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

Abstract

This study represents an advanced approach to road weather information system (RWIS) network planning. Here, a methodological framework is developed to determine optimal RWIS locations by integrating two analysis domains: space and time. Using a case study, the application of the proposed method is demonstrated using three critical RWIS variables: air temperature, road surface temperature, and dew point temperature. With these three variables, a series of geostatistical semivariogram analyses are performed to construct a single spatiotemporal model named joint semivariogram, which is able to preserve both spatial and temporal aspects. The constructed joint semivariogram is then used to find the optimal RWIS locations for a randomly generated study area using a popular heuristic algorithm—spatial simulated annealing. The proposed method enhances the previously developed RWIS location allocation model by considering both spatial and temporal components of multiple variables. The finding from this analysis reveals that optimal RWIS location strongly depends on the spatiotemporal autocorrelation structure of the variable of interest. Consequently, location solutions generated using the three variables are found to be different from each other. The variation among the RWIS location solutions is then further quantified by developing a spatial similarity index that is used to measure the degree of spatial similarities between different variables. Overall, the findings documented in this study will provide RWIS planners with a more complete and conclusive location allocation strategy and can act as a decision support tool for long-term RWIS network planning.

Safety Impact Assessment of Optimal RWIS Networks—An Empirical Examination

“…Spatiotemporal analysis was performed by constructing empirical semivariograms from the processed data, which optimizes parameter estimations for unsampled locations and captures the possible autocorrelation associated with the RWIS variables. Joint semivariogram models were then developed by combining spatial and temporal semivariograms to evaluate the spatiotemporal variability of RWIS measurements [19]. Based on parameters obtained from the joint semivariogram, kriging interpolation was used to estimate values at unsampled locations and their estimation error or kriging variance.…”

“…Spatial and temporal semivariograms can be combined using spatiotemporal anisotropy to estimate the joint semivariogram that can preserve both spatial and temporal effect. The joint semivariogram models developed in our previous effort are adopted in this study for conducting kriging interpolation [19].…”

“…This finding makes intuitive sense since both spatial and temporal attributes are preserved in the joint semivariogram. The readers are referred to our previous work for a detailed investigation on joint semivariogram analysis for multiple weather variables [19].…”

“…Considering the resurgent need to build a systematic and generalized RWIS network planning guideline, our previous efforts have developed a comprehensive and transferable methodological framework to optimize the design of a regional RWIS network by incorporating the use of multiple RWIS variables for improved spatiotemporal inference [17][18][19]. Two crucial network planning questions were answered through that research: (i) how many RWIS stations are needed in a region with varying environments to provide sufficient coverage over space and time?…”

“…While developing the optimal RWIS location solution, it was implicitly assumed that each solution set is associated with a unique spatial configuration tied to an objective function value or sum of kriging variance that represents RWIS' monitoring capability. The solution set associated with the lowest objective function value (lowest kriging variance) would be considered the solution with the highest network coverage and thus assumed to be most beneficial [19]. Based on this presumption that network coverage is a vital parameter for determining the goodness of an RWIS configuration, there is a resurgent need to extend this effort by investigating if it could also be used to explain its impact on traffic safety-a worthwhile attempt that has never been made in existing literature pertaining to quantifying the safety benefits of RWIS location solutions.…”