Markov Monte Carlo Simulation Research Articles

Reliability analysis and redundancy optimization of k-out-of-n systems with random variable k using continuous time Markov chain and Monte Carlo simulation

This article discusses the problems associated with the redundancy of structures k-out-of-n as a method of increasing system availability. A parallel k-out-of-n system was considered, with k repairable and homogeneous components. The other (n – k) objects are in hot redundancy mode. Failures and repairs are independent processes, and each repaired object is treated as full-fledged in terms of operation. The system operates under dynamically changing conditions, so the minimum number of operable components essential for its proper operation is a random variable with a specific distribution. Component damages and repairs are independent stochastic processes. A probabilistic and simulation approach based on Continuous Time Markov Chain and Monte Carlo simulation is proposed. This study aims to optimize the number of components in the k-out-of-n structure, which will ensure the system operation with a certain availability and performance. The proposed methods were applied to real transport systems, for which models were developed with parameters estimated on the basis of empirical data. The convergence of the results obtained, using two methods, testifies to the correctness of the methodology used and the reliability of the models developed. The risk to system performance associated with changing input parameters was assessed using model sensitivity analysis. As indicated, the model is not susceptible to changes in the damage and repair intensity. It has also been shown that optimization of the k-out-of-n structure brings a significant reduction in the costs incurred for system development and operation.

MATHEMATICAL MODEL AND SIMULATION FOR DESIGNING A COST-OPTIMIZED OFF-GRID HOUSE SOLAR ENERGY STORAGE SYSTEM

Solar power is a renewable energy source suitable for residential usage due to its affordability and the small area required for installation. However, it requires an effective energy storage system that can minimize power outages within a limited budget. This challenge was addressed by analyzing energy storage systems regarding climate and user behavior. In this study, a mathematical model has been developed to design a cost-effective energy storage system for an off-grid household. We utilized the Markov weather process and Monte Carlo simulation, considering various scenarios of available energy supply, energy consumption, and user behavior. In accordance with the outcome, the size and type of battery system from our model minimized power outages throughout the year. We applied our model to a 1600-square foot house in Rayong, Thailand, with the meteorological data and typical electricity loads from user data. For a budget threshold of 40,000 USD, our model suggests that the best battery is Tesla Powerwall+, which results in an average power outage of only around 3 hours per year, outperforming other batteries under identical economic conditions. Additionally, our model can be widely applied to real scenarios by considering a broader range of batteries, environmental impact and available space, and hybrid systems as well.

Arthroscopic Bankart repair versus nonoperative management for first-time anterior shoulder instability: A cost-effectiveness analysis.

Arthroscopic Bankart repair (ABR) may be more effective than nonoperative management for patients with anterior shoulder instability following first-time dislocation. The purpose of the study was to determine the most cost-effective treatment strategy by evaluating the incremental cost-effectiveness ratio (ICER) for ABR versus nonoperative treatment. This cost-effectiveness study utilized a Markov decision chain and Monte Carlo simulation. Probabilities, health utility values, and outcome data regarding ABR and nonoperative management of first-time shoulder instability derived from level I/II evidence. Costs were tabulated from Centers for Medicaid & Medicare Services. Probabilistic sensitivity analysis was performed using >100,000 repetitions of the Monte Carlo simulation. A willingness-to-pay (WTP) threshold was set at $50,000. The expected cost for operative management higher than nonoperative management ($32,765 vs $29,343). However, ABR (5.48 quality-adjusted life years (QALYs)) was the more effective treatment strategy compared to nonoperative management (4.61 QALYs). The ICER for ABR was $3943. Probabilistic sensitivity analysis showed that ABR was the most cost-effective strategy in 100% of simulations. ABR is more cost-effective than nonoperative management for first-time anterior shoulder dislocation. The threshold analysis demonstrated that when accounting for WTP, ABR was found to be the more cost-effective strategy.

Electrical Vehicle Charging Load Mobility Analysis Based on a Spatial–Temporal Method in Urban Electrified-Transportation Networks

Charging load mobility evaluation becomes one of the main concerns for charging services and power system stability due to the stochastic nature of electrical vehicles (EVs) and is critical for the robust scheduling of economic operations at different intervals. Therefore, the EV spatial–temporal approach for load mobility forecasting is presented in this article. Furthermore, the reliability indicators of large-scale EV distribution network penetration are analyzed. The Markov decision process (MDP) theory and Monte Carlo simulation are applied to efficiently forecast the charging load and stochastic path planning. A spatial–temporal model is established to robustly forecast the load demand, stochastic path planning, traffic conditions, and temperatures under different scenarios to evaluate the charging load mobility and EV drivers’ behavior. In addition, the distribution network performance indicators are explicitly evaluated. A Monte Carlo simulation is adopted to examine system stability considering various charging scenarios. Urban coupled traffic-distribution networks comprising 30-node transportation and 33-bus distribution networks are considered as a test case to illustrate the proposed study. The results analysis reveals that the proposed method can robustly estimate the charging load mobility. Furthermore, significant EV penetrations, weather, and traffic congestion further adversely affect the performance of the power system.

Triangulated irregular network-based probabilistic 3D geological modelling using Markov Chain and Monte Carlo simulation

Three-dimensional geological modelling based on triangulated irregular networks (TINs) often results in deterministic models that do not capture the uncertainty of stratigraphic attributes. This study presents a method for building a 3D TIN geological model that incorporates probability analysis and random simulation to account for this uncertainty. First, we generated the Markov chain sequence of the stratum and combined it with the correlation of boreholes to obtain the transition probability matrix of the virtual borehole. This matrix was then used to describe the probability of changes in the stratum. Second, we constructed a probabilistic model of stratum thickness and generated the stratum state of virtual boreholes through Monte Carlo simulation. Using the TIN algorithm, we built an overall probabilistic model and added validation boreholes to adjust the virtual boreholes and obtain the maximum probabilistic model of the study area. Finally, we performed a case analysis in South China to verify the modelling method, including visualisation, probability analysis and comparative analysis. Our results demonstrate that the proposed modelling method effectively visualises the stratigraphic configuration and quantifies the stratigraphic uncertainty, providing a reference for 3D geological modelling.

The Influencing Mechanisms on Global Industrial Value Chains Embedded in Trade Implied Carbon Emissions from a Higher-Order Networks Perspective

As the division of labor in global industrial value chains deepens, the embedded relationships and carbon emission relationships among countries become more complex. First, calculate the embedding indices of forward and backward global industrial value chains and establish the Multi-Regional Input Output (MRIO) model to calculate trade-implied carbon emissions. Second, construct higher-order weighted networks characterized by hypergraphs from 2000 to 2018, and calculate a high-dimensional vector of characteristic indicators based on apices and hyperedges. Finally, time exponential random graph models are constructed using maximum pseudo-likelihood estimation and Markov Monte Carlo simulation methods to dynamically observe the evolution of the impact mechanism of forward and backward industrial value chains embedded in trade-implied carbon emissions networks. The conclusions obtained are as follows: First, most countries tend to develop backward industries when embedded in global industrial value chains. Second, based on the Global Industry Classification Standard (GICS) criteria, industries deeply embedded in global forward value chains are mainly concentrated in materials and utilities, etc., while industries more deeply embedded in global backward value chains are mainly concentrated in consumer discretionary and real estate industries, etc. Third, “carbon transfer” and “carbon leakage” gradually widen the gap between developed and developing countries, both on the production and consumption sides. Fourth, we decompose the factors influencing industrial carbon emissions into carbon intensity effects, industrial structure effects, and output scale effects and analyze their influence mechanisms. Fifth, for countries with different carbon flow attributes, their forward and backward embedded global industrial value chains have different effects on trade-implied carbon emissions. Sixth, the effective paths of trade that lead to a reduction in carbon emissions are different for countries with different carbon flow characteristics.

Cost-benefit of serum pepsinogen screening for gastric adenocarcinoma in the Mexican population.

Helicobacter pylori (H. pylori) is known to be capable of causing chronic inflammation of the gastric mucosa that slowly progresses through the premalignant stages, reaching localized gastric adenocarcinoma (GAC). Its outcome is closely related to the stage at which diagnosis is made. The aim of the present study was to determine cost-benefit by comparing esophagogastroduodenoscopy, serum pepsinogen detection, and no screening at all. Utilizing Markov chains and Monte Carlo simulation, the costs and effects of various detection modalities were simulated to analyze the cost-benefit of each strategy. For our population, we used the published data of patients with gastric cancer, applicable to the Mexican population. The results were reported as incremental cost-effectiveness ratios. The best strategy was serum pepsinogen determination, followed by the strategy of endoscopic examination with continued monitoring every 3 years. The performance of serum pepsinogen serology and directed endoscopic examination (and continued monitoring, if necessary) for GAC screening could be a cost-effective intervention in Mexico, despite the low-to-moderate general prevalence of the disease.

Cost-effectiveness of screening for atrial fibrillation in a single primary care center at a 3-year follow-up

Objectives The aim of this study was to estimate the cost-effectiveness of intermittent electrocardiogram (ECG) screening for atrial fibrillation (AF) among 70–74-year old individuals in primary care. We also aimed to assess adherence to anticoagulants, severe bleeding, stroke and mortality among screening-detected AF cases at three-year follow-up. Methods A post hoc analysis based on a cross-sectional screening study for AF among 70–74-year old patients, who were registered at a single primary care center, was followed for three years for mortality. Data about adherence to anticoagulants, incidence of stroke and severe bleeding among screening-detected AF cases, were collected from patient’s records. Markov model and Monte Carlo simulation were used to assess the cost-effectiveness of the screening program. Results The mortality rate among screening-detected AF cases (n = 16) did not differ compared to the 274 individuals with no AF (hazard ratio 0.86, CI 0.12–6.44). Adherence to anticoagulants was 92%. There was no stroke or severe bleeding. The incremental cost-effectiveness ratio of screening versus no screening was EUR 2389/quality-adjusted life year (QALY) gained. The screening showed a 99% probability of being cost-effective compared to no screening at a willingness-to-pay threshold of EUR 20,000 per QALY. Conclusion Screening for AF among 70–74-year olds in primary care using intermittent ECG appears to be cost-effective at 3-year follow-up with high anticoagulants adherence and no increased mortality.

Cost-benefit of serum pepsinogen screening for gastric adenocarcinoma in the Mexican population

Introduction and aimHelicobacter pylori (H. pylori) is known to be capable of causing chronic inflammation of the gastric mucosa that slowly progresses through the premalignant stages, reaching localized gastric adenocarcinoma (GAC). Its outcome is closely related to the stage at which diagnosis is made. The aim of the present study was to determine cost-benefit by comparing esophagogastroduodenoscopy, serum pepsinogen detection, and no screening at all. Material and methodsUtilizing Markov chains and Monte Carlo simulation, the costs and effects of various detection modalities were simulated to analyze the cost-benefit of each strategy. For our population, we used the published data of patients with gastric cancer, applicable to the Mexican population. ResultsThe results were reported as incremental cost-effectiveness ratios. The best strategy was serum pepsinogen determination, followed by the strategy of endoscopic examination with continued monitoring every 3 years. ConclusionsThe performance of serum pepsinogen serology and directed endoscopic examination (and continued monitoring, if necessary) for GAC screening could be a cost-effective intervention in Mexico, despite the low-to-moderate general prevalence of the disease.

Germline testing and somatic tumor testing for BRCA1/2 pathogenic variants in ovarian cancer: What is the optimal sequence of testing?

10585 Background: In 2020 ASCO recommended that all women with epithelial ovarian cancer have germline testing (GT) for BRCA1/2 mutations, and those without a germline pathogenic variant (PV) should have somatic tumor testing (TT), to determine eligibility for PARP inhibitor (PARPi) therapy (GT-TT strategy). An alternate strategy is to start with tumor testing first, and to conduct germline testing only in those with a PV in the tumor, or a significant family history (TT-GT strategy). The objective was to conduct a cost-effectiveness analysis comparing the 2 testing strategies. Methods: A Markov Monte Carlo simulation model compared the costs (USD) and benefits of the 2 testing strategies. According to local empiric data, a sufficient tissue sample for TT was available in 99% of cases, otherwise the patient would only have GT. Sensitivity of TT was 99% for detecting germline PV. Only those with BRCA1/2 PV were eligible for PARPi. Primary outcomes included the number of women eligible for PARPi, with progression-free years of life (PFLY) gained based on SOLO1 data, and the incremental cost-effectiveness ratio (ICER). Monte Carlo simulation estimated the number of women who would have GT and TT, and the total with germline or somatic BRCA1/2 PV eligible for PARPi. Sensitivity analyses accounted for uncertainty around various parameters. Results: The GT-TT strategy was more effective but more costly than TT-GT in identifying patients eligible for PARPi. Table summarizes the average lifetime costs, benefits, and Monte Carlo simulation estimates for 10,000 women diagnosed with advanced epithelial ovarian cancer annually in the USA. The incremental benefit from the GT-TT strategy would be achieved at substantial cost to the health care system, with an ICER of $119,340 per PFLY gained relative to the TT-GT strategy. The results were highly sensitive to the sensitivity of TT to detect germline PV, and the costs of GT and TT. Assuming that GT was less than 50% of the cost of TT, the sensitivity of TT had to exceed 98% for the TT-GT strategy to be cost-effective. Conclusions: Although the ASCO recommended strategy of BRCA germline testing followed by tumor testing for those without a pathogenic variant may be more effective in identifying ovarian cancer patients for PARP inhibitor therapy, it is more costly. The ASCO strategy is justified if the sensitivity of tumor testing is not sufficiently high. However, assuming high tumor testing performance rates, tumor testing first followed by germline testing if there is a PV in the tumor and/or family history is a cost-effective strategy.[Table: see text]

Modelling changes in healthcare demand through geographic data extrapolation

ABSTRACT Stay Well Plans are a new programme of care offered to frail and elderly people in Newport. In 2016 a roll out the programme to be offered in all five counties serviced by Aneurin Bevan University Health Board was planned. This paper presents the data analysis and modelling used to determine the programme's effects on the demand of the wider system, and the effects of a Gwent-wide roll out. We extrapolate information from data from a geographical subset of the model domain to a larger geographical area, adjusting for population sizes, deprivation, and distances to healthcare facilities. These parametrise a Markov model and Monte Carlo simulation to predict changes in demand due to different levels of roll out. We conclude that a programme roll out may result in a large reduction on demand at residential care, however at the expense of an increase in demand at community care services.

Unseen Costs: The Direct and Indirect Impact of U.S. Immigration Policies on Child and Adolescent Health and Well-Being.

Shifts in migration and border control policies may increase the likelihood of trauma exposure related to child–parent separation and result in costs to the health system and society. In the present study, we estimated direct and indirect costs per child as well as overall cohort costs of border control policies on migrant children and adolescents who were separated from their parents, detained, and placed in the custody of the United States following the implementation of the 2018 Zero Tolerance Policy. Economic modeling techniques, including a Markov process and Monte Carlo simulation, based on data from the National Child Traumatic Stress Network's Core Data Set (N = 458 migrant youth) and published studies were used to estimate economic costs associated with three immigration policies: No Detention, Family Detention, and Zero Tolerance. Clinical evaluation data on mental health symptoms and disorders were used to estimate the initial health state and risks associated with additional trauma exposure for each scenario. The total direct and indirect costs per child were conservatively estimated at $33,008, $33,790, and $34,544 after 5 years for No Detention, Family Detention, and Zero Tolerance, respectively. From a health system perspective, annual estimated spending increases ranged from $1.5 million to $14.9 million for Family Detention and $2.8 million to $29.3 million for Zero Tolerance compared to baseline spending under the No Detention scenario. Border control policies that increase the likelihood of child and adolescent trauma exposure are not only morally troubling but may also create additional economic concerns in the form of direct health care costs and indirect societal costs.

Construction of electric vehicle driving cycle for studying electric vehicle energy consumption and equivalent emissions.

This paper proposes an effective and scientific method for the construction of a representative driving cycle for electric vehicles (EV) and takes it as a foundation for studying the energy consumption and equivalent emissions for EV. First, a test route is developed through the analysis of the topology of the Xi'an road structure and traffic flow. Second, the vehicle driving pattern data is gathered through an integrated method of chase car method and on-board method. The velocity-acceleration (V-A) grid method is used to divide speed and acceleration data into micro-states. Third, the proposed driving cycle construction method incorporates the Markov chain and Monte Carlo (MCMC) simulation method. Then, a filter process is designed to screen out the most representative driving cycle. Finally, the comparison of the simulation result and test results shows the constructed EV driving cycle is in line with reality, and estimating the EV's energy consumption per kilometer, driving range, and equivalent emissions under official driving cycles results in large relative errors. Therefore, the construction of a real-world driving cycle for specific cities or areas is necessary to evaluate energy consumption, driving range, and equivalent emissions of EV.

Reliability assessment of an isolated hybrid microgrid using Markov modeling and Monte Carlo simulation

The importance of conducting adequacy assessments of standalone systems with integrated renewable generation sources is growing. In this work, the focus is placed on the reliability assessment of an isolated microgrid operating on renewable energy generated by wind turbines (WTs) and photovoltaic (PV) panels. Batteries for storage were included in the model because of their crucial role in the system’s feasibility. Additional micro-gas turbines (MGTs) served as conventional backup. The sequential Monte Carlo simulation (SMCS) method was used to carry out simulations of the system, which was modeled using Markov matrices. Input data, such as wind speed, solar irradiance, and ambient air temperature, were used to simulate the power outputs of the generators. These historical data were fitted into appropriate distributions to extract corresponding parameters when simulating essential key factors necessary to produce the renewable power generation models. The adequacy model of the MGTs was obtained by employing the two-state reliability model, which was also superimposed with the generation models of WTs, PV panels, and batteries. The IEEE Roy Billinton test system (RBTS) was used for demand modelling. Common reliability indices were computed, and the system availability margins were evaluated.

The cost-effectiveness of limiting federal housing vouchers to use in low-poverty neighborhoods in the United States

ObjectiveResidents of low-income neighborhoods are exposed to relatively higher rates of crime, fewer opportunities to exercise, poorer schools, and few opportunities to eat healthy foods than residents of middle-class neighborhoods. Policies that influence neighborhood context could therefore serve as health interventions. We seek to inform the policy debate over the wisdom of spending health dollars on non-health sectors of the economy by defining the opportunity cost of doing so. Study designCost-effectiveness analysis with Markov model and Monte Carlo simulation. MethodsWe assess the long-term health and economic benefits of Moving to Opportunity–type housing vouchers vs traditional public housing. Our Markov model draws heavily from decades of follow-up data from a large randomized-controlled trial, from which we make projections about health outcomes and costs. ResultsRestricted housing vouchers cost less over the lifetime of recipients than traditional vouchers ($186,629 [95% credible interval: $148,856–$229,235] vs $194,077 [$153,831-$240,904]), while improving health and longevity (19.39 quality-adjusted life years [15.83–21.35] vs 19.16 [15.65–21.03]). Over 99% of the model simulations favored restricted housing vouchers over traditional public housing or non-restrictive vouchers. ConclusionsRestrictive vouchers appear to improve population health, save money, and save lives.

Lifetime Risk of Lower-Extremity Peripheral Artery Disease Defined by Ankle-Brachial Index in the United States.

BackgroundThere are no available lifetime risk estimates of lower‐extremity peripheral artery disease (PAD).Methods and ResultsUsing data from 6 US community‐based cohorts and the vital statistics, we estimated the prevalence and incidence of PAD, defined as an ankle‐brachial index < 0.90, at each year of age from birth to 80 years for white, black, and Hispanic men and women. Then, we used Markov Monte Carlo simulations in a simulated cohort of 100 000 individuals to estimate lifetime risk of PAD. On the basis of odds ratios of PAD for traditional atherosclerotic risk factors (eg, diabetes mellitus and smoking), we developed a calculator providing residual lifetime risk of PAD. In an 80‐year horizon, lifetime risks of PAD were 30.0% in black men and 27.6% in black women, but ≈19% in white men and women and ≈22% in Hispanic men and women. From another perspective, 9% of blacks were estimated to develop PAD by 60 years of age, while the same proportion was seen at ≈70 years for whites and Hispanics. The residual lifetime risk within the same race/ethnicity varied by 3.5‐ to 5‐fold according to risk factors (eg, residual lifetime risk in 45‐year‐old black men was 19.9% when current smoking, diabetes mellitus, and history of cardiovascular disease were absent versus 70.4% when all were present).ConclusionsIn the United States, ≈30% of blacks are estimated to develop PAD during their lifetime, whereas the corresponding estimate is ≈20% for whites and Hispanics. The residual lifetime risk within the same race/ethnicity substantially varies according to traditional risk factors.

BRCA mutation testing for first-degree relatives of women with high-grade serous ovarian cancer

BackgroundWomen with high-grade serous ovarian cancer (HGSC) have a 20% chance of carrying a BRCA1 or 2 mutation. Not all undergo genetic testing, and there is a large legacy group of untested patients. Their female first-degree relatives (FDR) may not qualify for testing unless they have specific ethnicity, or personal/family cancer history. We conducted a cost-effectiveness analysis to evaluate risk-reducing strategies for these FDR who are ineligible for testing. MethodsA Markov Monte Carlo simulation model estimated the costs and benefits of 3 strategies for female FDR of HGSC patients whose BRCA status is unknown: (1) no BRCA testing; (2) universal BRCA testing, followed by risk-reducing bilateral salpingo-oophorectomy (RRBSO) for mutation carriers; (3) universal RRBSO, without BRCA testing. Effectiveness was estimated in quality-adjusted life year (QALY) gains over a 50-year time horizon. Sensitivity analyses accounted for uncertainty around various parameters. ResultsUniversal BRCA testing for female FDR of women with HGSC yielded a higher average QALY gain at acceptable cost compared to no BRCA testing, with an incremental cost-effectiveness ratio of $7888 per QALY. Universal BRCA testing was more effective and less costly than universal RRBSO (19.20 QALYs vs. 18.52 QALYs, and $10,135 vs. $14,231, respectively). Results were stable over wide ranges of plausible costs and estimates. Compliance with hormone replacement therapy had to exceed 79.3% for universal RRBSO to be the most effective strategy. ConclusionBRCA mutation testing should be offered to all female first-degree relatives of women with high-grade serous ovarian cancer when BRCA mutation status is unknown.

A new non-parametric multivariate EWMA sign control chart for monitoring process dispersion

In this paper, a new non-parametric multivariate exponentially weighted moving average (NMEWMA) sign chart is proposed for monitoring the process dispersion. The run length characteristics of the NMEWMA sign chart are computed with the help of Markov chain and Monte Carlo simulations. Moreover, the NMEWMA sign chart is also used to detect changes in the process mean and dispersion simultaneously. An illustrative example is also used to explain the implementation of proposed control chart.

BRCA Mutation Testing for First-Degree Relatives of Women With High-Grade Serous Ovarian Cancer [4OP

INTRODUCTION: Women with high-grade serous ovarian cancer (HGSC) have a 20% chance of carrying a BRCA1 or BRCA2 mutation and are eligible for BRCA testing. Many are untested, therefore their female first-degree relatives (FDR) may not qualify for testing unless they have specific ethnicity or other personal/family cancer history. We conducted a cost-effectiveness analysis to evaluate BRCA mutation testing for these FDR who are otherwise ineligible for testing. METHODS: A Markov Monte Carlo simulation model estimated the costs and benefits of 3 strategies for female FDR of HGSC patients whose BRCA status is unknown: 1) no BRCA testing; 2) universal BRCA testing, followed by risk-reducing bilateral salpingo-oophorectomy (RRBSO) for mutation carriers (“BRCA testing”); 3) universal RRBSO, without BRCA testing (“RRBSO”). Effectiveness was estimated in quality-adjusted life year (QALY) gains. Sensitivity analyses accounted for uncertainty around various parameters. The time horizon was 50 years. RESULTS: BRCA testing for female FDR of HGSC patients yielded a higher average QALY gain at acceptable cost compared to no BRCA testing, with an ICER of $7,729 per QALY. BRCA testing was more effective and less costly than RRBSO (19.20 QALYs vs 18.48 QALYs, and $10,108.35 vs $13,959.20, respectively), therefore BRCA testing is the dominant strategy. Results were stable over a wide range of plausible costs and estimates. Compliance with hormone replacement therapy had to exceed 84% for RRBSO to be the preferred strategy. CONCLUSION: BRCA mutation testing should be offered to all female first-degree relatives of women with high-grade serous ovarian cancer when their BRCA mutation status is unknown.

Application of Markov chains and Monte Carlo simulations for developing pavement performance models for urban network management

Existing performance models developed for interurban pavements are not applicable to urban pavements due to differences in traffic demands and deterioration trends. The objective of the study was to develop performance models for the management of urban pavement networks. Markov chains and Monte Carlo simulation were applied to account for the probabilistic nature of pavements deterioration over time, using data collected in the field. One of the advantages of this methodology is that it can be used by local agencies with scarce technical resources and historical data. Eight performance models were developed and successfully validated for asphalt and concrete pavements in humid, dry and Mediterranean climates with different functional hierarchies. The resulting models evidence the impact of design, traffic demand, climate and construction standards on urban pavements performance. Predicted service life of asphalt and concrete pavements in primary networks are consistent with design standards. However, pavements in secondary and local networks present shorter and longer service life compared to design life, respectively. Climate is a relevant factor for asphalt pavements, where higher deterioration was observed compared to that expected. Opposite to this, no relevant differences between design and performance can be attributed to climate in concrete pavements.