No description available.

No description available.

No description available.

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.

This thesis research has attempted to address two challenging problems in winter road maintenance, namely road surface condition (RSC) estimation and forecasting. For RSC estimation, the goal of the research was to develop models to discriminate RSC classes based on continuous friction measurements (CFM) and other available data such as temperature and precipitation history. A systematic exploratory study was conducted on an extensive field data set to identify the categorical relationship between RSC and various aggregate CFM measures, such as those related to probability distribution and spatial correlation. A new multi-level model structure was designed, under which binary logistic regression models were calibrated and validated utilizing several carefully chosen aggregate measures to classify major RSC types. This model structure was found to be effective in capturing the overlapping nature of CFM ranges over different RSC types -- a problem which has not been addressed adequately in the past studies. An alternative model with support vector machine (SVM) was also developed for benchmarking the performance of the proposed logit model. It was found that the two types of models are comparative in performance, confirming the high performance of the proposed multi-level model. For road surface condition forecasting, a novel conceptual framework for short-term road surface condition forecasting is proposed, under which the short-term changing process of surface temperature, friction level and contaminant layer depths, is comprehensively explored and analyzed. This study framework is designed to consider all important conditional factors, including weather, traffic and maintenance operations. The maintenance operations, especially salting, are handled by loosening the strict Markovian assumption, i.e., a history instead of one single time interval of salting operations is considered. In this way, the variation of snow/ice melting speed caused by both residual salt amounts and salt/contaminant mixing states is incorporated in the forecasting model, which enables accurate short-term forecasting for contaminant layers. This approach practically circumvents a major limitation of previous studies, making the post-salting RSC forecasting more reliable and accurate. Under the proposed model framework, several advanced time series modelling methodologies are introduced into the analysis, which can capture the highly complex interactions between RSC measures and conditional factors simultaneously. Those methodologies, especially the univariate and multivariate ARIMA methods, are for the first time applied to the winter RSC evolution process. The forecasting errors of surface temperature, friction level and contaminant layer depths are all found to be small, implying that both the proposed study framework and the resulting solutions closely match the real-world observations. The proposed forecasting models are simple in structure, easy to interpret and mostly consistent with physical knowledge. Compared to the existing models, the proposed models provide extra flexibility for refactory, tuning and deployment. Furthermore, all the modelled RSC measures are numerical and the forecast errors are relatively small, suggesting empirical models could be an efficient alternative to physical models. With the well-designed modelling methods, the resulting empirical models as calibrated in our study can be implemented into a decision support and simulation tool with high temporal resolution and accuracy.

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.

No description available.

Several past studies have attempted to quantify the impact of winter weather conditions on highway mobility in terms of traffic volume, speed, and capacity. While consistent in their general findings, these studies have shown considerably different results in terms of effect size and contributing factors. More importantly, most of these studies have not attempted to model the effects of winter maintenance operations on mobility or isolate these effects from those due to snowstorm characteristics, rendering their results and the proposed methods of limited use for estimating the benefits of maintenance activities. This research attempts to address this gap through a statistical analysis of a data set that is unique in terms of spatial and temporal coverage and data completeness. The data set includes both event based and hourly observations of road weather and surface conditions, maintenance operations, traffic volume and speed, as well as several other measures, from 21 highway sections across the province of Ontario. Event based information is available for six winter seasons (2000 to 2006) at 19 of the sites. For this event based data a matched pair technique was employed to determine the changes in traffic volumes and speeds under matched conditions with and without snow events. A regression analysis was subsequently performed to relate the changes in traffic volume and speed over an event to changes in various contributing factors such as highway type, snow event characteristics and road surface conditions. A case study was conducted to illustrate the application of the developed models for quantifying the mobility impact of road surface condition and the mobility benefit of winter maintenance operations. Complete hourly records were available for all 21 sites for three winter seasons. This was used to perform the evaluation on an hourly basis. A matching technique is employed to assign hour-by-hour median speeds observed under typical weather and road surface conditions to each hour of a snowstorm event. A regression analysis is subsequently performed to relate changes from average hourly speed to various contributing factors such as highway type, weather conditions and maintenance operations. Effects of maintenance operations are represented by an intermediate variable called road surface condition index (RSI). A case study is conducted to illustrate the application of the developed models for quantifying the mobility impact of winter snowstorms and the mobility benefit of maintenance operations. The models developed in these analyses confirmed the relationships between weather variables and traffic volume and speed described in the literature. In addition a strong association between road surface condition and traffic volumes and speed was identified.

The thesis develops a driving assistant system that can locate the positions of the lane boundaries and detects the existence of the front-vehicle. It can also provide warning mechanism so as to avoid the danger as possible collides with previous vehicle. In lane recognition, we utilized the largest gradient of luminance value to detect possible road surface marking, then cooperated with the marking static and dynamic behavior of road surface characteristic to set up road surface marking and detect system. On the other hand, we considered vehicle detection leach the vehicle bottom shade characteristic from dynamic area threshold processing, and then judge and label where the vehicle exits. By the principle of the optics image formation, we estimated the relative distance from the previous vehicle. In this thesis, we proposed an easy and fast measure for previous vehicle of 96% correct rate in different environment. Running on typical 1.7Ghz processor system results up to 80 frames per second.