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Two papers on consistent estimation of a route choice model and link speed using sparse GPS data
KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport and Location Analysis.ORCID iD: 0000-0002-0089-6543
2013 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Global Positioning System and nomad devices are increasingly used to provide data from individuals in urban traffic networks. In these two papers we focus on consistent estimators of a route choice model and link speed.

In many different applications, it is important to predict the continuation of an observed path, and also, given sparse data, predict where the individual (or vehicle) has been. Estimating the perceived cost functions is a difficult statistical estimation problem, for different reasons. First, the choice set is typically very large.

Second, it may be important to take into account the correlation between the (generalized) costs of different routes, and thus allow for realistic substitution patterns. Third, due to technical or privacy considerations, the data may be temporally and spatially sparse, with only partially observed paths. Finally, the position of vehicles may have measurement errors. We address all these problems using an indirect inference (II) approach. We demonstrate the feasibility of the proposed estimator in a model with random link costs, allowing for a natural correlation structure across paths, where the full choice set is considered.

In the second paper, we develop an estimator for the mean speed and travel time based on indirect inference when the data are spatially and temporally sparse. With sparse data, the full path of vehicles are not observed, which is typically addressed using map matching techniques.

First, we show how speed can be estimated using an auxiliary model which includes map matching and a model of route choice. Next, we further develop the estimator and show how both speed and the route choice model can be jointly estimated by using iteration between an II estimator of speed and the II estimator of the route choice model (developed in Paper I). Monte Carlo evidence is provided which demonstrates that the estimator is able to accurately estimate both speed and parameters of the route choice model.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2013. , xi, 21 p.
Series
Trita-TSC-LIC, ISSN 1653-445X ; 13:003
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:kth:diva-122285ISBN: 978-91-87353-06-2 (print)OAI: oai:DiVA.org:kth-122285DiVA: diva2:621825
Presentation
2013-06-07, Salen Nash-Wardrop,, Teknikringen 10A, KTH, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

QC 20130521

Available from: 2013-05-21 Created: 2013-05-17 Last updated: 2013-05-21Bibliographically approved
List of papers
1. Estimating flexible route choice models using sparse data
Open this publication in new window or tab >>Estimating flexible route choice models using sparse data
2012 (English)In: Intelligent Transportation Systems (ITSC), 2012 15th International IEEE Conference on, IEEE conference proceedings, 2012, 1215-1220 p.Conference paper, Published paper (Refereed)
Abstract [en]

GPS and nomad devices are increasingly used to provide data from individuals in urban traffic networks. In many different applications, it is important to predict the continuation of an observed path, and also, given sparse data, predict where the individual (or vehicle) has been. Estimating the perceived cost functions is a difficult statistical estimation problem, for different reasons. First, the choice set is typically very large. Second, it may be important to take into account the correlation between the (generalized) costs of different routes, and thus allow for realistic substitution patterns. Third, due to technical or privacy considerations, the data may be temporally and spatially sparse, with only partially observed paths. Finally, the position of vehicles may have measurement errors. We address all these problems using a indirect inference approach. We demonstrate the feasibility of the proposed estimator in a model with random link costs, allowing for a natural correlation structure across paths, where the full choice set is considered.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2012
Series
IEEE International Conference on Intelligent Transportation Systems-ITSC, ISSN 2153-0009
Keyword
Correlation structure, Flexible routes, Indirect inference, Random links, Sparse data, Statistical estimation, Substitution patterns, Urban traffic networks
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-115858 (URN)10.1109/ITSC.2012.6338676 (DOI)000312599600201 ()2-s2.0-84871199918 (Scopus ID)978-146733064-0 (ISBN)
Conference
2012 15th International IEEE Conference on Intelligent Transportation Systems, ITSC 2012; Anchorage, AK; 16 September 2012 through 19 September 2012
Note

QC 20130123. QC 20160214. QC 20160221

Available from: 2013-01-15 Created: 2013-01-15 Last updated: 2016-02-21Bibliographically approved
2. Consistently estimating link speed using sparse GPS data with measured errors
Open this publication in new window or tab >>Consistently estimating link speed using sparse GPS data with measured errors
2014 (English)In: Transportation: Can we do more with less resources? – 16th Meeting of the Euro Working Group on Transportation – Porto 2013, Elsevier, 2014, 829-838 p.Conference paper, Published paper (Refereed)
Abstract [en]

Data sources using new technology such as the Geographical Positioning System are increasingly available. In many different applications, it is important to predict the average speed on all the links in a network. The purpose of this study is to estimate the link speed in a network using sparse GPS data set. Average speed is consistently estimated using Indirect Inference approach. in the end, the Monte Carlo evidence is provided to show that the results are consistent with parameter estimates.

Place, publisher, year, edition, pages
Elsevier, 2014
Series
Procedia Social and Behavioral Sciences, ISSN 1877-0428 ; 111
Keyword
Travel time, Sparse GPS data, Indirect inference, Map matching, route choice
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-122284 (URN)10.1016/j.sbspro.2014.01.117 (DOI)000335582500085 ()
Conference
16th Euro Working Group on Transportation, Porto, Portugal, 4-6 September 2013.
Note

QC 20140613. Updated from manuscript to proceedings paper. QC 20160214. QC 20160221

Available from: 2013-05-17 Created: 2013-05-17 Last updated: 2016-02-21Bibliographically approved

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Fadaei Oshyani, Masoud

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