Explore the vast range of titles available.

Keeping in view the severe lack of research in the field of quantification of pavement quality, an analysis of suitable methods for quantifying the quality of pavement construction and rehabilitation was carried out in this study. The main and sub-factors that significantly impact the quality of pavement construction and rehabilitation from managerial and constructional perspectives were identified using a questionnaire survey. A pavement construction and rehabilitation quality index equation has been developed that yields a quantified level of quality achieved in a project in terms of percentage. Ranking analysis of the top six influencing main factors revealed that constructional factors, such as paving and compaction practices and subsurface drainage, are more important than managerial factors, such as the client’s and quality consultant’s capability, payment and finances, and contractor’s capability. Sensitivity analysis revealed that each sub-factor has its individual significance on the overall quality index, irrespective of the rank of its main factor, and the quality index equation developed in this study duly encompasses the impact of each sub-factor and main factor, collectively, on the overall quality index. The practical application of the developed index was also validated through a case study. This study is one of the pioneer studies that specifically explored the quantification of pavement rehabilitation and construction quality in terms of a quality index, duly encompassing both managerial and constructional aspects. The results of this study and recommendations for future research can be effectively used as a benchmark checklist to improve particular main and sub-factors in order to enhance the overall quality of pavement rehabilitation and construction in future projects.

Mounting degradation in the environmental quality (EQL), specifically from the transport industry, is a big threat and challenge for sustainable development. The transport sector’s emission has gained researchers’ attention on climate change and transportation because of its increasing share in global emission. This study, thus, aims to analyze the links among road infrastructure (RIN), road transport energy consumption (RTEC), and environmental quality with the moderating role of population density (PDN). The study has used a dataset of five South Asian countries from 1971 to 2014. The study applies the Breusch–Pagan LM test to identify the issue of cross-sectional dependence. CIPS (second-generation unit root test) is applied to check the stationarity properties of the data, whereas the Westerlund (Oxf. Bul. Econ. Stat., 2007, 69 (6), 709–748) co-integration test is used to confirm the long-run association among the variables. Moreover, a fully modified ordinary least square (FMOLS) model is applied to analyze the effect that road transportation has on environmental quality. The study finds a positive effect of road infrastructure, road density (RDN), energy intensity (EIN), and road transport energy consumption on transport-generated emissions, which indicates that road transportation is harmful to environmental quality. Our results confirm the significant moderating role of population density in strengthening the relations of road infrastructure, road transport energy consumption, and environmental quality. It is concluded that population density works as a bridge between road infrastructure, road transport energy consumption, and environmental quality, which helps capture a strong impact of road transportation. We offer the planners of road transportation with a novel and practical approach to examine population density changes policy in the growing countries to analyze the environmental quality.

Road surface monitoring and maintenance are essential for driving comfort, transport safety and preserving infrastructure integrity. Traditional road condition monitoring is regularly conducted by specially designed instrumented vehicles, which requires time and money and is only able to cover a limited proportion of the road network. In light of the ubiquitous use of smartphones, this paper proposes an automatic pothole detection system utilizing the built-in vibration sensors and global positioning system receivers in smartphones. We collected road condition data in a city using dedicated vehicles and smartphones with a purpose-built mobile application designed for this study. A series of processing methods were applied to the collected data, and features from different frequency domains were extracted, along with various machine-learning classifiers. The results indicated that features from the time and frequency domains outperformed other features for identifying potholes. Among the classifiers tested, the Random Forest method exhibited the best classification performance for potholes, with a precision of 88.5% and recall of 75%. Finally, we validated the proposed method using datasets generated from different road types and examined its universality and robustness.

Background and objective Carnage on roads is a growing concern in Nigeria. Over 27 persons, equivalent to more than 4 families, die daily from road traffic crashes. Two direct factors of a road crash are road quality and vehicle quality. To interrogate and compare both factors to road traffic accidents, the longitudinal study regressed secondary data on death tolls against road quality and vehicle quality. Materials and methods Data on the estimated number of vehicles imported into Nigeria (1992–2021) served as the indicator of vehicle quality on Nigerian roads. The longitudinal study regressed secondary data on death tolls (2013–2019) against road quality (2006–2019) and vehicle quality (1992–2021). Results Results showed that road quality is degenerating as well as vehicle quality in Nigeria, resulting in increase in the number of road traffic crashes and the attendant death tolls. For every 1% decrease in road quality, death tolls from road traffic crashes in Nigeria increased by 0.00642% at 5% significance, and for every decrease in vehicle quality, death tolls from road traffic crashes in Nigeria increased by 0.327% at 5% significance. Conclusion The study recommended increased advocacy on the sanctity of life and the need for all tiers of government to prioritize policy and implementation of improving the road quality and vehicle quality to reduce road traffic crashes and save lives on Nigerian roads. Significance Growing road traffic crashes account for the death of more than 4 Nigerian families daily. This study analyzed road and vehicle qualities as the major factors of the carnage on Nigerian roads. Findings showed that degenerating road and vehicle qualities are majorly responsible for increase in number of road traffic crashes and attendant death tolls. The study is significant in highlighting the need for increased advocacy on the sanctity of life and the need for tiers of government to prioritize policy and its implementation to improve the road and vehicle qualities to reduce road traffic crashes and save lives and valuable resources in Nigerian.

Abstract Forest roads allow access for silvicultural operations, harvesting, recreational activities, wildlife management, and fire suppression. In British Columbia, Canada, roads that are no longer required must be deactivated (temporarily, semipermanently, or permanently) in order to minimize the impact on the overall forested ecosystem. However, the remoteness and size of the road network present challenges for monitoring. Our aim was to examine the utility of airborne laser scanning data to assess the status and quality of forest roads across 52,000 hectares of coastal forest in British Columbia. Within the forest estate, roads can be active or deactivated, or have an unknown status. We classified road segments based on the vegetation growth on the road surface, and edges, by classifying the height distribution of airborne laser scanning returns within each road segment into four groups: no vegetation, minor vegetation, dense understory vegetation, and dense overstory vegetation. Validation indicated that 73 percent of roads were classified correctly when compared to independent field observations. The majority were classified as active roads with no vegetation or deactivated with dense vegetation. The approach presented herein can aid forest managers in verifying the status of the roads in their management area, especially in remote areas where field assessments are costly and time-consuming.

Egypt has been fighting the issue of ensuring road safety‚ reducing accidents‚ preserving the lives of citizens since its inception. For these reasons‚ precisely identifying the road condition‚ followed by effective and timely maintenance and rehabilitation measures‚ leads to an increase in the road network's safety level and lifespan. This paper presents a multi-input deep learning framework that combines BiLSTM and Depthwise separable convolution to work in parallel for automatic recognition of road surface quality and different road anomalies. Furthermore, we performed an investigation to compare deep networks approaches against other traditional approaches using real-time data sensed and collected from the Egyptian road network. The proposed deep model has achieved an average accuracy of 93.1%‚ which is superior compared to other evaluated approaches. Finally, we utilized the proposed model to estimate a road quality index in the Egyptian cities.

In this study, we measured the in situ international roughness index (IRI) for first-degree roads spanning more than 1300 km in East Azerbaijan Province, Iran, using a quarter car (QC). Since road quality mapping with in situ measurements is a costly and time-consuming task, we also developed new equations for constructing a road quality proxy map (RQPM) using discriminant analysis and multispectral information from high-resolution Sentinel-2 images, which we calibrated using the in situ data on the basis of geographic information system (GIS) data. The developed equations using optimum index factor (OIF) and norm R provide a valuable tool for creating proxy maps and mitigating hazards at the network scale, not only for primary roads but also for secondary roads, and for reducing the costs of road quality monitoring. The overall accuracy and kappa coefficient of the norm R equation for road classification in East Azerbaijan province are 65.0% and 0.59, respectively.

Travel time is less costly if it is comfortable or can be used productively. One could hence argue that the value of travel time (VTT) of car travellers in economic appraisal should be differentiated by road type, reflecting differences in road quality. We explain the theoretical foundation for such a differentiation, review the relevant literature and show the results of an empirical case study based on actual route choice of highway drivers in Norway. We find little existing literature discussing the link between road type and VTT, but closely related findings suggest that that the impact on VTT could be substantial. Our empirical case study also suggests that the VTT is lower on higher quality road types. Applying this to economic appraisal would imply higher user benefits of road projects that improve road quality.

International Roughness Index (IRI) is one of the most widely used indices to determine the roughness of different road surface. Roughness index helps in identifying the quality of the road surface and suggest improvement measures. In this paper Roughness index values are measured from REVA University to Kodigehalli Gate. International Roughness Index are measured using mobile application. IRI value depends on vehicle speed and road. Roughness indicates the present road condition and helps in making decision related to management and maintenance of different types of roads. Based on Carbin app [1] results road user can identify 1) Safety, 2) Traffic, 3) Comfort of driving, 4) Fuel Consumption, 5) Quality of Air and 6) Road condition. Carbin application is used to collect Road quality, Speed, CO2 savings. In the study stretch Maximum value of IRI = 5.6m/km obtained from Bagalur cross to on BSF Cross and this stretch road user face all the above 6 problems. Minimum value of IRI= 2.44m/km obtained from Kodigehalli Cross to Bagalur cross (Airport Road-NH), the road is recently maintained by overlaying, in this stretch, road users comfortably use this stretch of road with respect above 6 aspects. As per the IRI value, new roads to be constructed to get IRI<1.2 m/km, once it reaches IRI=4 m/km road need to be maintained to get IRI < 2.2 m/km.

This study proposes a novel IoT model, the Smart Auto IoT-based Geo-Spatial Rover (Auto-GeRo), which integrates a multi-sensor fusion approach for detection of road anomalies in real-time. The model proposed here utilizes machine learning techniques to achieve a high precision rate of 0.43548 with Random Forest in pothole detection, thereby leading from existing models in both accuracy and cost-effectiveness. The work presented here follows a multi-sensor fusion approach that integrate a motion processing unit (IMU-MPU 9250) sensor with other sensors to capture 3-axial XYZ data for real-time road surface analysis. The primary objective is to identify and detect different road conditions, specifically distinguishing between flat and rough patches. By applying various machine learning algorithms, the research presented here tries to evaluate and optimize the performance of road surface classification based on sensor data collected during the simulation of proposed Auto-GeRo simulation. While Logistic Regression exhibits high accuracy (0.96038), the focus shifts towards precision for effective pothole detection, leading to the selection of the Random Forest model due to its superior precision (0.43548) in identifying roads with potholes. Thus the research highlights the potential of IoT-enabled multi sensor fusion techniques in road transportation network for better road safety and early maintenance thereby reducing the maintenance cost and vehicle damage.