Pavement Surface Temperature Research Articles

Temperature-related thermal properties of paving materials: experimental analysis and effect on thermal distribution in semi-rigid pavement

ABSTRACT Thermal properties of paving materials are essential for estimating temperature distribution in semi-rigid pavements. Literature reported that the thermal parameters varied widely and were most independent of temperature. This paper proposed an innovative test setup to determine the thermal conductivity and diffusivity of hot mix asphalt (HMA) and cement-treated base (CTB) materials. The experimental results indicated that the thermal properties of the paving materials linearly increased with surface temperature. Field temperature monitoring in semi-rigid pavement was also conducted at different depths of HMA and CTB. The heating rate of any location inside the pavement was found approximately twice the heat release. Such obtained thermal parameters and the pavement surface temperature from field monitoring served as inputs for numerical simulations based on ANSYS programme. Using temperature-related thermal properties in the modelling exhibited higher accuracy in predicting temperature distribution in the semi-rigid pavement than those independent of temperature from the literature.

Experimental and Numerical Investigations on an Organic Phase Change Material Incorporated Cool Concrete Pavement

Background: Traditionally, cool pavements have been designed as reflective, evaporative, etc. Though the reflective pavements reduce the pavement surface temperature significantly, they increase glare, the thermal burden on pedestrian traffic, and the temperature of nearby buildings. In the case of evaporative pavements, the absence of water, reduced thermal inertia and solar reflection result in a higher pavement temperature. As a result, there has been a pressing need to investigate new low-side-effect cool pavement options. Objective: The study aims to analyze the effect of phase change material (PCM) incorporation on the thermal performance of concrete pavements and to develop a total enthalpy-based numerical heat transfer model for such cool pavements. Materials and Methods: A paraffin-based organic PCM with a melting point of 42 to 45 °C was used in this work, and expanded clay aggregate (ECA) was used as an encapsulation medium. Concrete slabs without and with the incorporation of PCM-impregnated ECAs were cast, and thermocouples were implanted in the concrete to monitor the pavement temperature continuously. A total enthalpy-based numerical heat transfer model was developed to predict the thermal performance of such cool concrete pavements. Results: The PCM incorporation reduced 2.24 °C in the annual average pavement surface temperature with a maximum reduction of 4.12 °C. Conclusion: PCM incorporation effectively reduces pavement surface temperature during the daytime and makes the pavements cooler. Increasing the encapsulating medium's porosity and the concrete slab's thermal conductivity enhances the cooling potential. However, the thermal characteristics of the encapsulating material may be neglected as their impact is less on the cooling potential.

Experimental study on the temperature-regulating function of road thermoelectric generator system

Road thermoelectric generator systems (RTEGSs) can convert the heat absorbed by asphalt pavement into electricity. In addition, they can regulate the temperature of the asphalt pavement, which could be useful in reducing the urban heat-island effect in summer and melting snow in winter. Along these lines, in this work, the development of a novel type of RTEGS was proposed and its power-generation and temperature-regulation characteristics in summer and winter using outdoor tests were systematically explored. In summer, the pavement surface temperature was reduced by up to 10 °C using a water bath temperature of 25 °C and water-flow rate of 2.0 L/min. In winter, with the utilization of a water bath temperature of 20 °C and a water-flow rate of 2.5 L/min, snow could be melted within 44 min. Moreover, the peak output power of RTEGS was 162.33 mW and 34.63 mW in summer (ΔT = 37.3 °C) and winter (ΔT = 13.7 °C), respectively. Hence, this type of RTEGS can provide climatic and safety benefits, while also heat could be converted into useful energy.

A large-sized thermoelectric module composed of cement-based composite blocks for pavement energy harvesting and surface temperature reducing

Roads carry a lot of green and renewable energy. Thermoelectric cement as a green material can convert thermal energy to electrical power by using the temperature difference between the road surface and the underground. Cement-based materials can meet the needs for large-scale application in pavements because of their wide source, low cost, and good mechanical properties. Due to the preparation of large-size thermoelectric cement materials, the thermoelectric performance will be reduced, but the existing thermoelectric cement materials with excellent thermoelectric performance are small in size and they cannot be used for large-area applications. It is still a problem that thermoelectric properties of large-size thermoelectric cement-based composites prepared by the traditional pouring method are prone to dry shrinkage crack. Herein, to solve the problem of poor thermoelectric properties of large-sized thermoelectric cement materials. A thermoelectric cement block prepared by dry pressing is reported to be assembled into a large thermoelectric module of cement-based composite more suitable for pavement energy harvesting. The thermoelectric cement block is 50mm × 50mm × 20 mm, and the size of assembled into a large thermoelectric module is 300mm × 300mm × 35 mm. The addition of expanded graphite and metal oxides raised the average conductivity of the cement blocks to 0.63 S/cm and the average Seebeck coefficient to 20.79 μV/K, respectively. Tests have found that using a series connection of thermoelectric conversion modules can yield higher energy. Compared to other pavement thermoelectric generator (TEG) energy harvesting systems, the thermoelectric modules are less expensive and allow for large areas to be laid on the pavement. The module assembled using thermoelectric cement-based composite materials can generate 0.5 kW h of energy for 24 h on a road with a length of 1 km and a width of 10 m. Meanwhile, the thermoelectric module of cement-based composite has the ability to reduce the surface temperature and mitigate the urban heat island effect. The test results showed that it can reduce the temperature by 1°C–3°C. The results of this study provide a reference for future large scale practical applications of thermoelectric cement-based composites.

Long-term multi source analysis for asphalt binder PG selection using deep learning high air temperature modelling

As asphalt binder is a temperature-dependent viscoelastic material, the asphalt pavement performance is affected by the temperature during its service life. Traditionally, selecting the asphalt binder performance grade (PG) is based on long-term meteorological data analysis, but this study used MODIS and ASTER remotely-sensed (RS), reanalysis ERA5-Land, and in-situ meteorological datasets to select the PG. The multiple linear regression (MLR), genetic algorithm (GA), and deep-learning (DL) techniques were used to create models to estimate the maximum air and road pavement surface temperatures, considering the Superpave specifications. Model parameters involved the land surface temperature, vegetation index, elevation, ERA5-Land air temperature, soil moisture, wind speed, thermal radiation, evapotranspiration, latitude, and climate type. Comparative analyses revealed that the DL model yielded the most reliable PG results and the proposed RS-based method was more accurate than conventional approaches and could determine the PG accurately with 1 km resolution independent of distance to meteorological stations.

Performance evaluation of temperature-regulating asphalt mixture with thermochromic materials and low freezing point materials

Asphalt pavement absorbs a lot of heat in the summer because of its dark surface, which makes it susceptible to high-temperature. By altering the asphalt pavement’s ability to reflect solar radiation, thermochromic materials can regulate the temperature of the road. However, the thermochromic modified asphalt’s ability to regulate temperature is weak during the winter, when the road surface is covered in snow and ice or when there is no direct sunlight. Materials with low freezing points can lower the pavement’s freezing temperature and produce the snow melting and deicing effects. A type of temperature-regulating road asphalt material can be created by mixing low freezing point material and thermochromic material in a certain ratio with road asphalt. The blue thermochromic materials of 3%, 5% and 7% by weight were applied to the matrix asphalt to prepare thermochromic asphalt binders, respectively. The low freezing point materials replaced 25%, 50% and 75% of the mineral filler volume.The pavement performance, deicing performance and cooling performance of the tempering asphalt mixture were evaluated by orthogonal test design. According to the findings, the temperature-regulating asphalt combination performs substantially better at cooling and deicing than the unmodified asphalt mixture. When the replacement rate of low freezing point materials changes from 25 % to 75 %, the bond strength loss of modified asphalt mixture increases from 46 % to 75 %. The pavement surface temperature can be reduced by a maximum of 4–9 °C during the day by using a temperature-regulating asphalt composition. In this study, it is advised to use 5 % thermochromic dosage and 50 % replacement rate of low freezing point materials, taking into account the overall balance of performance enhancement.

Climate change and asphalt binder selection across ontario: A quantitative analysis towards the end of the century

The climate across the world and especially across Canada is becoming warmer. Recent research suggests that, on average, past and projected warming in Canada are about twice those of global values. As part of the Superpave Mix design system, Performance Grade (PG) of the asphalt binder is selected based on historical climatic conditions in relation to the expected in-service temperature spectrum of the pavement surface. Given the effects of climate change, it is important to ascertain how projected ambient conditions may impact pavement surface temperatures and therefore the suitable PG towards more durable and resilient pavement construction in the future. In this study, new regression models, to predict pavement surface temperatures, were developed using air temperatures and latitude of a site. These models were developed to determine the relationships between asphalt pavement surface temperature and ambient weather data from weather stations within the Ontario Ministry of Transportation's Road Weather Information System (RWIS). The developed models were used to predict the pavement temperatures for future climate data from different Global Circulation Models (GCMs) and Representative Concentration Pathways (RCPs), and the results were compared with that of latest Long-Term Pavement Performance (LTPP) models. According to the results of this study, based on the scale of the expected warming, changes in the asphalt binder grades across Ontario are expected in the future. The results of this study also suggest that different types of PGs including 52–34, 58–28, 64–28and 70–22 may need to be adopted by the end-century. This study provides practical recommendations that road authorities and planners may use to adapt paving materials to changing temperature conditions.

A review on the influencing factors of pavement surface temperature.

Pavement surface temperature is of great significance to pavement performance and pavement design, as well as the development of cool pavements. The variation of a pavement surface temperature is complicated as it is jointly affected by various factors, including air temperature, solar irradiance, wind speed, and pavement texture. This study overviews the internal and external factors that affect the pavement surface temperature in the field. It is found that air temperature is the main external climatic factor affecting the pavement surface temperature during the course of a day. Although solar radiation dictates the thermal partition at the pavement surface, it mainly influences daytime pavement temperature but vanishes at night. Pavements in calm weather can be 3-10°C hotter than those in windy weather, depending on the time of the day and the season. Other external factors such as passing vehicles also influence the pavement surface temperature at a degree 1-3°C. Also, the shading effect of urban trees can affect pavement surface temperature and urban microclimate. Internal factors that vary pavement surface temperature include albedo, thermal conductivity, heat capacity, and emissivity. Among them, albedo controls the pavement surface temperature while other factors play a secondary role. The results of this review provide a scope of research for developing sustainable and advanced solutions for future municipal pavement construction and urban heat island (UHI) mitigation.

Thermal performance assessment of PCM incorporated cool concrete pavements using numerical analysis

ABSTRACT Incorporating Phase Change Materials (PCM) through encapsulation is an effective method to regulate the pavement surface temperature. The present study investigates the effect of the thermal properties of concrete and encapsulating material on the cooling potential of PCM incorporated concrete pavements. An enthalpy-porosity based pore-scale heat transfer model was developed that simulates the melting of PCM in concrete pavements subjected to solar radiation. Two types of PCMs, i.e. Organic Mixture 35 (OM 35) and Organic Mixture 42 (OM 42) were incorporated into concrete slabs with the help of expanded clay aggregates (ECA) as PCM carrier. In the top 0.1 m layer of concrete, a total of 98 numbers of ECA-PCM balls of 1 cm diameter were distributed uniformly. The cooling potential of pavements with OM 35 and OM 42 improved by 82.67 % and 81.70 %, respectively with increase in the porosity of ECA from 0.1 to 0.9. Further, as the thermal conductivity of concrete increased from 1.2 to 2.4 W/mK, the cooling potential of OM 35 and OM 42 incorporated pavements increased by 36.48 % and 38.29 %, respectively. However, the thermal conductivity of the encapsulating material showed marginal influence on the cooling potential and therefore may be neglected.

Emerging Trends in Overcoming the Weather Barrier to Sustainable Mobility in Gulf and Tropical Cities

Several studies predicted that in case of the continuous rising concentrations of anthropogenic greenhouse gases (GHGs), the gulf region might experience intolerable temperatures to humans. Such terrible weather may have a severe consequence on different vital activities in different Gulf countries, such as the Muslim rites of Hajj in Mecca city. However, supporting the global mitigation efforts would significantly minimize the seriousness of the expected impacts. With that in mind, hot weather was reported as the significant barrier to sustainable mobility in those countries. Their current mobility is heavily dependent on privately-owned fossil-fueled vehicles rather than other sustainable transport choices. This paper reviewed the most recent innovative and solutions to overcome the problems associated with the hot climate. The review focused on the new techniques concerned with reducing the pavement surface temperature and the corresponding decreases in the ambient air temperature, aiming to promote sustainable mobility modes in hot climate cities. The paper also identified future research needs to fulfill each proposed solution's scientific and practical gap and overcome the weather barrier.

Evaporative cooling performance estimation of pervious pavement based on evaporation resistance

Pervious pavement is an efficient cooling strategy to alleviate the urban heat island (UHI) effect because it can retain percolating rainwater for subsequent evaporative cooling. The cooling effect simulation of pervious pavement before implementation is necessary to understand its contribution to the thermal environment. However, it still confronts challenges, due to limited estimation methods of evaporation rate. In this study, we proposed an approach to simulate the evaporation rate of pervious pavements by using the evaporation resistance method (ERM). First, the exponential relationship between evaporative resistance and volumetric water content of two widely used pervious paving materials was regressed by wind tunnel experiments. Second, this ERM was validated by outdoor field experiments of pervious pavements after being integrated into a heat transfer model. Lastly, the best water-sprinkling time was determined based on the model. The results revealed that the evaporation resistance sharply increased when the volume water content of ceramic brick (CB) and pervious concrete (PC) was below 6.5% and 11.8%, respectively. The regressed relationships revealed a high accuracy with all R2 above 0.85, and the root mean square error of the surface temperature of the pervious pavement between measurement and simulation demonstrated a high accuracy with CB of 1.31 °C and PC of 1.64 °C. Sprinkling during 07:00 to 11:00 under typical hot-humid climate summer days could gain a win-win effect with sizeable evaporation cooling effects and water-saving.

Parametric analysis and new performance correlation of the surface conventional rigid pavement temperature

A statistical model for predicting pavement surface temperature has been developed for the first time in this study. The main objective of this work is to analyse the impact of the input factors (solar radiation, ambient temperature, wind speed, and albedo) most affecting the conventional rigid pavement surface temperature. A multi-physics numerical model combining radiative, conductive, and convective heat transfers with a response surface methodology (RSM) model was created as an innovative and integrated approach. The numerical model of two-dimensional heat transport based on volume finite techniques is performed using FORTRAN software. Moreover, the analysis of variance (ANOVA) is used to establish the polynomial model or new correlation (quadratic regression) and analyse each input factor's statistical importance. Heat transfer model findings were in good agreement with the experimental data. For pavement surface temperature, the results show that the adjusted Radj2and predicted determination coefficients R2 achieved are 0.9912 and 0.9604, respectively. A good agreement is obtained between the statistical model's prediction values and the heat transfer model's numerical data. Based on the four analysed input factors, a simple polynomial statistical model is developed to forecast and reduce the conventional rigid surface pavement temperature. A wind speed of 1.215 m/s, an air temperature of 293.634 K, solar radiation of 222.305 W/m2 and albedo of 0.782 were found to be the optimal operating conditions for obtaining the lowest possible temperature on the pavement's surface.

Analysis of Asphalt Geogrid Reinforced Pavement Rutting by Finite Element Method

Pavement is a complex structure consisting of several layers of different materials that influence its stressful behavior. Permanent deformation can occur in pavement layers of insufficient hardness at high temperatures. Significant rutting normally only occurs during hot weather, especially when the flexible pavement surface temperature is 60 ° C or higher. 2D model analysis using ABAQUS software can predict the rutting behavior. The modeling procedure assumes that all materials performance is a linear elastic. Surface, base, subbase and subgrade layers consist of models. Models in every pavement model, subgrade layers are supposed to have endless depth. This paper presents an element-finite model (FE) for the behavior analysis of the dynamic loading unreinforced and geogrid reinforced paving. Increased loading of the model and critical pavement responses for unreinforced or geogrid-reinforced flexible paving, such as vertical stress and vertical surface deflection, were determined. The results indicated a difference in the displacement results when adding the geogrid layer. The results also showed a significant improvement in the behavior of the pavement system. A parametric study was carried out on a type of Truck (3-S1) and the applied pressure was 36 tons with different thicknesses of the asphalt layer once 150 mm and again 25 cm at different temperatures of 20, 40 and 60 ° C. It was found that the higher the temperatures, the higher the displacement as well.

Thermal Performance Enhancement of Asphalt Solar Collector by Using Extended Surfaces

The Urban Heat Island (UHI) effect occurs when the temperature of the asphalt pavement surface exceeds 70°C during the summer. Rutting is a significant temperature-related problem that occurs when the temperature rises too high on asphalt surfaces. Additionally, this phenomenon increases the amount of energy required to cool buildings adjacent to pavements and degrades air quality. The Asphalt Solar Collector (ASC) was examined in this work by inserting tubes into the pavement's construction and circulating working fluid within it to capture thermal energy generated by asphalt pavement. A low-carbon steel-alloy cheap waste materials have been investigated as an extended surface with HMA. The effect of various extended surfaces attached to the embedded tubes on the thermal performance of ASC has been studied to determine whether it satisfies specified aforementioned demands. The performance of several ASC models with bare, continuous finned, and mesh grid serpentine embedded tubes was investigated with same Conductive Hot Mixture Asphalt (C-HMA) by using a numerical 3-D model developed by COMSOL Multiphysics Software. when the Reynolds Number is increased, it is found that ASC efficiency increases from 66.74% for bare serpentine tubes to approximately 75.488% and 69.4% for continuous finned and mesh grid serpentine embedded tubes, respectively. A maximum value of about 398.53 W can be gained (from a total of 850 W/m2 incident solar radiation) by utilizing an extended surface. Additionally, the surface temperature of HMA decreases significantly from 52.67 to 46.07℃. For all models under investigation, it is clear that the optimum average Reynolds Number is about 600. It is found that the continuous fins model can capture more solar radiation than the mesh grid model by about 8.77%.

Effects of Albedo and Thermal Inertia on Pavement Surface Temperatures with Convective Boundary Conditions—A CFD Study

The urban heat island (UHI) effect increases the ambient temperatures in cities and alters the energy budget of building materials. Urban surfaces such as pavements and roofs absorb solar heat and re-emit it back into the atmosphere, contributing towards the UHI effect. Over the past few decades, researchers have identified albedo and thermal inertia as two of the most significant thermal properties that influence pavement surface temperatures under a given solar load. However, published data for comparisons of albedo and thermal inertia are currently inadequate. This work focuses on asphalt and concrete as two important materials used in the construction of pavements. Computational fluid dynamics (CFD) analyses are performed on asphalt and concrete pavements with the same dimensions and under the same ambient conditions. Under given conditions, the pavement top surface temperature is evaluated with varying albedo and thermal inertia values. The results show that the asphalt surface temperatures are consistently higher than the concrete surface temperatures. Surface temperatures under solar load reduce with increasing albedo and thermal inertia values for both asphalt and concrete pavements. The CFD results show that increasing the albedo is more effective in reducing pavement surface temperatures than increasing the thermal inertia.

Probability prediction of pavement surface low temperature in winter based on bayesian structural time series and neural network

Reliable pavement surface temperature prediction models are important for decision making in winter road maintenance. Due to the complexity of thermal environment in winter, there is inevitably uncertainty in pavement temperature. Therefore, the prediction results of pavement surface temperature should not be a single value, but a probability distribution. This paper developed a prediction model for evaluating the probability distribution of pavement surface temperature in winter. The model consisted of two modules, namely, a Bayesian Structural Time Series module (BSTS) and a Bayesian Neural Network module (BNN). The BSTS module provided a relatively rough prediction of pavement surface temperature, while the BNN module provided refined prediction based on the results from the BSTS module. The model was validated with data collected from the field test. The impact of model structure, model parameters, and probability threshold on the predictions was analyzed. Results show that most measured pavement temperatures fell into the 95% confidence interval of the predicted values, indicating the prediction was reliable. For the BSTS module, the regression component plays the most important role in the prediction. The seasonal component also has obvious influence on the prediction results. While the influence of local linear trend component on the prediction result is not obvious. For the BNN module, although the slippery index and pavement surface condition do not directly contribute to the heat transfer process, these two factors have correlation with the pavement surface temperature. The value of probability threshold should be properly determined to balance the reliability and computational cost of the prediction model. The model presented in this paper is useful for transportation agencies in winter road maintenance

Thermal performance of a layered asphalt pavement system for snow melting

Recently, electrically heated cable has been used to melt snow and ice from roads around the world. To increase the heat transfer efficiency of asphalt pavement and improve its snow and ice removal ability, but decrease summer solar heating, which could cause rutting, a new asphalt pavement system with various thermal conductivity for layered structures was proposed. This pavement system includes a bottom high-conductivity layer and a top low-conductivity layer. The heat transfer of this pavement system was simulated with the finite element method and tested in a real environment. The results show that a structural combination of a top low-conductivity layer and a bottom high-conductivity layer could be helpful in increasing the surface temperature of pavement during the bottom heating at winter, but decreasing the inner temperature of pavement subjected to summer solar radiation, therefore reducing its rutting potential at summer.

CORRELATION ANALYSIS OF DYNAMIC MODULUS AND RESILIENT MODULUS OF ASPHALT MIXTURE USING THE PG76 MODIFIED ASPHALT

Road infrastructure is still a mainstay for mobility. The primary issue that happened frequently is early damage due to overload and high pavement surface temperatures. Existing materials, such as penetration grade asphalt and modified asphalt, could not overcome these problems. However, the development of materials using the modified asphalt equivalent to PG76 (EPG76) is expected to provide better performance obtained through mechanistic testing,i.e., Marshall, Dynamic Modulus, and Resilient Modulus. The rheology test for EPG76 modified asphalt was carried out using DSR and showed that the value of EPG76 modified asphalt has increased to above Pen 60/70 asphalt. The polymer content of EPG76 modified asphalt can prevent asphalt properties became more rigid and more resistant to high temperatures. This mixture has a higher Resilient Modulus than Pen 60/70 asphalt at high temperatures and gives better crack resistance. Overall, the EPG76 modified asphalt mixture shows better performance under overload and high-temperature conditions.

Properties and Characterization of Temperature-Responsive Asphalt by Using Thermochromic Powder

Abstract Asphalt pavement absorbs solar energy efficiently during the summer and loses heat rapidly during the winter because of its black color, which leads to rutting and cracking distresses. The thermochromic powders were used in this study to reverse the process with its ability to dynamically change color (albedo in essence) subject to temperature variations. Three types of thermochromic powders (the red, blue, and black ones) were applied to base asphalt at the contents of 3, 6, and 10 % to prepare the thermochromic asphalt. The gel permeation chromatography, Fourier transform infrared spectroscopy, physical, rheological, and thermodynamic properties of base and thermochromic asphalt binders were investigated via laboratory tests. The temperature-regulating function of thermochromic asphalt binders was examined in the field. Throughout the study, it is revealed that with the addition of thermochromic powders, the penetration and creep rate of asphalt binder decreased, whereas the softening point, viscosity, rutting parameter, and stiffness increased. When the powder content is 3 %, the intermolecular force within asphalt is enhanced; therefore, the consistency of asphalt is improved while the flow ability is weakened. While the content continues to raise, the thermochromic powder is prone to produce particle agglomeration. Thermochromic asphalt has a much higher specific heat capacity and latent heat than base asphalt, reflecting the principle to regulate temperature. Temperature reduction of 4°C∼5°C was observed for thermochromic asphalt binders compared with base asphalt in the field test. Considering the performance balance and economics, the content of thermochromic powders was recommended to be 5∼6 %. After adding thermochromic powders, the surface temperature of asphalt pavement can be effectively reduced during the summer and the road temperature during the winter will remain adequate to prevent cracking or icing.

Hourly road pavement surface temperature forecasting using deep learning models

Road authorities in cold climates regularly apply salt on roads, during winter, to ensure public safety. Pavement surface temperature is a significant parameter affecting snow and ice melting at the onset of a storm. Road temperature below the freezing point of the applied brine causes ice to form on the road surface. Excessive application of salt on the road can have adverse environmental impacts, especially to soil and water quality. Therefore, forecasting pavement temperature can optimize road salt application, while reducing costs, improving public safety, and reducing environmental impacts. This research aims to develop a reliable and accurate pavement surface temperature prediction tool using machine learning techniques. This study employs advanced deep neural network (DNN) learning techniques to predict pavement surface temperature for road salt management purposes. To validate the proposed methodology, this work used hourly solar radiation and air temperature data from Environment Canada, and pavement surface temperature data collected from the Road Weather Information System (RWIS), for sites around the city of Toronto, Ontario. The performance of the proposed DNN model, that integrates a Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM), on pavement surface temperature forecasting, was evaluated against four other comparative machine learning methods, LSTM, Convolutional-LSTM (ConvLSTM), Sequence-to-Sequence (Seq2Seq), and Wavelet neural network (Wavenet) models. A dataset of 10,895 samples was collected as an hourly pavement surface temperature record in three timeframes from November 2009 to March 2014 on Highway 401 in southern Ontario, Canada. Experiments included predictions for 1-, 2-, 4- and 6-hours ahead, using as input features air temperature, solar radiation, and present pavement temperature. The proposed pavement temperature forecasting approach resulting from this investigation suggests our new approach is more accurate than previous models. These results reveal that the CNN-LSTM outperforms the four other models and creates predictions closer to the true pavement surface temperature.