RWIS based road condition prediction using machine learning algorithms

The need for a forecasting model of road conditions is becoming evermore critical, given the effects of ever-increasing severity in weather. Drastic changes in weather, especially cold fronts, often lead to dangerous roads. Consequently, traffic efficiencies are diminished and, even worse, accidents resulting in loss of life and property could increase. Across the nation, states are responsible for anticipating inclement weather and treating roads accordingly. Treatment costs can be reduced with more precise road condition predictions. The development of machine learning capabilities has enhanced the utilization of Big Data Systems throughout various sectors, including road climatology, making weather forecasting much more efficient and reliable. The study reported in this thesis analyzed various road climatology data, including sub-surface temperature at two- and six-inches from Road and Weather Information Systems (RWIS) deployed by Oklahoma Department of Transportation (ODOT) along the I-35 corridor at various road-bridge intersections aimed at producing a reliable and robust forecast model for predicting road surface temperature in the near and distant future. The predicting importance of each factor is analyzed statistically, and then manually, to determine its requirements for the forecast model. The study also determined the best forecast model after comparing a newly developed neural network with common regression techniques previously available through Machine Learning. Results showed that the novel neural network model offered a reliable 12-hour prediction for road surface temperature at a frequency of five minutes, depending on available historical data from RWIS. Two additional classification models provided Road Conditions Classes. The first was based on time series historical data from RWIS, and the second was based on historical and future data from a GFS (Global Forecast System). Together, these models accurately forecast local road surface temperatures 12-hour in advance of inclement weather in five-minutes frequencies at RMSE of ±1.67. They also accurately classified road conditions at a rate of more than 87.984%

Methodology to Forecast Road Surface Temperature with Principal Components Analysis and Partial Least-Square Regression: Application to an Urban Configuration

A forecast road surface temperature (RST) helps winter services to optimize costs and to reduce the deicers environmental impacts. Data from road weather information systems (RWIS) and thermal mapping are considered inputs for forecasting physical numerical models. Statistical models include many meteorological parameters along routes and provide a spatial approach. It is based on typical combinations resulting from treatment and analysis of a database from measurements of road weather stations or thermal mapping, easy, reliable, and cost effective to monitor RST, and many meteorological parameters. A forecast dedicated to road networks should combine both spatial and time forecasts needs. This study contributed to building a reliable RST forecast based on principal component analysis (PCA) and partial least-square (PLS) regression. An urban stretch with various weather conditions and seasons was monitored over several months to generate an appropriate number of samples. The study first consisted of the identification of its optimum number to establish a reliable forecast. A second aspect is aimed at comparing RST forecasts from PLS model to measurements. Comparison indicated a forecast over an urban stretch with up to 94% of values within ±1°C and over 80% within ±3°C

Road Friction Estimation for Connected Vehicles using Supervised Machine Learning

In this paper, the problem of road friction prediction from a fleet of connected vehicles is investigated. A framework is proposed to predict the road friction level using both historical friction data from the connected cars and data from weather stations, and comparative results from different methods are presented. The problem is formulated as a classification task where the available data is used to train three machine learning models including logistic regression, support vector machine, and neural networks to predict the friction class (slippery or non-slippery) in the future for specific road segments. In addition to the friction values, which are measured by moving vehicles, additional parameters such as humidity, temperature, and rainfall are used to obtain a set of descriptive feature vectors as input to the classification methods. The proposed prediction models are evaluated for different prediction horizons (0 to 120 minutes in the future) where the evaluation shows that the neural networks method leads to more stable results in different conditions.Comment: Published at IV 201

Recent Advances in Sustainable Winter Road Operations – A Book Proposal

Investing in winter transportation operations is essential and beneficial to the public and the economy. The U.S. economy cannot afford the cost of shutting down highways, airports, etc., during winter weather. In the northern U.S. and other cold-climate areas, winter maintenance operations are essential to ensure the safety, mobility, and productivity of transportation systems. Agencies are continually challenged to provide a high level of service and improve safety and mobility in a fiscally and environmentally responsible manner. To this end, it is desirable to use the most recent advances in the application of materials, practices, equipment, and other technologies. Such best practices are expected to improve the effectiveness and efficiency of winter operations, to optimize material usage, and to reduce associated annual spending, corrosion, and environmental impacts. Currently, no professional societies, scientific journals, or textbooks are dedicated solely to sustainable winter road operations, and key information is scattered across a variety of disciplines. The objective of the proposed book is to summarize the best practices and recent advances in sustainable winter road operations for the purposes of education and workforce development. This book is now in press and can be cited as follows: Shi, X., Fu, L. (2017). Sustainable Winter Road Operations (Eds.). ISBN: 978-1-119-18506-2. Wiley-Blackwell

Surface water flood warnings in England: overview, Assessment and recommendations based on survey responses and workshops

Following extensive surface water flooding (SWF) in England in summer 2007, progress has been made in improving the management and prediction of this type of flooding. A rainfall threshold-based extreme rainfall alert (ERA) service was launched in 2009 and superseded in 2011 by the surface water flood risk assessment (SWFRA). Through survey responses from local authorities (LAs) and the outcome of workshops with a range of flood professionals, this paper examines the understanding, benefits, limitations and ways to improve the current SWF warning service. The current SWFRA alerts are perceived as useful by district and county LAs, although their understanding of them is limited. The majority of LAs take action upon receipt of SWFRA alerts, and their reactiveness to alerts appears to have increased over the years and as SWFRA superseded ERA. This is a positive development towards increased resilience to SWF. The main drawback of the current service is its broad spatial resolution. Alternatives for providing localised SWF forecast and warnings were analysed, and a two-tier national-local approach, with pre-simulated scenario-based local SWF forecasting and warning systems, was deemed most appropriate by flood professionals given current monetary, human and technological resources