Arterial Travel Time Validation and Augmentation with Two Independent Data Sources
Abstract
Travel time data are a key input to applications of intelligent transportation systems. Advancement in vehicle tracking and reidentification technologies and proliferation of location-aware and connected devices have made networkwide travel time data available to transportation management agencies. The trend started with data collection on freeways and has been quickly extended to arterials. Although the freeway travel time data have been validated extensively in recent years, the quality of arterial travel time data is not well known. This paper presents a comprehensive validation scheme for arterial travel time data based on GPS probe and Bluetooth data as two independent sources. Since travel time on arterials is subject to a higher degree of variation than that on freeways, mainly because of the presence of signals, a new validation methodology based on the coefficient of variation is introduced. Moreover, a context-dependent travel time fusion framework is developed to improve the reliability of travel time information by fusing data from multiple sources. All 2012 data from a busy arterial corridor in Maryland are used to demonstrate the proposed comparison and augmentation model.
Get full access to this article
View all access and purchase options for this article.
References
1. Turner S.M., Eisele W. L., Benz R.J., and Holdener D.J. Travel Time Data Collection Handbook. FHWA-PL-98-035. FHWA, U.S. Department of Transportation, 1998.
2. Tam M.L., and Lam W.H. Using Automatic Vehicle Identification Data for Travel Time Estimation in Hong Kong. Transportmetrica, Vol. 4, No. 3, 2008, pp. 179–194.
3. Liu H.X., and Ma W. A Virtual Vehicle Probe Model for Time-Dependent Travel Time Estimation on Signalized Arterials. Transportation Research Part C: Emerging Technologies, Vol. 17, No. 1, 2009, pp. 11–26.
4. Kwong K., Kavaler R., Rajagopal R., and Varaiya P. Arterial Travel Time Estimation Based on Vehicle Re-identification Using Wireless Magnetic Sensors. Transportation Research Part C: Emerging Technologies, Vol. 17, No. 6, 2009, pp. 586–606.
5. Schrank D., Lomax T., and Turner S. TTI's 2010 Urban Mobility Report Powered by INRIX Traffic Data. Texas Transportation Institute, Texas A&M University System, College Station, 2010.
6. Haghani A., Hamedi M., Sadabadi K. F., Young S., and Tarnoff P. Data Collection of Freeway Travel Time Ground Truth with Bluetooth Sensors. In Transportation Research Record: Journal of the Transportation Research Board, No. 2160, Transportation Research Board of the National Academies, Washington, D.C., 2010, pp. 60–68.
7. Hamedi M., Haghani A., and Sadabadi F. Using Bluetooth Technology for Validating Vehicle Probe Data. Proc., 16th ITS World Congress and Exhibition on Intelligent Transport Systems and Services, Stockholm, Sweden, 2009.
8. Haghani A., Hamedi M., and Sadabadi K.F. I-95 Corridor Coalition Vehicle Probe Project: Validation of INRIX Data. Technical report. I-95 Corridor Coalition, College Park, Md., 2009.
9. Anusha S.P., Anand R.A., and Vanajakshi L. Data Fusion Based Hybrid Approach for the Estimation of Urban Arterial Travel Time. Journal of Applied Mathematics, Vol. 2012, 2012, Article 587193.
10. Hofleitner A., Herring R., Abbeel P., and Bayen A. Learning the Dynamics of Arterial Traffic from Probe Data Using a Dynamic Bayesian Network. IEEE Transactions on Intelligent Transportation Systems, Vol. 13, No. 4, 2012, pp. 1679–1693.
11. Herring R., Hofleitner A., Abbeel P., and Bayen A. Estimating Arterial Traffic Conditions Using Sparse Probe Data. Proc., 13th International Conference on Intelligent Transportation Systems, IEEE, 2010, pp. 929–936.
12. Aliari Y., and Haghani A. Bluetooth Sensor Data and Ground Truth Testing of Reported Travel Times. In Transportation Research Record: Journal of the Transportation Research Board, No. 2308, Transportation Research Board of the National Academies, Washington, D.C., 2012, pp. 167–172.
13. Faouzi N. E. E., Leung H., and Kurian A. Data Fusion in Intelligent Transportation Systems: Progress and Challenges—A Survey. Information Fusion, Vol. 12, No. 1, 2011, pp. 4–10.
14. Kong Q.J., Li Z., Chen Y., and Liu Y. An Approach to Urban Traffic State Estimation by Fusing Multisource Information. IEEE Transactions on Intelligent Transportation Systems, Vol. 10, No. 3, 2009, pp. 499–511.
15. Soriguera F., and Robusté F. Highway Travel Time Accurate Measurement and Short-Term Prediction Using Multiple Data Sources. Transportmetrica, Vol. 7, No. 1, 2011, pp. 85–109.
16. Bloch I. Information Combination Operators for Data Fusion: A Comparative Review with Classification. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, Vol. 26, No. 1, 1996, pp. 52–67.
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: 59
*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: 9
- Evaluating the Quality of High-Resolution Private Sector Data for Prov...
- Evaluation and augmentation of traffic data including Bluetooth detect...
- Integrating transportation data with emergency medical service records...
- Quality of location-based crowdsourced speed data on surface streets: ...
- Graph Analytics and Optimization Methods for Insights from the Uber Mo...
- Application of High-Resolution Trip Trace Stitching to Evaluate Traffi...
- Probabilistic Data Fusion for Short-Term Traffic Prediction With Semip...
- Is dynamic traffic sensor network profitable for network-level real-ti...
- Bicycle infrastructure and traffic congestion: Evidence from DC's Capi...
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.
