Road Asset Research Articles

SEGMENTATION OF TRAFFIC SIGNS FROM POLES WITH MATHEMATICAL MORPHOLOGY APPLIED TO POINT CLOUDS

Abstract. Traffic signs are one of the most relevant road assets for driving, as the safety of drivers depends to a great extent on their correct location. In this paper two methods are compared for the segmentation of the sign and the pole supporting it. Both methods are based on the morphological opening to identify the sign points, the first one directly employs the mathematical morphology directly applied to point clouds and the second one through point cloud rasterization into images. The comparison was conducted on twenty real traffic signs acquired with Mobile Laser Scanning obtaining point clouds from environments with signposts, traffic lights and lampposts. The results showed a correct segmentation of the signs, obtaining a F-score of 0.81 by the point-based method and a 0.75 by 2D image method. In particular, the point-based mathematical morphology proved to be more accurate in the segmentation of traffic sings installed on traffic lights and lampposts, avoiding over detection shown by the 2D image method.

Development and evaluation of relationships between surface condition rating and objective pavement condition parameters

ABSTRACT Highway authorities make efforts to relate visual ratings with directly measured pavement condition data to reduce or remove the necessity of manual pavement condition surveys that involve subjectivity and potential safety risk of assessors. This research develops a set of relationships between pavement surface condition rating and objective pavement condition parameters to assist road asset managers in triggering periodic bituminous resurfacing programs at the network level. These condition parameters include cracking (% area affected), rutting (mm), texture loss (% of left wheel path texture), and roughness (m/km). In the literature, deterministic and probabilistic modelling approaches are used to predict visual surface inspection rating (SIR) from directly measured pavement distresses. The Factorial ANOVA results that are typically used have inferred that cracking and rutting interact with each other significantly for the asphalt surfaced road network. However, the percentages of variation explained by the linear regression models that predict SIR from cracking/rutting are low (24–31%). Alternatively, developed ordinal logistic models for predicting the probability of a road section being in any particular surface condition, with any quantified cracking/rutting data, prove to be statistically better with overall success rates of 46% and 51% for sprayed sealed and asphalt surfaced network, respectively.

Development of structural deterioration models for flexible pavement using traffic speed deflectometer data

ABSTRACT A road's performance is influenced by several factors. Owing to the limited availability of structural data, road agencies are forced to use rutting and fatigue cracking data as a substitute for structural bearing capacity to identify rehabilitation treatment options. However, this approach fails to assist in the development of investment strategies to reduce the life-cycle cost of the road asset nor will it maximise the safety of vulnerable road users. The introduction of traffic speed deflectometer (TSD) has made it more affordable for many road agencies to collect deflection data. The use of TSD has created a knowledge gap on methodology/mechanism as road agencies are yet to identify the best means of utilising and incorporating these structural data at network-level analysis. The structural deterioration model is a critical input in the pavement management system for predicting pavement rehabilitation needs. A practically implementable concept was adopted in this study to develop the models. These deterioration models were developed by utilising five years' time series TSD deflection data as a function of other readily available data. This study successfully established structural deterioration models that will enable road agencies to incorporate available TSD data in the pavement management system for developing investment strategies.

Silk Road Heritage Branding and Polycentric Tourism Development

Cultural heritage, considered as a tool for sustainable tourism development and place branding, makes a destination appealing to visitors; hence, cultural heritage tourism can be a driving force for economic growth in cities and regions. Polycentricity is a useful multi-scalar concept in spatial theory that describes how adjacent urban centers can interact with each other, creating synergies and generating broader spatial networks. Cultural heritage and tourism, perceived as important factors of integration in a polycentric spatial structure, can further promote regional branding strategies. In this paper, a polycentricity index is introduced as a methodological tool for networking cultural heritage destinations, with an application to the Silk Road heritage. Silk Road cultural assets traced on the historical Silk Road routes linking East and West, can serve as tourist attraction poles and as an essential component for branding destinations through networking at various spatial scales. The Region of Eastern Macedonia and Thrace in Northern Greece, endowed with a plethora of Silk Road cultural assets, most of which are still untapped, is used to highlight the proposed methodology. The ultimate objective is the designation of polycentric destination networks based on Silk Road assets, in order to build regional branding opportunities over the Region.

Geohazard vulnerability and condition assessment of the Asian highway AH-48 in Bhutan

Transport networks are frequently exposed to geohazards, and losses can be observed frequently by notable disaster incidents such as landslides, earthquakes, and floods. Although losses due to natural disasters are frequently reported, limited attention is paid to assess the condition of strategic road networks in the Himalayas. Thus, to fulfill this gap, this study performs condition assessment of Asian Highway AH-48 in Bhutan and develops a system to rate the performance level of road assets. The assessment is conducted along a 157.6 km stretch of AH-48. The sum of findings highlights that about 55% of the road assets along the AH-48 are highly vulnerable to geohazards. Drains are observed to be the most vulnerable, with 52% drains lying below the standard rating among the considered assets followed by retaining walls and pavement. Landslide vulnerability function for highways that are situated in mountain regions is developed using the newly proposed landslide intensity scale. The sum of findings highlights that the Himalayan roads will be affected mostly by landslides and the drainage system is the most affected road asset showing the least serviceability among the common road assets.

Development of fragility curves for road embankments exposed to perpendicular debris flows

Debris flows cause recurrent interruptions and permanent damages in rural road networks, representing significant economic losses. Embankments are road assets frequently exposed to debris flows, especially in mountainous areas. The potential risk of infrastructure exposed to debris flows can be assessed in terms of the probability of expected damage based on fragility curves. The aim of the study is to develop fragility curves for road embankments exposed to debris flows representative to the expected physical damage and capacity loss. Two conceptual models are proposed to describe the impact and erosion of debris flows that run perpendicularly to road embankments. Probabilistic models are then developed in terms of limit state functions and the simulation of potential scenarios. The resulting models are finally fit to log-normal distributions and compared to historical data. Headcut erosion has higher probabilities of occurrence compared to sliding failure caused by the impact of debris flow. Lower road embankments present higher probabilities of failure to the impact of debris flows, especially for dense flows. Whereas, longer interaction times of less dense flows increase the probability of headcut erosion. Two-lane roads may present 50% more probabilities of headcut erosion compared to multilane roads for similar debris flows intensity.

Roads Infrastructure Digital Twin: A Step Toward Smarter Cities Realization

Digital Twin is a new concept that consists of creating an up-to-date virtual asset in cyberspace which mimics the original physical asset in most of its aspects, ultimately to monitor, analyze, test, and optimize the physical asset. In this article, we investigate and discuss the use of the digital twin concept of the roads as a step toward realizing the dream of smart cities. To this end, we propose the deployment of a Digital Twin Box to the roads that is composed of a 360° camera and a set of IoT devices connected to a Single Onboard Computer. The Digital Twin Box creates a digital twin of the physical road asset by constantly sending real-time data to the edge/cloud, including the 360° live stream, GPS location, and measurements of the temperature and humidity. This data will be used for realtime monitoring and other purposes by displaying the live stream via head-mounted devices or using a 360° web-based player. Additionally, we perform an object detection process to extract all possible objects from the captured stream. For some specific objects (person and vehicle), an identification module and a tracking module are employed to identify the corresponding objects and keep track of all video frames where these objects appeared. The outcome of the latter step would be of utmost importance to many other services and domains such as national security. To show the viability of the proposed solution, we have implemented and conducted real-world experiments where we focus more on the detection and recognition processes. The achieved results show the effectiveness of the proposed solution in creating a digital twin of the roads, a step forward to enable self-driving vehicles as a crucial component of smart mobility, using the Digital Twin Box.

Low-Power Drone-Mountable Real-Time Artificial Intelligence Framework for Road Asset Classification

With the continuous increase in interstate highway traffic and demand for higher safety standards, there is a growing need for rapidly scalable road inspection. Currently, inspection and condition assessment of roadways involve manual operations which increase labor costs and limit the scalability and inspection coverage. Furthermore, manually inspecting highways adds additional safety risks for highway workers and road inspectors. To address these challenges, we envision a fully automated process of highway inspection. This paper presents a novel low-power drone-mountable real-time artificial intelligence (AI) framework for road asset classification through visual sensing, which is the first step toward a fully automated inspection system. We analyzed a state DOT dataset, consisting of 14 different kinds of defected road assets. To this end, we developed our baseline framework using MobileNet-V2, which is a convolutional neural network (CNN) specially developed for mobile and embedded platforms. Since our target dataset was small and CNNs networks require a huge amount of data, we leveraged transfer learning, by pretraining MobileNet-V2 using the ImageNet dataset and then fine-tuned it on our target dataset. This new framework was ported to embedded platforms Nvidia Jetson Nano with the capability to perform on-board drone processing. Overall, our results demonstrate 81.33% accuracy on the test set while processing 7.4 frames per second and occupying a total power of 1.9 W. It achieved a Power Reduction Factor (PRF) of 21.17 over Nvidia TitanV implementation, with only 8.74% impact on the projected drone flight time.

Characterisation of cement grouted bituminous mixes using marginal aggregates

This study details the results of a laboratory study conducted to evaluate the properties of cement grouted bituminous mixes (CGBM) prepared with marginal aggregates. A conventional dense-graded bituminous concrete mix was also considered for the purpose of comparison. The CGBM specimens prepared were then evaluated in terms of its strength (Stability and Indirect Tensile strength), Elastic properties (Resilient modulus and Indirect Tensile Stiffness Modulus), Durability (Tensile strength ratio) and ravelling resistance (Cantabro Abrasion Test). Testing was conducted at two different curing periods (7 and 28 days) to consider the long-term strength gain of cement. Two test temperatures (25°C and 40°C) were considered to simulate the average and high-temperature properties. Further, both moisture conditioned and unconditioned states were examined to evaluate the susceptibility to moisture. Obtained results were statistically analysed to determine the critical effects of the curing period, temperature and moisture conditioning on the CGBM properties. An example of a low volume road was used to assess the cost related to the use of CGBM as a structural layer. Overall, laboratory results showed CGBM to be an effective method for the utilisation of marginal aggregates in routine road construction with enhanced material properties as compared to the control mix. Statistical analysis showed a significant influence of curing, temperature and moisture on the properties of CGBM. Further, the replacement of the conventional bituminous layer with CGBM resulted in a substantial reduction in material use, indicating that the use of marginal materials in CGBM could lead to sustainable road assets.

DeepAutoMapping: low-cost and real-time geospatial map generation method using deep learning and video streams

Field data collection and geospatial map generation are critical aspects in different fields such as road asset management, urban planning, and geospatial applications. However, one of the primary impediments to data collection is the availability of spatial and attribute data. This issue is aggravated by the high cost of conventional data collection and data processing methods and by the lack of geospatial data collection policies. This study proposes an inexpensive approach that enables real-time field data observation and geospatial data generation from video streams connected to a laptop and positioning sensors using deep learning technology. This proposed method was evaluated via an application called “DeepAutoMapping”, which was built on top of Python, then underwent through two different evaluation scenarios. The results demonstrated that the proposed approach is quick, easy to use and that it provides a high detection accuracy and an acceptable positioning accuracy in the outdoor environment. The proposed solution may also be considered as a pipeline for efficient and economical method of geospatial data collection and auto-map generation in the future.

Dynamic energy and carbon footprints of urban transportation infrastructures: Differentiating between existing and newly-built assets

Transportation infrastructures play an important role in building energy-efficient and low-carbon cities. Yet, how existing and newly-built assets contribute differently to the footprints of transportation infrastructures is not clear. In this paper, we develop a life-cycle approach to quantify the energy and carbon footprints of urban transportation infrastructures over time, with a clear differentiation between existing and newly-built assets. Taking Guangzhou, China as a case study, we evaluate the dynamic footprints of various types of urban transportation infrastructure (roads, subway, and railway) across their life-cycle stages until 2017, and perform a scenario analysis to 2035 to explore future pathways of footprint mitigation. We find that the energy and carbon footprints of road assets built prior to 2005 are around 36 times higher than those of the subway and 5 times higher than those of the railway. However, this situation has begun to shift over 2006–2017 as the subway and railway have become more popular means of transportation and bigger contributors to the city’s energy and carbon footprints. The pace of technological advances in building transportation infrastructure needs to exceed that of the average urban economy to steadily reduce the energy and carbon footprints of the new projects planned. These findings offer a dynamic life-cycle approach and tool to evaluate long-term regulatory policies toward a more sustainable transportation infrastructure system in cities.

DEVELOPMENT OF A GPS-GIS-BASED ROAD SIGNAGE MANAGEMENT SYSTEM FOR SELECTED ARTERIAL ROADS

Traffic signs, pavement markings and other road safety assets such as speed bumps are provided on roadway facilities to enhance traffic flow and safety. They will however perform their functions effectively if they meet the basic requirements of i. fulfill a need ii. command attention iii. convey a clear simple meaning iv. command respect from road users and v. give adequate time for proper response. It is noticed and rightfully observed by the Nigeria National Council on Works that these assets on Nigeria roads are not adequately managed and maintained. Some of them are not well positioned, some are missing and some others are damaged. There is therefore the need to assess the existing roadway assets, their conditions and adequacy, and generate a database needed for their effective operation, management and maintenance. The study thus aims at addressing the observed inadequacies by proffering a GPS-GIS-based solution. The objectives are to i. carry out an inventory of existing road signs and markings; ii. determine their adequacy or otherwise and iii. develop a GIS- based road signage management system. It is anticipated that the application of the developed system will enhance the performance of the agencies responsible for the operations of the road assets.

DEVELOPMENT OF A GPS-GIS-BASED ROAD SIGNAGE MANAGEMENT SYSTEM FOR SELECTED ARTERIAL ROADS

Traffic signs, pavement markings and other road safety assets such as speed bumps are provided on roadway facilities to enhance traffic flow and safety. They will however perform their functions effectively if they meet the basic requirements of i. fulfill a need ii. command attention iii. convey a clear simple meaning iv. command respect from road users and v. give adequate time for proper response. It is noticed and rightfully observed by the Nigeria National Council on Works that these assets on Nigeria roads are not adequately managed and maintained. Some of them are not well positioned, some are missing and some others are damaged. There is therefore the need to assess the existing roadway assets, their conditions and adequacy, and generate a database needed for their effective operation, management and maintenance. The study thus aims at addressing the observed inadequacies by proffering a GPS-GIS-based solution. The objectives are to i. carry out an inventory of existing road signs and markings; ii. determine their adequacy or otherwise and iii. develop a GIS- based road signage management system. It is anticipated that the application of the developed system will enhance the performance of the agencies responsible for the operations of the road assets.

Investigation on Identifying Road Anomalies using In-Vehicle Sensors for Cooperative Applications and Road Asset Management

One of the main challenges for road authorities is to maintain the quality of the road infrastructure. Road anomalies can have a significant impact on traffic flow, the condition of vehicles, and the comfort of occupants of vehicles. Strategies such as pavement management systems use pavement evaluation vehicles that are equipped with state-of-the-art devices to assist road authorities in identifying and repairing these anomalies. The quantity of data available is limited, however, by the limited availability and, therefore, coverage of these vehicles. To address this problem, several investigations have been conducted on the use of smartphones or equipping vehicles with additional sensors to identify the presence of road anomalies. This paper aims to add to this arsenal by using sensors already available in production vehicles to identify road anomalies. If production vehicles could be used to identify road anomalies, then road authorities would be equipped with an additional fleet of mobile sensors (vehicles traveling on a particular road) to receive initial insights into the presence of anomalies. This information could then be used to assist road authorities to deploy their staff and equipment more precisely at these locations, such that appropriate equipment reaches the right place at the right time. In this paper, an algorithm that uses lateral acceleration and individual wheel speed signals, which are commonly available vehicular variables, was developed to detect potholes using machine learning techniques. The results of the algorithm were validated with real life test scenarios.

Development of road sector’s lend valuation method for aims of asset management

The object of research is the land management processes of the road sector. The studies are based on the use of a multistructure approach, the basic principles of the theory of complex systems and the methodology of AHP (Analytic Hierarchy Process) by Thomas Saaty. The main hypothesis of the study is the application of a multi-structure approach to valuing assets, which are not comprehensively evaluated in the study, but are considered as complex objects and are divided into simpler levels, components and elements that can be evaluated separately. The features of the valuation of land of the road sector are considered, and the main stages of the evaluation sequence are identified. It has been established that the land valuation is the main component of the valuation of the asset of the road sector, which is located on it. The results of a theoretical study make it possible to develop an algorithm for valuing land of the road sector for asset management purposes. The developed algorithm has a clear sequence in the formation of the methodology for the land valuation of the road economy, it makes it possible to justify the adoption of managerial decisions regarding the land plot, which are consistent with the goals and correspond to the nature of the tasks of the functioning and development of the road sector. The testing of the developed algorithm is performed on a real example. It is found that the algorithm allows to optimize the process of valuation and management of road assets, which is especially important within limited financial resources. The choice of land valuation options is based on criteria of economic efficiency, based on the main limitations and features of land valuation of the road sector. The results of the calculations can be a weighty basis for the formulation of managerial goals for the assets of the road sector and will form the basis for further scientific studies of the process of valuation of assets under financial constraints.

An overview road data collection, visualization, and analysis from the perspective of developing countries

Road data collection went through dramatic changes over the course of technology enhancement. It started with simple paper sketches of pavement distresses, then manual and automatic road data collection, current trends are mobile phone-based road data collection and in near future connected/autonomous vehicles based road data collection will dominate. The evolution of road data collection methods shows how technology is shaping the way how data is collected, stored and analyzed. Even though the technology is rapidly progressing, most of the developing countries are still practicing manual (visual) road data collection practices. The paper overviews the advantages and disadvantages each approach describes data quantity requirements proposes an approach for road asset management systems in developing countries. It also highlights how to visualize road data by utilization open-source GIS software and briefly overviews turnkey solutions for road asset management.

Automatic Road Survey by Using Vehicle Mounted Laser for Road Asset Management

In most countries local roads (i.e., urban and rural) form over 80% of the entire road network and constitute the country's largest asset value. In order for local roads to remain fit for purpose and maintain their value, they require periodic maintenance. To make the best use of scarce maintenance resources, road maintenance needs to be preventative which requires the condition of the road to be assessed periodically. Traditional road surveys suffer from the lack of repeatability and reproducibility, are high cost and time consuming. This work proposes a vehicle mounted point laser system for the automated, rapid and inexpensive measurement of a major mode of local road deterioration, namely fretting. Compared to other technologies such as Ground Penetrating Radar (GPR), visual sensors and the Mobile Laser Scanning (MLS) system, the point laser requires less computational power, is less sensitive to the surrounding environment and is of comparatively low cost. A robust approach is proposed which consists of a number of pre-processing algorithms to deal with noise and the effects of the vehicles dynamic motion, and a signal processing algorithm which analyses histograms of the distance from the road surface measured by the laser to account for changes in road texture. Road fretting measured by the proposed system on a variety of roads is compared with fretting determined using a standard visual assessment process. The results indicate that the proposed system can measure road fretting to the levels of detail which are suitable for planning, programming and preparations road management functions.

Post-Disaster Reconstruction of Road Infrastructure

The rehabilitation and reconstruction of damaged road infrastructure plays a vital role in the recovery of disaster affected regions. The methods and processes adopted by road-asset owners during the reconstruction phase influence the longer term effects in disaster hit communities. While the decision making processes are intended to reduce impacts, mistakes at the decision making stage can lead to an increase in social and economic impacts in the longer term. It is thus imperative to understand how decision making takes place with regard to post-disaster reconstruction of road infrastructure. The objective of this paper is to understand how road asset owners assess and prioritise post-disaster reconstruction projects in order to identify how decision making could be improved in Australia and similar regions. The results of in-depth interviews conducted with road infrastructure practitioners in disaster affected regions are presented. The findings showed that there is a gap between the research community and practitioners in the use of systematic methods to aid prioritisation and decision making. The interviews also showed that the consideration of only a limited set of engineering and financial elements can lead to unintended consequences that impede resilience. A causal loop diagram was developed to illustrate the interrelationship between factors and showed that active intervention based on a systems thinking approach will benefit post-disaster decision making. These findings suggest that the development of more localised decision making tools can increase their adoption among practitioners.

A Comparison of Network Level Pavement Condition Assessment in Road Asset Management

Transportation agencies face the challenging task to maintain, preserve and improve infrastructure condition while with limited funding. Pavements are one of the major assets of roadway systems and pavement management system (PMS) are broadly accepted and implemented by agencies and organizations to maintain pavement structures at a high level of service. PMS is a set of tools to support the decision-making process for determining the demand of maintenance, prioritizing projects and optimizing funding allocation. Pavement condition monitoring may be evaluated or assessed by means of various indicators. Performance indicators are an essential part in a PMS, individual performance indicators (IPIs) and combined performance indicators (CPIs) are proposed to monitor and report pavement conditions. IPIs characterize the general condition of the various types of pavement distress which can be related to road performance. The CPI for each road type can be developed or calculated from IPIs. Focus on network level analysis of road pavements, the objective of this paper is to review and compare the development and application of performance indicators for assessment of pavement condition of different country’s guidelines. The utilization and integration mechanism of individual indicator are described and compared among selected country guidelines. The prospective indicators and techniques for future application are further discussed. It can be conclude from this study that the majority studied guidelines have placed great emphasis on surface distress and roughness for pavement condition assessment; international roughness index (IRI) is the most commonly used parameter for evaluation of road roughness due to its objectivity while the determination of surface distress is more subjective. The integration methods from IPIs into CPIs can be summarized as “deduct system method”, “sum system method”, “weighted sum method” and “equation method”.

Road Asset Management Practices in Ethiopia the Case of Addis Ababa

Currently, the construction industry is one of the biggest industries in Ethiopia contributing 10% to the Gross Domestic Product (GDP). It needs to be emphasized that road construction in Ethiopia is the means through which development are achieved. The economic growth of the country depends on availability and utilization of physical infrastructures. Road construction and utilization in Ethiopia are the means through which development strategies are achieved. The development of road construction projects can be enhanced through well-established Asset Management system that will improve tactical and operational decisions by applying the required knowledge to gain wisdom for provision of strategic infrastructures. However, most of the road construction in Ethiopia encounters problems due to lack of proper Asset Management practices. In this respect, this study’s objective was to investigate Roads Asset Management practices in Ethiopia the case of AACRA. In order to find answers to the research questions and to achieve the objectives of the study, quantitative and qualitative ways of research adapted. Interviews and questionnaires analyzed quantitatively whereas the desk study analyzed qualitatively explanatorily to assess alternatives, diagnose a situation, and discover new ideas. Accordingly, interpretation and discussion were made on the basis of results. The findings of the study revealed that there is lack of: Asset management system components implementation rated as low with a result of 54.5%, 63.6%, 64%, 82% and 73% respectively except condition assessment rated as medium with a result 45.5%, technologies like individual management system implementation in AACRA is in the planning phase as the response of the (7) professional in aggregate revealed, Integration of those management systems also is not there yet. Decision making levels are not explicitly defined in AACRA except programming and budgeting level rated as 54.5%. 91% of respondents revealed that there is lack of professional associations, conferences, short courses and 73% of the respondents disclosed that there is lack of organized asset management department. Some of the specific recommendations forwarded are decision making levels should be explicitly defined, applying different individual management systems and integration of those management systems is necessary and etc. Finally framework were developed for effective and efficient Road Asset Management.