Real-time computing of pavement conditions in cold regions: A large-scale application with road weather information system

Liu, Zhen; Bland, John M.; Bao, Ting; Billmire, Michael; Biniyaz, Aynaz

doi:10.1016/j.coldregions.2021.103228

Cited by 7 publications

(2 citation statements)

References 6 publications

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“…T HE functioning transport system is an integral component of modern society. It directly contributes to the everyday well-being of its population and the efficiency of industries [1]. However, according to statistics from the European Transport Safety Council, in 2018, more than 25,000 people lost their lives in road accidents in the European Union [2].…”

Section: A Background and Aimmentioning

confidence: 99%

Non-Visual Sensing of Metallic Pavement Markers From a Moving Vehicle

Niskanen

Kolli

Immonen

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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Snow and ice covering paved surfaces cause problems for roads and other vehicles operating in cold regions. There are notable risks for road accidents and reduced performance of vehicle movements, e.g., in industrial facilities such as ports, when road markings and marked pathways are not visually observable. In this study, metallic paved surface markers identified by metal sensors are tested. The sound signals captured by a metal detector attached to the vehicle are used to detect the markings.Results indicate that the tested method for reading markings through snow is effective at lower speeds. The main advantages of the tested technology are effectiveness in snowy and icy surface conditions, low upfront cost detection technology in some environments, robust system without moving parts, high sampling rate, and low operating costs. The procedure is not restricted to specific environmental conditions, such as snow and ice; it also has the potential to detect markings through layers of mud, tree leaves or sand. The detectors have application prospects in intelligent transportation system applications at airports, logistics terminals and industrial facilities.

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Section: A Background and Aimmentioning

confidence: 99%

Non-Visual Sensing of Metallic Pavement Markers From a Moving Vehicle

Niskanen

Kolli

Immonen

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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“…By definition, the road friction can be affected by various factors including, but not limited to the texture of tires and pavement, and the contaminants on road surface [9]. Besides, it has also been found that road friction is highly dependent on environmental factors [10][11][12][13][14][15][16][17][18], which can subsequently be estimated using weather information from Road Weather Information Systems (RWIS) [19]. RWIS has been considered one of the most important highway intelligent transportation system (ITS) infrastructures as it provides real-time weather, environmental, and generalized road conditions information.…”

Section: Introductionmentioning

confidence: 99%

Winter Road Friction Estimations via Multi-Source Road Weather Data—A Case Study of Alberta, Canada

Ding

Kwon

2022

Future Transportation

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Road friction has long been recognized as one of the most effective winter road maintenance (WRM) performance measures. It allows WRM personnel to make more informed decisions to improve their services and helps road users make trip-related decisions. In this paper, a machine-learning-based methodological framework was developed to model road friction using inputs from mobile road weather information systems (RWIS) that collect spatially continuous road weather data and road grip. This study also attempts to estimate friction using data from stationary RWIS that are installed far from each other, thereby leaving large areas unmonitored. To fill in the spatial gaps, a kriging interpolator was developed to create a continuous friction map. Slippery road risk levels were classified to provide an overview of road conditions via a risk warning map. The proposed method was evaluated with a selected highway segment in Alberta, Canada. Results show that the models developed herein are highly accurate (93.3%) in estimating friction and identifying dangerous road segments via a color-coded risk map. Given its high performance, the developed model has the potential for large-scale implementation to facilitate more efficient WRM services while also improving the safety and mobility of the traveling public.

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Probability prediction of pavement surface low temperature in winter based on bayesian structural time series and neural network

Chen

Dan

et al. 2022

Cold Regions Science and Technology

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Real-time computing of pavement conditions in cold regions: A large-scale application with road weather information system

Cited by 7 publications

References 6 publications

Non-Visual Sensing of Metallic Pavement Markers From a Moving Vehicle

Non-Visual Sensing of Metallic Pavement Markers From a Moving Vehicle

Winter Road Friction Estimations via Multi-Source Road Weather Data—A Case Study of Alberta, Canada

Probability prediction of pavement surface low temperature in winter based on bayesian structural time series and neural network

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