Estimation of Travel Time on Urban Networks with Midlink Sources and Sinks
Abstract
This paper presents a methodology for estimation of average travel time on signalized urban networks by integrating cumulative plots and probe data. This integration aims to reduce the relative deviations in the cumulative plots due to midlink sources and sinks. During undersaturated traffic conditions, the concept of a virtual probe is introduced, and therefore, accurate travel time can be obtained when a real probe is unavailable. For oversaturated traffic conditions, only one probe per travel time estimation interval–-360 s or 3% of vehicles traversing the link as a probe–-has the potential to provide accurate travel time.
Get full access to this article
View all access and purchase options for this article.
References
1. Wardrop J. G. Journey Speed and Flow in Central Urban Areas. Traffic Engineering and Control, Vol. 9, 1968, pp. 528–532.
2. Young C. P. A Relationship Between Vehicle Detector Occupancy and Delay at Signal-Controlled Junctions. Traffic Engineering and Control, Vol. 29, 1988, pp. 131–134.
3. Sisiopiku V. P., and Rouphail N. M. Toward the Use of Detector Output for Arterial Link Travel Time Estimation: A Literature Review. In Transportation Research Record 1457, TRB, National Research Council, Washington, D.C., 1994, pp. 158–165.
4. Sisiopiku V. P., Rouphail N. M., and Santiago A. Analysis of Correlation Between Arterial Travel Time and Detector Data from Simulation and Field Studies. In Transportation Research Record 1457, TRB, National Research Council, Washington, D.C., 1994, pp. 166–173.
5. Oh J. S., Jayakrishnan R., and Recker W. Section Travel Time Estimation from Point Detection Data. Presented at 82nd Annual Meeting of Transportation Research Board, Washington, D.C., 2003.
6. Nam D. H., and Drew D. R. Automatic Measurement of Traffic Variables for Intelligent Transportation Systems Applications. Transportation Research Part B: Methodological, Vol. 33, No. 6, 1999, pp. 437–457.
7. You J., and Kim T. J. Development and Evaluation of a Hybrid Travel Time Forecasting Model. Transportation Research Part C: Emerging Technologies, Vol. 8, No. 1, 2000, pp. 231–256.
8. Bajwa S. I., Chung E., and Kuwahara M. A Travel Time Prediction Method Based on Pattern Matching Technique. Proc., 21st ARRB and 11th REAAA Conference, Cairns, Australia, 2003.
9. Robinson S., and Polak J. W. Modeling Urban Link Travel Time with Inductive Loop Detector Data by Using the k-NN Method. In Transportation Research Record: Journal of the Transportation Research Board, No. 1935, Transportation Research Board of the National Academies, Washington, D.C., 2005, pp. 47–56.
10. Dailey D. J. Travel-Time Estimation Using Cross-Correlation Techniques. Transportation Research Part B: Methodological, Vol. 27, No. 2, 1993, pp. 97–107.
11. Coifman B., and Krishnamurthy S. Vehicle Reidentification and Travel Time Measurement Across Freeway Junctions Using the Existing Detector Infrastructure. Transportation Research Part C: Emerging Technologies, Vol. 15, No. 3, 2007, pp. 135–153.
12. Park D., and Rilett L. R. Forecasting Multiple-Period Freeway Link Travel Times Using Modular Neural Networks. In Transportation Research Record: Journal of the Transportation Research Board, No. 1617, TRB, National Research Council, Washington, D.C., 1998, pp. 163–170.
13. Chen H., Grant-Muller S., Mussone L., and Montgomery F. A Study of Hybrid Neural Network Approaches and the Effects of Missing Data on Traffic Forecasting. Neural Computing and Applications, Vol. 10, 2001, pp. 277–286.
14. Liu H., van Zuylen H. J., van Lint H., and Salomons M. Predicting Urban Arterial Travel Time with State-Space Neural Networks and Kalman Filters. In Transportation Research Record: Journal of the Transportation Research Board, No. 1968, Transportation Research Board of the National Academies, Washington, D.C., 2006, pp. 99–108.
15. Hellinga B. R., and Fu L. Reducing Bias in Probe-Based Arterial Link Travel Time Estimates. Transportation Research Part C: Emerging Technologies, Vol. 10, No. 4, 2002, pp. 257–273.
16. Srinivasan K. K., and Jovanis P. P. Determination of Number of Probe Vehicles Required for Reliable Travel Time Measurement in Urban Network. In Transportation Research Record 1537, TRB, National Research Council, Washington, D.C., 1996, pp. 15–22.
17. Berka S., Tarko A., Rouphail N. M., Sisiopiku V. P., and Lee D.-H. Data Fusion Algorithm for ADVANCE Release 2.0. Advance Working Paper Series, No. 48. University of Illinois at Chicago, 1995.
18. Westerman M., Litjens R., and Linnartz J.-P. Integration of Probe Vehicle and Induction Loop Data: Estimation of Travel Times and Automatic Incident Detection. California Partners for Advanced Transit and Highways (PATH). Research Papers: Paper UCB-PRR-96-13, 1996. http://repositories.cdlib.org/its/path/reports/UCB-ITS-PRR-96-13.
19. Choi K., and Chung Y. A Data Fusion Algorithm for Estimating Link Travel Time. Journal of Intelligent Transportation Systems, Vol. 7, 2002, pp. 235–260.
20. El Faouzi N. E. Data-Driven Aggregative Schemes for Multisource Estimation Fusion: A Road Travel Time Application. Proc., SPIE—International Society for Optical Engineering, Orlando, Fla., 2004, pp. 351–359.
21. Bhaskar A., Chung E., de Mouzon O., and Dumont A.-G. Analytical Modeling and Sensitivity Analysis for Travel Time Estimation on Signalized Urban Networks. Presented at 87th Annual Meeting of the Transportation Research Board, Washington, D.C., 2008.
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: January 1, 2009
Issue published: January 2009
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: 16
*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: 19
- Network-Wide Link Travel Time Inference Using Trip-Based Data From Aut...
- Understanding parking decisions with structural equation modeling
- A survey of advances in vision-based vehicle re-identification
- Arterial link travel time estimation considering traffic signal delays...
- A Methodological Framework of Travel Time Distribution Estimation for ...
- Travel Time Estimation Using Freeway Point Detector Data Based on Evol...
- Is Bus Overrepresented in Bluetooth MAC Scanner data? Is MAC-ID Really...
- Bluetooth Vehicle Trajectory by Fusing Bluetooth and Loops: Motorway T...
- Real‐time estimation of travel speed using urban traffic information s...
- Estimating Dynamic Queue Distribution in a Signalized Network Through ...
- Macroscopic Fundamental Diagram for Brisbane, Australia...
- Hybrid Model for Motorway Travel Time Estimation considering Increased...
- Fundamental understanding on the use of Bluetooth scanner as a complem...
- Urban link travel time estimation based on sparse probe vehicle data
- Unscented Kalman filter for urban link travel time estimation with mid...
- A framework for neighbour links travel time estimation in an urban net...
- Average Travel Time Estimations for Urban Routes that Consider Exit Tu...
- Model for Predicting Distribution of Link Travel Times for Urban Signa...
- Analysis for the Use of Cumulative Plots for Travel Time Estimation on...
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.
