Traffic Signal Battery Backup Systems: Use of Event-Based Traffic Controller Logs in Performance-Based Investment Programming
Abstract
A stable supply of power is a critical element in reliable traffic signal operation. Battery backup systems (BBSs) are often used to prevent traffic signals from experiencing power disruption. BBSs are designed through the use of engineering judgment because of a lack of operational requirements and well-defined performance measures. New high-resolution traffic controllers have the ability to record event-based data for power failures and traffic counts at a resolution of 0.1 s. With these high-resolution data, this paper proposes performance-based investment programming that uses the average annual signal downtime (AASD) over the analysis period as a performance measure. The AASD is stochastically estimated through the use of hazard-based duration models developed with power failure data. A volume and functional class–weighted stochastic optimization scheme is then presented for a BBS planning project, in which battery capacity is sized to minimize the AASD for a network under given budget constraints.
Get full access to this article
View all access and purchase options for this article.
References
1. Wallace W. A., Wojtowicz J., Torrey D., Renna N., and Tan J. Guidelines for Traffic Signal Energy Back-Up Systems. Technical Report 09-05. Center for Infrastructure and Transportation Studies, Rensselaer Polytechnic Institute, Troy, N.Y., 2009.
2. Smaglik E. J., Sharma A., Bullock D. M., Sturdevant J. R., and Duncan G. Event-Based Data Collection for Generating Actuated Controller Performance Measures. In Transportation Research Record: Journal of the Transportation Research Board, No. 2035, Transportation Research Board of the National Academies, Washington, D.C., 2007, pp. 97–106.
3. Transportation Project Prioritization and Performance-Based Planning Efforts in Rural and Small Metropolitan Regions. National Association of Development Organizations Research Foundation, Washington, D.C., 2011.
4. IEEE Standard Computer Dictionary: A Compilation of IEEE Standard Computer Glossaries. IEEE, New York, 1990.
5. DTE Energy. Outage Causes. https://www2.dteenergy.com/wps/portal/dte/business/problemsSafety/details. Accessed June 5, 2014.
6. Hensher D. A., and Mannering F. L. Hazard-Based Duration Models and Their Application to Transport Analysis. Transport Reviews, Vol. 14, No. 1, 1994, pp. 63–82.
7. Bhat C. R. A Hazard-Based Duration Model of Shopping Activity with Nonparametric Baseline Specification and Nonparametric Control for Unobserved Heterogeneity. Transportation Research Part B, Vol. 30, No. 3. 1996, pp. 189–207.
8. Kim S.-G., and Mannering F. L. Panel Data and Activity Duration Models: Econometric Alternatives and Applications. In Panels for Transportation Planning, Transportation Research, Economics, and Policy, Springer, New York, 1997, pp. 349–373.
9. Nam D., and Mannering F. An Exploratory Hazard-Based Analysis of Highway Incident Duration. Transportation Research Part A, Vol. 34, 2000, pp. 85–102
10. Washington S. P., Karlaftis M. G., and Mannering F. L. Statistical and Econometric Methods for Transportation Data Analysis, 2nd ed. CRC Press, Boca Raton, Fla., 2010.
11. Lee E. Statistical Methods for Survival Data Analysis, 2nd ed. John Wiley & Sons, New York, 1992.
12. Bendell A., Walley M., Wightman D. W., and Wood L. M. Proportional Hazards Modelling in Reliability Analysis: An Application to Brake Discs on High Speed Trains. Quality and Reliability Engineering International, Vol. 2, No. 1, 1986, pp. 45–52.
13. Silverman B. W. Density Estimation for Statistics and Data Analysis. Chapman & Hall, London, 1986.
14. Hu H. Risk-Conscious Design of Off-Grid Solar Energy Houses. PhD dissertation. Georgia Institute of Technology, Atlanta, 2009.
15. Billinton R., and Sankarakrishnan A. System State Transition Sampling Technique for Reliability Evaluation. Reliability Engineering and System Safety, Vol. 44, No. 2, 1994, pp. 131–134.
16. Billinton R., and Li W. Reliability Assessment of Electric Power Systems Using Monte Carlo Methods. Plenum Press, New York, 1994.
17. Multi-Criteria Analysis: A Manual. Department for Communities and Local Government, London, 2009.
18. Traffic Signals Manual. Texas Department of Transportation, Austin, 2011. http://onlinemanuals.txdot.gov/txdotmanuals/tff/tff.pdf. Accessed May 5, 2014.
19. Goldberg D. E. Genetic Algorithm in Search, Optimization, and Machine Learning. Addison–Wesley, Boston, Mass., 1989.
20. Khosravi A., Mazloumi B., Nahavandi S., Creighton D., and Van Lint J. W. C. A Genetic Algorithm-Based Method for Improving Quality of Travel Time Prediction Intervals. Transportation Research Part C, Vol. 19, No. 6, 2011, pp. 1364–1376.
21. Indiana Department of Transportation. Roadway Inventory. https://gis.in.gov/apps/DOT/RoadwayInventory/. Accessed July 5, 2014.
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: 25
*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: 1
- A Holistic Intersection Rating System (HIRS)—A Novel Methodology to Me...
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.
