Towards Strategic and Sustainable Region-wide Road Weather Information Systems (RWIS) Network Planning and Management

• Author / Creator

  Biswas, Simita

• Road Weather Information Systems (RWIS) are considered one of the most critical highway intelligent transportation system (ITS) infrastructures, combining several advanced technologies to collect, process, and disseminate road weather information. The collected information is used by road maintenance authorities to make operational decisions aimed at improving safety and mobility before, during, and after inclement weather events. Acknowledging their significant operational and environmental benefits, many North American transportation agencies have invested millions of dollars in deploying RWIS stations to strengthen the monitoring coverage of winter road surface conditions. However, considering their high deployment costs and the seemingly random nature of road weather fluctuations, little is known about the optimal distribution density required to provide adequate monitoring coverage under varying circumstances. What is also resurging is the development of comprehensive RWIS siting guidelines in an effort to maximize return on investment while keeping our roads safe and mobile.
  As an initial step, a series of geostatistical semivariogram models were constructed and compared using topographic position index (TPI) and weather severity index (WSI). A geostatistical approach in conjunction with large-scale optimizations were then conducted to determine the optimum number of RWIS stations across several topographic and weather zones using nationwide weather, geographical, and topographical datasets covering 20 different states in the US. The findings indicate that RWIS density strongly depends on the varying environmental characteristics of the region under investigation.
  In the subsequent step, a new methodological framework was developed to determine optimal RWIS locations by taking into account spatial characteristics of multiple critical RWIS variables; namely, air temperature, road surface temperature, and dew point temperature. A multi-variable semivariogram model was developed by integrating the impact of multiple crucial weather parameters. This integrated model, combined with the traffic parameters, was employed to refine the location optimization algorithm; which was then solved using a popular metaheuristic algorithm; namely, spatial simulated annealing. The developed location allocation model was then illustrated using a case study for the region-wide RWIS network planning and statewide gap analysis.
  In addition to this, this study involved sensitivity analysis of optimal locations generated for various planning scenarios to further validate the conclusiveness of the findings and to furnish decision-makers with a range of solutions, providing flexibility in the decision-making process. The findings revealed that the weighting of weather and traffic parameters influences optimal location selection. The resulting solution sets from the sensitivity analysis offer adaptability in selecting parameter weights tailored to the requirements of decision-makers, encompassing considerations of both weather variables and safety implications associated with traffic.
  In addition to location determination, this study introduced a novel bi-level sequential optimization model for comprehensive RWIS network planning, which addresses the need to pinpoint both the location and type of RWIS stations; namely, Regular RWIS and Mini-RWIS. Regular RWIS stations capture regional weather trends while Mini-RWIS stations capture local trends. Therefore, considering the type is critical for achieving cost-effectiveness and maximizing coverage. By comparing the gap in monitoring coverage, substantial enhancement in RWIS network planning is achieved by offering a method to fine-tune both station placement and type. This decision is pivotal as the choice directly impacts the network’s financial sustainability and operational efficiency. Therefore, determining the optimal type of station in conjunction with its placement is essential for constructing an effective and economically viable RWIS network.
  At the last step, the impact of optimal RWIS network on traffic safety was assessed by introducing a new parameter, named network coverage index (NCI). NCI analyzes and quantifies the advantages of an optimized RWIS network through the enhancement of transportation safety. The findings reveal a strong dependency between the NCI and the RWIS network configuration. Based on the findings obtained in this study, road agencies and RWIS planners can now be assisted with conceptualizing the capabilities of an optimized RWIS network, which will help them increase monitoring coverage, and in the process, gain a quantitative understanding on its potential impact on traffic safety.
  The methodologies developed and analyzed in this thesis provide RWIS planner with the evidence-based RWIS planning and management strategies, which in turn will benefit winter travelers with improved safety, mobility, and a more environmentally sustainable RWIS network. Moreover, RWIS network planning solutions derived from this research are conveniently implemented for real-world applications.

• Subjects / Keywords

  • RWIS
  • Topography
  • Weather
  • Density Optimization
  • Location Allocation
  • Safety Assessment

• Graduation date

  Fall 2024

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/r3-q2mq-t878

• License

  This thesis is made available by the University of Alberta Library with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.