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Ett hållbart samhälle behöver ha en klimatvänlig snöröjning. Den traditionella snöröjningen är associerad med en del problem, exempelvis bidrar saltspridning till ökad korrosion av vägar och fordon, förorening av både ytvatten och grundvatten samt ökad mobilitet av tungmetaller. Ett hållbart alternativ är hydronisk markvärme, även kallat Hydronic Asphalt Pavement, HAP. Snösmältning med ett HAP-system sker genom att en varm fluid cirkulerar i rör under ytan som ska hållas snöfri. HAP- systemets energianvändning och snösmältningskapacitet är beroende av hur de värmande rören är placerade samt vilket styrsystem som används. Rapporten syftar till att öka förståelsen för hur styrsystemet och geometrin påverkar HAP-systemets energianvändning och snösmältningstid. En numerisk 2D-modell konstrueras i COMSOL Multiphysics vilken användes för att simulera styrsystemets och geometrins påverkan på HAP-systemet. Snön förenklades som en värmesänka till vilken modellen överförde värme via ett värmeflöde. En avgränsning i rapporten var att det bortsågs från vatten på ytan för att förenkla modellen. Resultatet bekräftar att HAP-systemets styrsystem och geometri har stor påverkan på dess energianvändning och snösmältningstid. Generellt ger en hög energianvändning kortare tid med snö på ytan. Det gör att om det är önskvärt att ha ett energisnålt system behöver en avvägning mellan energianvändning och tid med snö på ytan göras. Ett intermittent styrsystem bedöms vara ett bra alternativ då det ger relativt låg energianvändning och kort tid med snö på ytan. Om det inte finns en begränsning i energianvändning finns det flera styrsystem som kan ge en snöfri yta hela året. Ytans temperatur är den bästa styrparametern att använda för att minska både energianvändning och snösmältningstid. Då värmerören placeras grundare ökar energibehovet och tiden med snö på ytan minskar. Det är möjligt att placera värmerören djupare med bibehållen snöfri tid på ytan om styrsystemet anpassas efter djupet. En viktig anpassning är att styrsystemet ger en förvärmningseffekt, exempelvis att vägen börjar värmas då vägytans temperatur understiger 1°C. En ökning av avståndet mellan värmerören, CCrör, minskar energibehovet och tiden med snö på ytan ökar. Det bedöms vara möjligt att öka CCrör till 350 mm utan att generera för stora skillnader i temperaturprofilen över ytan då rördjupet är 100 mm eller 160 mm. Det styrsystem som gynnas mest av att öka CCrör till 350 mm är ”Grundfall”, vilken värmer vägen under hela vinterhalvåret. Energianvändningen minskar då med 132 kWh/m2 (22,9%) och den längsta ihållande tiden med snö på ytan ökar från 0 h till 4 h. Beroende på vad kraven på ytan är kan det vara möjligt att ha 350 mm som CCrör för de andra styrsystemen. HAP-systemet blir resurseffektivare och billigare vid konstruktion ju större CCrör som används, vilket är önskvärt. Resultatet visar att det är en liten minskning i energianvändning och snösmältningstid då isolering är under värmerören jämfört med ingen isolering. Detbedöms därför vara omotiverat ur både energisynpunkt och snösmältningsmässigt att använda isolering under värmerören på det sätt som undersökts i detta arbete. Det är en markant skillnad i energianvändning mellan ett styrsystem som är enklare och ett som är mer komplext. Om styrsystemet ”Intermittent” används i stället för ”Grundfall” vid Hamngatan i Karlstad skulle det generera en minskad energianvändning av 4,37 GWh fjärrvärme (58,5%), vilket motsvarar 199 ton CO2 per år. Resultatet understryker vikten att ett optimalt styrsystem används. Även en liten skillnad i energianvändning kan ge stora energimässiga besparingar eftersom det ofta är stora ytor som värms med ett HAP-system. För att kunna avgöra vilket styrsystem som är bäst lämpat behöver kraven på ytan bestämmas, vilket inte görs i arbetet, utan resultaten hålls generella. / A sustainable society need to have a climate friendly snow removal system. The traditional snow removal systems generate some problems, for example increased corrosion of roads and vehicles, contamination of both surface- and ground water and increased mobility of heavy metals. A sustainable alternative is Hydronic Asphalt Pavement, HAP. Snow melting with a HAP-system is generated by circulating a warm fluid in pipes underneath the surface that is to be snow free. Both the energy usage and snow melting time is affected by how the heat pipes are placed and which control system that is used. The report aims to increase the knowledge of how both the control system and geometry of the heating pipes affect the energy use and snow melting time of a HAP-system. A numerical 2D-model was constructed in COMSOL Multiphysics which was used to simulate how the control system and geometry of the heating pipes effects the HAP-system. The snow was simplified to a heat sink, to which the model could transfer heat through a convective heat flux. A demarcation of the study is that water on the surface is ignored to simplify the model. The results confirms that both the control system and geometry of the heat pipes greatly affects the energy usage and snow melting time. In general, a large energy usage generates a shorter total time with snow on the surface. It is therefore needed to do a balancing between energy usage and the total time with snow on the surface if the energy usage is to be restricted. An intermittent control system is considered to be a good alternative as it gives a relative low energy usage and short time with snow on the surface. If there is no limitation on the energy use, there is several control systems that gives a snow free surface throughout the year. The surface temperature is the best parameter for the control system as it minimizes both the energy usage and snow melting time. When the heating pipes is placed shallower the energy usage is increased and the time with snow on the surface decreases. It is possible to place the heating pipes at a greater depth and still have the same functionality of the HAP-system if the control system is adjusted accordingly. One important adjustment for the control system is preheating, for example that the heating is turned on when the air temperature is less than 1°C. An increase of CCrör decrease the energy usage and increase the time with snow on the surface. It is possible to increase CCrör to 350 mm and still have a smooth temperature profile if the heating pipes is placed 100 mm or 160 mm beneath the road surface. The control system that gains the most out of an increase in !!!ö! to 350 mm is “Grundfall”, which reduce its energy usage with 132 kWh/m2 (22,9%) and the longest time with snow on the surface is increased from 0 h to 4 h. Depending on which demands the surface is to meet, it is possible to have 350 mm as CCrör for the other control systems. An increase in CCrör makes the HAP-system more resource efficient and cheaper to build, which is desirable. The results show a small decrease in energy usage and snow melting time when isolation is underneath the heating pipes compared to without isolation. It is therefore deemed to be unmotivated to use isolation as it is used in this paper, in both energy use- and snow melting time-perspective. There is a significant difference in energy use between a simple and more complex control system. If the control system “Intermittent” is used instead of “Grundfall” at Hamngatan in Karlstad the energy usage would decrease with 4,37 GWh heat (58,5%) and 199 ton of CO2. The result underlines the importance of an optimal control system for a HAP-system. Even a small change in energy consumption can generate large energy savings due to the scale of the surfaces that is heated with HAP-systems. To be able to decide which control system that is the best suited, the demand on the surface needs to be set. The demands are not set in this paper in order to keep the results general.

Energy dissipation, fuel consumption, real-estate property prices and health issues are some of the aspects related to the tyre/pavement interaction and its functional properties of rolling resistance and noise. The first two aspects are affected by the tyre/road interaction as energy is dissipated mostly by hysteretic losses as the tyre is subjected to dynamic deformations when contacting the pavement surface. The other effect of this contact system that is analysed in this thesis is noise. Excessive noise exposure leads to a decrease in real-estate property values and even health issues such as increased blood pressure, sleep disturbance, cognitive impairment in children, among others. To mitigate such issues, a good understanding of the underlying causes is crucial and therefore a holistic approach was used to analyse the contact interaction in a more comprehensive way, encompassing the pavement, tyre, environmental and contact media (contaminations). Both noise and rolling resistance were analysed after subjecting the contact system to controlled interventions in one variable while maintaining the other variables constant and then comparing to a reference condition. In the first part of the investigative work, different tyres were tested while maintaining the pavement, environment and contact media constant, allowing an evaluation of the impact of winter tyres on noise and rolling resistance. In the second part, an intervention in the pavement was applied while maintaining the other variables constant. allowing an evaluation of the impact of surface grinding on noise and rolling resistance. The first part quantified how noisier studded tyres are in comparison to non-studded tyres, yet no substantial difference in rolling resistance was found. The second part revealed the potential of the horizontal grinding to reduce noise and rolling resistance, having a limitation, on the duration of such effects, especially for Swedish roads where studded tyres are used. / Energiförluster, bränsleförbrukning, fastighetspriser och hälsoproblem är några av de effekter som följer av däckens interaktion med vägytan och som relaterar till de funktionella egenskaperna för rullmotstånd och buller. De första två effekterna uppkommer av väg- och däckinteraktionen eftersom energi förbrukas mestadels genom hysteresförluster när däcken utsätts för dynamiska deformationer vid kontakt med vägytan. Den andra effekten av däck/vägkontakten som analyseras i avhandlingen är buller. Alltför hög bullerexponering intill boendemiljöer leder till minskning av fastighetspriser och även till hälsoproblem såsom ökat blodtryck, sömnstörning, kognitiv försämring hos barn, med mera. För att mildra sådana problem, är det avgörande att ha en god förståelse av de bakomliggande orsakerna, och därför används här ett helhetsgrepp för att genomföra en mer omfattande analys av interaktionen. Analysen inkluderar egenskaper hos vägytan, däcken, omgivande miljö samt kontaktmedier (förorenande skikt).. Både buller och rullmotstånd har analyserats efter att på ett kontrollerat sätt ha varierat en viss variabel medan de andra variablerna hållits konstanta, och sedan jämföra resultaten med referensförhållandet. I den första delen av denna undersökning provades flera olika däck medan vägytan, miljö- och kontaktmedier hölls konstanta. Detta möjliggjorde en utvärdering av effekterna av olika vinterdäck på rullmotstånd och buller. I den andra delen beskrivs effekter av en förändring av vägytan, utförd genom att slipa bort topparna i vägtexturen, medan de andra variablerna hålls konstanta.. Den första delen kvantifierar bullerökningen vid användning av dubbdäck i jämförelse med odubbade vinterdäck. Några betydande skillnader i rullmotstånd hittades däremot inte mellan de två däckgrupperna. Den andra delen av texten visar på potentialen av horisontell slipning för att minska buller och rullmotstånd, dock är varaktigheten begränsad; särskilt för svenska vägar där dubbdäck används. / <p>QC 20180212</p>

A pavement management system (PMS) is a valuable tool and one of the critical elements of the highway transportation infrastructure. Since a vast amount of pavement data is frequently and continuously being collected, updated, and exchanged due to rapidly deteriorating road conditions, increased traffic loads, and shrinking funds, resulting in the rapid accumulation of a large pavement database, knowledge-based expert systems (KBESs) have therefore been developed to solve various transportation problems. This dissertation presents the development of theory and algorithm for a new decision tree induction method, called co-location-based decision tree (CL-DT.) This method will enhance the decision-making abilities of pavement maintenance personnel and their rehabilitation strategies. This idea stems from shortcomings in traditional decision tree induction algorithms, when applied in the pavement treatment strategies. The proposed algorithm utilizes the co-location (co-occurrence) characteristics of spatial attribute data in the pavement database. With the proposed algorithm, one distinct event occurrence can associate with two or multiple attribute values that occur simultaneously in spatial and temporal domains. This research dissertation describes the details of the proposed CL-DT algorithms and steps of realizing the proposed algorithm. First, the dissertation research describes the detailed colocation mining algorithm, including spatial attribute data selection in pavement databases, the determination of candidate co-locations, the determination of table instances of candidate colocations, pruning the non-prevalent co-locations, and induction of co-location rules. In this step, a hybrid constraint, i.e., spatial geometric distance constraint condition and a distinct event-type constraint condition, is developed. The spatial geometric distance constraint condition is a neighborhood relationship-based spatial joins of table instances for many prevalent co-locations with one prevalent co-location; and the distance event-type constraint condition is a Euclidean distance between a set of attributes and its corresponding clusters center of attributes. The dissertation research also developed the spatial feature pruning method using the multi-resolution pruning criterion. The cross-correlation criterion of spatial features is used to remove the nonprevalent co-locations from the candidate prevalent co-location set under a given threshold. The dissertation research focused on the development of the co-location decision tree (CL-DT) algorithm, which includes the non-spatial attribute data selection in the pavement management database, co-location algorithm modeling, node merging criteria, and co-location decision tree induction. In this step, co-location mining rules are used to guide the decision tree generation and induce decision rules. For each step, this dissertation gives detailed flowcharts, such as flowchart of co-location decision tree induction, co-location/co-occurrence decision tree algorithm, algorithm of colocation/co-occurrence decision tree (CL-DT), and outline of steps of SFS (Sequential Feature Selection) algorithm. Finally, this research used a pavement database covering four counties, which are provided by NCDOT (North Carolina Department of Transportation), to verify and test the proposed method. The comparison analyses of different rehabilitation treatments proposed by NCDOT, by the traditional DT induction algorithm and by the proposed new method are conducted. Findings and conclusions include: (1) traditional DT technology can make a consistent decision for road maintenance and rehabilitation strategy under the same road conditions, i.e., less interference from human factors; (2) the traditional DT technology can increase the speed of decision-making because the technology automatically generates a decision-tree and rules if the expert knowledge is given, which saves time and expenses for PMS; (3) integration of the DT and GIS can provide the PMS with the capabilities of graphically displaying treatment decisions, visualizing the attribute and non-attribute data, and linking data and information to the geographical coordinates. However, the traditional DT induction methods are not as quite intelligent as one's expectations. Thus, post-processing and refinement is necessary. Moreover, traditional DT induction methods for pavement M&R strategies only used the non-spatial attribute data. It has been demonstrated from this dissertation research that the spatial data is very useful for the improvement of decision-making processes for pavement treatment strategies. In addition, the decision trees are based on the knowledge acquired from pavement management engineers for strategy selection. Thus, different decision-trees can be built if the requirement changes. / Ph. D.

Increasing demands, shrinking financial and human resources, and increased infrastructure deterioration have made the task of maintaining the infrastructure systems more challenging than ever before. Life-cycle cost analysis (LCCA) is an important tool for transportation infrastructure management, which is used extensively to support project level decisions, and is increasingly being applied to enhance network level analysis. However, traditional LCCA tools cannot practically and effectively utilize expert knowledge and handle ambiguous uncertainties. The main objective of this dissertation was to develop enhanced LCCA models using soft computing (mainly fuzzy logic) techniques. The proposed models use available "real-world" information to forecast life-cycle costs of competing maintenance and rehabilitation strategies and support infrastructure management decisions. A critical review of available soft computing techniques and their applications in infrastructure management suggested that these techniques provide appealing alternatives for supporting many of the infrastructure management functions. In particular, LCCA often utilizes information that is uncertain, ambiguous and incomplete, which is obtained from both existing databases and expert opinion. Consequently, fuzzy logic techniques were selected to enhance life-cycle cost analysis of transportation infrastructure investments because they provide a formal approach for the effective treatment of these types of information. The dissertation first proposes a fuzzy-logic-based decision-support model, whose inference rules can be customized according to agency's management policies and expert opinion. The feasibility and practicality of the proposed model is illustrated by its implementation in a life-cycle cost analysis algorithm for comparing and selecting pavement maintenance, rehabilitation and reconstruction (MR&R) policies. To enhance the traditional probabilistic LCCA model, the fuzzy-logic-based model is then incorporated into the risk analysis process. A fuzzy logic approach for determining the timing of pavement MR&R treatments in a probabilistic LCCA model for selecting pavement MR&R strategies is proposed. The proposed approach uses performance curves and fuzzy-logic triggering models to determine the most effective timing of pavement MR&R activities. The application of the approach in a case study demonstrates that the fuzzy-logic-based risk analysis model for LCCA can effectively produce results that are at least comparable to those of the benchmark methods while effectively considering some of the ambiguous uncertainty inherent to the process. Finally, the research establishes a systematic method to calibrate the fuzzy-logic based rehabilitation decision model using real cases extracted from the Long Term Pavement Performance (LTPP) database. By reinterpreting the model in the form of a neuro-fuzzy system, the calibration algorithm takes advantage of the learning capabilities of artificial neural networks for tuning the fuzzy membership functions and rules. The practicality of the method is demonstrated by successfully tuning the treatment selection model to distinguish between rehabilitation (light overlay) and do-nothing cases. / Ph. D.

Increasingly tighter budgets, changes in government role/function, declines in staff resources, and demands for increased accountability in the transportation field have brought unprecedented challenges for state transportation officials at all management levels. Systematic methodologies for effective management of a specific type of infrastructure (e.g., pavement and bridges) as well as for holistically managing all types of infrastructure assets are being developed to approach these challenges. In particular, the intrinsic characteristics of highway system make the use of multi-objective optimization techniques particularly attractive for managing highway assets. Recognizing the need for effective tradeoff tools and the limitations of state-of-practice analytical models and tools in highway asset management, the main objective of this dissertation was to develop a performance-based asset management framework that uses multi-objective optimization techniques and consists of stand-alone but logically interconnected optimization models for different management levels. Based on a critical review of popular multi-objective optimization techniques and their applications in highway asset management, a synergistic integration of complementary multi-criteria optimization techniques is recommended for the development of practical and efficient decision-supporting tools. Accordingly, the dissertation first proposes and implements a probabilistic multi-objective model for performance-based pavement preservation programming that uses the weighting sum method and chance constraints. This model can handle multiple incommensurable and conflicting objectives while considering probabilistic constraints related to the available budget over the planning horizon, but is found more suitable to problems with small number of objective functions due to its computational intensity. To enhance the above model, a hybrid model that requires less computing time and systematically captures the decision maker's preferences on multiple objectives is developed by combining the analytic hierarchy process and goal programming. This model is further extended to also capture the relative importance existent within optimization constraints to be suitable for allocations of funding across multiple districts for a decentralized state department of transportation. Finally, as a continuation of the above proposed models for the succeeding management level, a project selection model capable of incorporating qualitative factors (e.g. equity, user satisfaction) into the decision making is developed. This model combines k-means clustering, analytic hierarchy process and integer linear programming. All the models are logically interconnected in a comprehensive resource allocation framework. Their feasibility, practicality and potential benefits are illustrated through various case studies and recommendations for further developments are provided. / Ph. D.

Traditional asphalt mix design procedures are empirical and need random and lengthy trials in a laboratory, which can cost much labor, material resources, and finance. The initiative (Material Genome initiative) was launched by President Obama to revitalize American manufacturing. To achieve the objective of the MGI, three major tools which are computational techniques, laboratory experiments, and data analytics methods are supposed to have interacted. Designing asphalt mixture with laboratory and computation simulation methods has developed in recent decades. With the development of data science, establishing a new design platform for asphalt mixture based on data-driven methods is urgent. A balanced mix design, defined as an asphalt mix design simultaneously considering the ability of asphalt mixture to resist pavement distress, such as rutting, cracking, IRI (international roughness index), etc., is still the trend of future asphalt mix design. The service life of asphalt pavement mainly depends on the properties of the asphalt mixture. Whether asphalt mixture has good properties also depends on advanced asphalt mix design methods. Scientific mix design methods can improve engineering properties of asphalt mixture, further extending pavement life and preventing early distress of flexible pavement. Additionally, in traditional asphalt mix design procedures, the capability to resist pavement distress (rutting, IRI, and fatigue cracking) of a mixture is always evaluated based on laboratory performance tests (Hamburg wheel tracking device, Asphalt Pavement Analyzer, repeated flexural bending, etc.). However, there is an inevitable difference between laboratory tests and the real circumstance where asphalt mixture experiences because the pavement condition (traffic, climate, pavement structure) is varying and complex. The successful application examples of machine learning (ML) in all kinds of fields make it possible to establish the predictive models of pavement distress, with the inputs which contain asphalt concrete materials properties involved in the mix design process. Therefore, this study utilized historical data acquired from laboratory records, the LTPP dataset, and the NCHRP 1-37A report, data analytics and processing methods, as well as ML models to establish pavement distress predictive models, and then developed an automated and balanced mix design procedure, further lying a foundation to achieve an MGI mix design in the future. Specifically, the main research content can be divided into three parts:1. Established ML models to capture the relationship between properties of the binder, aggregates properties, gradation, asphalt content (effective and absorbed asphalt content), gyration numbers, and mixture volumetric properties for developing cost-saving Superpave and Marshall mix design methods; 2. Developed pavement distress (rutting, IRI, and fatigue cracking) predictive models, based on the inputs of asphalt concrete properties, other pavement materials information, pavement structure, climate, and traffic; 3. Proposed and verified an intelligent and balanced asphalt mix design procedure by combining the mixture properties prediction module, pavement distress predictive models and criteria, and non-dominated Sorting genetic algorithm-Ⅱ (NSGA-Ⅱ). It was discovered determining total asphalt content through predicting effective and absorbed asphalt content indirectly with ML models was more accurate than predicting total asphalt content directly with ML models; Pavement distress predictive models can achieve better predictive results than the calibrated prediction models of Mechanistic-Empirical Pavement Design Guide (MEPDG); The design results for an actual project of surface asphalt course suggested that compared to the traditional ones, the asphalt contents of the 12.5 mm and 19 mm Nominal Maximum Aggregate Size (NMAS) mixtures designed by the automated mix design procedure drop by 7.6% and 13.2%, respectively; the percent passing 2.36 mm sieve of the two types of mixtures designed by the proposed mix design procedure fall by 17.8% and 10.3%, respectively. / Doctor of Philosophy / About 96% of roads are paved with asphalt mixture. Asphalt mixture consists of asphalt, aggregates, and additives. Asphalt mix design refers to the process to determine the proper proportion of aggregates, asphalt, and additives. Traditional asphalt mix design procedures in laboratories are empirical and cost much labor, material resources, and finance. Pavement distresses, for example, cracks are important indicators to assess pavement condition. With the development of data science, machine learning (ML) has been applied to various fields by predicting desired targets. The multi-objective optimization refers to determining the optimal solution of a multiple objectives problem. The study applied ML methods to predict asphalt mixture components' proportions and pavement distress with historical experimental data and pavement condition records from literature and an open-source database. Specifically, the main research content can be divided into three parts:1. Established ML models to predict the proportion of asphalt when aggregates are given; 2. Built ML models to predict pavement distress from pavement materials information, pavement structure, climate, and traffic; 3. Develop a digital asphalt mix design procedure by combining the pavement distress prediction models and a multi-objective optimization algorithm.

When a vehicle changes speed or direction, the interaction between the contacting surfaces of the tire and the pavement form frictional forces. The pavement's contribution to tire-pavement friction is referred to as skid resistance and is provided by pavement microtexture and macrotexture. The amount of skid resistance depreciates over time due to the polishing action of traffic, and for this reason, the skid resistance should be monitored with friction testing equipment. The equipment use one of four test methods to measure network-level friction: ASTM E 274 locked-wheel, ASTM E 2340 fixed-slip technique, ASTM E 1859 variable-slip technique, and sideways-force coefficient (SFC) technique. The fixed-slip, variable-slip, and SFC techniques are used in continuous friction measurement equipment (CFME). In the United States, skid resistance is traditionally measured with a locked-wheel skid trailer (LWST) equipped with either a ASTM E 501 ribbed or a ASTM E 524 smooth 'no tread' tire. Since the LWST fully-locks the test wheel to measure friction, it is only capable of spot testing tangent sections of roadway. By contrast, the remaining three test methods never lock their test wheels and, therefore, they can collect friction measurements continuously on all types of roadway, including curves and t-intersections. For this reason, highway agencies in the U.S. are interested in transitioning from using a LWST to using one of three continuous methods. This dissertation explores the use of continuous friction measurements, collected with a Sideways-force Coefficient Routine Investigation Machine (SCRIM), in a systemic highway safety management approach to reduce crashes that result in fatalities, injuries, and property damage only. The dissertation presents four manuscripts. In the first manuscript, orthogonal regression is used to develop models for converting between friction measurements with a SCRIM and LWST with both a ribbed and smooth tire. The results indicated that the LWST smooth tire measured friction with greater sensitivity to changes in macrotexture than the SCRIM and LWST ribbed tire. The SCRIM also had greater correlation to the LWST ribbed tire than the LWST smooth tire. The second investigation establishes the relationship between friction measured with a SCRIM and the risk of crashes on dry and wet pavement surfaces. The results of this showed that increasing friction decreases both dry and wet pavement crashes; however, friction was found to have greater impact in wet conditions. Due to the negative relationship between friction and crashes, eventually there will be a point where further losses in friction can result in a rapid increase in crash risk. This point can be identified with a friction threshold known as an investigatory level. When measured friction is at or below the investigatory level, an in- and out-of-field investigation is required to determine whether a countermeasure is necessary to improve safety. The third manuscript proposes a statistical regression approach for determining investigatory levels. Since this approach relies on statistical regression, the results are objective and should be the same for any analyst reviewing the same data. The investigatory levels can be used in a systemic approach that identifies locations where crashes can be reduced based on a benefit-cost analysis of surface treatments. Last, the forth manuscript demonstrates a benefit-cost analysis that selects surface treatments based on crash reductions predicted with continuous friction measurements. / Doctor of Philosophy / When a vehicle changes speed or direction, the tires slide over the pavement surface, creating friction that produces the traction that is necessary for the vehicle to change speed or direction. Friction can diminish when water, dust, and other contaminants are present, or over time due to traffic. Over time, the loss in friction causes the risk of a crash to increase. However, this relationship is non-linear, and therefore, eventually there will be a point where further losses in friction can cause a rapid increase in crash risk. For this reason, the pavement friction is monitored with equipment that slides a rubber tire with known properties over a pavement surface. Since friction is lowest when the pavement is wet, the equipment applies a film of water to the surface directly in front of the sliding tire. There are different types of equipment used to measure friction. The physical designs of the equipment and their method of testing may be different. For example, some devices measure friction by sliding a wheel that is angled away from the path of the vehicle, while others slide a wheel that is aligned with the vehicle but reduced in speed compared to the vehicle. The factors that make the equipment different can affect the quantity of friction that is measured, as well as the timing between each consecutive measurement. The advantages that some equipment offers can entice highway agencies to transition from a pre-existing system to a more advantageous system. Before transitioning, the measurements from the two types of equipment should be compared directly to determine their correlation. Statistical regression can also be used to develop models for converting the measurements from the new equipment to the units of the current, which can help engineers interpret the measurements, and to integrate them into an existing database. The presence of water on a pavement surface can result in a temporary loss of friction that can increase the risk of a crash beyond the normal, dry pavement state. This does not guarantee that dry pavements have sufficient friction as is suggested in most literature. In this dissertation, the relationship between friction and the risk of a crash on dry and wet pavements are evaluated together. The results show that increasing friction can decrease the crash risk on both dry and wet pavement surfaces. The amount of friction that is needed to maintain low crash risk is not the same for every section of road. Locations such as approaches to curves or intersections can increase the risk of a crash, and for that reason, some sections of roadway require more friction than others. Minimum levels of friction called investigatory levels can be established to trigger an in- and out-of-field investigation to determine whether improving friction can improve safety when the measured friction is at or below a specific value. This dissertation proposes a methodology for determining the investigatory levels of friction for different sections of roadway using a statistical regression approach. The investigatory levels are then used to identify locations where pavement surface treatments can reduce crashes based on a benefit-cost analysis. Last, the ability of a surface treatment to reduce crashes is evaluated using another statistical regression approach that predicts changes in crash risk using friction measurements. Since there are several treatment options, a treatment is selected based on estimated cost and benefit.