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Short-Term Traffic Prediction in Large-Scale Urban Networks
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning. (Urban Mobility Group)ORCID iD: 0000-0002-8499-0843
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

City-wide travel time prediction in real-time is an important enabler for efficient use of the road network. It can be used in traveler information to enable more efficient routing of individual vehicles as well as decision support for traffic management applications such as directed information campaigns or incident management. 3D speed maps have been shown to be a promising methodology for revealing day-to-day regularities of city-level travel times and possibly also for short-term prediction. In this paper, we aim to further evaluate and benchmark the use of 3D speed maps for short-term travel time prediction and to enable scenario-based evaluation of traffic management actions we also evaluate the framework for traffic flow prediction. The 3D speed map methodology is adapted to short-term prediction and benchmarked against historical mean as well as against Probabilistic Principal Component Analysis (PPCA). The benchmarking and analysis are made using one year of travel time and traffic flow data for the city of Stockholm, Sweden. The result of the case study shows very promising results of the 3D speed map methodology for short-term prediction of both travel times and traffic flows. The modified version of the 3D speed map prediction outperforms the historical mean prediction as well as the PPCA method. Further work includes an extended evaluation of the method for different conditions in terms of underlying sensor infrastructure, preprocessing and spatio-temporal aggregation as well as benchmarking against other prediction methods.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2019. , p. 21
Series
TRITA-ABE-DLT ; 1915
Keywords [en]
travel time prediction, short-term travel time prediction, traffic prediction, clustering, partitioning, spatio-temporal partitioning, large-scale prediction, PPCA, 3D speed map
National Category
Transport Systems and Logistics
Research subject
Transport Science
Identifiers
URN: urn:nbn:se:kth:diva-250650ISBN: 978-91-7873-224-1 (print)OAI: oai:DiVA.org:kth-250650DiVA, id: diva2:1312971
Presentation
2019-05-31, B2, Brinellvägen 23, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

QC 20190531

Available from: 2019-05-02 Created: 2019-05-01 Last updated: 2019-05-02Bibliographically approved
List of papers
1. Integrated framework for real-time urban network travel time prediction on sparse probe data
Open this publication in new window or tab >>Integrated framework for real-time urban network travel time prediction on sparse probe data
2018 (English)In: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 12, no 1, p. 66-74Article in journal (Refereed) Published
Abstract [en]

The study presents the methodology and system architecture of an integrated urban road network travel time prediction framework based on low-frequency probe vehicle data. Intended applications include real-time network traffic management, vehicle routing and information provision. The framework integrates methods for receiving a stream of probe vehicle data, map matching and path inference, link travel time estimation, calibration of prediction model parameters and network travel time prediction in real time. The system design satisfies three crucial aspects: computational efficiency of prediction, internal consistency between components and robustness against noisy and missing data. Prediction is based on a multivariate hybrid method of probabilistic principal component analysis, which captures global correlation patterns between links and time intervals, and local smoothing, which considers local correlations among neighbouring links. Computational experiments for the road network of Stockholm, Sweden and probe data from taxis show that the system provides high accuracy for both peak and off-peak traffic conditions. The computational efficiency of the framework makes it capable of real-time prediction for large-scale networks. For links with large speed variations between days, prediction significantly outperforms the historical mean. Furthermore, prediction is reliable also for links with high proportions of missing data.

Place, publisher, year, edition, pages
Institution of Engineering and Technology, 2018
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-219361 (URN)10.1049/iet-its.2017.0113 (DOI)000426045200009 ()2-s2.0-85041135912 (Scopus ID)
Note

QC 20180206

Available from: 2017-12-04 Created: 2017-12-04 Last updated: 2019-05-02Bibliographically approved
2. Spatio-Temporal Partitioning of Large Urban Networks for Travel Time Prediction
Open this publication in new window or tab >>Spatio-Temporal Partitioning of Large Urban Networks for Travel Time Prediction
2018 (English)In: 2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), IEEE , 2018, p. 1390-1395Conference paper, Published paper (Refereed)
Abstract [en]

The paper explores the potential of spatiotemporal network partitioning for travel time prediction accuracy and computational costs in the context of large-scale urban road networks (including motorways/freeways, arterials and urban streets). Forecasting in this context is challenging due to the complexity, heterogeneity, noisy data, unexpected events and the size of the traffic network. The proposed spatio-temporal network partitioning methodology is versatile, and can be applied for any source of travel time data and multivariate travel time prediction method. A case study of Stockholm, Sweden considers a network exceeding 11,000 links and uses taxi probe data as the source of travel times data. To predict the travel times the Probabilistic Principal Component Analysis (PPCA) is used. Results show that the spatio-temporal network partitioning provides a more appropriate bias-variance tradeoff, and that prediction accuracy and computational costs are improved by considering the proper number of clusters towards robust large-scale travel time prediction.

Place, publisher, year, edition, pages
IEEE, 2018
Series
IEEE International Conference on Intelligent Transportation Systems-ITSC, ISSN 2153-0009
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-244586 (URN)000457881301060 ()2-s2.0-85060452125 (Scopus ID)978-1-7281-0323-5 (ISBN)
Conference
21st IEEE International Conference on Intelligent Transportation Systems (ITSC), NOV 04-07, 2018, Maui, HI
Note

QC 20190304

Available from: 2019-03-04 Created: 2019-03-04 Last updated: 2019-05-10Bibliographically approved
3. 3D Speed Maps and Mean Observations Vectors for Short-Term Urban Traffic Prediction
Open this publication in new window or tab >>3D Speed Maps and Mean Observations Vectors for Short-Term Urban Traffic Prediction
2019 (English)In: TRB Annual Meeting Online, Washington DC, US, 2019, p. 1-20Conference paper, Published paper (Refereed)
Abstract [en]

City-wide travel time prediction in real-time is an important enabler for efficient use of the road network. It can be used in traveler information to enable more efficient routing of individual vehicles as well as decision support for traffic management applications such as directed information campaigns or incident management. 3D speed maps have been shown to be a promising methodology for revealing day-to-day regularities of city-level travel times and possibly also for short-term prediction. In this paper, we aim to further evaluate and benchmark the use of 3D speed maps for short-term travel time prediction and to enable scenario-based evaluation of traffic management actions we also evaluate the framework for traffic flow prediction. The 3D speed map methodology is adapted to short-term prediction and benchmarked against historical mean as well as against Probabilistic Principal Component Analysis (PPCA). The benchmarking and analysis are made using one year of travel time and traffic flow data for the city of Stockholm, Sweden. The result of the case study shows very promising results of the 3D speed map methodology for short-term prediction of both travel times and traffic flows. The modified version of the 3D speed map prediction outperforms the historical mean prediction as well as the PPCA method. Further work includes an extended evaluation of the method for different conditions in terms of underlying sensor infrastructure, preprocessing and spatio-temporal aggregation as well as benchmarking against other prediction methods.

Place, publisher, year, edition, pages
Washington DC, US: , 2019
Keywords
3D speed map, short-term prediction, travel time prediction, traffic prediction, large-scale prediction, clustering, partitioning, spatio-temporal partitioning
National Category
Transport Systems and Logistics
Research subject
Transport Science
Identifiers
urn:nbn:se:kth:diva-250647 (URN)
Conference
Transportation research board annual meeting (TRB)
Note

QC 20190502

Available from: 2019-05-01 Created: 2019-05-01 Last updated: 2019-08-27Bibliographically approved

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Citation style
  • apa
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More styles
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More languages
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