Thermal mapping, the measurement of road surface temperatures (RSTs) with an infra-red thermometer (IRT) mounted in a moving vehicle, seeks to identify a 'characteristic and repeatable' thermal fingerprint (temperature profile) for any stretch of road. A number of uses have been suggested for the process, including ice detection sensor network design and identifying stretches of road for selective gritting, with potential financial and environmental benefits due to reduced salt usage. The project 'Thermal Mapping for a Highway Gritting Network' has resulted in the most extensive survey yet undertaken. The aims were to investigate the reliability/repeatability of fingerprints and establish confidence limits. Comprehensive mapping of Sheffield roads took place during winters 1988/89- 1991/92. Significant errors (+/-3°C) in RST readings were identified after the first winter. Laboratory and road tests confirmed errors were produced due to warming/cooling of the IRT. Operating the IRT in a temperature control box eliminated these errors. Seven Sheffield routes were mapped during winters 89/90 and 90/91 with route 1 fingerprints (100) used for most of the analysis. The main factors affecting the variation in RSTs were confirmed as altitude and land-use with localised peaks occurring under bridges and by trees and tall buildings. The occurrence of cold air drainage on clear/calm (extreme') nights resulted in 'low' RSTs at relatively low altitudes. Differences were identified between what should have been identical extreme fingerprints. These were related to variations in the behaviour of cold air drainage. rom night to night and variations in wind direction/speed interacting with local relief. Confidence limits for extreme fingerprints and maps, taking into account possible errors in mapping and differences between fingerprints, were +/-20C and +/- 2.5°C respectively. With important decisions concerning gritting made when RSTs are +/-5°C confidence limits of this magnitude have important implications for thermal mapping. Future use should be restricted to sensor network design and assessment/re-design of gritting network.

Each winter, Britain spends up to £120 million spreading approximately 2 million tonnes of rock salt on our roads to keep them free of ice and snow. This thesis shows that it would be possible to significantly reduce the amount of salt spread, by improving the accuracy of the Road Danger Warnings issued to Highway Authorities. Each day in winter, the maintenance engineer receives a Road Danger Warning from his local weather centre. Unfortunately these Warnings are not very accurate because they are based on forecasts of minimum air temperature alone, rather than using road surface temperatures. During the winter of 1982/83, of 102 Road Danger Warnings issued to Hereford and Worcester County Council, only 32 were correct in predicting icy conditions on the MS motorway. This thesis presents a computer model to predict ice formation on roads up to 24 hours ahead. During the winter of 1978/79 instruments were installed in the M4 motorway to measure road surface temperature and wetness. The computer model has been tested retrospectively for 30 nights when the road surface temperature fell below 5°C. The predicted minimum road surface temperature has a root mean square error of 0.9°C. During the winters of 1982/83 and 1983/84, the model was tested in 'real time' against road surface temperatures measured automatically on the M5 and M6 motorways, giving a root mean square error of 1.5°C for 80 nights during 1.982/83, and 1.3°c for 120 nights during 1983/84. The form of the issued Road Danger Warnings has been changed from a simple sentence issued over the telephone or using telex, to a graph of predicted road surface temperature and wetness. An optimistic and a pessimistic graph is issued to give the maintenance engineer an idea of the certainty of the forecast. The thesis proposes a national network of automatic road surface monitoring sites. Each site would be linked to microcomputers in local weather centres, which would then run the prediction model and issue Road Danger Warnings accordingly. The information could then be sent to maintenance engineers using Prestel.

The need for improved cloud parameterisations in a road surface temperature model is demonstrated. Case studies from early 1994 are used to investigate methods of tracking cloud cover using satellite imagery and upper level geostrophic flow. Two of these studies are included in this thesis. Errors encountered in cloud tracking methods were investigated as well as relationships between cloud height and pixel brightness in satellite imagery. For the first time, a one dimensional energy balance model is developed to investigate the effects of erroneous cloud forecasts on surface temperature. The model is used to determine detailed dependency of surface freezing onset time and minimum temperature on cloud cover. Case studies from the 1995/96 winter in Devon are undertaken to determine effects of differing scenarios of cloud cover change. From each study, an algorithm for predicting road surface temperature is constructed which could be used in future occurrences of the corresponding scenario of the case study. Emphasis is strongly placed on accuracy of predictions of surface freezing onset time and minimum surface temperature. The role o f surface and upper level geostrophic flow, humidity and surface wetness in temperature prediction is also investigated. In selected case studies, mesoscale data are also analysed and compared with observations to determine feasibility of using mesoscale models to predict air temperature. Finally, the algorithms constructed from the 1995/96 studies are tested using case studies from the 1996/97 winter. This winter was significantly different from its preceding one which consequently meant that the algorithm from only one scenario of the 1995/96 winter could be tested. An algorithm is also constructed from a 1996/97 winter case study involving a completely different scenario Recommendations for future research suggest testing of existing algorithms with guidance on additional scenarios.

Transportation networks play a significant role in the economy of Canadians during winter seasons; thus, maintaining a safe and economic flow of traffic on Canadian roads is crucial. Winter contaminants such as freezing rain, snow, and ice cause reduced friction between vehicle tires and pavement and thus increased accident-risk and decreased road capacity. The formation of ice and frost caused by snowfall and wind chill makes driving a very difficult task. Pavement surface temperature is an important indicator for road authorities when they are deciding the optimal time to apply anti-icer/deicer chemicals and when estimating their effect and the optimal amounts to apply. By forecasting pavement temperature, maintenance crews can figure out road surface conditions ahead of time and start their operations in a timely manner, thereby reducing salt use and increasing the safety and security of road users by eliminating accidents caused by slipperiness. This research investigates the feasibility of applying simple statistical models for forecasting road surface temperatures at locations where RWIS data are available. Two commonly used modeling techniques were considered: time-series analysis and artificial neural networks (ANN). A data set from an RWIS station is used for model calibration and validation. The analysis indicates that multi-variable SARIMA is the most competitive technique and has the lowest number of forecasting errors.

Transportation networks play a significant role in the economy of Canadians during winter seasons; thus, maintaining a safe and economic flow of traffic on Canadian roads is crucial. Winter contaminants such as freezing rain, snow, and ice cause reduced friction between vehicle tires and pavement and thus increased accident-risk and decreased road capacity. The formation of ice and frost caused by snowfall and wind chill makes driving a very difficult task. Pavement surface temperature is an important indicator for road authorities when they are deciding the optimal time to apply anti-icer/deicer chemicals and when estimating their effect and the optimal amounts to apply. By forecasting pavement temperature, maintenance crews can figure out road surface conditions ahead of time and start their operations in a timely manner, thereby reducing salt use and increasing the safety and security of road users by eliminating accidents caused by slipperiness. This research investigates the feasibility of applying simple statistical models for forecasting road surface temperatures at locations where RWIS data are available. Two commonly used modeling techniques were considered: time-series analysis and artificial neural networks (ANN). A data set from an RWIS station is used for model calibration and validation. The analysis indicates that multi-variable SARIMA is the most competitive technique and has the lowest number of forecasting errors.