Real-Time Travel Time Prediction Framework for Departure Time and Route Advice
Abstract
Heavily used urban networks remain a challenge for travel time prediction because traffic flow is rarely homogeneous and is also subject to a wide variety of disturbances. Various models, some of which use traffic flow theory and some of which are data driven, have been developed to predict traffic flow and travel times. Many of these perform well under set conditions. However, few perform well under all or even most urban traffic conditions. As part of the Amsterdam Practical Trial, a comprehensive field operation test for traffic management, a real-time travel time prediction framework, was developed to make use of an ensemble of traffic modeling techniques to predict travel times with great accuracy for arterial roads as well as urban roads. The various models in the framework include both traffic theoretical models and data-driven approaches, making use of some of the largest real-time traffic data sets currently available to limit errors to less than 20% for any time of day or week. The impending implementation of the framework sets it at the forefront of practical real-time implementation of urban travel time prediction.
Get full access to this article
View all access and purchase options for this article.
References
1. Bellemans T., De Schutter B., and De Moor B. Models for Traffic Control. Journal A, Vol. 43, No. 3–4, 2002, pp. 13–22.
2. Hegyi A. Model Predictive Control for Integrating Traffic Control Measures. Netherlands TRAIL Research School, Delft University of Technology, 2004.
3. Knoop V. L., van Lint J. W. C., Vries J., Kester L., and Passchier I. Relationship Between Application Scale and Maximum Time Latency in Intelligent Transport Solutions. In Transportation Research Record: Journal of the Transportation Research Board, No. 2380, Transportation Research Board of the National Academies, Washington, D.C., 2013, pp. 1–9.
4. Laval J. A. Hysteresis in Traffic Flow Revisited: An Improved Measurement Method. Transportation Research Part B: Methodological, Vol. 45, No. 2, 2011, pp. 385–391.
5. Daganzo C. F. The Cell Transmission Model: A Dynamic Representation of Highway Traffic Consistent with the Hydrodynamic Theory. Transportation Research Part B: Methodological, Vol. 28, No. 4, 1994, pp. 269–287.
6. Daganzo C. F. The Cell Transmission Model, Part II: Network Traffic. Transportation Research Part B: Methodological, Vol. 29, No. 2, 1995, pp. 79–93.
7. Yperman I. The Link Transmission Model for Dynamic Network Loading. PhD dissertation. Katholieke Universiteit Leuven, Belgium, 2007.
8. Tampère C. M. J., Corthout R., Cattrysse D., and Immers L. H. A Generic Class of First-Order Node Models for Dynamic Macroscopic Simulation of Traffic Flows. Transportation Research Part B: Methodological, Vol. 45, No. 1, 2011, pp. 289–309.
9. Corthout R., Himpe W., Viti F., Frederix R., and Tampere C. M. Improving the Efficiency of Repeated Dynamic Network Loading Through Marginal Simulation. Transportation Research Part C: Emerging Technologies, Vol. 41, pp. 90–109.
10. Corthout R., Tampère C. M. J., Frederix R., and Immers L. H. Marginal Dynamic Network Loading for Large-Scale Simulation-Based Applications. Presented at 90th Annual Meeting of the Transportation Research Board, Washington, D.C., 2011.
11. Himpe W., and Tampère C. Efficient Dynamic Network Loading Modeling: The Fixed Point Link Transmission Model. Proceedings of the BIVEC-GIBET Transport Research Day (Hesse M., Caruso G., Gerber P., and Viti F., eds.). University Press, Zelzate, Belgium, 2013, pp. 95–98.
12. van Hinsbergen C. P. I., and van Lint J. W. C. Bayesian Combination of Travel Time Prediction Models. In Transportation Research Record: Journal of the Transportation Research Board, No. 2064, Transportation Research Board of the National Academies, Washington, D.C., 2008, pp. 73–80.
13. Van Lint J., Hoogendoorn S., and van Zuylen H. J. Accurate Freeway Travel Time Prediction with State-Space Neural Networks Under Missing Data. Transportation Research Part C: Emerging Technologies, Vol. 13, No. 5, 2005, pp. 347–369.
14. Chen C., Kwon J., Rice J., Skabardonis A., and Varaiya P. Detecting Errors and Imputing Missing Data for Single-Loop Surveillance Systems. In Transportation Research Record: Journal of the Transportation Research Board, No. 1855, Transportation Research Board of the National Academies, Washington, D.C., 2003, pp. 160–167.
15. Vlahogianni E. I., Karlaftis M. G., and Golias J. C. Short-Term Traffic Forecasting: Where We Are and Where We're Going. Transportation Research Part C: Emerging Technologies, Vol. 43, 2014, pp. 3–19.
16. Viti F., Hoogendoorn S. P., Immers L. H., Tampère C. M. J., and Hoogendoorn-Lanser S. National Data Warehouse: How the Netherlands Is Creating a Reliable, Widespread, Accessible Data Bank for Traffic Information, Monitoring, and Road Network Control. In Transportation Research Record: Journal of the Transportation Research Board, No. 2049, Transportation Research Board of the National Academies, Washington, D.C., 2008, pp. 176–185.
17. Treiber M., and Helbing D. Reconstructing the Spatio-Temporal Traffic Dynamics from Stationary Detector Data. Cooper@tive Tr@nsport@tion Dyn@mics, Vol. 1, No. 3, 2002, pp. 3.1–3.21.
18. Treiber M., Kesting A., and Wilson R. E. Reconstructing the Traffic State by Fusion of Heterogeneous Data. Computer-Aided Civil and Infrastructure Engineering, Vol. 26, No. 6, 2011, pp. 408–419.
19. Van Lint J., and Hoogendoorn S. P. A Robust and Efficient Method for Fusing Heterogeneous Data from Traffic Sensors on Freeways. Computer-Aided Civil and Infrastructure Engineering, Vol. 25, No. 8, 2010, pp. 596–612.
20. Schreiter T., van Lint H., Treiber M., and Hoogendoorn S. Two Fast Implementations of the Adaptive Smoothing Method Used in Highway Traffic State Estimation. Proc., 13th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2010, pp. 1202–1208.
21. van Lint J. W. C. Empirical Evaluation of New Robust Travel Time Estimation Algorithms. In Transportation Research Record: Journal of the Transportation Research Board, No. 2160, Transportation Research Board of the National Academies, Washington, D.C., 2010, pp. 50–59.
Cite article
Cite article
Cite article
OR
Download to reference manager
If you have citation software installed, you can download article citation data to the citation manager of your choice
Information, rights and permissions
Information
Published In
Article first published online: April 28, 2019
Issue published: January 2015
Authors
Metrics and citations
Metrics
Journals metrics
This article was published in Transportation Research Record: Journal of the Transportation Research Board.
VIEW ALL JOURNAL METRICSArticle usage*
Total views and downloads: 65
*Article usage tracking started in December 2016
Altmetric
See the impact this article is making through the number of times it’s been read, and the Altmetric Score.
Learn more about the Altmetric Scores
Articles citing this one
Receive email alerts when this article is cited
Web of Science: 0
Crossref: 2
- A data-driven traffic modeling for analyzing the impacts of a freight ...
- Improvement of Network Performance by In-Vehicle Routing Using Floatin...
Figures and tables
Figures & Media
Tables
View Options
Get access
Access options
If you have access to journal content via a personal subscription, university, library, employer or society, select from the options below:
loading institutional access options
Alternatively, view purchase options below:
Purchase 24 hour online access to view and download content.
Access journal content via a DeepDyve subscription or find out more about this option.
