International Roughness Index Research Articles

Enhancing pavement performance prediction with traffic speed deflection data

ABSTRACT Pavement performance modelling is an essential part of pavement management. Past research has shown that a structural indicator can be included in pavement performance models to capture differences in performance between weak and strong pavements. Weak pavements are expected to deteriorate more rapidly than strong pavements, as consistently shown in past research. The work presented in this paper uses the Traffic Speed Deflectometer (TSD) to calculate multiple structural indexes and compares their ability to differentiate between poor and well performing pavements. Historical condition data from the US National Parks pavement network is used in this assessment. Two approaches for calculating the subgrade resilient modulus and two approaches for calculating the effective structural number from the TSD results are compared. The temperature corrected centre deflection, surface curvature index and ratio of effective to required structural number are all calculated from the TSD data. Performance models are developed for two condition indexes and the International Roughness Index (IRI). In all cases, the addition of the structural indexes improved the quality of predictions, and the IRI prediction showed the most improvement from including the indexes. Ultimately, it was shown that the TSD testing results can be used to inform the pavement management process.

Reliability-Based Pavement Roughness Progression Modeling Using Bayesian Approach

This paper aims to develop reliability-based roughness progression models for bituminous pavements across different climatic zones in India. The methodology involves dividing the Indian geographical region into six climatic zones with specific climatic characteristics that influence pavement deterioration. This delineation is achieved using K-means clustering and considers parameters such as temperature, rainfall, and humidity. The roughness progression models and reliability interpretations are developed through a Bayesian regression framework. Initially, International Roughness Index (IRI) progression models are created for three climatic zones: hot and dry, warm and humid, and moderate. These models are based on time series data from 2015 to 2016, gathered from 130 pavement sections in each zone. To validate the models, additional time series data from 2016 to 2017 are utilized. The forecast results indicate that the hot and dry zone exhibits the highest IRI progression rate for specified causal variable values, followed by the warm and humid zone and the moderate zone. The adoption of the Bayesian regression framework provides probabilistic parameter distributions for model coefficients, enabling the assessment of model-level reliability. The study demonstrates that, by adjusting the quartile of causal variables, the overall reliability of the model can be improved, thereby reducing the deviation from actual IRI values. Moreover, it is possible to evaluate variable-level reliability by examining the influence of individual variables on roughness progression.

Modeling of Traffic Noise along Urban Arterials in Irbid City of Jordan

Traffic-related noise pollution is a recurring problem in large and medium-sized cities. The objective of this study is to quantify traffic-noise levels along selected urban arterials and model the generated traffic noise based on traffic and pavement characteristics. Urban arterial and collector streets in Irbid city, Jordan, were taken as a case study. The city is considered an example of a medium-sized city. 65 urban arterial and collector sections were selected to achieve the stated objective. For each section, ten noise measurements were taken using a time interval of 2.5 minutes for each observation. The statistical pass-by method was used to measure 650 externalnoise observations. In addition, data on traffic, pavement and section geometric characteristics was obtained through field measurements. The collected traffic characteristics, including traffic flow, percentage of trucks and speed in each direction of travel, were obtained. In addition to the measurement of pavement macrotexture depth, an international roughness index was measured using a smartphone application. Investigation of the collected noise data indicated that urban streets experienced high noise levels, reaching maximum and average values of 82 and 77.2 dB(A), respectively. The results of the analyses showed that an increase in each of the included traffic characteristics resulted in significantly higher noise levels. For example, an increase in speed from 35 to 55 km/h would increase noise by 2.7 dB(A). In addition, the interaction term of roughness index and pavement macrotexture depth was found to increase the generated noise. Finally, the results of the analysis indicated that both multivariate linear -and exponential-regression models are suitable to model the generated traffic noise. Each model explained approximately 54% of the noise variability. Probably, traffic composition and vehicle-power type heterogeneity might reduce the level of explained variability. Keywords: Traffic noise, Urban arterials, Pass-by method, Noise modeling, Jordan.

Enhancing Pavement Sustainability: Prediction of the Pavement Condition Index in Arid Urban Climates Using the International Roughness Index

Municipalities and transportation departments worldwide are striving to keep road pavement conditions acceptable, thus enhancing pavement sustainability. Although the pavement condition index (PCI) is widely used to assess distress conditions, traditional visual surveys used for PCI estimation can be laborious, expensive, and time-consuming. The international roughness index (IRI) can be measured more economically and conveniently than PCI; however, it does not directly indicate the surface condition of the pavement. In this study, a PCI–IRI correlation is proposed for urban roads located in the New Beni-Suef region, Egypt. For this purpose, a total of 44 km of urban roads was divided into homogenous sections. A visual distress survey was conducted to measure PCI considering typical distress patterns. The IRI values for the same sections were measured using an ultrasonic distance sensor mounted on an automobile. An exponential model was proposed to capture the relationship between IRI and PCI. With a coefficient of determination of 0.82, the exponential model seems to outperform reported IRI-PCI correlations. Model validation, along with a comparison to the existing models, supports its applicability to a wide range of roads. The proposed model provides a cost-effective means for accurately predicting PCI based on IRI, which is particularly useful for pavement maintenance management programs on limited budgets.

Design, Placement, and Laboratory and Field Evaluation of Rejuvenated Cold Recycled Asphalt Mixtures

Cold recycled (CR) pavements, the combination of reclaimed asphalt pavement and a recycling agent (foamed or emulsified asphalt) at ambient temperatures, can provide a sustainable method to rehabilitate, reconstruct, and construct roadways. However, CR pavement is not a one-for-one replacement of typical asphalt base mixtures. To utilize CR pavements as an alternative to a typical asphalt base mixture or surface course, additional materials such as rejuvenating agents may be needed. This research focuses on the formulation, plant production, and placement on the National Center for Asphalt Technology pavement test track off-ramp, and laboratory evaluation of three rejuvenated CR mixtures, one foamed asphalt CR mixture, and an engineered emulsion CR mixture. These mixtures were subjected to strength and moisture susceptibility testing, along with the indirect tensile asphalt cracking test (IDEAL-CT) and high-temperature indirect tensile test (HT-IDT). The influence of laboratory production versus plant production and the impact of rejuvenators on mixture performance and density were investigated. International roughness index and rut depth data for the off-ramp sections indicate adequate performance, except for the anionic emulsion with bio-based rejuvenator section. Laboratory test results indicate that the addition of rejuvenators into two of the three CR mixtures increased density and decreased moisture susceptibility and increased the CTIndex by at least 35% compared to non-rejuvenated CR mixtures. HT-IDT strengths revealed that two of the three rejuvenated mixtures performed slightly below the non-rejuvenated mixtures.

Ride Comfort Prediction on Urban Road Using Discrete Pavement Roughness Index

The prediction of ride comfort holds significant potential for enhancing the driving experience of both human drivers and autonomous vehicles, as it is closely correlated with pavement roughness. However, in urban road scenarios, the presence of shorter road segments and local irregularities introduces added complexity to ride comfort prediction. To better capture and characterize the irregularities and short road sections’ unevenness, we adopt the discrete roughness index (DRI) instead of the commonly used international roughness index (IRI) for assessing road profile unevenness, which is more suitable for urban roads. Ride comfort prediction is developed through numerical simulations using an eight-degree-of-freedom full-car model. The maximum transient vibration value (MTVV) is adopted to assess ride comfort. Through comparing the correlations between the MTVV and pavement roughness indices, it is indicated that the fitting degree of MTVV-DRI outperforms that of MTVV-IRI on short sections. Then, a set of speed-related DRI thresholds to estimate ride comfort distribution on a given road section is proposed, with considerations of vehicle speed, time period, and wheel paths. A hyperbolic-tangent-based speed control strategy is also proposed to avoid abrupt speed and acceleration changes during deceleration. This prediction method can assist drivers or autonomous vehicles in generating driving control strategies and maintaining a high level of ride comfort.

Revolutionising Egyptian pavement design: a comprehensive E* database and advanced modeling approach for contextually informed performance predictions

ABSTRACT This research assesses the dynamic modulus (E*) as a pivotal rheological attribute for characterizing viscoelastic behaviour in various indigenous asphalt mixtures across diverse scenarios. An exhaustive investigation involving 32 laboratory-designed asphalt mixes and two field mixes explores the influence of traffic loading patterns, air voids, binder sources, and climatic conditions on E*. Utilizing Superpave gyratory compaction, 68 E*-specimens are compacted and subjected to E*-testing, leading to master curve development. Evaluation of NCHRP 1-37A and NCHRP 1-40D Witczak models demonstrates their fair to excellent accuracy in predicting E* for Egyptian mixes. The NCHRP 1-40D model stands out with an R² of 0.94, showcasing superior accuracy and minimal bias. This study integrates AASHTOW are Pavement ME Design (PMED) to analyse pavement sections, revealing the impact of design factors, characterized by measured E*, on predicted pavement performance, encompassing asphalt concrete rutting, alligator cracking, longitudinal cracking, and terminal international roughness index. The investigation highlights the marginal impact of substituting predicted E* for measured E* on AASHTOWare pavement performance indicators for Egyptian mixes under default binder characteristic values. This research contributes valuable insights into the interplay of E* and pavement performance, facilitating wellinformed decisions in pavement design and analysis across diverse conditions in the Egyptian context.

Analysis of Pavement Conditions and Handling of Road Damage at the Surface Layer

The Cukul - Buni Kasih road section at the Garut Regency Boundary in Pangalengan District, West Bandung Regency, West Java Province, is pivotal in facilitating economic growth and regional development. Spanning 3.493 km with a width of 3.12 m, this study aims to assess its condition and identify road damage types. Key indices, including SDI (Surface Distress Index), RCI (Road Condition Index), IRI (International Roughness Index), and PCI (Pavement Condition Index), were employed to evaluate road health. Visual analysis revealed an average SDI value of 112, indicating minor damage. The RCI method yielded an average value of 5, denoting slightly damaged conditions. IRI assessment resulted in an average value of 9, reflecting slight damage. The PCI method yielded an average of 46, signifying poor (bad) conditions. Predominant damage types include potholes, alongside alligator cracking, patching, edge cracking, weathering/raveling, and rutting. In summary, the Cukul – Buni Kasih road section (Garut Regency Boundary) is predominantly damaged, necessitating urgent attention. The assessment utilizing SDI, RCI, IRI, and PCI methods provides valuable insights for planning and executing necessary road maintenance measures.

Novel Instance-Based Transfer Learning for Asphalt Pavement Performance Prediction

The deep learning method has been widely used in the engineering field. The availability of the training dataset is one of the most important limitations of the deep learning method. Accurate prediction of pavement performance plays a vital role in road preventive maintenance (PM) and decision-making. Pavement performance prediction based on deep learning has been widely used around the world for its accuracy, robustness, and automation. However, most of the countries in the world have not built their pavement performance historical database, which prevents preventive maintenance using the deep learning method. This study presents an innovative particle swarm optimization (PSO) algorithm-enhanced two-stage TrAdaBoost.R2 transfer learning algorithm, which could significantly increase the pavement performance prediction database. The Long-Term Pavement Performance (LTPP) database is used as the source domain data, and one of the highways in China is chosen as the target domain to predict pavement performance. The results show that the proposed PSO-Two-stage TrAdaBoost.R2 model has the highest accuracy compared with AdaBoost.R2 model and traditional regression decision tree model. The validation case study shows significant consistency between the predicted International Roughness Index (IRI) and the whole-year measurement data with an R2 of 0.7. This study demonstrates the great potential of the innovative instance-based transfer learning method in pavement performance prediction of a region’s lack of data. This study also contributes to other engineering fields that could greatly increase the universality of deep learning.

Machine Learning Modeling of Wheel and Non-Wheel Path Longitudinal Cracking

Roads degrade over time due to various factors such as traffic loads, environmental conditions, and the quality of materials used. Significant investments have been poured into road construction globally, necessitating regular evaluations and the implementation of maintenance and rehabilitation (M&R) strategies to keep the infrastructure performing at a satisfactory level. The development and refinement of performance prediction models are essential for forecasting the condition of pavements, especially to address longitudinal cracking distress, a major issue in thick asphalt pavements. This research leverages multiple machine learning methods to create models predicting non-wheel path (NWP) and wheel path (WP) longitudinal cracking using data from the Long-Term Pavement Performance (LTPP) program. This study highlights the marked differences in distress conditions between WP and NWP, underscoring the importance of precise models that cater to their unique features. Aging trends for both types of cracking were identified through correlation analysis, showing an increase in WP cracking with age and a higher initial International Roughness Index (IRI) linked to NWP cracking. Factors such as material characteristics, kinematic viscosity, pavement thickness, air voids, particle size distribution, temperature, KESAL, and asphalt properties were found to significantly influence both WP and NWP cracking. The Exponential Gaussian Process Regression (GPR) emerged as the best model for NWP cracking, showcasing exceptional accuracy with the lowest RMSE of 89.11, MSE of 7940.72, and an impressive R-Squared of 0.63. For WP cracking, the Squared Exponential GPR model was most effective, with the lowest RMSE of 12.00, MSE of 143.93, and a high R-Squared of 0.62. The GPR models, with specific kernels for each cracking type, proved their adaptability and efficiency in various pavement scenarios. A comparative analysis highlighted the superiority of our new machine learning model, which achieved an R2 of 0.767, outperforming previous empirical models, demonstrating the strength and precision of our machine learning approach in predicting longitudinal cracking.

COMPARATIVE ANALYSIS OF EVALUATION CRITERIA FOR UNPAVED RURAL ROADS: A CASE STUDY

This study focused on the evaluation of the condition of the unpaved rural road: Sauces Norte-Imbana in the province of Loja, using two main indicators: Unsurfaced Road Condition Index (URCI) and the International Roughness Index (IRI). The IRI was measured using three different devices: MERLIN, Roughometer III, and a mobile application. The collected data were statistically analyzed to determine their distribution and the correlation between the variables. The results revealed a weak correlation between these variables, indicating that each methodology may be assessing different aspects of the road condition or due to the particular conditions of the road. The lack of a strong relationship may be related to the specific characteristics of the studied road, such as its sinuosity and the influence of environmental conditions on roughness measurements. This study underscores the importance of considering a variety of factors when evaluating the condition of unpaved roads and the need to develop and apply suitable evaluation techniques for the specific conditions of each road.

Estimating impact of pavement surface condition and geometrics design on two-wheeler run-off road crashes on horizontal curves

Introduction-Motorized Two-Wheeler (MTW) is a convenient and affordable mode of transportation despite being highly vulnerable to rollover crashes. The possible fact may be that MTW is a single-track vehicle and thus lack longitudinal stability, particularly on horizontal curves (HC). This stability depends on external factors such as curve geometrics and contact surface properties. Therefore, this research aims to identify the impact of pavement surface conditions and roadway geometrics on MTW run-off road crashes on HC. Method-To consider the temporal effect of pavement surface conditions, this study analyzed different combinations of light and surface moisture conditions for pre- and post-pavement surfacing conditions. Thus, Generalized Estimating Equation (GEE) linked with the log function was used to account for repeated observations. Sample data were collected bi-annually from 76 HC for two years of pre- and post-resurfacing. Pavement surface condition parameter includes skid resistance, rut depth, and international roughness index (IRI). However, to restrain the impact of pavement surface conditions, a separate analysis was carried out to investigate the impact of geometrical design attributes. Results-The analysis concluded that surface moisture condition, light condition, rut depth, and their interactions with AADT were found significant for pre-resurfacing. For post-resurfacing, surface moisture condition, and light condition, IRI and its interaction with AADT were found significant. Similarly, roadway geometrics such as curve radius and their interaction with the presence of sight distance, public activity centre and cross-slope were found significant. Conclusion-Overall, the study insights and confirms how and to what extent pavement surface properties and geometric attributes impact the subject crashes. The results will enhance the understanding of roadway architecture and may help highway designers to implement, either at the intervention or treatment level, to improve safety and comfort.

Quantifying the Effects of Material Input Levels on Jointed Plain Concrete Pavement (JPCP) Performance and Slab Thickness

The mechanistic-empirical pavement design guide (MEPDG) is a commonly accepted design principles guide that aids in jointed plain concrete pavement (JPCP) design and performance analysis. The MEPDG uses three different design parameter input levels, referred to as level one, level two, and level three, providing increasing confidence in the analysis at the lower numbered levels, which use more locally relevant (level two) or project-specific (level one) data. The state-of-the-art pavement ME software (version 2.6.2) uses MEPDG design principles to predict pavement performance. The three performance indicators for JPCP systems (international roughness index (IRI), joint faulting, and transverse cracking) experience significant changes when simulating under a different input level. The IRI and faulting indicator changed by 78 percent when using inputs varying from level one to level three, with the cracking indicator change being more severe at 87 percent. To accommodate the change in performance indicator values between input level one and input level three, increasing the concrete slab thickness is necessary to achieve comparable pavement performance. An increase in the Portland cement concrete (PCC) layer from one inch to two inches is required when input level three simulations are performed, demonstrating the economic and sustainability benefits of using project-specific level one inputs. Understanding the impact of simulation input levels will help to meet design and sustainability goals and improve the lifecycle performance of JPCP systems.

Physics-informed neural networks to advance pavement engineering and management

Physics-informed neural networks (PINN) are machine learning (ML) algorithms that can bridge the gap between our understanding of physical phenomena and the corresponding empirical observations. This paper discusses applications of physics-integrated ML to advance pavement engineering. To demonstrate an example, a PINN model was pretrained to approximate the simulation of vehicles’ suspension responses to longitudinal road profiles. The parameters of the outer layers were finetuned to adapt model output to the standard International Roughness Index (IRI), while keeping the pretrained inner layers to preserve the embedded physical knowledge of the suspension behaviour. The PINN model showed low bias and standard error in predicting IRI values on training, test, and an independent dataset from an autonomous vehicle study by the Ford Motor Company. This approach to reconcile and supplement infrequent survey data with spatiotemporally continuous data (from connected vehicles) can enhance data-driven practices for pavement design, maintenance and asset management.

Applications of using connected vehicle data for pavement quality analysis

Current quantitative methods to evaluate pavement conditions in the United States are most commonly focused on construction acceptance using the International Roughness Index (IRI). However, from an asset management perspective, qualitative visual inspection techniques are the most prevalent. Modern vehicles with factory-equipped sensors drive these roadways daily and can passively assess the condition of infrastructure at an accuracy level somewhere between qualitative assessment and rigorous construction acceptance techniques. This paper compares crowdsourced ride quality data with an industry standard inertial profiler on a 7-mile bi-directional construction zone. A linear correlation was performed on 14 miles of I-65 that resulted in an R2 of 0.7 and a p-value of <0.001, but with a modest fixed offset bias. The scalability of these techniques is illustrated with graphics characterizing IRI values obtained from 730,000 crowdsourced data segments over 5,800 miles of I-80 in April of 2022 and October 2022. This paper looks at the use of standard original equipment manufacturer (OEM) on-board sensor data from production vehicles to assess approximately 100 miles of roadway pavements before, during, and after construction. The completed construction projects observed IRI improvements of 10 in/mi to 100 in/mi. These results suggest that it is now possible to monitor pavement ride quality at a system level, even with a small proportion of connected vehicles (CV) providing roughness data.

Modeling of road damage prediction due to overload using HDM-4 method

The road use load should essentially conform to the plan load as stated in the design guidelines. But in reality, the load received by the road is more than standard, so it has the potential to accelerate road damage. This study aims to predict the increase in the value of the International Roughness Index (IRI) and model the increase in value (IRI) due to overload. The approach method used in the analysis is the prediction of an increase in the IRI value due to excess load. The data collection method is carried out on road sections which include IRI values, vehicle load testing, CBR values and environmental conditions. The location of data collection is carried out on BTS Provincial Road. Surabaya City – Legundi. The analysis method for the prediction of the IRI value is the HDM-4 method, while to model the prediction of the increase in the IRI value is used exponential method with various parameters related to the results. The prediction results show that overload affects the increase in the IRI value. From the initial IRI value of 2.37, if the road is traversed by vehicles with standard loads for 20 years, the IRI value increases to 10.42. While the road traveled by overloaded vehicles within 20 years from an IRI value of 2.37 will increase to 16.88. With overload, the IRI value increases very sharply with the exponential model y=2.3583e0.0982x. While the standard load by generating an exponential model y=2.3825e0.0739x. From the results of the exponential model, load overload resulted in a significant increase in the IRI value with a growth rate of 0.0982. When compared to normal expenses which only experienced a growth rate of 0.0739. The modeling results show that overload will increase the growth of very high IRI values with the exponential model increase graph

Development of a Relationship between Pavement Condition Index and Riding Quality Index on Rural Roads: A Case Study in China

The current standard for evaluating road conditions worldwide relies primarily on the Pavement Condition Index (PCI) and the International Roughness Index (IRI). The IRI can be further calculated to obtain the Riding Quality Index (RQI). To assess pavement damage, various imaging equipment is commonly utilized, providing consistent results that align with actual road conditions. For roughness detection, the Laser Profilometer offers excellent results but may not be suitable for rural roads with poor conditions due to its high inspection cost and the need for a stable environmental setting. Therefore, there is a pressing need to develop cost-effective, rapid, and accurate roughness inspection methods for these roads, which constitute a significant portion of the road network. This study examined the relationship between PCI and RQI using nonlinear regression on 30,088 valid pavement inspection records from various regions in China (totaling 24,624.222 km). Our objective was to estimate RQI solely from PCI data, capitalizing on its broad coverage and superior accuracy. Additionally, we explored how PCI levels impact RQI decay rates. The models in this study were compared to several models published in previous studies at last. Our findings indicate that the model performs best for low-grade roads with low PCI scores, achieving over 90% accuracy for both cement concrete and asphalt concrete pavements. Furthermore, different levels of pavement damage have distinct effects on RQI decay rates, with the most significant impact observed when the pavement is severely damaged. The models in this study outperformed all the other available models in the literature. Consequently, under limited inspection conditions in rural areas, pavement damage inspection results can effectively predict riding quality or roughness, thereby reducing inspection costs. Overall, this study offers valuable insights but has limitations, including limited global generalizability and the model’s applicability to high-grade roads. Future research is needed to address these issues and enhance practical applications.

Cycling on rough roads: a model for resistance and vibration

Minimising opposing forces is a matter of interest to most cyclists. These forces arise from passage through air (‘drag’) and interaction with the road surface (‘resistance’). Recent work recognises that resistance forces arise not only from the deformation of the tyre (‘rolling resistance’) but also from irregularities in the road surface (‘roughness resistance’), which lead to power dissipation in the body of the rider through vibration. The latter effect may also have an adverse impact on human health. In this work we offer a quantitative theory of roughness resistance and vibration that links these effects to a surface characterisation in terms of the International Roughness Index (IRI). We show that the roughness resistance and the Vibration Dose Value (or VDV, the usual vibration dosage metric) can be expressed in terms of elementary formulae. The roughness resistance depends only on the vertical stiffness of the bicycle and the roughness index. Surprisingly, other apparently relevant parameters, such as physiological characteristics of the bicycle rider and other features of the bicycle, do not enter. For roads of moderate roughness, roughness resistance is larger than rolling resistance. For very rough roads, roughness resistance is larger than aerodynamic drag. So only on roads of high quality (in most jurisdictions, accounting for less than 10% of the total) can roughness resistance be ignored. Roughness resistance can be mitigated by reducing the vertical stiffness of the bicycle. In common with other recent reports, we find that almost any cycling activity will breach public health guidelines relating to Vibration Dose Value.

Feasibility of Modernizing the Acceptable International Roughness Index Value

The U.S. National Highway System has set a standard for acceptable pavement roughness, measured by the international roughness index (IRI) at less than 2.68 m/km (170 in./mi). This value corresponds to a present serviceability rating (PSR) of 2.5, where half of the highway drivers perceive the pavement ride quality as unacceptable with a repair need. For over 3 decades, public transportation agencies in the U.S. have used this IRI value to determine when pavement surface rehabilitation is needed. However, with advancements in vehicle suspension systems improving ride quality, the current acceptable IRI value corresponding to 2.5 PSR has been questioned. To our knowledge, studies in the literature have yet to explore this issue. This study aims to evaluate the feasibility of modernizing the acceptable IRI value through nationwide, large-scale field testing, which is typically expensive and time-consuming. The IRI-present serviceability index (PSI) equation that is capable of converting the acceptable IRI value into the estimated 2.5 PSR (i.e., the PSI) was selected and calibrated by applying test quarter-car parameters synthesized through a literature review. Then, the calibrated IRI-PSI equation was used to find a new acceptable IRI value that was evaluated for the statistical significance and the financial benefits of the new value. The evaluation results suggest a need for a nationwide field test to modernize the current acceptable IRI value. Public transportation agencies can easily adapt the technical approach we used to calibrate the IRI-PSI equation without altering their current IRI measurement systems.

Research and comparison of pavement performance prediction based on neural networks and fusion transformer architecture

<abstract> <p>The decision-making process for pavement maintenance from a scientific perspective is based on accurate predictions of pavement performance. To improve the rationality of pavement performance indicators, comprehensive consideration of various influencing factors is necessary. To this end, four typical pavement performance indicators (i.e., Rutting Depth, International Roughness Index, Longitudinal Cracking, and Alligator Cracking) were predicted using the Long Term Pavement Performance (LTPP) database. Two types of data, i.e., local input variables and global input variables, were selected, and S-ANN and L-ANN models were constructed using a fully connected neural network. A comparative analysis of the predictive outcomes reveals the superior optimization of the L-ANN model. Subsequently, by incorporating structures such as self-attention mechanism, a novel predictive approach based on the Transformer architecture was proposed. The objective is to devise a more accurate predictive methodology for pavement performance indices, with the goal of guiding pavement maintenance and management efforts. Experimental results indicate that, through comparative analysis of three quantitative evaluation metrics (root mean square error, mean absolute error, coefficient of determination), along with visual scatter plots, the predictive model employing the fused Transformer architecture demonstrates higher robustness and accuracy within the domain of pavement performance prediction when compared to the L-ANN model. This outcome substantiates the efficacy and superiority of the model in terms of predictive performance, establishing it as a reliable tool for accurately reflecting the evolution of asphalt pavement performance. Furthermore, it furnishes a theoretical reference for determining optimal preventive maintenance timing for pavements.</p> </abstract>