Use of Topography, Weather Zones, and Semivariogram Parameters to Optimize Road Weather Information System Station Density across Large Spatial Scales

Abstract

A road weather information system (RWIS) is a combination of advanced technologies which collect, process, and disseminate road weather and condition information. This information is used by road maintenance authorities to make operative decisions that improve safety and mobility during inclement weather events. Many North American transportation agencies have invested millions of dollars to deploy RWIS stations to improve the monitoring coverage of winter road surface conditions. The design of these networks often varies by region, however, and it is not entirely clear how many stations are necessary to provide adequate monitoring coverage under different conditions; substantial gaps remain in knowledge about optimal design. To fill these gaps, an investigation was conducted to determine how optimized RWIS station densities relate to topographic and weather characteristics. A series of geostatistical semivariogram models were constructed and compared using topographic position index (TPI) and weather severity index (WSI) to measure relative topographic variation and weather severity, respectively. The geostatistical approach was then applied to map the optimum number of RWIS stations across several topographic and weather zones. The study area captured varying environmental characteristics, including regions with flat or varied terrain and warm or cold regions. This study suggests that RWIS data collected from a specific region can be used to estimate the number of stations required for regions with similar zonal characteristics. The outcome of this study can be used as a decision-making tool for RWIS network expansion, thus maximizing monitoring capability of RWIS networks using topographic and weather-related zonal classifications.

Development of a Novel Road Weather Information System Location Allocation Model Considering Multiple Road Weather Variables over Space and Time

“…An innovative RWIS location modeling framework was developed in our previous efforts where the problem was formulated as an integer programming problem with the objective of minimizing the spatially averaged kriging variance (in other words, maximizing spatial coverage) across the road network ( 3 , 7 , 27 , 28 ). For the first time in the literature, the method developed there provided decision makers with a tool needed to optimize and expand their existing RWIS networks.…”

Developing Statewide Optimal RWIS Density Guidelines Using Space-Time Semivariogram Models

“…The study also asserted that a region with a longer spatial autocorrelation range would require fewer stations than a region with a shorter range [4]. Our recent effort [9] further extends the former work by investigating the dependency of optimal RWIS density on two different measures, namely, topography and weather severity, in an effort to improve generalization potentials and design a long-term strategic RWIS deployment plan. Despite the uniqueness of the method developed and analyses undertaken therein, the study dealt solely with the spatial domain, which does not account for the inherent temporal correlation of road weather and surface conditions.…”

“…RWIS density for each TPI-WSI region is calculated by combining the topographic and weather effects with equal weightage. In our previous analysis, only the spatial domain was used for density optimization, and the output showed a minimum of one to a maximum of two RWIS stations per unit area [9]. Here, in this study, both spatial and temporal domains were used for density optimizations.…”

“…Characterizations of Topography and Weather. The study area was characterized based on topography and weather severity taken from our recent study [9]. These two parameters were selected as zonal classification standards.…”

Pemodelan Spasial Produksi Ikan Pada Industri Budidaya Perikanan Di Kota Cilegon

“…Salah satu alat yang digunakan untuk mengukur korelasi spasial antar observasi adalah variogram. Selain itu variogram dilakukan dalam pengukuran variabilitas dan keterkaitan antar lokasi (Biswas, 2019). Variogram akan menggambarkan korelasi spasial pada produksi ikan di setiap kecamatan di Kota Cilegon.…”