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PurposeThe purpose of this paper was to study the possibility of using smartphone roughness measurements for developing pavement roughness regression models as a function of pavement age, traffic loading and traffic volume variables. Also, the effects of patching and pavement distresses on pavement roughness were investigated. The work focused on establishing pavement roughness prediction models and applying these models to pavement management systems (PMS) to help decision-makers choose the best maintenance and rehabilitation (M&R) options by using cost-effective methods.Design/methodology/approachSignal processing techniques including filtering and processing techniques were used to obtain the International Roughness Index (IRI) from raw acceleration data collected from smartphone accelerometer sensors. The obtained IRI values were inputted as a dependent variable in analytical regression models as well as several independent variables with proper transformations.FindingsAccording to the study results, several regression models were developed with a big variation in the coefficients of determination (R2). However, the best models included pavement age, accumulated traffic volume (∑TV) and construction quality factor (CQF) with R2 equal to 0.63. It was also found that the effects of pavement distresses and patching was significant at a-level < 0.05. The patching effect on pavement roughness was found higher than the effect of other pavement distresses.Practical implicationsThe presented results and methods in this paper could be used in the future predictions of pavement roughness and help the decision-makers to estimate M&R needs. The work focused on establishing IRI prediction models and applying these models to the PMS to help decision-makers choose the best M & R options.Originality/valueTo develop sound pavement roughness models, it is essential to collect roughness data using automated procedures. However, applying these procedures in developing countries faces several difficulties such as the high price and operation costs of roughness equipment and lack of technical experience. The advantage of using IRI values taken from smartphones is that the roughness evaluation survey may be expanded to cover the full road network at a cheaper cost than with automated instruments. Therefore, if the roughness survey covers more roads, the prediction model’s accuracy will be improved.

The main objectives of this research were to evaluate the pavement condition of the primary roads in Jordan using the Strategic Highway Research Program (SHRP) procedure and to investigate the effects of pavement age and traffic loading on pavement performance. One of the most important products of SHRP was the Pavement Condition Rating (PCR) on a scale between 0 and 100 and it was calculated based on the existing distresses and pavement roughness. An integrated data base was developed for the selected pavement sections, including information on pavement identification, pavement condition, pavement age, traffic volumes and traffic loading. The results of pavement evaluation showed that most of the pavement sections were in fair condition. Based on the suggested Maintenance and Rehabilitation (M & R) strategies for the rural roads in Jordan, it was found that about 40% of the evaluated pavement sections were in need of major maintenance and reconstruction. Regression analysis was used to develop sound pavement performance models. The effects of pavement age and traffic loading on PCR in these models were found statistically significant at α � level < 0.01 with relatively high R� value (0.841). Also, the analysis indicated that most of the primary roads in Jordan failed prematurely and require major M & R before the end of their design lives because of heavy traffic loading.

Allocated budgets for maintenance of road networks are normally limited. Therefore, not all roads receive the required attention they deserve in a timely manner. These roads are left to deteriorate until the next maintenance round. The cost associated with delayed maintenance is significantly excessive. A Pavement Maintenance Management System (PMMS) can be a useful tool for evaluation, prioritization of Maintenance and Rehabilitation (M&R) projects, and determination of funding requirements and allocations. The pavement condition is normally indexed using a parameter called Pavement Condition Index (PCI) which represents an overall assessment of surface defects by type, severity and extent. Periodic collections of PCI over time for different sections within the roadway network provide an approach to monitor changes in pavement serviceability over time and can produce useful data to predict and evaluate required maintenance solutions and their associated cost. The researchers intend to use available data collected over the span of a year and a half on sections within the roadway network at the campus of Al-Zaytoonah University of Jordan (ZUJ) to study the relation between the maintenance cost and the pavement deterioration rate. This study may incorporate variables such as pavement age, traffic volumes, maintenance history and pavement condition assessment results. The available records of PCI will be analyzed and the findings will be clearly presented. The practical inclusion of the findings within the current PMMS used at the university will also be detailed.

This study investigated the performance of rural public bus transport services in Jordan Valley during COVID-19. Jordan Valley consists of three brigades; Southern Shouneh, Deir Alla, and Northern Shouneh. The performance measures included availability, comfort and convenience, waiting time, mobility, productivity, and safety for the external and internal bus routes. The names, number of buses, and fares for bus routes were obtained from Land Transport Regulatory Commission of Jordan (LTRC). The field survey consisted of interviews with passengers and drivers in addition to direct field observations. The average waiting time for both the minibuses and microbuses at off-peak hours was found twice and half the waiting time at peak hours. The minimum and maximum values of the average speed varied between 40 to 100 km/h for the external routes and between 30-90 km/h for the internal routes. As a productivity measure, the average operating ratio for the internal routes was found 2.09 and 1.38 for the external routes. 60% of the microbuses obliged to the stated fare in comparison to minibuses in which all of them obliged to the stated fare. It was found that 40% of the external bus routes were within the range of overall Level of Service (LOS) between C & D, 26.67% within the range of LOS between B & D, 13.33% within the range of LOS between B & C, 13.33% within the range between C & E, and 6.67% within the range between D & E. Also, it was found that 60% of internal bus routes were within the range of LOS between C & D, 20% within the range of LOS between C & E, and 20% within the range of LOS C. The developed regression models between the average perception waiting time as dependent variable and travel time as independent variable were found significant at α-level < 0.05, with r2 = 0.505 at peak periods and r2 = 0.673 at off-peak period.

The main objective of this research was to investigate the contribution of pavement characteristics, traffic, and physical and mechanical properties of asphaltic mixtures to highway pavement rutting. A total of 51 pavement sections from the rural highway network in Jordan were selected for a case study. The average rut depths for these sections were measured and three cores were drilled for comprehensive laboratory testing. The investigation was performed using four approaches. The first approach considered pavement characteristics represented by surface thickness, last overlay thickness, pavement age, and subgrade California bearing ratio. The average annual equivalent single axle load was also included in this approach. The second approach included Marshall test parameters such as stability, flow, stiffness, and Marshall modulus. The third approach dealt with the effect of mixture air voids on rutting. The variables examined in this approach include air void content within the ruts, voids between ruts, voids near the pavement centerline, and the difference between centerline and rut voids. The fourth approach considered the dynamic permanent deformation characteristics of the pavement surface layer represented by the dynamic modulus. Regression analysis techniques were employed to develop statistical relationships between average rut depths and the parameters examined in each individual approach. The combined effect of these significant parameters on pavement rutting was also examined for prediction purposes. Rutting formation was found to be most dependent on the traffic loading, dynamic modulus of the bituminous mixture and its susceptibility to further compaction, and foundation soil strength.Key words: pavement rutting and characteristics, Marshall test, traffic loading, air voids, static creep, dynamic permanent deformation.

The main objective of this work was to investigate the potential of integrating a stereometric vision system, i.e., using digital stereo images, and a knowledge-based system for flexible pavement distress classification. Classification process includes distress type, severity level, and options for repair. A hybrid stereo vision and knowledge-based system (called K-PAVER) was developed. The system extracts distress measurements using a PC-based stereo vision system. Geometric surface measurements such as point locations, distances, areas, volumes, and surface areas could also be computed. The knowledge-based system developed utilizes a set of if...then rules from the PAVER system (a pavement maintenance management system for roads and streets) and related literatures. New parameters, including shape parameters, orientation, and some geometrical measurements, were introduced to the system in order to facilitate the distress classification process. A criterion for maintenance priorities based on four parameters was developed. These parameters are pavement condition index, average daily traffic, location of distressed pavement, and street class. Surface measurements and automatic classification decision-making were validated and tested for all distress types. The developed system gives accurate results in both the measurement mode and the decision-making phase. This result opens the door for a fully automated distress classification process without any human intervention.Key words: knowledge-based systems, vision systems, stereo measurements, flexible pavement distresses, maintenance priorities, pavement maintenance management systems.