Microclimates of various road weather stations in Devon were examined. Road surface temperatures were measured during various synoptic conditions. Data from the thermal mapping exercise conducted by Vaisala TMI were analysed and categorised as clear and calm, cloudy and windy, and a condition between the two extremes. Results indicate valleys to be relative cold spots in clear conditions, and high altitude stations are cold spots during cloudy conditions. A separate case study during the cold spell of February 1991 reinforced the conclusion and extended these views county wide. Coastal stations with surface winds were observed to be 1 to 3 deg.C warmer than inland stations. Reaction times, the delay in road temperature reaction due to synoptic change, showed for the most difficult forecasting scenario on the passage of a cold front, small reaction times (less than 30 minutes) at exposed sites when clear skies resulted, sheltered sites having a reaction time of up to 2 hours. Sheltered sites in valleys had the largest temperature drop due to cold air drainage. A surface climate model was used in retrospect to predict road surface temperatures at night, each station having its own characteristic exposure. Cloud change was estimated from satellite images. Results indicate predicted minimum temperature within 0.5 deg.C of observation. Real time forecasts were tried and an accuracy of 65% at this level was achieved. These results were equal or better than the existing "Open Road" forecasts.

A good understanding of the relationship between highway performance, such as crash rates and travel delays, and winter road maintenance activities under different winter weather and traffic conditions is essential to the development of cost-effective winter road maintenance policies and standards, operation strategies and technologies. This research is specifically concerned about the mobility benefit of winter road maintenance. A microscopic traffic simulation model is used to investigate the traffic patterns under adverse weather and road surface conditions. A segment of the Queen Elizabeth Way (QEW) located in the Great Toronto Area, Ontario is used in the simulation study. Observed field traffic data from the study segment was used in the calibration of the simulation model. Different scenarios of traffic characteristics and road surface conditions as a result of weather events and maintenance operations are simulated and travel time is used as a performance measure for quantifying the effects of winter snow storms on the mobility of a highway section. The modeling results indicate that winter road maintenance aimed at achieving bare pavement conditions during heavy snowfall could reduce the total delay by 5 to 36 percent, depending on the level of congestion of the highway. The simulation results are then applied in a case study for assessing two maintenance policy decisions at a maintenance route level.

A good understanding of the relationship between highway performance, such as crash rates and travel delays, and winter road maintenance activities under different winter weather and traffic conditions is essential to the development of cost-effective winter road maintenance policies and standards, operation strategies and technologies. This research is specifically concerned about the mobility benefit of winter road maintenance. A microscopic traffic simulation model is used to investigate the traffic patterns under adverse weather and road surface conditions. A segment of the Queen Elizabeth Way (QEW) located in the Great Toronto Area, Ontario is used in the simulation study. Observed field traffic data from the study segment was used in the calibration of the simulation model. Different scenarios of traffic characteristics and road surface conditions as a result of weather events and maintenance operations are simulated and travel time is used as a performance measure for quantifying the effects of winter snow storms on the mobility of a highway section. The modeling results indicate that winter road maintenance aimed at achieving bare pavement conditions during heavy snowfall could reduce the total delay by 5 to 36 percent, depending on the level of congestion of the highway. The simulation results are then applied in a case study for assessing two maintenance policy decisions at a maintenance route level.

Winter road maintenance activities are crucial for maintaining the accessibility and traffic safety of the road network at northerly latitudes during winter. Common winter road maintenance activities include snow ploughing and the use of anti-icing agents (e.g. road salt, NaCl). Since the local weather is decisive in creating an increased risk of slippery conditions, understanding the link between local weather and conditions at the road surface is critically important. Sensors are commonly installed along roads to measure road weather conditions and support road maintenance personnel in taking appropriate actions. In order to improve winter road maintenance, more precise information about road surface conditions is essential. In this thesis, different methods for estimation of road weather are developed, discussed and tested. The methods use the principles of infrared thermometry, image analysis and spectroscopy to describe ice formation, snow accumulation and road surface wetness in specific patches or along road sections. In practical applications, the methods could be used for better planning of snow clearing operations, forecasting of ice formation and spreading of road salt. Implementing the proposed methods could lead to lower maintenance costs, increased traffic safety and reduced environmental impact. / <p>QC 20121116</p>

Scheduling of winter maintenance operations such as plowing or salting is a difficult and complex problem. Proper selection and timing of such operations is critical to their effectiveness, however scheduling decisions must often be made with strict time and resource limitations imposed upon them. A decision support system which analyses current road conditions and makes scheduling suggestions based on them would be a valuable step toward improving the quality of treatment, while simultaneously reducing the burden of scheduling on maintenance managers. This thesis proposes a real-time scheduling model based on an Operations Research framework that can be used by maintenance managers to develop and evaluate alternative resources allocation plans for winter road maintenance operations. The scheduling model is implemented as an Integer Linear Program and is solved using off-the-shelf software packages. The scheduling model takes into account a wide range of road and weather condition factors such as road network topology, road class, weather forecasts, and contractual service levels, and produces a vehicle dispatch schedule that is optimal with respect to operating costs and quality of service. A number of heuristics are also explored to aid in efficient approximations to this problem.

Scheduling of winter maintenance operations such as plowing or salting is a difficult and complex problem. Proper selection and timing of such operations is critical to their effectiveness, however scheduling decisions must often be made with strict time and resource limitations imposed upon them. A decision support system which analyses current road conditions and makes scheduling suggestions based on them would be a valuable step toward improving the quality of treatment, while simultaneously reducing the burden of scheduling on maintenance managers. This thesis proposes a real-time scheduling model based on an Operations Research framework that can be used by maintenance managers to develop and evaluate alternative resources allocation plans for winter road maintenance operations. The scheduling model is implemented as an Integer Linear Program and is solved using off-the-shelf software packages. The scheduling model takes into account a wide range of road and weather condition factors such as road network topology, road class, weather forecasts, and contractual service levels, and produces a vehicle dispatch schedule that is optimal with respect to operating costs and quality of service. A number of heuristics are also explored to aid in efficient approximations to this problem.

In countries with severe winters such like Canada, winter road maintenance (WRM) operations, such as plowing, salting and sanding, play an indispensible role in maintaining good road surface conditions and keeping roads safe. WRM is, however, also costly, both monetarily and environmentally. The substantial direct and indirect costs associated with WRM have stimulated significant interest in quantifying the safety and mobility benefits of winter road maintenance, such that systematic cost-benefit assessment can be performed. A number of studies have been initiated in the past decade to identify the links between winter road safety and factors related to weather, road, and maintenance operations. However, most of these studies have focused on the effects of adverse weather on road safety. Limited efforts have been devoted to the problem of quantifying the safety benefits of winter road maintenance under specific road weather conditions. Moreover, the joint effects of and complex interactions between road driving conditions, traffic and maintenance and their impact on traffic safety have rarely been studied. This research aims to determine the effect of WRM on road safety during snow storm events and develop models that can be used to quantify the safety benefit of alternative winter road maintenance policies, strategies and practices. Two integral aspects of collision risk were investigated, namely, collision frequency and severity. Collision frequency models were developed using winter storm collision data compiled for six winter seasons (2000 to 2006) for a total of 31 highway routes across Ontario. A comprehensive measure, namely, road surface condition index (RSI), was proposed to represent the road surface conditions during a variety of snow events. RSI was used as a surrogate measure to capture the effects of WRM. Other factors related to weather, traffic and road features were also accounted for in the analysis. Problems associated with data aggregation were also investigated. For this purpose, two different datasets were formed, namely, event-based data (EBD) which aggregates data by snow storm events and hourly based data (HBD) which includes hourly records of collision counts and other related factors. These two data sets of different aggregation levels were then used to investigate the effects of data aggregation and correlation (within – event) as well as to develop models for different purposes of benefit analyses. For EBD, Negative Binomial models and Generalized Negative Binomial models were calibrated whereas for HBD, Generalized Negative Binomial models and multilevel Poisson Lognormal models were calibrated. Generalized Negative Binomial models were found to best fit the data for both datasets. It was found that addition of site specific variables improves model fit. RSI and exposure were found significant for all the models and datasets. Weather factors such as visibility, wind speed, precipitation, and air temperature were also found to have statistically significant effects on collision frequency. All the models were consistent in terms of effects of different variables. The EBD models are useful to quantify the effect of different maintenance service standards and policies with limited information on the details of the weather events and traffic. On the other hand, HBD models have a higher level of reliability capable of providing more accurate estimates on road accidents. As a result, they are useful for determining the effects of different treatment operations. Several examples were employed to demonstrate the application of the developed models, such as quantifying the benefits of alternative maintenance operations and evaluating the effects of different service standards using safety as a performance measure. To enable a comprehensive risk analysis, collisions under both all-weather conditions and snow storm conditions over the six winter seasons were analyzed to identify the relationship between collision severity and various factors related to road weather and surface conditions, road characteristics, traffic, and vehicles etc., on collision severity. A multilevel modeling framework was introduced to capture the inherent hierarchy between collisions, vehicles and persons involved within the collision data. For each collision data set, three alternative severity models, namely, multinomial models, ordered logit models and binary logit models, were calibrated and compared. It was found that multilevel multinomial logit models were best fit to the data. Moreover issues related to different levels of aggregation were also discussed and results from occupant based data were found to be more reasonable and in line with general literature. Different individual, vehicle, environment and accident location factors were found to have a statistically significant effect on the injury severity levels. Contributing factors at the individual and vehicle levels include driver condition, driver sex, driver age, position in vehicle, use of safety device such as seat belt, vehicle type, vehicle age and vehicle condition. Roadway and environment factors include number of lanes, speed limit, road alignment, RSI/road surface condition, wind speed, and visibility. Other factors include light, and traffic volume. Two case studies were conducted to demonstrate the application of the developed models in conjunction with the accident frequency models for cost benefit analysis. This research was the first to investigate the direct link between road surface conditions and collisions at an operational level. It has been shown that the developed models are capable of evaluating alternative winter road maintenance policies and operations and assessing the safety benefit of a particular winter road maintenance strategy or decision. This research is also the first to conduct an in-depth analysis on the problem of winter road safety at a disaggregate level that captures detailed temporal variation (e.g., hourly and by storm event)) within small spatial aggregation units (road sections corresponding to actual patrol routes). The safety models developed from this research could be easily incorporated into a decision support tool for conducting what-if analysis of alternative winter road maintenance policies and methods. Moreover these models could provide a mechanism to estimate road safety level based on road surface as well as weather and traffic conditions and therefore could potentially be used for generating safety related information for travelers as part of a winter traffic management scheme. Directions for future work are also provided at the end of this document.

In countries with severe winters such like Canada, winter road maintenance (WRM) operations, such as plowing, salting and sanding, play an indispensible role in maintaining good road surface conditions and keeping roads safe. WRM is, however, also costly, both monetarily and environmentally. The substantial direct and indirect costs associated with WRM have stimulated significant interest in quantifying the safety and mobility benefits of winter road maintenance, such that systematic cost-benefit assessment can be performed. A number of studies have been initiated in the past decade to identify the links between winter road safety and factors related to weather, road, and maintenance operations. However, most of these studies have focused on the effects of adverse weather on road safety. Limited efforts have been devoted to the problem of quantifying the safety benefits of winter road maintenance under specific road weather conditions. Moreover, the joint effects of and complex interactions between road driving conditions, traffic and maintenance and their impact on traffic safety have rarely been studied. This research aims to determine the effect of WRM on road safety during snow storm events and develop models that can be used to quantify the safety benefit of alternative winter road maintenance policies, strategies and practices. Two integral aspects of collision risk were investigated, namely, collision frequency and severity. Collision frequency models were developed using winter storm collision data compiled for six winter seasons (2000 to 2006) for a total of 31 highway routes across Ontario. A comprehensive measure, namely, road surface condition index (RSI), was proposed to represent the road surface conditions during a variety of snow events. RSI was used as a surrogate measure to capture the effects of WRM. Other factors related to weather, traffic and road features were also accounted for in the analysis. Problems associated with data aggregation were also investigated. For this purpose, two different datasets were formed, namely, event-based data (EBD) which aggregates data by snow storm events and hourly based data (HBD) which includes hourly records of collision counts and other related factors. These two data sets of different aggregation levels were then used to investigate the effects of data aggregation and correlation (within – event) as well as to develop models for different purposes of benefit analyses. For EBD, Negative Binomial models and Generalized Negative Binomial models were calibrated whereas for HBD, Generalized Negative Binomial models and multilevel Poisson Lognormal models were calibrated. Generalized Negative Binomial models were found to best fit the data for both datasets. It was found that addition of site specific variables improves model fit. RSI and exposure were found significant for all the models and datasets. Weather factors such as visibility, wind speed, precipitation, and air temperature were also found to have statistically significant effects on collision frequency. All the models were consistent in terms of effects of different variables. The EBD models are useful to quantify the effect of different maintenance service standards and policies with limited information on the details of the weather events and traffic. On the other hand, HBD models have a higher level of reliability capable of providing more accurate estimates on road accidents. As a result, they are useful for determining the effects of different treatment operations. Several examples were employed to demonstrate the application of the developed models, such as quantifying the benefits of alternative maintenance operations and evaluating the effects of different service standards using safety as a performance measure. To enable a comprehensive risk analysis, collisions under both all-weather conditions and snow storm conditions over the six winter seasons were analyzed to identify the relationship between collision severity and various factors related to road weather and surface conditions, road characteristics, traffic, and vehicles etc., on collision severity. A multilevel modeling framework was introduced to capture the inherent hierarchy between collisions, vehicles and persons involved within the collision data. For each collision data set, three alternative severity models, namely, multinomial models, ordered logit models and binary logit models, were calibrated and compared. It was found that multilevel multinomial logit models were best fit to the data. Moreover issues related to different levels of aggregation were also discussed and results from occupant based data were found to be more reasonable and in line with general literature. Different individual, vehicle, environment and accident location factors were found to have a statistically significant effect on the injury severity levels. Contributing factors at the individual and vehicle levels include driver condition, driver sex, driver age, position in vehicle, use of safety device such as seat belt, vehicle type, vehicle age and vehicle condition. Roadway and environment factors include number of lanes, speed limit, road alignment, RSI/road surface condition, wind speed, and visibility. Other factors include light, and traffic volume. Two case studies were conducted to demonstrate the application of the developed models in conjunction with the accident frequency models for cost benefit analysis. This research was the first to investigate the direct link between road surface conditions and collisions at an operational level. It has been shown that the developed models are capable of evaluating alternative winter road maintenance policies and operations and assessing the safety benefit of a particular winter road maintenance strategy or decision. This research is also the first to conduct an in-depth analysis on the problem of winter road safety at a disaggregate level that captures detailed temporal variation (e.g., hourly and by storm event)) within small spatial aggregation units (road sections corresponding to actual patrol routes). The safety models developed from this research could be easily incorporated into a decision support tool for conducting what-if analysis of alternative winter road maintenance policies and methods. Moreover these models could provide a mechanism to estimate road safety level based on road surface as well as weather and traffic conditions and therefore could potentially be used for generating safety related information for travelers as part of a winter traffic management scheme. Directions for future work are also provided at the end of this document.

Baigiamajame magistro darbe yra nagrinėjamos Lietuvoje žiemos metu naudojamos intelektinės transporto sistemos. Didžiausias dėmesys yra skiriamas KOSIS trūkumo – nesugebėjimo prognozuoti – pašalinimui. Darbo tikslas yra išsiaiškinti, ar yra galimybė, pasinaudojus minėtos sistemos teikiamais duomenimis, prognozuoti kritulių pradžios laiką VĮ „Vilniaus regiono keliai" prižiūrimuose keliuose. Darbo aktualumui atskleisti yra aptarta kritulių prognozės svarba tiek eismo dalyviams tiek kelių priežiūros įmonėms ypač šaltuoju metų laiku. Tikslui pasiekti yra aprašoma tyrimo metodiką, kurios pagrindą sudaro vėjo, nešančio kritulių debesis, greitis. Greičiui apskaičiuoti yra pateikiami teoriniai pagrindai, padedantys pasirinkti atitinkamus parametrus šio dydžio nustatymui. Žinant debesų judėjimo greitį yra parengtos prognozės, kurių rezultatai gretinami su realiai užfiksuotais laiko intervalais. Atlikus tyrimą yra nustatyta, kokiu spinduliu yra tikslinga atlikti tokias prognozes ir kokiam laikotarpiui galima prognozuoti. Darbą sudaro 6 dalys: įvadas, literatūros apžvalga, Lietuvoje naudojamos intelektinės transporto sistemos kelių priežiūroje žiemą, klimatinių sąlygų prognozavimo tyrimas, išvados ir pasiūlymai, literatūros sąrašas. Darbo apimtis – 71 p. teksto be priedų, 35 iliustracijos, 13 lentelės, 29 bibliografiniai šaltiniai. Atskirai pridedami darbo priedai. / In this master thesis there are analysed intelligent transport systems used for winter road maintenance in Lithuania. The main attention is payed to Road Weather Information system. The aim of this thesis is to find out the posibility of forecasting the precipitation start time in Vilnius region using Road Weather stations information. All stations are located within a 200 km radius to the southwest of Vilnius region. At first there is a disputed importance of precipitation forecasting to drivers and road maintenance personnel especially during the winter season. In order to achieve the aim of this thesis, there are described forecasting method based on the wind speed. Also there is a theory that explains how to select basic parameters to calculate the wind speed. There are some forecasts made in this paperwork and their results are compared with real data. It helps to find out which stations are useful for further forecasting and what is the longest time of forecasting. The thesis includes 6 parts: introduction, survey of literature, Road Weather Information systems used for winter road maintenance in Lithuania, the reserach of weather forecasting, conclusions and suggestions, references. Pages - 71 p . text, 35 figures . , 13 tables . , 39 bibliographic sources . All appendixes are separately attached.

Winter road maintenance activities are crucial for maintaining the accessibility and traffic safety of the road network during winters. Common winter road maintenance activities include plowing and the use of de-icing agents (e.g. NaCl) to avoid freezing. Effective winter road maintenance strives towards keeping the roads free from snow and ice while reducing negative side effects of winter road maintenance, such as ground water contamination from road salt. Since the weather is decisive for when there is an increased risk of slipperiness, the understanding and continuous observation and forecasts of the road weather are of highest importance. Sensors are commonly installed along roads to measure road weather conditions to support the road maintenance personnel in taking appropriate actions. Different types of errors and uncertainties related to sensors used for frost warnings along roads have been investigated by using a regional scale dataset from south-western Sweden. The results from this study indicate that various types of uncertainties originate from both measurements and models which have a significant impact on the winter road maintenance efficiency. To provide better information about the road surface conditions, a new method for detecting ice formation on roads is presented. Infrared sensors were used to detect temperature patterns which may occur when ice formation take place on a road surface. The investigations demonstrate the potential to improve winter road maintenance by introducing new methods to better describe the road surface conditions. / QC 20101206