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Estimating flexible route choice models using sparse data
KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport and Location Analysis. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.ORCID iD: 0000-0002-0089-6543
KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport and Location Analysis. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport and Location Analysis. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.ORCID iD: 0000-0001-5290-6101
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. 1215-1220 p.
Series
IEEE International Conference on Intelligent Transportation Systems-ITSC, ISSN 2153-0009
Keyword [en]
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: urn:nbn:se:kth:diva-115858DOI: 10.1109/ITSC.2012.6338676ISI: 000312599600201Scopus ID: 2-s2.0-84871199918ISBN: 978-146733064-0 (print)OAI: oai:DiVA.org:kth-115858DiVA: diva2:588438
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
In thesis
1. Two papers on consistent estimation of a route choice model and link speed using sparse GPS data
Open this publication in new window or tab >>Two papers on consistent estimation of a route choice model and link speed using sparse GPS data
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:nbn:se:kth:diva-122285 (URN)978-91-87353-06-2 (ISBN)
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
2. New Opportunities in Urban Transport Data: Methodologies and Applications
Open this publication in new window or tab >>New Opportunities in Urban Transport Data: Methodologies and Applications
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The deployment of Information and Communication Technologies (ICT) is growing in transportation which may contribute to a more efficient and effective service. The data acquired from ICT based systems could be used for many purposes such as statistical analysis and behavior learning and inference. This dissertation addresses the question of how transportation data that was collected for a specific application can be used for other applications. This thesis consists of five separate papers, each addressing a subset of the topic.

The first paper estimates a route choice model using sparse GPS data. This paper demonstrates the feasibility of an Indirect Inference based estimator in a model with random link costs, allowing for a natural correlation structure across paths, where the full choice set is considered.

The second paper presents an estimator for the mean speed and travel time at network level based on indirect inference when the data are spatially and temporally sparse.

The third paper proposes an evaluation framework which outlines a systematic process to quantify and assess the impacts of public transport preferential measures on service users and providers in monetary terms, using public transport data sources.

In the fourth and fifth papers, a methodology is developed and implemented for integrating different prediction models and data sources while satisfying practical requirements related to the generation of real-time information. Then the performance of the proposed prediction method is compared with the prediction accuracy obtained by the currently deployed methods.

Abstract [sv]

Användandet av informations- och kommunikationsteknologier (eng. ICT) ökar inom transportområdet, vilket kan bidra till ökad effektivitet. Insamlad data från system med ICT skulle kunna användas för många ändamål såsom statistisk analys, beteendeinlärning och inferens. Denna avhandling tar upp frågan om huruvida transportdata insamlat för en viss tillämpning kan användas för andra. Avhandlingen innehåller fem forskningsartiklar, var och en inriktar sig på sin del av ämnet.

De två första uppsatserna fokuserar på konsistenta estimatorer för hastighet på länkar och ruttval. För många olika tillämpningar är det viktigt att förutsäga en observerad rutts fortsättning, och, givet att det är glest med data, att även avgöra var individen (eller fordonet) har varit. Att skatta den upplevda restiden (och nyttan) av en vald rutt är ett statistiskt svårt skattningsproblem av flera olika skäl. För det första är valmängden ofta mycket stor. För det andra kan det vara viktigt att ta hänsyn till korrelationen mellan de (generaliserade) kostnaderna för olika rutter och därigenom tillåta realistiska ersättningsmönster. För det tredje, på grund av överväganden gällande teknik och den personliga integriteten, kan data vara temporalt och spatialt gles och med endast partiellt observerade rutter. Slutligen, kan det finnas mätfel av fordonens position. Vi utvecklar en estimator för upplevd nytta av en rutt (i den första artikeln) samt för restid på länkar i vägnätverket (i den andra artikeln).

I den tredje artikeln föreslås ett ramverk för utvärdering innefattande en systematisk process som kvantifierar och bedömer inverkan av preferensstyrmedel inom kollektivtrafiken på tjänsteanvändare och leverantörer.

I fjärde och femte artikeln utvecklas och implementeras en metodologi för att integrera olika prediktiva modeller och data-källor i beaktande av praktiska krav kopplade till skapandet av realtidsinformation. Den resulterande prediktionsmetoden jämförs med de metoder som i nuläget används i Stockholm och Brisbane.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2015. xvii, 24 p.
Series
TRITA-TSC-PHD, 15:008
National Category
Transport Systems and Logistics
Research subject
Transport Science
Identifiers
urn:nbn:se:kth:diva-177489 (URN)978-91-87353-80-2 (ISBN)
Public defence
2015-12-11, L1, Drottning Kristinas väg 30, KTH, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

QC 20151123

Available from: 2015-11-23 Created: 2015-11-20 Last updated: 2015-11-23Bibliographically approved

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Fadaei, MasoudKarlström, Anders

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