. However, the efficiently strongly depends, among other factor, on the quality of the seed matrix. This paper proposes an integrated computational framework in which an off-line procedure generates the time-sliced OD matrices, which are the input to an on-line estimator. The paper also analyzes the sensitivity of the on-line estimator with respect to the available ICT traffic measurements.
Starting Page	612
Ending Page	619
File Size	493632
Page Count	8
File Format	PDF
ISSN	21530017
e-ISBN	9781467365963
DOI	10.1109/ITSC.2015.106
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-15
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vehicles Estimation Vehicle dynamics Kalman filters Layout Detectors Time measurement ICT data Dynamic OD Matrices Matrix Estimation Kalman filtering
Content Type	Text
Resource Type	Article

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