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 flowthroughout the road links.However,
human error and the weaknesses of using GPS devices in urban settingsstill 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.
References
[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. http://dx.doi.org/10.3141/2254-12
[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. http://dx.doi.org/10.1016/j.trb.2013.05.001
[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.