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Current solutions for monitoring the progress of pavement construction (such as collecting, processing and analysing data) are inefficient, labour-intensive, time-consuming, tedious and error-prone. In this study, an automated solution proposes sensors prototype mounted unmanned ground vehicle (UGV) for data collection, an LSTM classifier for road layer detection, the integrated algorithm for as-built progress calculation and web-based as-built reporting.

The crux of the proposed solution, the road layer detection model, is proposed to develop from the layer change detection model and rule-based reasoning. In the beginning, data were gathered using a UGV with a laser ToF (time-of-flight) distance sensor, accelerometer, gyroscope and GPS sensor in a controlled environment. The long short-term memory (LSTM) algorithm was utilised on acquired data to develop a classifier model for layer change detection, such as layer not changed, layer up and layer down.

In controlled environment experiments, the classification of road layer changes achieved 94.35% test accuracy with 14.05% loss. Subsequently, the proposed approach, including the layer detection model, as-built measurement algorithm and reporting, was successfully implemented with a real case study to test the robustness of the model and measure the as-built progress.

The implementation of the proposed framework can allow continuous, real-time monitoring of road construction projects, eliminating the need for manual, time-consuming methods. This study will potentially help the construction industry in the real time decision-making process of construction progress monitoring and controlling action.

This first novel approach marks the first utilization of sensors mounted UGV for monitoring road construction progress, filling a crucial research gap in incremental and segment-wise construction monitoring and offering a solution that addresses challenges faced by Unmanned Aerial Vehicles (UAVs) and 3D reconstruction. Utilizing UGVs offers advantages like cost-effectiveness, safety and operational flexibility in no-fly zones.

The purpose of this study is to develop a cost-effective autonomous wheelchair robot navigation method that assists the aging population.

Navigation in outdoor environments is still a challenging task for an autonomous mobile robot because of the highly unstructured and different characteristics of outdoor environments. This study examines a complete vision guided real-time approach for robot navigation in urban roads based on drivable road area detection by using deep learning. During navigation, the camera takes a snapshot of the road, and the captured image is then converted into an illuminant invariant image. Subsequently, a deep belief neural network considers this image as an input. It extracts additional discriminative abstract features by using general purpose learning procedure for detection. During obstacle avoidance, the robot measures the distance from the obstacle position by using estimated parameters of the calibrated camera, and it performs navigation by avoiding obstacles.

The developed method is implemented on a wheelchair robot, and it is verified by navigating the wheelchair robot on different types of urban curve roads. Navigation in real environments indicates that the wheelchair robot can move safely from one place to another. The navigation performance of the developed method and a comparison with laser range finder (LRF)-based methods were demonstrated through experiments.

This study develops a cost-effective navigation method by using a single camera. Additionally, it utilizes the advantages of deep learning techniques for robust classification of the drivable road area. It performs better in terms of navigation when compared to LRF-based methods in LRF-denied environments.

This paper aims to present a novel scheme of multiple vanishing points (VPs) estimation and corresponding lanes identification.

The scheme proposed here includes two main stages: VPs estimation and lane identification. VPs estimation based on vanishing direction hypothesis and Bayesian posterior probability estimation in the image Hough space is a foremost contribution, and then VPs are estimated through an optimal objective function. In lane identification stage, the selected linear samples supervised by estimated VPs are clustered based on the gradient direction of linear features to separate lanes, and finally all the lanes are identified through an identification function.

The scheme and algorithms are tested on real data sets collected from an intelligent vehicle. It is more efficient and more accurate than recent similar methods for structured road, and especially multiple VPs identification and estimation of branch road can be achieved and lanes of branch road can be identified for complex scenarios based on Bayesian posterior probability verification framework. Experimental results demonstrate VPs, and lanes are practical for challenging structured and semi-structured complex road scenarios.

A Bayesian posterior probability verification framework is proposed to estimate multiple VPs and corresponding lanes for road scene understanding of structured or semi-structured road monocular images on intelligent vehicles.

Across Europe, on average more than 95% of all passenger cars and half of all light commercial vehicles are permanently available to a household. This includes both privately owned vehicles and company cars. The profiles of vehicle use can be specified as average annual distance driven per vehicle and for the fleet as a total, purpose of travel (trip destination), infrastructure use (urban, interurban or motorway road transport) and also fuel consumption together with data on CO₂ emissions. Indicators on vehicle use can be tracked in various ways:

• self-administered panels of households, which permit their vehicles to be followed for several years;

• national or local household travel surveys (with a seven-day trip diary);

• official vehicle inspection and vehicle registration files;

• ‘vehicle surveys’ based on vehicle registry data;

• traffic counts;

• data collected for road-charging purposes.

self-administered panels of households, which permit their vehicles to be followed for several years;

national or local household travel surveys (with a seven-day trip diary);

official vehicle inspection and vehicle registration files;

‘vehicle surveys’ based on vehicle registry data;

traffic counts;

data collected for road-charging purposes.

The paper will present a review of mainly vehicle-based survey methods used in France, Germany, Finland, the United Kingdom, the United States and Canada, describing existing sampling frames to their scope, advantages and limitations, as well as their costs. Issues addressed in this context will be further examined in terms of their methodological challenges as well as their purpose.

The leading questions underlying this paper as well as the corresponding workshop are: why is it necessary to have data on passenger travel or transportation; and, looking at international experience, how good are vehicle-based surveys in delivering the required information? In discussing problems experienced in the different countries with data collection and evaluation methods, emphasis will be put on potential strategies for methodological and technological improvement and problem solving. One example is the potential use, benefits and constraints of new survey technologies presented by vehicle tracking techniques.

To proactively draw efficient maintenance plans, road agencies should be able to forecast main road distress parameters, such as cracking, rutting, deflection and International Roughness Index (IRI). Nonetheless, the behavior of those parameters throughout pavement life cycles is associated with high uncertainty, resulting from various interrelated factors that fluctuate over time. This study aims to propose the use of dynamic Bayesian belief networks for the development of time-series prediction models to probabilistically forecast road distress parameters.

While Bayesian belief network (BBN) has the merit of capturing uncertainty associated with variables in a domain, dynamic BBNs, in particular, are deemed ideal for forecasting road distress over time due to its Markovian and invariant transition probability properties. Four dynamic BBN models are developed to represent rutting, deflection, cracking and IRI, using pavement data collected from 32 major road sections in the United Arab Emirates between 2013 and 2019. Those models are based on several factors affecting pavement deterioration, which are classified into three categories traffic factors, environmental factors and road-specific factors.

The four developed performance prediction models achieved an overall precision and reliability rate of over 80%.

The proposed approach provides flexibility to illustrate road conditions under various scenarios, which is beneficial for pavement maintainers in obtaining a realistic representation of expected future road conditions, where maintenance efforts could be prioritized and optimized.

The purpose of this paper is to attempt to put forth an innovative geographic information system (GIS)‐based methodology for demarcating stretches of mountain roads with differential probability of road accidents. The proposed methodology has been tested in a sample road network of Uttarkashi district in Uttaranchal (India) and exhibits potential of reducing the frequency of road accidents by adopting suitable site‐specific measures along accident‐prone stretches.

The paper is based on the hypothesis that road accidents in the mountain roads are largely due to the three basic road parameters that distinguish mountain roads from those in the plains; sinuosity, gradient, and width. The sinuosity of the road is calculated for every 500 meter stretch of the road map layer while for delineating the gradient of the road topographic data of Survey of India maps have been used. The paper utilises GIS‐based environment for correlating these parameters and delineating accident‐prone road stretches.

The proposed new methodology for delineating differential accident risk in mountain roads has been utilised for demarcating road stretches with differential probability of road accidents and the output has been correlated with the actual road accident database of Uttarkashi district in Uttaranchal. The correlation exhibits the potential of this methodology for practical mitigative planning‐related purposes. The same can also be utilised for better aligning the planned roads.

Human factor is the most important determinant of road accidents and non‐incorporation of this parameter is the biggest limitation of the proposed methodology. Further, the effectiveness of the proposed methodology is the function of the validity of the hypothesis. The methodology is, however, highly flexible and has ample scope for accommodating other parameters as well. The effectiveness of the output is, however, a function of the accuracy of the input maps. Road layer considered in this paper has been prepared from the maps available with the State Government Department (Public Works Department) and their alignment does not depict the ground details. Input road layer prepared with precision Geographical Positioning System (GPS), preferably differential, mapping would produce more realistic results. The positions of the past accident sites for the purpose of correlations are taken from the data provided by the State Police Department and these are not very accurately determined. GPS‐based database of the accident locations would help in effective correlations.

The methodology proposed in this paper is an attempt to scientifically delineate differential accident‐prone stretches of the mountain roads. This would pave the way for implementation of site‐specific measures for reducing probability of road accidents and better aligning of the proposed roads.

Previously a large number of workers have used GIS‐based techniques for delineating hazard and risk related largely to landslides, floods and earthquakes but the same has never been employed for delineating road accident risk. The methodology is simple, unique, original and functional and has immense practical utility for reducing the menace of road accidents in mountain roads.

Many road authorities considered contracting out road maintenance to the private sector based on performance measures as an alternative and better solution than traditional methods of contracting. It highlights issues of interest to road authorities in the context of saving maintenance costs and managing contracting times effectively. This method is named as performance based maintenance by contracting (PBMC) and has substantial success records in minimizing infrastructure maintenance costs in many developed and developing countries over the last two decades. It has received the attention of researchers and practitioners. However, the literature on PBMC is reasonably high although the concept of PBMC is relatively new. The purpose of this paper is to carry out a comprehensive state of the art review of the literature that has been conducted in the recent years.

A total of 62 published report and journal articles related to performance based maintenance by contracting for road network system has been analysed and reviewed in this paper.

This paper analyses the literature on PBMC and presents examples of developed and developing countries that have been successfully maintaining their road network systems using PBMC as their preferred method of contracting.

The potential of reducing maintenance costs, increasing the quality of works and reducing the chance of corruption in the long run in developing countries are the challenging issues for PBMC, which needs more attention. This paper can be used as a base or platform for future research in the area of PBMC such as developing optimal policies and cost models.

This paper would be useful for the research on PBMC. It would be beneficial for the engineers or professionals in improving the performance of road maintenance and management.