Road Classification Research Articles

Evaluating the Effectiveness of Mulching for Reducing Soil Erosion in Cut Slope and Fill Slope of Forest Roads in Hyrcanian Forests

Forest operations often enhance runoff and soil loss in roads and skid trails, where cut slopes and fill slopes are the most important source of sediment. This study evaluated the effectiveness of four erosion control treatments applied to cut slope and fill slope segments of forest roads of different ages in the Hyrcanian forest in northern Iran. The treatment combinations, each replicated three times, included four classes of mulch cover (bare soil [BS], wood chips cover [WCH], sawdust cover [SC], and rice straw cover [RSC]), two levels of side slope (cut slope and fill slope), two levels of side slope gradient (20–25% and 40–45%), and three levels of road age (three, 10 and 20 years after construction). Mulch cover treatments significantly reduced average surface runoff volume and sediment yield compared to BS. Regardless of erosion control treatment, greater surface runoff volume and soil loss under natural rainfall occurred on steeper slope gradients in all road age classes and decreased with increasing road age on both slope gradients. On cut slopes, average runoff and soil loss from the plots covered with WCH (17.63 l per plot, 2.43 g m–2) was lower than from those covered with SC (22.81 l per plot, 3.50 g m–2), which was lower than from those covered with RSC (29.13 l per plot, 4.41 g m–2 and BS (34.61 l per plot, 4.94 g m–2). On fill slopes, average runoff and soil loss from the plots covered with WCH (14.13 l per plot, 1.99 g m–2) was lower than from plots covered with SC (20.01 l per plot, 3.23 g m–2), which was lower than from plots covered with RSC (24.52 l per plot, 4.06 g m–2) and BS (29.03 l per plot, 4.47 g m–2). Surface cover successfully controlled erosion losses following road construction, particularly on steep side slopes with high erosion potential.

Road surface type classification based on inertial sensors and machine learning

The demand for several sources of situational data from the traffic environment has intensified in recent years, through the development of applications in intelligent transport systems (ITS), such as autonomous vehicles and advanced driver assistance systems. Among these situational data, the road surface type classification is one of the most important and can be used throughout the ITS domain. However, in order to have a wide application, the development of a safe and reliable model is necessary. Therefore, in addition to the application of safe technology, the model developed must operate correctly in different vehicles, with different driving styles and in different environments in which vehicles can travel to. For this purpose, in this work we collect nine datasets with contextual variations using inertial sensors, represented by accelerometers and gyroscopes. These data were produced in three different vehicles, with three different drivers, in three different environments in which there are three different surface types, in addition to variations in conservation state and presence of obstacles and anomalies, such as speed bumps and potholes. After a pre-processing step, these data were used in 34 different computational models for road surface type classification, employing both Classical Machine Learning and Deep Learning techniques. Through several experiments, we analyze the learning and generalization capacity of each technique. The best model developed was a CNN-based deep neural network, which obtained validation accuracy of 93.17%, classifying surfaces between segments of dirt, cobblestone or asphalt roads.

RCNet: road classification convolutional neural networks for intelligent vehicle system

Vision-based techniques for intelligent vehicles in heterogeneous road environments are gaining significant attention from researchers and industrialists. Unfortunately, the mechanisms in this domain suffer from limited performance due to scene complexity, varying road structure, and improper illumination conditions. These challenging situations may lead an intelligent vehicle into dangerous situations such as collisions or road accidents and may cause higher mortality. The application of intelligent methods and other machine learning techniques for road surface classification is little explored in the existing literature. Thus, we propose a convolutional neural network-based road classification network (RCNet) for the accurate classification of road surfaces. This procedure includes the classification of five major categories of road surfaces: curvy, dry, ice, rough, and wet roads. The experimental results reveal the behavior of the proposed RCNet under various optimizer techniques. The standard performance evaluation measures have been used to test and validate the proposed method on the Oxford RobotCar dataset. RCNet achieves classification accuracy, precision, and sensitivity of 99.90%, and 99.97% of specificity. Results of implemented work are significantly higher than available state-of-the-art techniques and show accurate and effective performance in the complex road environment.

Nonlinear Vibration Control Device for a Vehicle Suspension Using Negative Stiffness Mechanism

Vibration suspension system is used in the automobiles to mitigate the excitations that can come from the travel over various road profiles. The conventionally used suspension systems are linear devices that are effective against high frequencies; however, they have limitations in isolation ability in the low-frequency ranges. Hence, nonlinear isolation system can be used to overcome such limitations. The quasi-zero stiffness (QZS) characteristics are efficient in the vibration isolation and can provide isolation efficiently against low-frequency excitations as well. This concept is taken into consideration and has designed a nonlinear vibration isolation system with QZS behaviour. The negative stiffness is achieved through the inclined spring arrangement, and the positive stiffness is obtained from the pneumatic system. The force–displacement model is used to develop the approximate dynamic modelling for small displacement. The performance is studied against various road profiles as per ISO 8608. The results showed that the isolation system effectively isolates the external excitations from the road profile and shows a high isolation capability in the higher velocities. The study shows the proposed system reduced on an average of 92% and 88% of external excitations in road class B–D and E–H, respectively. Based on the computational study conducted in this work on the developed design, the isolation system is found to be effective in vibration isolation. The QZS system parameters highly influence the performance of the isolation system. The system developed in this work can be used to test for real-time automobile applications.

Development of a methodology for monitoring the environmental impact of waste of the year-round maintenance of highways

The analysis of environmental impact of the public highway maintenance was carried out. The dynamics of the road traffic, the length of highways and the road safety was revealed for the 1990-2019 period. The analysis of the existing regulatory framework for monitoring the impact on the environment in the year-round maintenance of highways has been carried out. The system of placement of monitoring points depending on the category of road, the category of land, the presence of roadside forest belts with the definition of zones of impact on the environment by various groups of pollutants was substantiated.

Monitoring of Road Conditions Based on Geographic Information Systems: A Case Study of Yangiyul District of Tashkent Region, Uzbekistan

Modern requirements for road management require the use of new technologies and methods in solving the problems of construction, reconstruction and maintenance of roads. Currently, road organizations have begun work on the creation of a geographic information system for roads; digital maps with the location and layer of roads are being assembled. Information about roads, is very important data for many social-economic sectors of the Republic of Uzbekistan. In this paper, creating geospatial digital and informative map of roads of Bukhara district of Tashkent region is revealed. The map covers all types and categories of roads. All availability conditions of roads inserted to attribute table of ArcGIS 10.2 and analyzed geospatial operations. On maps, created by not road organizations, information about roads is presented in a standard form, which leads to the lack of modern information on the condition of roads.

Pathways of crime: Measuring crime concentration along urban roadways

Some urban spaces are associated with disproportionate numbers of criminal events, while other areas are relatively free from disorder and crime. The relationship between urban space and crime concentration has received increased attention in recent years, with the location quotient frequently presented as a tool to identify and quantify such concentration. This measure has several limitations, with one significant concern surrounding the choice of denominator with which to standardize local and global rate calculations. In response, we present a new methodological adaptation to the location quotient, improving the measurement of crime concentration along linear features. To test this adaptation, we measure how crime concentrates by road classification at both a macro and micro level within two Canadian suburban municipalities. Using transportation network data, we identify the road types that are associated with a disproportionate concentration of criminal events, and illustrate how these relationships change alongside the level of aggregation. Results support the use of the adapted location quotient, finding that criminal events concentrate along specific road types, and emphasize the importance of spatial scale in understanding local relationships between crime and the built urban landscape.

Implementation of Dark and Raining Environmental Conditions to Investigate Lane Support System Performance for Passenger Cars in Southeast Asia: Case Study for Malaysian Domestic Road

This paper presents a study on the implementation of dark and raining environment test conditions to investigate Lane Support System (LSS) performance for passenger cars in Southeast Asia. The case study has been performed for Malaysian domestic roads. LSS systems involved have been the Lane Departure Warning (LDW) and Lane Keep Assist (LKA). The literature review conducted has revealed that there is not yet a similar study performed for vehicle LSS system. The motivation of this study has been to identify the effect of both environmental conditions on the vehicle LSS performance. This pioneering work is also aimed to help in establishing a new LSS test protocol that could reflect better the driving situations in Southeast Asia countries. The experimental works have been carried out at state road in Melaka, Malaysia. LSS on-road test parameters have been adopted from LSS test protocols from the European New Car Assessment Programme (Euro NCAP) and the Japan National Agency for Automotive Safety and Victim’s Aid (NASVA). A new test procedure to carry out the study has been also developed in this study in order to complement the referred test protocols, including customized rain-making testing equipment to simulate the raining environment conditions. Results from the study have showed that both LDW and LKA systems performance could successfully be evaluated when subjected to dark and raining environment conditions using the new test procedure. Furthermore, the new test procedure developed in this study is also highly applicable for implementation in future LSS on-road tests involving any type of road category (both domestic and highway) with minimal setting up process.

Mapping Drainage Structures Using Airborne Laser Scanning by Incorporating Road Centerline Information

Wide-area drainage structure (DS) mapping is of great concern, as many DSs are reaching the end of their design life and information on their location is usually absent. Recently, airborne laser scanning (ALS) has been proven useful for DS mapping through manual methods using ALS-derived digital elevation models (DEMs) and hillshade images. However, manual methods are slow and labor-intensive. To overcome these limitations, this paper proposes an automated DS mapping algorithm (DSMA) using classified ALS point clouds and road centerline information. The DSMA begins with removing ALS ground points within the buffer of the road centerlines; the size of the buffer varies according to different road classes. An ALS-modified DEM (ALS-mDEM) is then generated from the remaining ground points. A drainage network (DN) is derived from the ALS-mDEM. Candidate DSs are then obtained by intersecting the DN with the road centerlines. Finally, a refinement buffer of 15 m is placed around each candidate DS to prevent duplicate DS from being generated in close proximity. A total area of 50 km2, including an urban site and a rural site, in Vermont, USA, was used to assess the DSMA. Based on the road functional classification scheme of the Federal Highway Administration (FHWA), the centerline information regarding FHWA roads was obtained from a public data portal. The centerline information on non-FHWA roads, i.e., private roads and streets, was derived from the impervious surface data of a land cover dataset. A benchmark DS dataset was gathered from the transport agency of Vermont and was further augmented using Google Earth Street View images by the authors. The one-to-one correspondence between the benchmark DS and mapped DS for these two sites was then established. The positional accuracy was assessed by computing the Euclidian distance between the benchmark DS and mapped DS. The mean positional accuracy for the urban site and rural site were 13.5 m and 15.8 m, respectively. F1-scores were calculated to assess the prediction accuracy. For FHWA roads, the F1-scores were 0.87 and 0.94 for the urban site and rural site, respectively. For non-FHWA roads, the F1-scores were 0.72 and 0.74 for the urban site and rural site, respectively.

Priority-based optimisation model for developing CIP using PASER for city roads: a case study

ABSTRACT This study developed a priority-based optimisation model for Pavement Management Systems (PMS) using Pavement Surface Evaluation and Ratings (PASER) for city roads. By synchronising the available budgets and pavement conditions, the Capital Improvement Plan (CIP) ensures that with a limited annual budget, the overall pavement condition is optimised. Factors used to develop the CIP include: budget, road length, road classification, PASER, and treatment types. The optimisation methodology used in this study selects the best set of projects to complete while maximising the budget and weighted average PASER. The methodology used in this study were tested using the city of Fort Wayne, Indiana as a case study.

Influence of road conditions on vehicles’ fuel consumption

The work is devoted to the solution of the issue of economic and safe transportation of goods and passengers by road. This transportation depends on the condition of roads, road surface, vehicle type and weather conditions. Weather conditions are taken into account both in terms of visibility (meteorological) and in terms of the coefficient of adhesion. The general criterion for assessing all conditions is the average speed of the vehicle, taking into account weather and road conditions. Weather conditions are determined by the type of visibillity: clear weather, rain, snowfall, blizzards, rain. By the coefficient of adhesion: dry surface, normal, wet, snow, ice. By road conditions: category of road, width of the travel section, radii of horizontal curves, longitudinal slopes, width of the road, the state of surface (coefficient of solidity). According to weather conditions, the calendar year is divided into three periods according to the conditions of cars’ movement. The first (winter) - December, January, February, March; second (spring-summer) - April, May, July, June, August; third (autumn) - September, October, November. The use of weather conditions in the Northern regions of Ukraine is presented in this work.

Spatial Analysis of Healthcare Facilities in the Federal Capital Territory, Abuja Nigeria

Over the second half of the twentieth century, the world has experienced enormous health improvements from the use of various technologies in the diagnosis, treatment, monitoring and reporting various disease. However, developing countries have benefited unequally from health gains, with many, especially in Sub-Saharan Africa (SSA), continuing to experience high mortality. This study aims to carry out a spatial analysis of healthcare facilities in the Federal Capital Territory (FCT) Abuja, Nigeria with a view of identifying the pattern of distribution of healthcare facilities. This aim was achieved by identifying and mapping the various health facilities in the FCT. The directory of Hospitals was collected from the Hospital Management Board of the FCT and the geographic location were collected using a handheld GPS device. The administrative map of Abuja and the Area Councils were digitized to obtain shapefiles for various classes of roads, railway lines, waterways and boundaries of the area councils. A geospatial database was created in the ArcMap 10.7 environment using the data collected. The locations of all healthcare facilities were plotted, and the spatial spread of healthcare facilities were analyses and queried. The healthcare facilities distribution shows a clustered pattern but underserving the study area with an average nearest neighbor ratio (NNR) for healthcare facilities in the study of 0.34 (p < 0.001) and the Z-score was −40.9 (p < 0.001), the low Z-score value indicate there is a less than 1% likelihood that this clustered pattern could be the result of deliberate planning. The healthcare facilities distribution pattern suggests a disorderly and inefficient distribution of healthcare facilities. Owing to the significance of the underserving of the urban area councils by healthcare facilities, policymakers may consider placing additional healthcare facilities to reduce the stress on the available healthcare facilities and personnel. Keywords: Spatial Analysis, GIS, Healthcare, Facilities DOI: 10.7176/JEES/11-1-06 Publication date: January 31 st 2021

Automated generation of traffic signals and lanes for MATSim based on OpenStreetMap

Abstract OpenStreetMap (OSM) offers information on traffic networks like the position of intersections and streets, road categories, speed limits, number of lanes etc. Based on this information, network input data for transport simulations, e.g. the agent-based transport simulation MATSim can be generated. A MATSim network is modeled as a graph with nodes (intersections) and links (streets). Previously, data about traffic signals and lanes could only be provided to MATSim simulations by manual data preparation or had to be emulated by reducing flow capacities of links that lead towards signalized intersections. To be able to model inner-city traffic more realistically, this study extends the input generation of MATSim. Information about traffic signals and lanes is extracted from OSM and automatically processed to create signal and lane input files for the simulation. Processing data about traffic signals and lanes is required to optimize fixed-time plans, as well as to develop and validate algorithms for traffic-adaptive signals. This study provides the basis for further development of traffic lights in the transport simulation MATSim. An application of a MATSim simulation including traffic signals and lanes in the Berlin metropolitan area verifies the developed method.

Farm Vehicle Crashes on U.S. Public Roads: A Review Paper

Farm vehicles (FVs) are not primarily designed for road transportation purposes, but often need to share the roads with other motorists. The interference of these large, slow-moving vehicles with the regular traffic can lead to hazardous situations and cause severe crashes. Farm Vehicle Crashes (FVCs) are rare compared to the total number of road crashes, and hence, have been neglected by many. This has been reflected in various aspects including data collection, education, legislation, and prevention. The number of FVCs in the U.S. has been steady over the last decade. This paper conducts a literature review to put forth a better understanding of this safety issue from a transportation standpoint. The main goal of this study is to identify the characteristics of FVCs, the unique contributing factors in FVCs, and possible strategies to mitigate the issues involved. It appears that despite their lower frequency, the rate of severe injuries and fatalities in FVCs is significantly higher than the average for all crashes. The percentage of fatal crashes in FVCs can be nearly five times higher than average for all road crashes. Further, it is estimated that FVs are overrepresented in road crashes considering their low exposure on the roads. The crash contributing factors including time- dependent variables, weather and road surface conditions, location, road classification, manner of collision, and driver’s characteristics are discussed based on the result of multiple studies in different states of the U.S. Several unique characteristics of FVCs distinct them from non-farm vehicle crashes which can result from the characteristics of FVs, rural roads, and the nature of farming tasks. Improvements in lighting and marking, equipping farm vehicles with Rollover Protective Structure (ROPS) and seatbelts, educating the public and farmers, and roadway improvements are recommended as potential safety improvement strategies for mitigating the frequency and severity of FVCs.

Картографирование сезонной автотранспортной доступности Арктического региона России

Transport accessibility is one of the main categories in transport research and planning of socio-economic development of territories. For Russia, which has the largest area of the northern and arctic territories, this problem is especially urgent. The presence and condition of the road network determines the territorial integrity and unity of the economic space, therefore fulfilling the most important strategic task of the spatial integration of the country. Automobile transport in the Arctic zone of the Russian Federation is poorly developed and is characterized by a low density of the public network. Its main features are the disunity of the road network, the heterogeneous degree of transport development of the territories and the noticeable role of winter roads in the road infrastructure. An integral part of the transport logistics of the Arctic regions of Russia are winter roads, which allow transportation in the winter season at the lowest cost. The purpose of this study is to develop a database structure and thematic mapping of automobile roads in the Arctic zone of the Russian Federation, taking into account the seasonality of their operation. As a result of this work, a classification of winter automobile roads and ice crossings of the Arctic territories was developed and an overview map “Seasonal road transport accessibility of the Arctic region of Russia” at a scale of 1:20,000,000 was compiled. The map demonstrates the contrast between the relatively good winter transport development of the territory and summer impassability. On the basis of statistical materials, maps “Density of all-season motorable roads” and “Level of motorization” were compiled, which give an idea of the differentiation of the regions of the Arctic zone of the Russian Federation by these indicators. A high level of motorization significantly exceeds the capacity of the existing roads. This is due to the fact that off-road transport is the only guarantor of mobility for the population of hard-to-reach settlements.

Influence of requirements in automotive roads and their status on the estimation of propertty valuation characteristics

Features of the structure of the road economy, as part of the country's economy, affect the creation of a systematic methodology for assessing its property, when individual components will determine the peculiarities of the use and development of road infrastructure. To this end, for the purpose of assessing the property status of highways, which are the main component of the road economy, it is recommended to develop different models of approach to the definition of property valuation of highways. In the article the directions and models of estimation of the property condition of highways are offered, with the use of different concepts of determining their needs in the infrastructure of the region's economy and the need to maintain them in the proper operating condition. The combination of needs and the state of highways is used as a conceptual criterion for the formation of models for their evaluation as property in the structure of the region's economy. According to this criterion, a road is considered as an object which: A - provides social and administrative needs for the organization of motor transport in the region and the implementation of passenger transportation; B - can make a profit as a result of its use; C - is a part of the main productive assets of the region and the state as a whole, which should be maintained in good condition; D - a national economic object of a specific purpose, which is used to provide activities of individual sectors of the economy. The proposed structural classification of highways according to the conceptual criterion of their needs and conditions - A, B, C and D - is the basis for the formation of different models for assessing their property status, the peculiarities of which are discussed in the article. The main principle of such a classification of motor roads and transport facilities in general for the purpose of their property assessment is that they are considered as objects or sub-entities in the models of calculating the property of owners or users of transport facilities.

Locally invariant analysis of forest road quality using two different pulse density airborne laser scanning datasets

Two different pulse density airborne laser scanning datasets were used to develop a quality assessment methodology to determine how airborne laser scanning derived variables with the use of reference surface can determine forest road quality. The concept of a reference DEM (Digital Elevation Model) was used to guarantee locally invariant topographic analysis of road roughness. Structural condition, surface wear and flatness were assessed at two test sites in Eastern Finland, calculating surface indices with and without the reference DEM. The high pulse density dataset (12 pulses m) gave better classification results (77% accuracy of the correctly classified road sections) than the low pulse density dataset (1 pulse m). The use of a reference DEM increased the precision of the road quality classification with the low pulse density dataset when the classification was performed in two-steps. Four interpolation techniques (Inverse Weighted Distance, Kriging, Natural Neighbour and Spline) were compared, and spline interpolation provided the best classification. The work shows that applying a spline reference DEM it is possible to identify 66% of the poor quality road sections and 78% of the good ones. Locating these roads is essential for road maintenance.–2–2

A Survey on Motion Prediction of Pedestrians and Vehicles for Autonomous Driving

Autonomous vehicle (AV) industry has evolved rapidly during the past decade. Research and development in each sub-module (perception, state estimation, motion planning etc.) of AVs has seen a boost, both on the hardware (variety of new sensors) and the software sides (state-of-the-art algorithms). With recent advancements in achieving real-time performance using onboard computational hardware on an ego vehicle, one of the major challenges that AV industry faces today is modelling behaviour and predicting future intentions of road users. To make a self-driving car reason and execute the safest motion plan, it should be able to understand its interactions with other road users. Modelling such behaviour is not trivial and involves various factors e.g. demographics, number of traffic participants, environmental conditions, traffic rules, contextual cues etc. This comprehensive review summarizes the related literature. Specifically, we identify and classify motion prediction literature for two road user classes i.e. pedestrians and vehicles. The taxonomy proposed in this review gives a unified generic overview of the pedestrian and vehicle motion prediction literature and is built on three dimensions i.e. motion modelling approach, model output type, and situational awareness from the perspective of an AV.

Road classification based on SVM and vehicle body vibration

Obtaining road surface information to make the vehicle run in the best condition can not only reduce energy consumption and vehicle loss, but also improve driving safety. In this paper, specific car body information was preprocessed as root mean square value, and SVM offline training was used. The recognition rate of off-road and highway can reach 98%. Compared with traditional threshold recognition, SVM has better adaptability and robustness. On the premise of keeping easy to obtain, the discrimination accuracy of the root mean square value is obviously better than the original value and the mean value.

Simulation of Pavement Random Excitation Based on Harmonic Superposition Method

The road input model is very important in the analysis of vehicle ride comfort and handling stability. Based on the analysis of the relationship between the spatial frequency power spectral density and the time power spectral density of the road, the road signal generation model is established. The simulation is carried out under different vehicle speeds, and the B and C-level random road time excitation signals are generated. The power spectral density is used to compare the simulation results of the model with the road classification standard. The experimental results show that the results are accurate and can provide reliable excitation signals for vehicle control research.