Strategic Planning for Equitable RWIS Implementation: A Comprehensive Study Incorporating a Multi-variable Semivariogram Model


  • Simita Biswas

    Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB, T6G2W2, Canada

  • Tae J. Kwon

    Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB, T6G2W2, Canada


Received: 19 September 2023 | Revised: 25 October 2023 | Accepted: 26 October 2023 | Published Online: 10 November 2023


This paper extends the previously developed method of optimizing Road Weather Information Systems (RWIS) station placement by unveiling a sophisticated multi-variable semivariogram model that concurrently considers multiple vital road weather variables. Previous research primarily centered on single-variable analysis focusing on road surface temperature (RST). The study bridges this oversight by introducing a framework that integrates multiple critical weather variables into the RWIS location allocation framework. This novel approach ensures balanced and equitable RWIS distribution across zones and aligns the network with areas both prone to traffic accidents and areas of high uncertainty. To demonstrate the effectiveness of this refinement, the authors applied the framework to Maine’s existing RWIS network, conducted a gap analysis through varying planning scenarios and generated optimal solutions using a heuristic optimization algorithm. The analysis identified areas that would benefit most from additional RWIS stations and guided optimal resource utilization across different road types and priority locations. A sensitivity analysis was also performed to evaluate the effect of different weightings for weather and traffic factors on the selection of optimal locations. The location solutions generated have been adopted by MaineDOT for future implementations, attesting to the model’s practicality and signifying an important advancement for more effective management of road weather conditions.


RWIS, Location optimization, Multi-variable semivariogram, Heuristics, Spatial simulated annealing (SSA), Collision rate (CR)


[1] Boon, C.B., Cluett, C., 2002. Road Weather Information Systems: Enabling Proactive Maintenance Practices in Washington State [Internet]. Available from:

[2] Pilli-Sihvola, E., Leviakangas, P., Hautala, R. (editors), 2012. Better winter road weather information saves money, time, lives and the environment. Proceedings of the 19th Intelligent Transport Systems World Congress (ITS); 2012 Oct 22-26; Vienna, Austria.

[3] Ölander, J., 2002. Winter Index by Using RWIS and MESAN [Internet]. PIARC 2002 XIth International Winter Road Congress 28-31 January 2002-Sapporo (Japan). Available from:

[4] Axelson, L., 2000. Development and Use of the Swedish Road Weather Information System [Internet]. Available from:

[5] White, S.P., Thornes, J.E., Chapman, L., 2006. A guide to road weather information systems, version 2. University of Birmingham: Birmingham, UK.

[6] Manfredi, J., Walters, T., Wilke, G., et al., 2008. Road Weather Information System Environmental Sensor Station Siting Guidelines, Version 2.0 [Internet]. Available from:

[7] Jin, P.J., Walker, A., Cebelak, M., et al., 2014. Determining strategic locations for environmental sensor stations with weather-related crash data. Transportation Research Record. 2440(1), 34-42.

[8] Eriksson, M., Norrman, J., 2001. Analysis of station locations in a road weather information system. Meteorological Applications. 8(4), 437-448.

[9] Zwahlen, H.T., Russ, A., Vatan, S., 2003. Evaluation of ODOT Roadway/Weather Sensor Systems for Snow and Ice Removal Operations: Part I: RWIS [Internet]. Available from:

[10] Mackinnon, D., Lo, A., 2009. Alberta transportation road weather information system (RWIS) expansion study. Alberta Transportation.

[11] Zhao, L., Chien, S., Meegoda, J., et al., 2016. Cost-benefit analysis and icroclimate-based optimization of a RWIS network. Journal of Infrastructure Systems. 22(2), 04015021.

[12] Fetzer, J., Caceres, H., He, Q., et al., 2018. A multi-objective optimization approach to the location of road weather information system in New York State. Journal of Intelligent Transportation Systems. 22(6), 503-516.

[13] Kwon, T.J., Fu, L., 2013. Evaluation of alternative criteria for determining the optimal location of RWIS stations. Journal of Modern Transportation. 21, 17-27.

[14] Valjarević, A., Filipović, D., Živković, D., et al., 2021. Spatial analysis of the possible first Serbian Conurbation. Applied Spatial Analysis and Policy. 14, 113-134.

[15] Timalsina, K.P., Subedi, B.P., 2022. Open space implications in urban development: Reflections in recent urban planning practices in Nepal. Journal of Geographical Research. 5(2), 69-81.

[16] Kwon, T.J., Fu, L., Melles, S.J., 2017. Location optimization of road weather information system (RWIS) network considering the needs of winter road maintenance and the traveling public. Computer‐Aided Civil and Infrastructure Engineering. 32(1), 57-71.

[17] Biswas, S., Kwon, T.J., 2022. Development of a novel road weather information system location allocation model considering multiple road weather variables over space and time. Transportation Research Record. 2676(8), 619-632.

[18] Biswas, S., Kwon, T.J., 2020. Developing state wide optimal RWIS density guidelines using space-time semivariogram models. Journal of Sensors. 1208692. DOI:

[19] Biswas, S., Wu, M., Melles, S.J., et al., 2019. Use of topography, weather zones, and semivariogram parameters to optimize road weather information system station density across large spatial scales. Transportation Research Record. 2673(12), 301-311.

[20] Olea, R.A., 2012. Geostatistics for engineers and earth scientists. Springer Science & Business Media: New York.

[21] Van Groenigen, J.W., Stein, A., 1998. Constrained optimization of spatial sampling using continuous simulated annealing. Journal of Environmental Quality. 27(5), 1078-1086.

[22] Van Groenigen, J.W., Siderius, W., Stein, A., 1999. Constrained optimisation of soil sampling for minimisation of the kriging variance. Geoderma. 87(3-4), 239-259.

[23] Brus, D.J., Heuvelink, G.B., 2007. Optimization of sample patterns for universal kriging of environmental variables. Geoderma. 138(1-2), 86-95.

[24] Heuvelink, G.B., Brus, D.J., de Gruijter, J.J., 2006. Optimization of sample configurations for digital mapping of soil properties with universal kriging. Developments in Soil Science. 31, 137-151.

[25] Golembiewski, G., Chandler, B.E., 2011. Roadway Safety Information Analysis: A Manual for Local Rural Road Owners [Internet]. Available from:

[26] Kahl, J.S., Norton, S.A., Cronan, C.S., et al., 1991. Maine. Acidic deposition and aquatic ecosystems: Regional case studies. Springer: New York. pp. 203-235.

[27] Greenleaf, M., 1829. A survey of the State of Maine: In reference to its geographical features, statistics and political economy. Maine State Museum Publications: Augusta.

[28] R: A Language and Environment for Statistical Computing [Internet]. Available from:

[29] Pebesma, E.J., 2004. Multivariable geostatistics in S: The gstat package. Computers & Geosciences. 30(7), 683-691.

[30] Johnston, K., Ver Hoef, J.M., Krivoruchko, K., et al., 2001. Using ArcGIS geostatistical analyst. Esri: Redlands.

[31] ArcGIS 10.4.1 for Desktop Quick Start Guide [Internet]. ESRI; 2015. Available from:


How to Cite

Biswas, S., & Kwon, T. J. (2023). Strategic Planning for Equitable RWIS Implementation: A Comprehensive Study Incorporating a Multi-variable Semivariogram Model. Journal of Geographical Research, 6(4), 54–72.


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