A geostatistical approach to winter road surface condition estimation using mobile RWIS data

2019 ◽  
Vol 46 (6) ◽  
pp. 511-521
Author(s):  
Lian Gu ◽  
Tae J. Kwon ◽  
Tony Z. Qiu
Keyword(s):  
Robust Regression ◽  
Surface Condition ◽  
Road Surface ◽  
Road Maintenance ◽  
Surface Conditions ◽  
Modelling Framework ◽  
Geostatistical Approach ◽  
Winter Road ◽  
Systematic Framework

In winter, it is critical for cold regions to have a full understanding of the spatial variation of road surface conditions such that hot spots (e.g., black ice) can be identified for an effective mobilization of winter road maintenance operations. Acknowledging the limitations in present study, this paper proposes a systematic framework to estimate road surface temperature (RST) via the geographic information system (GIS). The proposed method uses a robust regression kriging method to take account for various geographical factors that may affect the variation of RST. A case study of highway segments in Alberta, Canada is used to demonstrate the feasibility and applicability of the method proposed herein. The findings of this study suggest that the geostatistical modelling framework proposed in this paper can accurately estimate RST with help of various covariates included in the model and further promote the possibility of continuous monitoring and visualization of road surface conditions.

scholarly journals A Geostatistical Investigation into the Effective Spatiotemporal Coverage of Road Weather Information Systems in Alberta, Canada

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Xu Wang ◽  
Lian Gu ◽  
Tae J. Kwon ◽  
Tony Z. Qiu
Keyword(s):  
Information Systems ◽  
Weather Conditions ◽  
Road Maintenance ◽  
The Road ◽  
Weather Information ◽  
Geostatistical Approach ◽  
Adverse Weather ◽  
Winter Road ◽  
Winter Road Maintenance

Inclement weather acutely affects road surface and driving conditions and can negatively impact traffic mobility and safety. Highway authorities have long been using road weather information systems (RWISs) to mitigate the risk of adverse weather on traffic. The data gathered, processed, and disseminated by such systems can improve both the safety of the traveling public as well as the effectiveness of winter road maintenance operations. As the road authorities continue to invest in expanding their existing RWIS networks, there is a growing need to determine the optimal deployment strategies for RWISs. To meet such demand, this study presents an innovative geostatistical approach to quantitatively analyze the spatiotemporal variations of the road weather and surface conditions. With help of constructed semivariograms, this study quantifies and examines both the spatial and temporal coverage of RWIS data. A case study of Alberta, which is one of the leaders in Canada in the use of RWISs, was conducted to indicate the reliability and applicability of the method proposed herein. The findings of this research offer insight for constructing a detailed spatiotemporal RWIS database to manage and deploy different types of RWISs, optimize winter road maintenance resources, and provide timely information on inclement road weather conditions for the traveling public.

scholarly journals Road Surface Condition Forecasting in France

2009 ◽  
Vol 48 (12) ◽  
pp. 2513-2527 ◽  
Author(s):  
L. Bouilloud ◽  
E. Martin ◽  
F. Habets ◽  
A. Boone ◽  
P. Le Moigne ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Initial Conditions ◽  
Meteorological Data ◽  
Surface Condition ◽  
Road Surface ◽  
Snow Layer ◽  
Surface Conditions ◽  
The Road ◽  
Road Surface Temperature ◽  
On The Road

Abstract A numerical model designed to simulate the evolution of a snow layer on a road surface was forced by meteorological forecasts so as to assess its potential for use within an operational suite for road management in winter. The suite is intended for use throughout France, even in areas where no observations of surface conditions are available. It relies on short-term meteorological forecasts and long-term simulations of surface conditions using spatialized meteorological data to provide the initial conditions. The prediction of road surface conditions (road surface temperature and presence of snow on the road) was tested at an experimental site using data from a comprehensive experimental field campaign. The results were satisfactory, with detection of the majority of snow and negative road surface temperature events. The model was then extended to all of France with an 8-km grid resolution, using forcing data from a real-time meteorological analysis system. Many events with snow on the roads were simulated for the 2004/05 winter. Results for road surface temperature were checked against road station data from several highways, and results for the presence of snow on the road were checked against measurements from the Météo-France weather station network.

scholarly journals Road Surface Monitoring Using Smartphone Sensors: A Review

2021 ◽  
Author(s):  
Shahram Sattar ◽  
Songnian Li ◽  
Michael A. Chapman
Keyword(s):  
Anomaly Detection ◽  
Surface Condition ◽  
Low Frequency ◽  
Road Surface ◽  
Detection Accuracy ◽  
Road Maintenance ◽  
Key Factor ◽  
Smartphone Sensors ◽  
Road Surface Monitoring ◽  
Surface Monitoring

Road surface monitoring is a key factor to providing smooth and safe road infrastructure to road users. The key to road surface condition monitoring is to detect road surface anomalies, such as potholes, cracks, and bumps, which affect driving comfort and on-road safety. Road surface anomaly detection is a widely studied problem. Recently, smartphone-based sensing has become increasingly popular with the increased amount of available embedded smartphone sensors. Using smartphones to detect road surface anomalies could change the way government agencies monitor and plan for road maintenance. However, current smartphone sensors operate at a low frequency, and undersampled sensor signals cause low detection accuracy. In this study, current approaches for using smartphones for road surface anomaly detection are reviewed and compared. In addition, further opportunities for research using smartphones in road surface anomaly detection are highlighted.

scholarly journals 2C3-5　A Study of Winter Road Surface Condition Presentation by Steering Vibration

2019 ◽  
Vol 55 (Supplement) ◽  
pp. 2C3-5-2C3-5
Author(s):  
Kazuya ITOH ◽  
Ryoma ITOH
Keyword(s):  
Surface Condition ◽  
Road Surface ◽  
Winter Road

scholarly journals Characteristics of Laser Backscattering Intensity to Detect Frozen and Wet Surfaces on Roads

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Jungil Shin ◽  
Hyunsuk Park ◽  
Taejung Kim
Keyword(s):  
Surface Condition ◽  
Laser Scanner ◽  
Time Of Day ◽  
Road Surface ◽  
Driving Safety ◽  
Surface Conditions ◽  
Active Sensor ◽  
Roadside Vegetation ◽  
Signal Characteristics ◽  
Wet Surface

A frozen or wet road surface is a cause of skidding and accidents, so road surface condition is important information for driving safety. Some instruments and methods have been developed to investigate road surface conditions based on optical imagery, although an active sensor is needed, regardless of the time of day. Recently, the laser scanner, which acquires backscattering intensity data related to reflectivity, has become popular in various fields. There is a need to investigate road surface conditions (frozen, wet, or dry) using laser backscattering intensity. This study tries to analyze signal characteristics of laser backscattering intensity to detect frozen and wet surfaces on roads. An ice target with a 7 cm thickness was placed on a road surface, and a wet surface was made due to the melting ice. The ice target, wet surface, dry surface, and roadside vegetation were scanned using a laser scanner. As a result, backscattering signals from the top surface of the ice target were missing due to its smoothness. Dry and wet asphalt surfaces showed distinguishable intensity ranges in their signals. The thick sidewall of the ice target and vegetation at the roadside showed overlapping intensity ranges. An ice sheet is only a few millimeters thick on a real road surface, and the roadside vegetation might be easily distinguished by using texture or auxiliary data. Therefore, laser backscattering intensity can be used to detect frozen, wet, and dry road surfaces, regardless of the time of day. The laser scanner can be installed to acquire information about road surface conditions from observation stations and vehicles in an application for transportation.

Real-time Winter Road Surface Condition Monitoring Using an Improved Residual CNN

2020 ◽  
Author(s):  
Guangyuan Pan ◽  
Matthew Muresan ◽  
Ruifan Yu ◽  
Liping Fu
Keyword(s):  
Real Time ◽  
Surface Condition ◽  
Model Performance ◽  
Road Surface ◽  
Specific Domain ◽  
Ice Coverage ◽  
Calibration Methods ◽  
Winter Road ◽  
The Relationship ◽  
Fine Tune

This paper proposes a real-time winter road surface condition (RSC) monitoring solution that automatically generates descriptive RSC information in terms of snow/ice coverage by using images from fixed traffic/weather cameras. Several state-of-the-art pre-trained deep neural networks are customized and fine-tuned to address a specific domain, classifying the amount of snow coverage on a road surface. A thorough evaluation is conducted to identify and select the best model. This evaluation uses an extensive set of experiments to test the accuracy and generalization of each model and uses transfer-learning to fine-tune each of the pre-trained models on independent images from different traffic/weather cameras. The transferability of each model, the relationship between model performance and data size, and the system settings of each model are then examined. Lastly, three online weight calibration methods are proposed to automatically update the model in new environments. The result shows that re-training the model using images from a mixed set of cameras has the most promising results.

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