Validation of Visibility Data from Road Weather Information Systems by Comparing Three Data Resources: Case Study in Ohio
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32769
Validation of Visibility Data from Road Weather Information Systems by Comparing Three Data Resources: Case Study in Ohio

Authors: Fan Ye

Abstract:

Adverse weather conditions, particularly those with low visibility, are critical to the driving tasks. However, the direct relationship between visibility distances and traffic flow/roadway safety is uncertain due to the limitation of visibility data availability. The recent growth of deployment of Road Weather Information Systems (RWIS) makes segment-specific visibility information available which can be integrated with other Intelligent Transportation System, such as automated warning system and variable speed limit, to improve mobility and safety. Before applying the RWIS visibility measurements in traffic study and operations, it is critical to validate the data. Therefore, an attempt was made in the paper to examine the validity and viability of RWIS visibility data by comparing visibility measurements among RWIS, airport weather stations, and weather information recorded by police in crash reports, based on Ohio data. The results indicated that RWIS visibility measurements were significantly different from airport visibility data in Ohio, but no conclusion regarding the reliability of RWIS visibility could be drawn in the consideration of no verified ground truth in the comparisons. It was suggested that more objective methods are needed to validate the RWIS visibility measurements, such as continuous in-field measurements associated with various weather events using calibrated visibility sensors.

Keywords: Low visibility, RWIS, traffic safety, visibility.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1129255

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1274

References:


[1] Goodwin, L. C. Weather Impacts on Arterial Traffic Flow, Mitretek Systems, Inc., Falls Church, VA, 2002. http://www.ops.fhwa.dot.gov/weather/best_practices/ArterialImpactPaper.pdf. Accessed in January, 2017.
[2] Pisano, P. A., L. Goodwin and M. A. Rossetti. U.S. highway crashes in adverse road weather conditions. Paper presented at the 24th Conference on International Interactive Information and Processing Systems for Meteorology, Oceanography and Hydrology, New Orleans, LA, 2008.
[3] Theofilatos, A. and G. Yannis. A Review of the Effect of Traffic and Weather Characteristics on Road Safety. Accident Analysis and Prevention, 72 (2014) 244-256.
[4] Hranac, R., E. Sterzin, D. Krechmer, H. Rakha, and M. Farzaneh. Empirical Studies on Traffic Flow in Inclement Weather. Report No. FHWA-HOP-07-073, U.S. Department of Transportation Federal Highway Administration, 2006.
[5] Kyte, M., Z. Khatib, P. Shannon and F. Kitchener. The Effect of Weather on Free Flow Speed, Transportation Research Board, Paper No. 01-3280, Washington, D.C, 2001.
[6] Abdel-Aty, M., A.-A. Ekrama, H. Huang, and K. Choic. A study on crashes related to visibility obstruction due to fog and smoke. Accident Analysis and Prevention, 43 (2011) 1730-1737.
[7] Abdel-Aty, M., H.M. Hassan, M. Ahmed, and A.S. Al-Ghamdi. Real-time Prediction of Visibility Related Crashes. Transportation Research Part C, 24 (2012) 288-298.
[8] Ahmed, M. M., M. Abdel-Aty, J. Lee, and R. Yu. Real-time Assessment of Fog-related Crashes using Airport Weather Data: A Feasibility Analysis. Accident Analysis and Prevention, 72 (2014) 309-317.
[9] Yu, R., Y. Xiong, and M. Abdel-Aty. A Correlated Random Parameter Approach to Investigate the Effects of Weather Conditions on Crash Risk for a Mountainous Freeway. Transportation Research Part C, 50 (2015) 68-77.
[10] SWOV Fact Sheet. The Influence of Weather on Road Safety. SWOV, Leindschendam, the Netherlands, 2012.
[11] Yu, R., and M. Abdel-Aty. Using Hierarchical Bayesian Binary Probit Models to Analyze Crash Injury Severity on High Speed Facilities with Real-time Traffic Data. Accident Analysis and Prevention, 62 (2014) 161-167.
[12] Koetse, M.J., and P. Rietveld. The Impact of Climate Change and Weather on Transport: An Overview of Empirical Findings. Transportation Research Part D, 14 (2009) 205-221.
[13] Murphy, R., R. Swick, B.A. Hamilton, and G. Guevara. Best Practices for Road Weather Management. FHWA Report No. FHWA-HOP-12-046, 2012.
[14] Ahmed M., M. Abdel-Aty, S. Qi, and M. Abuzwidah. Synthesis of State-of-the-Art in Visibility Detection Systems’ Applications and Research. Journal of Transportation Safety & Security, 6 (2014) 183-206.
[15] Hill, C.J. Concept of Operations for Road Weather Connected Vehicle Application. Report No. FHWA-JPO-13-047, U.S. Department of Transportation, 2013.
[16] Automated Surface Observing System (ASOS) User’s Guide. National Oceanic and Atmospheric Administration, 1998. http://www.nws.noaa.gov/asos/pdfs/aum-toc.pdf, Accessed in January, 2017.
[17] An Introduction to Standards for Road Weather Information Systems (RWIS). FHWA Report, Publication No. FHWA-OP-02-079, 2002.
[18] GOES-R Aviation Products-Visibility. http://www.goesr.gov/education/docs/fs_visibility.pdf. Accessed in July, 2015.
[19] Shinar, D., J.R. Treat, and S.T. McDonald. The Validity of Police Reported Accident Data. Accident Prevention and Analysis, 15 (3), (1983) 175-191.
[20] Andrey, J., B. Mills, M. Leahy, and J. Suggett. Weather as a Chronic Hazard for Road Transportation in Canadian Cities. Natural Hazards 28 (2003), 319-343.