Road Classification Research Articles

Identify Location Characteristics of Tourism Accommodation that are Influenced by e-Commerce Case Study: Lembang Tourism Area, Kab. West Bandung

The presence of the Industrial Revolution 4.0, which is marked by the rapid development of the internet, has fundamentally changed the way humans live, work, and communicate with each other. This development has also influenced the development of the business of providing accommodation in tourist areas. The ease of obtaining information via an internet network that is connected to regional areas opens up opportunities for the public to market tourist accommodation through e-commerce accommodation. The Lembang tourist area is an area with high potential for the development of e-commerce accommodation, covering 25.64% of tourist attractions in West Bandung Regency. This research aims to determine trends and developments in the characteristics of business locations providing accommodation in the Lembang tourist area based on observations via digital platforms. Data was obtained using a spatial analysis method with two different functions. The classification function is used to review location characteristics in businesses providing accommodation based on road class. The neighborhood function is used to review location characteristics based on proximity to tourist centers. The data was then analyzed descriptively and qualitatively. The results of the research show that the development trend of e-commerce accommodation in accommodation provision businesses continues to grow from year to year. Observation results via digital platforms show that as many as 136 accommodation business units were incorporated into e-commerce accommodation in 2019 in the Lembang tourist area. The location characteristics of the business providing accommodation tend to be concentrated around tourist areas with a radius of 1 km from the tourist center. Judging from the characteristics of business locations providing accommodation on roads, it is known that 59% of business units are predominantly located on neighborhood roads, while 41% are on main roads. The ease of access offered by this digital platform influences the development of business providing accommodation and location on the road class but does not significantly affect the distance to tourist centers.

The Measurement of the Noise Level Based on Roads Classifications in Urban Area (Case Study: Harjamukti, Depok)

Harjamukti Village is located in Depok City which is a buffer zone for the capital city of DKI Jakarta, the rapidly growing housing sector shows that this location has an attractiveness. The next problem that arises is the potential for noise from vehicular traffic due to community mobility. Noise measurements carried out using SNI 8427: 2017 show the LSM value on each road: Tumaritis Road (68.87 dBA), Bungur Road (61.29 dBA), Putri Tunggal Road (73.67 dBA), Putri Tunggal District Road (65.7 dBA) and Sementara Road (61.29 dBA). This study was conducted to see the road class based on the noise and capacity parameters, so that from these two parameters, can we clearly show the road class being analyzed. The measurements were carried out for 24 hours using the standard SNI 8427: 2017. The quality standard for the residential area used was 55 Dba based on the Minister of Environment Decree No. 48 of 1996. The road capacity used is analyzed based on the number of vehicles and the class of each road. Based on the noise parameters (sig 0.006) and capacity (sig 0.000) so that from the noise parameter there is no significant difference in the 5 research locations, while the road capacity shows a significant difference from the 5 research locations. Prediction of road class cannot be determined from the noise value and road capacity, because the noise obtained needs to be analyzed for the types of activities around whether there is an influence from other activities that are also dominant, such as toll roads and road capacity, because there are local road classes whose capacity is not detected different from collector road class.

Analisa Sistem Penerangan Lampu Jalan Di Desa Kenten Laut Banyuasin

In evaluating street lighting systems, the main factors are safety, comfort, and beauty for road users who must receive special attention. Street lighting is very useful at night or during dark conditions such as rain or cloudiness. Therefore, the authors evaluate the street lamp lighting system on the street .A.Nakowi .No17.Rt14 in the village of KentenLaut. From the results obtained, the length of the road is 240 meters with a road width of 4 meters for the calculation of the installation of street lights based on the reality in the field by taking into account road conditions and road classification. The lamp used is SON-T 150W, after an evaluation, the street lamp lighting system on the street. A.Nakowi.No17..Rt14.In the village of KentenLaut does not meet the standards, so the street lamps in the village of KentenLaut must be repaired or replaced with lamps so that the lighting can match the standard of street lamp lighting, the standard of good street lamp lighting is 20 lux so that road users feel comfortable with bright lighting.

IMPROVING THE EFFICIENCY OF PROCESS MANAGEMENT IN THE FIELD OF TRAFFIC SAFETY ACCORDING TO ADDITIONAL CRITERIA

Known methods of assessing the level of traffic safety on highway sections are considered. When developing or improving road safety assessment methods, it is necessary to establish, among other things, the influence of variable road factors on traffic safety and to give a quantitative and qualitative assessment of the degree of their change. Factors that can significantly affect forecasting accuracy have been identified. All methods of detecting dangerous areas that are currently available are offered, based on traffic accident statistics. At different times, the following methods were proposed: analysis of statistical data by the methods of probability theory; use of multivariate correlation analysis data; analysis of traffic speed charts (methods of safety factors and "acceleration noise"); analysis using coefficients of relative influence of individual elements of the road (method of accident rates); method of conflict situations. It is proposed to improve the existing method by introducing additional parameters, which were obtained as a result of the formalization of accident statistics at the places of concentration of traffic accidents. It is proposed to evaluate traffic safety conditions by the final sum of points (on a 100-point scale), which takes into account a number of road construction characteristics. In our opinion, in conditions where the assessment of road safety conditions is entrusted to the representatives of the highway owner, this method looks more effective, since the road owner does not always or does not fully possess reliable statistical information about road accidents in the area being evaluated. The proposed method of evaluating the level of traffic safety is an undoubted step forward on the path of simplification and clarification existing methods. In the future, work on the construction of a simulation model of the interaction of the driver and the road as a subsystem of the general system "driver-car-road-environment" looks promising, by relying on criteria that were previously neglected - technical means of traffic management. This will make it possible to calibrate the proposed method and transfer its action to the software environment. In this way, the research will receive a significant platform for the involvement of additional variables - taking into account the road category, time of day, informativeness of technical means, their deformability, etc.

Identification of illumination source types using nighttime light images from SDGSAT-1

ABSTRACT The constant need for decarbonization has led to the replacement of artificial light at night (ALAN) with light-emitting diodes (LEDs), inducing blue light pollution and its consequent adverse effects. As a result, there is an urgent need for the development of a technique for the rapid, accurate, and large-scale discrimination of the various illumination sources. The newly launched Sustainable Development Science Satellite-1 (SDGSAT-1) can play this role by supplementing the existing nighttime light data with multispectral and high-resolution features. Along these lines, in this work, a novel approach to identify various types of illumination sources using machine learning in SDGSAT-1 images was proposed, taking Beijing as a worked example. The results indicate that: (1) The method can effectively distinguish the various types of light sources with an overall accuracy of 0.92 for ALAN and 0.95 for streetlights. (2) The illumination patterns can be clearly depicted, indicating distinct spatial heterogeneity in ALAN along Beijing’s 5th Ring Road. (3) Statistically significant disparities between road classes and streetlight types were detected, with a notable increase in LED streetlight usage as the road class diminishes. This work emphasizes the crucial role of SDGSAT-1 in analysing ALAN, providing valuable insights in urban lighting management.

Desain lapisan Perkerasan Jalan Pada Ruas Jalan Desa Mbangga – Woloboa Kecamatan Wolowaru Kabupaten Ende

The Mbangga-Woloboa road section, Rindiwawo Village, Wolowaru District is included in the local road type with rigid pavement and a road width of 3 meters. Every year the condition of the Mbangga-Woloboa road section experiences damage, where several parts of the road have many holes and when it rains there are lots of puddles of water, which is very disturbing for road users. Even though the Ministry of Public Works, with the available funds, has repaired the existing damage by overlaying it repeatedly, it has not been able to solve the existing problem completely because the road condition has deteriorated again before it reaches its design life. Therefore, it needs to be redesigned by repairing the subgrade layer and replacing the road surface layer with new rigid pavement according to the load of vehicles passing through the road. So by using the AASHTO method, the traffic growth rate (i) of the planned road is 5.23% per year with collector road class. The effective Resilient Modulus (MR) value on the road section is 2770 psi with a CBR value of 1.87%, and DDT 2.70 kg/cm2. The thickness of the pavement layer is found to be D1 = 13 cm, D2 = 20 cm, and D3 = 41 cm.

A novel framework for road vectorization and classification from historical maps based on deep learning and symbol painting

Road networks in the past are imperative for understanding evolution of transportation infrastructure, urban sprawl, and route planning, etc. Various approaches have been developed for road extraction from historical maps, among which deep learning techniques stand out as the most effective ones. However, little attention has been paid to investigating road vectorization and classification from historical maps. Moreover, road classification via machine learning methods usually requires large amounts of dedicated training data. To address these issues, this paper proposes a novel and comprehensive framework for road vectorization and classification on the basis of road segmentation from historical maps. First, deep learning is used to get pixel-wise raster road segmentation results, which are further skeletonized using morphological operations. Then, considering that each road class is represented with a certain symbol, a painting function is defined for each class able to paint the corresponding symbol. These painting functions are then used to draw road segments along the skeletons. Since the start and end points in each painting function are used to vectorise the segment, this method achieves vectorization and classification at the same time. Our method is validated on four Siegfried map sheets in Switzerland, and evaluated via both visual and quantitative assessments. The results indicate that the method is capable of classifying roads accurately. In particular, two evaluation metrics completeness and correctness achieve 90.69% and 72.71% respectively for road class 2 which accounts for the highest portion in the map. Moreover, the results of this method avoid the saw-toothed issue of vectorised road lines. This research is beneficial for creating complete vector road network datasets with class information to support decision-making in urban planning and transportation.

УСТАНОВЛЕННЯ НАЦІОНАЛЬНИХ ВИМОГ ДО ЗЕРНОВОГО СКЛАДУ АСФАЛЬТОБЕТОННИХ СУМІШЕЙ ЗГІДНО З ДСТУ EN 13108-1

Introduction. DSTU EN 13108-1 establishes requirements for asphalt mixtures used for the arrangement of pavement layers of roads, airfields, etc. These asphalt mixtures are used for arranging the base layer, binding layer, and a surface layer. Problem statement. A special feature of DSTU EN 13108-1 is that it establishes general requirements for all indicators, in particular for grading. That is there are no specific requirements for the grading of asphalt mixtures depending on the road category, traffic loading and the location of the asphalt concrete layer in the road pavement structure. Therefore, it is advisable to consider in detail the requirements for the grading of asphalt mixtures, compare them with the requirements of national standards and propose appropriate national requirements. Purpose. Establishment of national requirements for the grading of asphalt mixtures according to DSTU EN 13108-1.

ПРОЄКТУВАННЯ АВТОМАГІСТРАЛЕЙ КОНТИНЕНТАЛЬНОГО, СУБКОНТИНЕНТАЛЬНОГО ТА ТРАНСРЕГІОНАЛЬНОГО СПОЛУЧЕННЯ В РОЗРІЗІ ПОЛОЖЕНЬ ЗАКОНОДАВЧИХ АКТІВ ЄС

Introduction. The introduction to the paper highlights the importance of the design and construction of highways of various link functions levels as a key factor in modern integration processes and also emphasizes the role of highways for substantial increasing of the road network safety. Problem statement. The issues of the paper concern aspects of highways design based on the experience of European colleagues in the context of the provisions of the relevant legislative acts of EU. Purpose. The goal of the paper is to analyze the main provisions for the design of continental, subcontinental and transregional highways considering, within the format of the publication, the design features of the highways elements which are universal for various design systems. Results. The highways categorization regarding their link functions levels and with this categorization linked classification by design classes were reviewed which accept the highways jurisdiction and the build-up areas related location and also the corresponding restrictions on vehicle speed limits. The highways characteristics were defined allowing the determination of highways as a separate category of motor roads in the world-wide practice of the road building industry.

Road friction estimation based on vision for safe autonomous driving

Road peak friction is a significant parameter determining vehicle driving safety. This paper focuses on road friction monitoring based on vision for autonomous vehicles. A deep learning classification model combined with training strategies adapted to the real-scenario road friction image dataset is proposed. The road peak friction coefficient is initially derived based on the classification probability and an empirical inference model. To enhance the usability and reliability of the estimated friction coefficient, a filter that concerns the classification model uncertainty and the variation of estimation output is then performed. The top-1 and top-2 classification accuracy on five road friction classes reach 94.84% and 99.22%, respectively. The developed friction preview scheme is further deployed and validated in real-vehicle scenarios. The results indicate that the proposed methods are robust to road environment change. It has potential in practical applications that provide crucial road information for safer autonomous driving.

Spatial Analysis of Risk Exposure of Urban Trees: A Case Study from Bologna (Italy)

In Europe, more than two-thirds of the population live in urban areas. The management of urban green areas and trees is becoming increasingly relevant involving different players and stakeholders, as well for keeping a focus on the Sustainable Development Goals. Society and policy makers are often unaware of the disservices that neglecting these areas can cause. Appropriate monitoring interventions can protect both citizens and trees themselves. The aim of the study was to assess the risk potential in urban trees in Bologna suburbs (Italy). For each tree in the city of Bologna, three target variables identifying the number and categories of streets, buildings, and walking and cycle paths near the tree were considered and used as covariates. A multiple regression model assessing the relationship between a dependent synthetic spatial variable (digital number, DN), proxy of the likelihood of tree failure, and the three independent covariates was estimated. Both the number of streets and walking and cycle paths in the area surrounding a tree are shown to be significantly associated with the DN value. The use of open data can assist in monitoring and maintaining urban green areas. The tool supports a virtuous circle between stakeholders in urban systems through sustainability and efficiency.

LAND COVERAGE ANALYSIS OF PAKISTAN USING SATELLITE IMAGERY

Abstract. Pakistan has a unique landscape geographically due to its strategic geo-political importance. It has played a vital role in global climate and politics. There are various semantic segmentation studies performed on remote sensing high-resolution imagery of various urban and rural areas into major classes of buildings, vegetation, water, and roads. These analyses have supported the land coverage study, which can facilitate urban infrastructure management, forestry, disaster management, and climate challenges. Recent climate reports have confirmed the importance of these studies, especially for Pakistan. It’s a critical location for the global south to observe the climate catastrophe. This research will focus on three major cities of Islamabad, Karachi, and Quetta and semantically segment the satellite imagery to study the land coverage. Our research contributes the dataset from major cities of Pakistan and compare the performance of state-of-the-art semantic segmentation networks to evaluate the dataset. Benchmark can help in selecting a highly effective deep learning network and generalizing those networks on our prepared dataset. Dataset can be downloaded from here: https://github.com/Abdullah-Sabir/Pakistan-Land-Coverage-Analysis-Dataset

Road Damage Detection and Classification

Abstract: This is a road damage detection desktop application. In the subject of transportation engineering, identifying road damage early on is essential because it can save maintenance costs and avoid accidents. Deep learning methods have demonstrated encouraging performance in a number of computer vision tasks recently, including the detection of road damage. In this study, we suggest a region-based convolutional neural network (R-CNN)-based method for detecting road degradation. Using a publicly accessible dataset of road photos with different kinds of damage, such as cracks, potholes, and patches, we trained our R-CNN. Our approach outperformed cutting-edge techniques with an accuracy of 85% in identifying road damage.

Advancements in road surface classification: a comprehensive review of computer vision and IoT approaches

Advancements in road surface classification: a comprehensive review of computer vision and IoT approaches

Where is it complex to reallocate road space?

Road space distribution has traditionally been based on the hierarchical classification of streets. In arterials, the majority of space is dedicated to traffic lanes, whereas local streets typically have fewer traffic lanes and more space for parking or sidewalks. Within urban areas, road space is contested between two main types of spaces: corridors of movement, and places for access and standing/stillness/staying. Given the limited availability of urban space, particularly in central areas, deciding how to allocate space for these functions poses a dilemma and requires tradeoffs. Nonetheless, certain areas experience underutilization and inefficiencies in space utilization over time. In this context, we propose a site selection methodology to identify complex zones within a city where different types of users and demands compete for space. These zones present the potential for dynamically allocating road space based on fluctuating demands and policy objectives. This methodology serves as an initial guide for planners to identify zones that require a thorough evaluation of activities and diverse temporal-spatial demands when reallocating road space. We use network centrality, land use indicators, traffic, and public transport dynamics indicators to detect complex zones and apply them to a Lisbon case study.

Numerical Simulation of the Effect of Variation in Subgrade CBR Values on Rigid Pavement

This study presents the results of research on the stress behavior of rigid pavement concrete slabs by varying CBR subgrade values. It aims to know the effect of changing the rigid pavement strss. Data used in this study are geometric data and material properties of the pavement structure. The load vehicle considered is National Road class standard. Rigid pavement is very sensitive with un-uniformity of underlaying supposrt. In this research, the effect of un-uniform support of subgrade was modeled numerically using Abaqus software. The friction coefficient of each layer was also considered in the model. Model simulation of existing rigid pavement calculated 13 variations of CBR values. Result shown that there is a stress increment by 26.27% in the longitudinal direction of the rigid pavement between models with uniform variations of CBR and models with non-uniform variations of CBR. This result show the sensitivity of rigid pavement to un-uniformity of subgrade.

Optimal planning of safety improvements on road sites belonging to different categories within large networks: An integrated multi-layer framework

Planning road safety interventions on large road networks implies several layers of complexity in the decision-making process. In fact, the following simultaneous problems should be addressed: estimating safety performances on the different road elements of the network, identifying sites showing high potential for improvement with respect to reference values, defining the possible types of safety measures to be implemented and their anticipated effect on traffic safety, limiting the number of interventions given fixed budget constraints.This study proposes an integrated multi-layer framework which takes into account the above-defined problems into a single optimization procedure which provides the number and type of safety interventions to be implemented over a wide road network composed of different categories of road elements. The proposed framework is based on the following peculiar aspects: the potential for safety improvement is quantitatively assessed based on the estimation of safety performances for each road category, a bi-level thresholding process integrated in the optimization process is used to highlight sites for interventions, the anticipated outcome of safety measures is quantitatively assessed as well through available crash reduction factors.The proposed methodology is applied to a case study which analyzes a sample of real roads belonging to a province-wide road network composed of various road elements (i.e., different categories of segments and intersections), under different budget constraints. Results demonstrate the applicability and flexibility of the proposed approach, which could be used for planning purposes, independently of the particular geographic location. Clearly, the approach is valid at the planning stage, given that several details of the different layers of analysis are necessarily simplified, while they should be studied in detail at the single intervention project stage.

Road Pattern Classification Using Deep Learning for Noise Data for Autonomous Driving Vehicle

Tire-pavement Interaction Noise (TPIN) is noise caused by interactions between rolling tires and road surfaces. After measuring the TPIN using a microphone, transform TPIN to images using continuous wavelet transform (CWT). The transformed images are used to classify the driving road and tire using convolutional neural network (CNN). The road and tire classification network using CNN is required for braking systems in autonomous vehicle. The CNN in this paper can classify snow road, asphalt road, and two types of tires with over 97% accuracy.

Characterization of road surfaces using three near-infrared diode lasers

Rapid road network expansion has heightened the importance of surface condition information for traffic accident prevention and route optimization. This article introduces a laser diode-based sensor that identifies seven surface conditions and accurately measure ice, water, and snow film thicknesses on roads. An optical module was developed to detect weak optical signals based on the characteristic absorption spectrum of the target surface. The module used three laser diodes (1310, 1440, and 1550 nm wavelengths) as light sources. Additionally, a road classification algorithm that is adaptable to foggy weather was developed using a multi-wavelength processing protocol. The sensor was subjected to numerous calibration and performance verification experiments. During thick foggy measurements, the processed spectra displayed a maximum variation of 2.372% across a 600 to 25,000 m visibility range with a relative standard deviation of only 0.328%. This demonstrated effective weakening of the effects of visibility variations. During winter field testing, the sensor classified road conditions effectively and accurately measured ice, snow, and water film thicknesses, with a correlation coefficient of 0.97444. The accuracy of the measurements was less than 0.5 mm. The sensor’s effectiveness for long-term field-based road testing has been verified.

Research on Street Color Environment Perception Based on CEP-KASS Framework

The color of urban streets plays a crucial role in shaping a city’s image, enhancing street appeal, and optimizing the experience of citizens. Nevertheless, the relationship between street color environment and residents’ perceptions has rarely been deeply discussed, and most of the existing studies adopt qualitative methods. To accurately and effectively assess the connection between street color environment and residents’ emotional perceptions, this paper introduces a quantitative research framework based on multi-source data called “Color Emotion Perception with K-Means, Adversarial Strategy, SegNet, and SVM (CEP-KASS)”. By combining K-Means unsupervised machine learning and SegNet computer vision techniques, it captures and analyzes visual elements and color data from Baidu Street View Images (BSVI). It then employs a human–machine adversarial scoring model to quantify residents’ perceptions of BSVI and uses the support vector machine regression model to predict the final perception scores. Based on these data, a Pearson correlation analysis and visual analysis were conducted on the elements and color in the urban environment. Subsequently, the streets were classified based on perception frequency and perception scores by integrating multi-source data, and areas within the third ring of Xuzhou City were selected for validating the research framework. The results demonstrate that utilizing street-view images and the CEP-KASS framework can quantitatively analyze urban color perception and establish a connection with residents’ emotions. In terms of color perception, red, orange, and blue all have a strong positive correlation with the interesting score, whereas black is positively correlated with a sense of safety. Regarding color attributes, low-saturation bright colors result in higher fun perception scores in urban spaces; too low saturation and brightness can affect their attractiveness to residents; brightness has an inverse relationship with the perception of safety, and adjusting brightness inversely can improve the perceived safety experience in certain urban external spaces. The street classification criteria based on perception frequency and perception scores proposed herein can provide references for planners to prioritize color transformation decisions, with a priority on emulating HSHF streets and transforming LSHF streets. When formulating color planning, suggestions for color adjustment can be given based on the correlation study of color with visual elements and perception scores, optimizing urban residents’ spatial perception and their emotional experiences. These findings provide robust theoretical support for further enhancing the visual quality of streets and refining urban color planning.