Using Real-Life Dual-Loop Detector Data to Develop New Methodology for Estimating Freeway Travel Time Reliability

Abstract

Travel time reliability is an important system performance measure for freeway traffic operations. It captures the variability experienced by individual travelers, and it is an indicator of the operational consistency of a facility over an extended period. Real-life transportation data were used to develop a new methodology for estimating travel time reliability of the I-4 corridor in Orlando, Florida. Four different travel time distributions were tested: Weibull, exponential, lognormal, and normal. The developed best-fit statistical distribution (lognormal) can be used to compute and predict travel time reliability of freeway corridors and report this information in real time to the public through traffic management centers. When compared with existing Florida and buffer time methods, the new reliability method showed higher sensitivity to geographical locations, which reflects the level of congestion and bottlenecks. Another advantage of the new method is its ability to estimate the travel time reliability as a function of departure time.

References

Shaw

NCHRP Synthesis 311: Performance Measures of Operational Effectiveness for Highway Segments and Systems. Transportation Research Board of the National Academies, Washington, D.C., 2003.

Kececioglu

Reliability Engineering Handout, Vol. 1. Prentice Hall, Upper Saddle River, N.J., 1991.

Chen

Skabardonis

and Varaiya

Travel-Time Reliability as a Measure of Service. In Transportation Research Record: Journal of the Transportation Research Board, No. 1885, Transportation Research Board of the National Academies, Washington, D.C., 2003, pp. 74–79.

Chen

and Recker

Considering Risk Taking Behavior in Travel Time Reliability. UCI-ITS-WP-00–24. Institute of Transportation Studies, University of California, Irvine. 2000. www.its.uci.edu. Accessed May 10, 2006.

Shaw

and Jackson

System Reliability Measures for Highway and Multimodal Systems. Presented at 82nd Annual Meeting of the Transportation Research Board, Washington, D.C., 2003.

Z. P.

and Nicholson

A. J.

Degradable Transportation Systems: Sensitivity and Reliability Analysis. Transportation Research B, Vol. 31, No. 3 1997, pp. 225–237.

Turner

Best

and Schrank

Measures of Effectiveness for Major Investment Studies. Texas Transportation Institute, Texas A&M University System, College Station, 1996.

Lomax

Turner

Hallenbeck

Boon

Margiotta

and O'Brien

Traffic Congestion and Travel Reliability: How Bad Is the Situation and What Is Being Done About It? FHWA, U.S. Department of Transportation. www.fhwa.dot.gov/congestion/cgstpapr.htm. Accessed May 20, 2004.

The Florida Reliability Method: In Florida's Mobility Performance Measures Program. Florida Department of Transportation. 2000. www.dot.state.fl.us/planning/statistics/mobilitymeasures/reliability.pdf. Accessed March 31, 2004.

10.

Al-Deek

H. M.

and Chilakamarri

C. V. S. R.

New Algorithms for Filtering and Imputation of Real Time and Archived Dual-Loop Detector Data in I-4 Data Warehouse. In Transportation Research Record: Journal of the Transportation Research Board, No. 1867, Transportation Research Board of the National Academies, Washington, D.C., 2004, pp. 116–126.

11.

Al-Deek

Kerr

Ramachandran

Pooley

Chehab

Emam

Chandra

Zuo

Petty

and Swinson

The Central Florida Data Warehouse, Phase 2: The Central ITS Office Funding. Transportation Systems Institute, Department of Civil and Environmental Engineering, University of Central Florida, Orlando, 2004.

12.

Shimokawa

and Liao

Goodness-of-Fit Tests for Type I Extreme-Value and 2-Parameter Weibull Distributions. IEEE Transactions on Reliability, Vol. 48, No. 1 1999, pp. 79–86.

13.

Birolini

Reliability Engineering Theory and Practice, 3rd ed. Springer-Verlag, New York, 1999.

14.

D'Agostino

R. B.

and Stephens

M. A.

Goodness-of-Fit Techniques. Marcel Dekker, New York, 1986.