Road Classification Research Articles

Typomorfology of building mass and lingkage in Malioboro, Yogyakarta

The Malioboro area of Yogyakarta is one of the main tourist destinations in Yogyakarta, which has the potential for the rapid development of regional structural patterns. Its strategic location in the city center and the presence of historic buildings and first-class shopping centers in the area attract tourists and investors to build in the Malioboro area. On the other hand, Malioboro Street is the philosophical axis of Yogyakarta City which has also been designated as a Cultural Conservation Area. The pattern of regional structure development based on building mass and linkage in this area is the focus of this research. Changes in the Typology of this area were analyzed using a comparative method by comparing data on patterns of building mass and linkage from different 4-year periods. This study aims to find out how the pattern of changes in the typology of buildings in the Malioboro area and its influence on the role of the Malioboro area as a cultural heritage area. Based on the results of the study, it is known that the Typology of building mass and form changes in the Malioboro area there are five types, namely the gradual addition of building mass, the sudden addition, the reduction in building mass (total loss) and the change in the shape of the building without changing the building mass. Meanwhile, from the linkage typology, there is no change in the road network pattern from the 4 study year periods. Changes in building typology in the Malioboro area tend to be close to the Beringharjo market, while there is no change in building typology for the Agung building. However, near another historical building, namely the Vredeburg fort, there are indications of an additional building mass on the east side of the building. Its also that changes in the typology of buildings in this area are influenced by the location of the building on the road class .This needs to be considered so that policy references or directions can be given to control developments in cultural heritage areas. Keywords: Building mass, linkage, malioboro, typology, transformation, urban

LiDAR intensity correction for road marking detection

The intensity data recorded by light detection and ranging (LiDAR) contains the spectral characteristics of a scanned target, making it an essential source of information in mobile laser scanning (MLS) related applications. However, the raw intensity can be distorted by distance and incidence angle, obstructing its use in autonomous driving. To correct the intensity distortion, an automatic demarcation selection, based on distance effect function, and a Blinn-Phong model-based angle effect correction, have been proposed to correct the raw intensity, establishing the relationship between intensity correction performance and the influencing factors, including modeling data selection, incidence angle span (IAS) and point cloud density. Separability (SEP) and balanced F score (BFS) were used to evaluate the separability, accuracy and totality of road marking detection. Experimental results showed that the background data-driven angle effect function (BDAF) used was beneficial for enhancing marking and road classification. IAS has a major influence on correcting intensity performance. SEP was improved by 35% when IAS was increased from 3.17° to 13.5°, with point density of 535 pt/m2. The IAS threshold could be adjusted according to the instrument performance used. It offers a meaningful investigation of detection and classification of planar target using corrected MLS intensity.

LIDAR INVESTIGATIONS OF HLYNSKE ARCHAEOLOGICAL COMPLEX

The identification, analysis and research of archeological sites is a difficult task, especially in areas covered with dense vegetation. The study of sites in such an area is limited by a number of factors. This applies to the vegetation cover, as well as the size and state of preservation of the archeological sites themselves. The difficulties in the documentation, survey, and material collection increase in areas characterized by dense understory vegetation. The presence of disparate components of archaeological complexes, in such conditions, further complicates their identification on the ground. To solve these research problems, remote sensing based on LiDAR (Light Detection and Ranging) can be fruitfully applied to achieve unique performance in detecting ancient settlements.
 In this paper, LiDAR scanning were applied to the medieval site near Hlynske village (Poltava region, Central Ukraine), selected for this study because it is located on a hilly area with complex topography and thick vegetation cover surveying archaeological features and remains, in densely vegetated areas.
 According to the paleo landscape and hypsometric analysis of the digital terrain model (DTM), the fortified parts of the settlements separated by ravines are evidence of direct anthropogenic impact on the relief. These ravines are the remnants of ditches that have increased their area due to erosion processes. Other ravines that divide the archeological complex into parts are the remains of trails, paths or roads. They appeared as a result of economic activity of the Romny culture population in the 9th century. Fragments of spindle whorls, weapons and personal ornaments were found in these places, which were probably lost by ancient inhabitants while traveling on these routes. The system of ravines, which is fixed to the west and south-west of the Small hillfort, also belongs to the category of ancient roads that connected Opishnya, Budyshche and Hlynske.
 Thus, as a result of the analysis of the DTM, information on microtopography and features of landscape formation on the monument was obtained. As a whole, the investigations allowed the discovery of an anthropogenic landscape from the 9th century and the detection of an unknown extra urban area abandoned in the Late Middle Ages.

Simulation and Application of Urban Road Landscape Based on Geographic Information Data

In this paper, we study and analyze the urban road landscape by integrating big data with GIS and designing a simulation system. Underwood is selected as the base model and calibrated for optimal density and capacity. The traffic flow model is optimized, and the minimum flow rate of each class of road is corrected; the traffic volume of highway and expressway in the early morning and at night is adjusted to make up for the discrepancy between the calculated flow rate of the model and the actual situation due to the unstable speed. Investigate and analyze the pollutants produced by enterprises along the road and select suitable and targeted plants to enhance the ecological protection of road landscape and optimize and improve the park environment; combine the regional culture and apply the ceramic culture to the design of road landscape vignettes; select suitable water and moisture-resistant plants for slope protection plant landscape design, to form a trinity of ecological protection, landscape road, and riverfront green space of industrial park road landscape. As a new basic geospatial infrastructure and carrier, 3D city has a unique spatiotemporal view to plan and manage the operation and maintenance information of the whole city. This part of the study takes urban planning, community management, urban publicity, and tourism management as demonstration cases and focuses on the elaboration and verification of how 3D city data and 3D urban geographic information are applied in the refined urban construction and management. Typical road measurement data are selected, and the applicability of the road traffic flow model of each level is judged by numerical analysis. An interactive road model editing method with the help of auxiliary information is proposed. With the help of auxiliary information displayed in different ways, the operation of adjusting road height in three-dimensional space is transformed into operation in a two-dimensional plane, which can effectively simplify the operation process and improve the accuracy of model editing.

Düşük Trafikli Mahalle Tasarımı İçin Planlanmış Davranış Teorisi’nin ve Zaman Coğrafyası ile Sentezinin İncelenmesi

This article presents an evaluation of the contribution of the synthesis of the Theory of Planned Behaviour (TPB) with Time Geography to the Low Traffic Neighbourhood (LTN) design stages. In the LTN design scheme, the evaluation of the human factor has importance at all stages of the design. However, the LTN design is currently based on stakeholder meetings, verbal statements and maps created with stakeholders in general. Social psychology, which includes concepts and theories to understand complex human behaviour, has been used in many transportation studies. TPB, one of the most well-known theories on this subject, its contributions to transportation studies and its primary deficiencies were identified within the article. It has been evaluated that the lack of spatial and temporal scope, one of these primary deficiencies, can be eliminated by synthesizing the Time Geography approach. As a result, this synthesis has the potential to increase the effects of the LTN design by integrating TPB, which has the potential to provide a basis for guiding people's behaviour, and Time Geography, which can reflect its spatial and temporal projection, in each of the stages of LTN design namely street classification, determination of neighbourhood boundaries, prioritization of neighbourhoods and determination of measures.

Rhythm of the streets: a street classification framework based on street activity patterns

As the living tissue connecting urban places, streets play significant roles in driving city development, providing essential access, and promoting human interactions. Understanding street activities and how these activities vary across different streets is critical for designing both efficient and livable streets. However, current street classification frameworks primarily focus on either streets’ functions in transportation networks or their adjacent land uses rather than actual activity patterns, resulting in coarse classifications. This research proposes an activity-based street classification framework to categorize street segments based on their temporal street activity patterns, which is derived from high-resolution de-identified and privacy-enhanced mobility data. We then apply the proposed framework to 18,023 street segments in the City of Boston and reveal 10 distinct activity-based street types (ASTs). These ASTs highlight dynamic street activities on streets, which complements existing street classification frameworks, which focus on the static or transportation characteristics of the street segments. Our results show that a street classification framework based on temporal street activity patterns can identify street categories at a finer granularity than current methods, which can offer useful implications for state-of-the-art urban management and planning. In particular, we find that our classification distinguishes better those streets where crime is more prevalent than current functional or contextual classifications of streets.

STUDI KERUSAKAN PRASARANA JALAN DI PASAR SENTRAL KALIBOBO KABUPATEN NABIRE TERHADAP AKTIVITAS EKONOMI MASYARAKAT

Damage to roads will cause many losses to community activities or activities, especially economic activities that can be felt by users directly and also economic actors, in this case local market traders.Similar problems can be seen in road conditions in Kalibobo Central Market, Nabire Regency, Province Papua, in almost the last one period, the road at Kalibobo Central Market, Nabire Regency, is very concerned about. Many people have complained to the local Regency Government to pay attention to this road but until now it has not been well realized. The objectives of this study are: "Knowing the effects of damage to road infrastructure at the Kalibobo Central Market on the Economic Activities of the Nabire Regency Community." The data analysis method used by the writer is descriptive qualitative with data collection techniques used, namely observation and interviews, the results obtained are the effect of road damage on economic activity, among others: Inadequate road class, vehicle intensity with large loads, parking area that is not regular, Lack of Government Attention. Therefore it is necessary to have a thorough road paving by the Nabire Regency government so that economic activity in the Kalibobo central market can work well.

Intelligent Novel IMF D-SWARA—Rough MARCOS Algorithm for Selection Construction Machinery for Sustainable Construction of Road Infrastructure

The quality of road infrastructure largely depends on the quality of road construction and adequate construction machinery. In order to reduce uncertainties and improve the performance of road infrastructure, it is necessary to apply modern and appropriate construction machinery. The aim of this study was to create a novel integrated multi-criteria decision-making (MCDM) model for the selection of pavers for the middle category of roads. A total of 16 criteria were defined and then divided into four main groups, on the basis of which the performance of 12 pavers was evaluated. Improved Fuzzy Stepwise Weight Assessment Ratio Analysis (IMF SWARA) with D numbers (IMF D-SWARA) was extended to determine the significance of the criteria for the selection of construction machinery based on two groups of experts. Rough measurement of choices and their ranking as a compromise solution (R-MARCOS) was used to evaluate and rank the performance of construction machinery. The results show that three alternatives out of the set considered can satisfy defined requirements. After that, we performed a multi-phase validity test in which different values of criterion weights were simulated. A comparative analysis with seven other Rough MCDM methods was also created, and the Spearman’s correlation coefficient (SCC) and WS coefficient were calculated to determine the correlation of ranks for sensitivity analysis and comparative analysis. Thus, the obtained results were verified.

Spatial Distribution of Soil Heavy Metals and Associated Environmental Risks near Major Roads in Southern Tibet, China.

Soil heavy metal pollution is becoming an increasingly serious environmental problem. This study was performed to investigate the contents of surface soil heavy metals (Cu, Zn, Pb, Cd) near six roads in the southern part of the Tibetan Plateau. Multivariate statistics, geoaccumulation index, potential ecological risk, and a human health assessment model were used to study the spatial pollution pattern and identify the main pollutants and regions of concern. The mean was ranked in the order Cd > Cu > Zn > Pb, with the average concentrations of Cd, Zn, and Cu exceeding their corresponding background levels 4.36-, 1.00-, and 1.8-fold, respectively. Soil Cd level was classified as posing a considerable potential risk near national highways and a high potential risk near non-national highways, whereas soil Cu, Zn, and Pb were associated with a low potential ecological risk for each class of roads. Furthermore, the non-carcinogenic risk due to soil heavy metals for each class of roads was within the acceptable risk level for three exposure pathways for both adults and children, but the carcinogenic risk attributable to soil Pb exceeded the threshold for children near highways G318, G562, and G219 and for adults near highway G318. Our work not only underscores the importance of assessing potential threats to ecological and human health due to soil heavy metal pollution on road surfaces but also provides quantitative guidance for remediation actions.

An End-to-End Multi-Task Learning Model for Drivable Road Detection via Edge Refinement and Geometric Deformation

This paper presents a road detection method for autonomous driving based on an end-to-end neural network model. Our method takes advantage of both the characteristics of road boundary and multi-task learning of a deep convolutional network. By reassigning the label and rebalancing the loss of road pixels, we focus on the learning of hard examples on the boundary to refine its performance. Then, a road geometric transformation-based data augmentation method is proposed to enable the network model to be robust under traffic scenes. Based on these two novel methods, a unified architecture consisting of a shared deep residual encoder network and multi-branch decoder sub-networks is integrated. It adopts road scene classification as a supervised learning task to realize road segmentation and scene classification simultaneously. Experiments demonstrate that the proposed method has achieved the highest MaxF value in most road scenes. Both qualitative and quantitative evaluations on the KITTI-Road benchmark demonstrate our superior performance.

Road Accidents Prediction and Classification

Abstract: Road accidents leads to death, disability and hospitalization of people across world which leads to loss of potential income of individual and also affects the economy of the country. For every 10 people killed during road accidents across world one person belongs to India. In year 2020, total of 3,66,138 road accidents occurred leading to loss of 1,31,714 persons lives, injuring 3,48,279 persons. Number of road accidents and damage caused by it can be reduced by identifying the factors leading to it. In this project, we are applying the concepts of data mining and machine learning to identify the various factors that affect road accidents and its severity. The application will take variety inputs such as age of vehicle, light condition, road surface condition, speed limit etc. and will use random forest machine learning algorithm to calculate the severity of a possible accident. The severity of a possible accident will be displayed on a scale of 1 to 3, 1 being the highest and 3 being the least severe so, that they drive safely and take precautions. This data can be used in future to analyze inputs and improves the accuracy of the system output. In case of severity 1 which is the case of possibility of fatal accident an alert message will be sent to police so that they can take any preventive measures and therefore this application can prove to be very helpful in reducing accident fatality rates in the country.

Experimental and Numerical Research of Paved Microcrack Using Histogram Equalization for Detection and Segmentation

The paved cracks are one of the major concerns for scientists and engineers in road maintenance and damage evaluation study. Digital image processing applications have been applied in road surface inspection, classification, and decomposition of paved roads. This paper has tested and proposed the process to evaluate the road cracks and their possible solution as we know that the key issues for analysis are enhancement and segmentation of image along with edge detection to attain the promising results we have gained and discussed under the heading of simulations for the experimental and numerical of crack detection. Using MATLAB, we examine the various gray-level image using better techniques based on their computational capability. The method is based upon one of the histogram modification techniques, which is coupled with the segmentation method and the crack edge detection. At last, three feature methods are used, namely, Harris, MSERF, and SURF, to wind up our research.

Road traffic crashes and emergency response optimization: a geo-spatial analysis using closest facility and location-allocation methods

Increased occurrence of road traffic crashes in Kano metropolis has resulted in a steady loss of lives, injuries, and increased people's risk exposure. This study looked into the emergency response to road traffic crashes in Kano, with a view to improving efficiency by developing linkages and synergy between Emergency Healthcare Facilities (EHCF), ambulances, and crash hotspots. The geographical location and attributes of the major EHCF, crash hotspots along highway intersections, and the two existent ambulances at the Kano State Fire Service (KSFS) and Federal Road Safety Corp head offices (FRSC) were obtained using GPS surveying. Road traffic network data (vector format) was digitized from satellite image, from which two major road classes (highways and minor roads) were identified, as well as their respective speed limits. The length and speed constraints were used to calculate time distances. Nearest Neighbor and Network (closest facility, shortest route, and location-allocation) analyses were carried out. Location-allocation analysis was to determine based on defined criteria the best locations to allocate EHCF or ambulance for optimum coverage. The results demonstrated that EHCF, ambulances, and crash places have different distribution patterns with almost no linkages. Closest ambulance facility analysis revealed the FRSC ambulance takes 9.41 minutes to arrive to crash spot 18 (Maiduguri Road, following NNPC) and 7.52 minutes to arrive at AKTH, the nearest EHCF. Comparatively, getting to Court road incident scene (spot 16) and IRPH as the closest EHCF takes about 3 times the time it takes to get to spot 18 and 4 times the time it takes to get to AKTH. This means that practically almost all victims in the city suffocate before reaching to the hospital. This signifies that, in cases of demand for CPR at the incident scene, there are higher likelihood of dying as it is expected to be provided within the first four minutes after the crash. Based on a maximum of 4 minutes impedance cutoff from all directions towards the occurrences areas, location-allocation analysis found eight new locations to maximize coverage and improve efficiency. It is concluded that current road traffic crash emergency response system has been determined to be ineffective. As a result, more ambulances should be strategically placed to improve emergency response times.

Scene Classification in the Environmental Art Design by Using the Lightweight Deep Learning Model under the Background of Big Data.

On the basis of scene visual understanding technology, the research aims to further improve the classification efficiency and classification accuracy of art design scenes. The lightweight deep learning (DL) model based on big data is used as the main method to achieve real-time detection and recognition of multiple targets and classification of the multilabel scene. This research introduces the related foundations of the DL network and the lightweight object detection involved. The data for a multilabel scene classifier are constructed and the design of the convolutional neural network (CNN) model is described. On public datasets, the effectiveness of the lightweight object detection algorithm is verified to ensure its feasibility in the classification of actual scenes. The simulation results indicate that compared with the YOLOv3-Tiny model, the improved IRDA-YOLOv3 model reduces the number of parameters by 56.2%, the amount of computation by 46.3%, and the forward computation time of the network by 0.2 ms. It means that the IRDA-YOLOv3 network obtained after the improvement can realize the lightweight of the network. In the scene classification of complex traffic roads, the classification model of the multilabel scene can predict all kinds of semantic information of a single image and the classification accuracy for the four scenes is more than 90%. In summary, the discussed classification method based on the lightweight DL model is suitable for complex practical scenes. The constructed lightweight network improves the representational ability of the network and has certain research value for scene classification problems.

The Optical Design of a LED-Reflector Module in Road Illumination

In recent years, there has been a trend towards energy-efficient, long-lasting, environmentally friendly, and technology-compatible LED road luminaires. In this study, the optical design and analysis of the LED road luminaire model for the M1 and M2 illumination classes roads, which are the roads with the highest importance, have been carried out. In the research, the unique reflector design was made using SolidWorks 3D design software. XLamp CXA1520 LED light source by Cree company was preferred during the design. 21 LEDs were placed on the reflector at the ideal distance and LED luminaire model was created in LightTools illumination design software. A simulation study was performed for the LED luminaire model created on LightTools software and the distribution curve of luminous intensity was obtained. At the same time, the EULUMDAT (.ldt) file, which is required for road illumination analysis, was produced with LightTools software. The obtained .ldt file was transferred to DIALux illumination calculation program and simulated for M1 and M2 class roads. Illumination parameters obtained from DIALux analysis have been optimized by comparing them with the road illumination criteria accepted by International Commission on Illumination (CIE). It has been observed that the designed LED luminaire model significantly meets the M1 and M2 class road illumination criteria defined by CIE. In the study, it was also discussed what changes can be made in the optical material to meet all criteria.

From uncertainty to further improving the accuracy and authenticity of test results

The article presents new test technologies for assessing the dependability (reliability) of motor vehicles, providing an increase in the accuracy and authenticity of test results through their integrated use with an accentuated allocation of different forms of loading of motor vehicles units and aggregates, in the form of: cyclic regular loading in different frequency ranges, forced loading of the engine and transmission units in the "acceleration—braking" mode and the normalized impact of broken and sodden dirt roads. Keywords: tests, types of tests, new testing technologies, categories of test roads, authenticity, dependability, reliability, accentuated types of loading, acceleration-braking mode, normalized impact of dirt roads

Formulation of Framework for Evacuation of Tsunami Disaster after COVID-19 Pandemic on The South Coast of Watulimo, Trenggalek

The research aims to identify and design the latest theory-based tsunami disaster evacuation after the COVID-19 pandemic. Analysis of temporary shelters and analysis of evacuation routes were used in research with scoring techniques and network analysis techniques. The conceptual framework related to tsunami disaster evacuation after the COVID-19 pandemic has been compiled in research and can be used as material for consideration to formulate government policies and determine contingency plans and operational plans that will be carried out in the event of a tsunami disaster following the COVID-19 pandemic. Parameters of temporary shelter variables that must be considered are building function, the number of floors, building capacity, road location, building area, distance from the beach, and building construction. The capacity of the temporary shelter before the COVID-19 pandemic was obtained from the building area divided by the minimum space requirement per person of 1.64 m2. After the COVID-19 pandemic, the minimum space requirement per person combined with a physical distance of 2 m, the total area required will be 6 m2/person. Meanwhile, the parameters of the evacuation route variables that must be considered are road width, travel time, road pavement conditions, and road class.

INVESTIGATIONS ON THE EFFECT OF HD PROCESSING IN LAND COVER CLASSIFICATION

Abstract. The requirement of automated Land Use/Land Cover (LULC) classification has arisen in ecosystem related applications, such as natural hazard assessments, urban and rural area planning, natural resource management, etc. The data source and the classification method used in the production of LULC maps depend on the study area size and the location, and also determined by taking the time and cost into account. Recently, MAXAR Technologies announced a new product, High Definition (HD) with 15 cm resolution, which is obtained by post-processing of images with 30 cm Ground Sampling Distance (GSD). The post-processing employs machine learning methods. On the other side, the effect of HD processing on the image quality, and the usability of such products in various applications are still needed to be investigated. In this study, the influence of HD processing algorithm on LULC classification results was investigated by using 15 cm HD and 30 cm resolution images provided by MAXAR. By using the Random Forest (RF) and Support Vector Machine (SVM) methods in two different study areas, image classification was performed to detect water, vegetation, asphalt road, building, shadow, agriculture and barren land classes. The results show that in HD products, the edges of objects were sharper, whereas the classification noise was higher inside agricultural fields. Considering the overall results, it can be concluded that with the use of HD products in urban areas, improved LULC maps can be obtained.

Smartphone-based road manhole cover detection and classification

Road surface condition detection is an important application for many intelligent transportation systems (ITSs). A manhole cover depression is one of the common factors affecting road conditions. Smartphones are equipped with different sensors, which can be used to collect image data and inertial data. A new large-scale manhole cover detection dataset is developed by using smartphones to collect road image data, and a hierarchical classification method based on the convolutional neural network is proposed in this paper. The proposed method first coarsely classifies the images into nonrainy and rainy types and then performs manhole cover detections based on the coarse classification results. As a result, the proposed method achieves an accuracy of approximately 86.3% for road manhole cover detection. Based on the observation that different degrees of manhole cover subsidence produce different degrees of inertial sensor data, this paper used a machine learning method, which can automatically classify the detected manhole covers into different degrees of subsidence, namely good, average, and poor. The average recalls, average precisions, and average F1-measures achieve approximately 87.3%, 86.9%, and 87.2% accuracy, respectively. The results show that the proposed approach can effectively detect manhole covers in different weather and road conditions, which can effectively reduce the cost of road manhole cover data collection and detection, providing a new method for road manhole cover detection.

Development of a Conceptual Data Model for 3D Geospatial Road Management Based on LandInfra Standard: A Case Study of Korea

In practice, road management data are typically managed in two-dimensional (2D) geospatial forms. However, 2D geographic information system (GIS)-based road infrastructure management data have limitations in their representation of complex roads, such as interchanges, bridges, and tunnels. As such, complex and large road network management data cannot be adequately managed in a 2D GIS-based form. This study discusses the use of the LandInfra standard for road infrastructure management in Korea, considering its focus on land and civil engineering infrastructure facilities. To facilitate the transition from 2D to 3D GIS, we analyzed existing road management models of road pavement and road register information and created Unified Modeling Language (UML) class diagrams depicting these models. Then, existing road management classes and LandInfra classes were mapped. Based on the results, we propose a road management model based on the Facility, Alignment, and Road parts of LandInfra. For its implementation, several classes of the proposed data model were encoded into InfraGML using real-world data input. Taken together, this study shows how the LandInfra standard can be extended and applied to the field of road infrastructure management in Korea, supporting the transition from a 2D to a 3D GIS-based model.