An enhanced spatial statistical method for continuous monitoring of winter road surface conditions

Abstract

To facilitate more efficient winter maintenance decision support, road weather information systems (RWIS) have been widely used by highway agencies. However, the cost of RWIS stations is high, and they have limited monitoring coverage. To address this challenge, this paper presents an innovative framework that applies regression kriging to integrate stationary and mobile RWIS data to improve the accuracy of road surface temperature (RST) estimation. Furthermore, an optimal RWIS network expansion strategy is introduced by incorporating a modified particle swarm optimization method with the objective of minimizing spatially averaged kriging estimation errors. A sensitivity analysis is also conducted to investigate the influence of station densities on model performance. The case study from Alberta, Canada, demonstrates the feasibility and applicability of the proposed method. The findings provide insights for continuous monitoring and visualization of both road weather and surface conditions and for optimizing RWIS network planning.

An Automatic Architecture Designing Approach of Convolutional Neural Networks for Road Surface Conditions Image Recognition: Tradeoff between Accuracy and Efficiency

“…A higher weight value means more importance will be placed on that corresponding factor during the optimization process. Equation (1) shows the formulation of the objective function. where w is the weight value or the importance level (when w = 0, the objective function becomes solely minimizing the normFLOPs, meaning that the optimization process will not consider the accuracy piece at all, and vice versa); norm Val Acc and normFLOPs are the normalized experimental validation accuracy and FLOPs after max-min normalization method for fair comparison on an equivalent scale [34].…”

“…Winter road maintenance (WRM) is a critical operation for meeting the safety and mobility needs of road users, especially for regions that reside in high latitudes. During the winter season, inclement weather events such as snow, sleet, ice, and frost lead to remarkable location and time variation in road surface conditions (RSC), which negatively affect drivers' performance and threaten all passengers' lives [1][2][3]. Due to the vast spatial distances covered by highway networks and the uncertain nature of the weather events, such variations are often hard to monitor and predict, making both WRM activities and public travel extremely challenging.…”

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

“…In this optimization process, a number of n-dimensional candidate points (particle) are placed in the search space of a function, and each of the particles evaluates the objective function at its current location. Each particle can be assumed as a potential solution presented by velocity and position [29,30]. Movement of each particle is determined based on its best fit location with one or more swarms, and the algorithm searches for optima by updating the generations [31,32].…”

“…The sigmoid function is utilized in BPSO where every dimension in the position becomes a number between 0 and 1. A modified BPSO was proposed by Gu et al in 2019 to solve the RWIS location optimization problem [29]. The similar method was adapted for use in our study for region-wise RWIS density optimization.…”

“…The parameters are chosen to ensure that the particles can fly slowly while eliminating their ability for selflearning and enhancing social-learning. The steps associated with the modified BPSO algorithm are listed as follows [29][30][31]:…”