Evaluation of the Accuracy and Automation of Travel Time and Delay Data Collection Methods


Travel time and delay are among the most important measures for gauging a transportation system’s performance. To address the growing problem of congestion in the US, transportation planning legislation mandated the monitoring and analysis of system performance and produced a renewed interest in travel time and delay studies. The use of traditional sensors installed on major roads (e.g. inductive loops) for collecting data is necessary but not sufficient because of their limited coverage and expensive costs for setting up and maintaining the required infrastructure. The GPS-based techniques employed by the University of Delaware have evolved into an automated system, which provides more realistic experience of a traffic flow throughout the road links. However, human error and the weaknesses of using GPS devices in urban settings still have the potential to create inaccuracies. By simultaneously collecting data using three different techniques, the accuracy of the GPS positioning data and the resulting travel time and delay values could be objectively compared for automation and statistically compared for accuracy. It was found that the new technique provided the greatest automation requiring minimal attention of the data collectors and automatically processing the data sets. The data samples were statistically analyzed by using a combination of parametric and nonparametric statistical tests. This analysis greatly favored the GeoStats GPS method over the rest methods.

Share and Cite:

R. Suarez, A. Faghri and M. Li, "Evaluation of the Accuracy and Automation of Travel Time and Delay Data Collection Methods," Journal of Transportation Technologies, Vol. 4 No. 1, 2014, pp. 72-83. doi: 10.4236/jtts.2014.41007.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] L. Zhang, W. Xu and M. Li, “Co-Evolution of Transportation and Land Use: Modeling Historical Dependencies in Land Use and Transportation Decision-Making,” No. OTREC-RR-09-08, 2009.
[2] S. Humphrey, A. Faghri and M. Li, “Health and Transportation: The Dangers and Prevalence of Road Rage within the Transportation System,” American Journal of Civil Engineering and Architecture, Vol. 1, No. 6, 2013, pp. 156-163.
[3] R. Frey, A. Faghri and M. Li, “The Development of an Expert System for Effective Countermeasure Identification at Rural Unsignalized Intersections,” International Journal of Information Science and Intelligent System, Vol. 3, No. 1, 2014, pp. 23-40.
[4] M. Li, X. Zhou and N. Rouphail, “Planning-Level Methodology for Evaluating Traveler Information Provision Strategies under Stochastic Capacity Conditions,” Transportation Research Board’s 90th Annual Meeting, No. 11-3002, Washington DC, 2011.
[5] M. Li, X. Zhou and N. Rouphail, “Quantifying Benefits of Traffic Information Provision under Stochastic Demand and Capacity Conditions: A Multi-Day Traffic Equilibrium Approach,” 14th International IEEE Conference on Intelligent Transportation Systems Conference (ITSC), Washington DC, 5-7 October 2011, pp. 2118-2123.
[6] X. Zhou, N. Rouphail and M. Li, “Analytical Models for Quantifying Travel Time Variability Based on Stochastic Capacity and Demand Distributions,” Transportation Research Board 90th Annual Meeting, No. 11-3603, Washington DC, 2011.
[7] A. Jia, X. Zhou, M. Li, N. Rouphail and B. Williams, “Incorporating Stochastic Road Capacity into a Day-to-Day Traffic Simulation and Traveler Learning Framework: Model Development and Case Study,” Transportation Research Record: Journal of the Transportation Research Board, Vol. 2254, No. 1, 2011, pp. 112-121.
[8] G. Comert, “Simple Analytical Models for Estimating the Queue Lengths from Probe Vehicles at Traffic Signals,” Transportation Research Part B: Methodological, Vol. 55, 2013, pp. 59-74.
[9] US Department of Transportation, “A Guide to Metropolitan Transportation Planning Under ISTEA: How the Pieces Fit Together,” US Department of Transportation, Washington DC, 1991.
[10] L. Berzina, “Evaluation of Travel Time Data Collection Techniques and GPS Method Post-Processing Automation,” University of Delaware, Newark, 2005.
[11] M. P. Hunter, S. K. Wu and H. K. Kim, “Practical Procedure to Collect Arterial Travel Time Data Using GPS-Instrumented Test Vehicles,” Transportation Research Record: Journal of the Transportation Research Board, Vol. 1978, 2006, pp. 160-168.
[12] A. Demers, G. F. List, W. A. Wallace, E. E. Lee and J. M. Wojtowicz, “Probes as Path Seekers: A New Paradigm,” Transportation Research Record: Journal of the Transportation Research Board, Vol. 1944, 2007, pp. 107-114.
[13] S. E. Shladover and T. M. Kuhn, “Traffic Probe Data Processing for Full-Scale Deployment of Vehicle-Infrastructure Integration,” Transportation Research Record: Journal of the Transportation Research Board, Vol. 2086, 2008, pp. 115-123.
[14] A. Faghri and K. Hamad, “Application of GPS in Traffic Management Systems,” GPS Solutions, Vol. 5, No. 3, 2002, pp. 52-60. http://dx.doi.org/10.1007/PL00012899
[15] H. D. Robertson, J. E. Hummer and D. C. Nelson, “Manual of Transportation Engineering Studies,” Prentice-Hall, Inc., Englewood Cliffs, 1994.
[16] Kowoma, “GPS System,” 2009.
[17] J. L. Devore, “Probability and Statistics for Engineering and the Sciences,” Brooks/Cole Publishing, Pacific Grove, 2000.
[18] A. D. May, “Traffic Flow Fundamentals,” Prentice-Hall, Inc., Englewood Cliffs, 1990.
[19] A. Faghri, “Application of Global Positioning System (GPS) for Monitoring Congestion,” University of Delaware, Newark, 1998.
[20] P. Sprent and N. C. Smeeton, “Applied Nonparametric Statistical Methods,” Chapman & Hall/CRC, New York, 2001.
[21] M. Hollander and D. A. Wolfe, “Nonparametric Statistical Methods,” John Wiley & Sons, New York, 1973.

Copyright © 2022 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.