Real-Time System for Daily Modal Split Estimation and OD Matrices Generation Using IoT Data: A Case Study of Tartu City.

Kaveh Khoshkhah, Mozhgan Pourmoradnasseri, Amnir Hadachi, Helen Tera, Jakob Mass, Erald Keshi, Shan Wu

Abstract read

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Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

–field-weighted citation impact

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1 citing paper in PubMed.

Uncovering latent urban mobility patterns via smart-card and survey data fusion.Nature communications · 2026
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5 · Who and what money

Authors and funding

7 authors.

Kaveh KhoshkhahITS Lab, Institute of Computer Science, University of Tartu, Narva mnt 18, 51009 Tartu, Estonia.

Mozhgan PourmoradnasseriITS Lab, Institute of Computer Science, University of Tartu, Narva mnt 18, 51009 Tartu, Estonia.ORCID 0000-0002-2092-816X

Amnir HadachiITS Lab, Institute of Computer Science, University of Tartu, Narva mnt 18, 51009 Tartu, Estonia.ORCID 0000-0001-9257-3858

Helen TeraITS Lab, Institute of Computer Science, University of Tartu, Narva mnt 18, 51009 Tartu, Estonia.

Jakob MassITS Lab, Institute of Computer Science, University of Tartu, Narva mnt 18, 51009 Tartu, Estonia.ORCID 0000-0002-2118-4641

Erald KeshiITS Lab, Institute of Computer Science, University of Tartu, Narva mnt 18, 51009 Tartu, Estonia.

Shan WuITS Lab, Institute of Computer Science, University of Tartu, Narva mnt 18, 51009 Tartu, Estonia.ORCID 0000-0002-8062-0853

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, we have witnessed the emergence of the implementation and integration of significant working solutions in transportation, especially within the smart city concept. A lot of cities in Europe and around the world support this initiative of making their cities smarter for enhanced mobility and a sustainable environment. In this paper, we present a case study of Tartu city, where we developed and designed a daily real-time system for extracting and performing a modal split analysis. Our web-based platform relied on an optimization approach for calibrating our simulation in order to perform the analysis with the use of real data streams from IoT devices installed around the city. The results obtained from our system demonstrated acceptable performance versus the quality of the available data source. In addition, our platform provides downloadable OD matrices for each mode of mobility for the community.

Indexed as

internet of things (IoT)mobility modelingmodal split analysisOD matrixreal-time systemsSUMO simulation

Identifiers

PMID35459014

DOI10.3390/s22083030

PMCPMC9030519

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