Secondary Roads Research Articles

The Impact of Physical Setting on the Development of Street Character (Case Study: Ahmad Yani Street, Tegal)

The development of city facilities and infrastructure is one of the indicators that can be seen to find out how the development of a city area is. The city of Tegal is one of the cities that is currently making improvements in its urban areas, namely by repairing and arranging public spaces in the city of Tegal. One of the results of the improvement and arrangement of public spaces in the City of Tegal is the arrangement of the corridor for Jalan Ahmad Yani, City of Tegal. Ahmad Yani Street in Tegal City is a secondary local road with the status of a city road in the center of Tegal City. Arrangements made on Jalan Ahmad Yani are such as implementing a one-way system for vehicles, providing parking pockets and making improvements to the pedestrian path area. This research will discuss how changes in the physical setting that occurred on Jalan Ahmad Yani after repairs and rearrangements have been carried out so that they affect the formation of a new character and identity of the street space. In addition, this research will also discuss the impact on the community and the environment around Jalan Ahmad Yani from changes in the physical setting. This study uses a qualitative research method with descriptive analysis which aims to explain what phenomena occur in the field and examine based on existing theory. The results of this study are expected to be able to explain how the physical setting of a street space can influence and create character for that street space.

Zoning of the city of Shelekhov, Irkutsk agglomeration, based on the assessment of acoustic pollution

The study carried out the zoning of the city of Shelekhov, based on the assessment of acoustic pollution of the territory. Field observations were made and the number of vehicles moving along intracity roads and the federal highway R-258 “Baikal” was calculated. With the help of sound level meters, instrumental measurements were made in the daytime and at night, and maps of the sound pressure level of the road network were built. The calculation of statistical characteristics was carried out and the daily course of the distribution of the noise level on the main and secondary roads was constructed. The analysis of the obtained results and their comparison with the established sanitary standards for their excess was carried out. Evidence has been identified to support the implementation of strategies aimed at reducing acoustic pollution and, as a result, creating favorable conditions for the population to live.

Quantifying and predicting air quality on different road types in urban environments using mobile monitoring and automated machine learning

Traffic emissions are a primary source of air pollution in urban areas, with air quality being influenced by different types of roads characterized by varying traffic volumes and speeds. Comprehending the distribution of air pollutants and the factors influencing it across different road types holds immense significance in endeavors to enhance air quality within urbanized regions. This study recorded concentrations of PM, SO2, NO2, CO, and O3 on different road types in Shenyang, China, using mobile monitoring. The impacts of road type and microclimatic factors on air quality were quantified using automated machine learning. Among the six road types, the suburban highway exhibited the highest PM, SO2, and NO2 pollution. On the other hand, secondary roads experienced the highest levels of CO and O3 pollution. The automated machine learning models provided accurate predictions for PM2.5, PM10, SO2, NO2, and O3 concentrations (R2 = 0.91, 0.83, 0.82, 0.83, 0.79, respectively). Relative humidity played the most significant role in PM2.5 and PM10 concentrations (55.93% and 59.39%, respectively), followed by air temperature (15.36% and 17.73%) and road types (14.28% and 8.74%). Road types contributed 24.33%, 20.60%, 16.61%, and 11.90% to SO2, CO, O3, and NO2 concentrations, respectively. Overall, this study addresses the limitations of previous research and provides a comprehensive understanding of the impact of road types on air pollutant concentrations in urban environments.

Data mining and spatio-temporal characteristics of urban road traffic emissions: A case study in Shijiazhuang, China

Accurate estimation of traffic emissions and analysis of spatio-temporal distribution on urban roads play a crucial role in the development of low-carbon transportation system. Traditionally, a region’s emission characteristics have been studied using numerous emission models with GPS-based spatio-temporal data. Due to the heavy data processing needs of GPS-based data, emission characteristics for a large region have been studied by dividing the region into a limited number of smaller areas or units. Additionally, GPS data are based on a few vehicles in the traffic which does not fully reflect road conditions. This paper proposed an approach that can be used to study and calculate the spatio-temporal emission pattern of a region at a roadway section level by using Baidu’s online traffic data and COPERT model. The proposed method can be used to estimate road-level emission patterns while avoiding the impact of redundant data in large datasets, making the dataset more reliable, applicable, and scalable. The proposed approach has been demonstrated through a study of spatio-temporal emission patterns in the Qiaoxi district within city of Shijiazhuang, China. Online data crawling technology was used to obtain data on urban road traffic speed and driving distance. The linear reference technology was used to construct a two-layer road network model to conduct the coupling and matching of traffic data with the road network data. The COPERT model was implemented to calculate the average traffic emissions on each road in the road network, and a traffic emission intensity index was proposed to quantify the CO, VOC, NOx and CO2 emissions on urban roads in the study area. The analysis results show that the traffic emission intensity of the expressway, trunk road, secondary road, and branch road is high during the morning peak (7 AM-9 AM) and evening peak (5 PM—7 PM). The sections with higher traffic emission intensity are mainly concentrated on the main roads and secondary roads such as Jiefang South Street, Shitong Road and Xinhua Road. Nearly one-third of 2nd Ring and 3rd Ring roads also have relatively high emission intensity. The research results provide new ideas for estimating traffic emissions in urban road networks and analyzing the spatio-temporal distribution of traffic emissions. The research results can also provide a decision-making basis for traffic management departments to formulate energy-saving and emission-reduction measures and promote the development of urban green and low-carbon transportation.

Unveiling the spatial differentiation drivers of major soil element behavior along traffic network accessibility

Advancements in transportation networks have induced a spatial-temporal convergence effect, accelerating socio-economic elements flow and dismantling the conventional "core-periphery" urbanization gradient. Accessibility of transportation networks emerges as a reliable indicator of urbanization. There has been a growing global and Chinese focus on the various forms of metal pollution in urban soil. This study aims to investigate the driving forces and effects of urbanization factors (Gross Domestic Product (GDP), value added of secondary industries (VA), night light (NL), population density (PD), and road density (Distance)), soil property factors (pH, electrical conductivity (EC), and total organic carbon (TOC)), and topographic factors (elevation (DEM), aspect, and slope) on toxic heavy metal elements (Cd, As, and Hg) and trace elements (Mn, Ti, V) in surface soil (0–20 cm) across varying accessibility levels in the Beijing-Tianjin-Hebei urban agglomeration. Results reveal significant influence of accessibility on Cd and Hg levels (p < 0.05), with higher accessibility areas displaying elevated element concentrations. According to the evaluation results of the single-factor pollution index, Cd and V have the highest pollution exceedance rates (93.18% and 75.76%, respectively). Moran's Index results highlight typical spatial clustering of elements, with hotspots in areas of high accessibility. Urbanization has led to distinct spatial agglomeration patterns in element concentrations and environmental factors. Geographic detector analysis reveal that in low accessibility areas, metal element pollution and distribution are influenced by a combination of complex factors, including soil properties (pH), terrain conditions (DEM), and the urbanization process (VA). In high accessibility areas, toxic heavy metal elements are primarily driven by urbanization factors, largely influenced by transportation activities, industrial development, and population density, while elements Mn, Ti, and V are still influenced by both natural processes and urbanization activities. These findings suggest that urbanization intensifies the impact on potential toxic elements in soil, and that trace elements are increasingly affected by urbanization, warranting further attention.

GLMDriveNet: Global–local Multimodal Fusion Driving Behavior Classification Network

Driving behavior classification plays an important role in many fields, such as Advanced Driving Assistance System (ADAS), traffic safety, and energy saving. In this paper, we propose a Global–local Multimodal Fusion Driving Behavior Classification Network (GLMDriveNet) which classifies driver behaviors into normal driving, aggressive driving, and drowsy driving. First of all, we design a Global–local Interaction Channel Attention Module (GLI-CAM) to extract effective features in both the roadside image and the spectrogram generated from the current prediction time and its previous four seconds of vehicle speeds. Furthermore, a learnable positional embedding is introduced to fuse the global and local information of the channels for better screening of the extracted features. Secondly, we propose a Multi-scale Feature Representation Fusion Module (MS-FRFM) to associate the high-scale and low-scale information of images and spectrograms and assign different importances for different modal information, making the network more inclined to useful modal information. Our model is evaluated on a public dataset UAH-DriveSet and achieves the best performance (98.4% F1-score on all roads, 97.4% F1-score on the motorway road, and 99.8% F1-score on the secondary road) compared to other state-of-the-art methods. Our model has a very fast speed (142 FPS) and strong generalization which has been verified through extensive experiments on multiple datasets. The code is available on https://github.com/liuwenzhuo1/GLMDrivenet.

URLA YARIMADASINDA ESKİ VE YENİ YOLLARIN KAVŞAĞI, BARBAROS OVASI

The Urla Peninsula stretches from the center of the western Anatolian coast to the Aegean Sea. Its fertile soils and maritime opportunities have made the peninsula economically and commercially important in every period. The route running on the east-west direction connects the peninsula to the Anatolian mainland. Both this route and the secondary roads connected to it converge and disperse in the Barbaros plain as required by geography. The high mountain masses that close the central part of the peninsula from north to south open up in the Barbaros plain. Before the construction of modern roads, the plain provided transportation facilities through its narrow passages. Archaeological data show that the plain was connected to the west by the Mataracı Gorge near the village of Birgi during the Early Bronze Age. The pottery finds observed on the commanding heights on both sides indicate the existence of an early derbent system. In Birgi, in the Hamamıkuyu area, there are the ruins of an accommodation place dating back to the 14th century. At the end of the 19th century, the Birgi connection lost its importance with the construction of a macadamized road passing through the southern part of the plain. The macadamized road coming from Urla follows the valley of Tatar Creek. Previously, Urla-plain connection was provided by the road passing through the Kavaklıdere valley to the north of it. Today's roads are also located on these two routes. Connecting the east-west main route, the plain is also the connection and distribution point for various secondary roads. These roads provide access to the southern sea via Ildırı (ancient Erythrai), Kıran (ancient Korakas/Korykos), Gülbahçe Bay and Zeytinler. Archaeological research provides data showing that the plain and its surroundings have been inhabited in various periods from the Neolithic period to the present day.

Nonlinear impact analysis of built environment on urban road traffic safety risk

With the rapid development of economy, the increasing number of motor vehicles and the total road mileage, which leads to the increasingly prominent traffic safety problems. In order to explore the quantitative relationship between the built environment and the risk of urban road traffic safety, this paper reconstructs the built environment system based on the ‘5D' element model of the built environment combined with the factors influencing traffic safety risks, and describes the built environment from multiple aspects such as density, diversity &traffic design etc, and then build the gradient lift decision tree model to explore the importance and dependency of variables. The empirical analysis selects a district in Chongqing as the research unit, and the results show that: the RMSE the model was 0.0036, the MAPE was 1.9%, and the determination coefficient R2 was 0.84. GBDT algorithm results shows: the cumulative importance of population density, road facilities, intersection density, secondary road and branch road density, average intersection distance, land use mix, and economic density reaches 77.87%. Some variables show obvious nonlinearity and threshold effect.

Assessing springtime vertebrate prey of sympatric mesopredators in the southeastern United States using metabarcoding analysis.

Coyotes (Canis latrans) colonized the eastern United States over the last century and formed a 3-species predator guild with bobcats (Lynx rufus) and gray foxes (Urocyon cinereoargenteus) across much of the southeastern United States. Diets among the three species vary along with respective impacts on game species such as white-tailed deer (Odocoileus virginianus) and wild turkeys (Meleagris gallopavo). To determine predation impacts on vertebrate prey and dietary overlap in consumption of prey items, we assessed diets of coyote, bobcat, and gray fox during spring, coinciding with white-tailed deer fawning and wild turkey nesting and brood rearing. We sampled across three sites along the Savannah River in South Carolina from mid-May through mid-June of 2020-2021. We collected 180 scat samples along 295.9 kilometers (71.1-122.4 km/site) of unpaved secondary roads and used DNA metabarcoding to determine vertebrate diet items. We identified predator species of scat using DNA metabarcoding and species-specific mtDNA fragment analysis (153 were coyote, 20 bobcat, and seven gray fox). Overall, we found evidence that two species, coyote and bobcat, consumed deer while all three consumed turkeys. Frequency of deer in the diet varied across sites for coyotes from 62-86% and wild turkey was present with a frequency of occurrence of 9% for coyotes, 5% for bobcats, and 14% for gray fox. Vertebrate diet specialization was evident across predator species with high frequency of deer in coyote diets, rabbits and small mammals in bobcat diets, and herpetofauna in gray fox diets. During deer fawning and wild turkey nesting and brood rearing, dietary overlap appears to be mediated by disparate selection of prey items, which reduced competition among coyotes, bobcats, and gray foxes. Use of DNA metabarcoding may augment our understanding of dietary preferences within this predator guild by providing increased resolution of diet composition among important game species.

Predicting population size at large scale: The case of two large felids

Approaches that allow capitalizing on local population estimates to derive global population estimates with associated uncertainty are urgently needed, especially for naturally rare species of conservation concern. Here we used published population density estimates to predict large-scale density patterns and derive global population estimates for two species of large felids, the leopard and the tiger. We modelled population density for the leopard (n = 392) and the tiger (n = 547) as a function of environmental and anthropogenic variables, while controlling for differences in sampling method and sampling area, time of data collection, spatial autocorrelation, subspecies and political protection. We used Bayesian inference to generate a distribution of plausible population sizes. Both species showed higher densities in high productivity areas, the leopard being more abundant in high precipitation, high level of terrain roughness and agricultural areas, and the tiger in areas with low croplands and low roughness. Primary roads density showed a negative effect on both species. Secondary roads density was associated to higher densities for the leopard but lower densities for the tiger. Livestock biomass showed a humped relationship with tigers’ densities. Temporal trends in average density were negative for the tiger, experiencing an average decline of 34% (IQR: 11% − 53%). In contrast, the trend for leopards showed a marginal, yet uncertain, increase in recent years 21% (IQR: −5% − 57%). We predicted a global population estimate of 261,636 (IQR = 146,768 − 461,512) and 5201 tigers (IQR = 2596 − 10,460). Large-scale models of population density that rely on unstructured data can contribute to our understanding of species ecology, produce robust population size estimates for conservation assessment and inform large-scale conservation planning. At the same time, the uncertainty around these estimates highlights the limited knowledge available for these species which should be accounted for in conservation assessments.

Morbidity and mortality burden of COVID-19 in rural Madagascar: results from a longitudinal cohort and nested seroprevalence study.

Three years into the pandemic, there remains significant uncertainty about the true infection and mortality burden of COVID-19 in the World Health Organization Africa region. High quality, population-representative studies in Africa are rare and tend to be conducted in national capitals or large cities, leaving a substantial gap in our understanding of the impact of COVID-19 in rural, low-resource settings. Here, we estimated the spatio-temporal morbidity and mortality burden associated with COVID-19 in a rural health district of Madagascar until the first half of 2021. We integrated a nested seroprevalence study within a pre-existing longitudinal cohort conducted in a representative sample of 1600 households in Ifanadiana District, Madagascar. Socio-demographic and health information was collected in combination with dried blood spots for about 6500 individuals of all ages, which were analysed to detect IgG and IgM antibodies against four specific proteins of SARS-CoV-2 in a bead-based multiplex immunoassay. We evaluated spatio-temporal patterns in COVID-19 infection history and its associations with several geographic, socio-economic and demographic factors via logistic regressions. Eighteen percent of people had been infected by April-June 2021, with seroprevalence increasing with individuals' age. COVID-19 primarily spread along the only paved road and in major towns during the first epidemic wave, subsequently spreading along secondary roads during the second wave to more remote areas. Wealthier individuals and those with occupations such as commerce and formal employment were at higher risk of being infected in the first wave. Adult mortality increased in 2020, particularly for older men for whom it nearly doubled up to nearly 40 deaths per 1000. Less than 10% of mortality in this period would be directly attributed to COVID-19 deaths if known infection fatality ratios are applied to observed seroprevalence in the district. Our study provides a very granular understanding on COVID-19 transmission and mortality in a rural population of sub-Saharan Africa and suggests that the disease burden in these areas may have been substantially underestimated.

Behavioral-dependent recursive movements and implications for resource selection

Within home ranges, animals repeatedly visit certain areas. Recursive movement patterns are widespread throughout the animal kingdom, but are rarely considered when developing resource selection models. We examined how behavioral state-dependent recursive movements influenced reource selection of eastern wild turkey (Meleagris gallopavo silvestris) broods as they aged from day 1 to 28. Because broods become more plastic in behaviors once they begin roosting off the ground, we separated data into broods that were ground roosting (1–13 days) and tree roosting (14–28 days). We used Hidden Markov Models to identify 2 behavioral states (restricted and mobile). We extracted state-specific recursive movements based on states and specific step lengths, which we integrated into a step selection analysis to evaluate resource selection. We found that in a restricted state, ground roosting broods spent less time in areas of mixed pine-hardwoods and more time in areas with greater vegetation density. Tree roosting broods revisited areas closer to shrub/scrub landcover types, and areas with greater vegetation density. Tree roosting broods also spent less time near mixed pine-hardwoods, while spending more time in areas with greater vegetation density. We found that in a mobile state, ground roosting broods revisited areas closer to secondary roads and mixed pine-hardwoods, but farther from hardwoods. Tree roosting broods revisited areas farther from secondary roads and with greater vegetation density. Tree roosting broods also spent more time in areas closer to pine. Resource selection varied depending on behavioral state and recursive movements. However, revisitation and residence time impacted selection in both ground and tree roosting broods. Our findings highlight the need to consider how behaviors can influence movement decisions and ultimately resource selection.

Spatial distribution characteristics and analysis of influencing factors on different manufacturing types in Shandong Province.

Investigating the spatial distribution characteristics and influencing factors of various industry types is critical for promoting the high-quality transformation and development of China's industry. This study combined the Getis-Ord Gi* statistic method, the random forest-based importance assessment method, and the geographically weighted regression method to determine the spatial distribution characteristics of four industry types and their influencing factors. The results revealed that the raw material industry was primarily concentrated in the surrounding districts and counties of Linyi and Qingdao. The food and light textile industry was mainly concentrated in the surrounding districts and counties of Qingdao, and a few were concentrated in some counties of Linyi. The processing and manufacturing industry was also concentrated in the surrounding districts and counties of Qingdao, and a few were concentrated in the belt regions connecting Jinan, Zibo, and Weifang. The high-tech industry was mainly concentrated in the surrounding districts and counties of Jinan and Qingdao. The key spatial influencing factors of the four industry types were different. The number of employees in the secondary industry and road density were most important in determining the spatial distribution of the raw material industry. The financial environment and number of research institutions were most important to the spatial distribution of the food and light textile industry. The gross domestic product and number of medical facilities were most important to the spatial distribution of the processing and manufacturing industry. Urbanization rate, number of research institutions, and gross domestic product were most important to the spatial distribution of the high-tech industry. Geographically weighted regression analysis revealed that the impact intensity of these key factors on the industry exhibits significant spatial heterogeneity. Taken together, these results are useful for formulating the development strategy for each industrial type in different regions.

Using case study on the performance of rural cement stabilizing construction waste recycling road base

In order to solve the problem of resource utilization of construction waste (C&W) recycled aggregate (RA) in rural road grassroots, natural aggregate (NA) was replaced by RA with 0%, 20%, 40%, 60%, 80% and 100% in the test. The unconfined compressive strength, splitting strength, compressive resilience modulus, dry shrinkage test, and frost resistance test were used to test the road performance of the recycled mixture. In order to improve the strength and crack resistance of cement stabilized regenerated mixture, three different admixtures (polypropylene fiber, expansion agent, and curing agent) were considered based on the orthogonal test to improve the crack resistance of the regenerated mixture, and their influence weights were analyzed. Relying on the Gaobeidian rural road network reconstruction project, the construction waste recycling test road was laid, and the construction quality of compaction, bending, and flatness was tested. The results show that the strength of cement-stabilized recycled mixture peaks when RA content is 20%. When RA content is 0% to 100%, the compressive strength decreases by 22.6%, compressive resilience modulus decreases by 18.7%, and tensile strength decreases by 33.2%. Adding 0.8‰ polypropylene fiber, 0.4% expansion agent, and 1.5% curing agent increased 21.2% 7d unconfined compressive strength, 7.8% in 7d splitting strength, and 43.2% in 14d drying shrinkage coefficient. The construction test results show that adding fiber and low-content construction waste increases the base load capacity, increases the compressive strength by 34.5%, reduces the base deflection by 10.5%, and has good road performance. Finally, considering the test results comprehensively, when the RA content is below 20%, and the polypropylene fiber content is between 0.6‰ and 0.8‰, it can meet the basic construction requirements of secondary and sub-secondary rural regeneration roads in China.

Use of logging roads by terrestrial mammals in a responsibly managed neotropical rainforest in Guyana

Selective logging is the most widespread use of tropical forests. Building logging roads facilitates access to previously remote rainforests, and so proper management is essential for ensuring biodiversity retention in logged landscapes. Terrestrial mammals often directly use logging roads (via movement corridors, hunting or foraging), making them vulnerable to poorly managed roads. Here we explore how the presence, arrangement and use of logging roads influence terrestrial mammal occupancy and detection within a Forest Stewardship Council (FSC) certified logged forest in Guyana. We compared camera trap data from20 natural ‘game’ trails in an unlogged area, with camera trap data from 23 sites set near to or on logging roads within the Iwokrama forest. Our findings showed high occupancy within logged areas with no statistically significant difference to unlogged areas. Higher detections were noted along secondary and feeder roads compared to skid trails and the natural trails in control areas. Additionally, our data showed a negative correlation between occupancy and distance to village for a scatter hoarding rodent, most likely driven by subsistence hunting by local communities. Our results indicate that proper road management geared towards the monitoring and guarded access of logging roads, can have a positive effect on terrestrial mammal occurrence within responsibly managed rainforests.

Role of Additives in Compacted Concrete Pavements

Roller compacted concrete (RCC) is an emerging pavement technology especially for secondary roads and is additionally valued for its lower installation costs, faster opening to traffic, high load-carrying capacity, and durability. One major drawback to use of RCC in secondary roads is the balance of smoothness and target surface durability over time. To achieve more consistent RCC mixes, the compaction (flow) performance and rheology of about 40 freshly prepared zero-slump graded aggregate concrete pastes were explored with selected types and levels of additives and water saturation levels. Viscosity modifiers were found to be critical to achieve the highest yield strength formulations. The high yield strength RCC formulations identified from this study followed the key guidance, namely graded aggregate, viscosity, and lubricity, of the Superpave program for fresh asphalt concrete.

The Function of Urban Green Space in Avoiding Disasters in Central Shanghai, China

Urban green space is an essential green infrastructure and spatial guarantee for sustainable development in Shanghai, China. Additionally, its spatial layout’s rationality directly influences the city’s disaster prevention and reduction effect. To use the buffer analysis method, this study selected the ArcGIS 9.0 software platform for making a quantitative analysis of disaster-proof green space service capacity in downtown Shanghai based on relevant construction standards and specifications. The results showed the following: (1) the distribution pattern of disaster-proof green space in downtown Shanghai is less homogeneously distributed in the central area but more homogeneously distributed in the edge area; (2) the service area of all disaster-proof green spaces in the central urban area of Shanghai accounted for 85.07% of the total area, with a service population of 12,516,600. Less than 15% of the area was still uncovered, meaning 2,196,700 people were unserved. These results suggest that the buffer radius of disaster-avoidance green space reaches 400 m. It covers the largest area and serves the largest population, accounting for more than 50% of the central urban area. The mismatch between urban green space distribution and population distribution patterns affected the service effect difference of green space for disaster avoidance. Therefore, new urban green spaces, stadiums, school playgrounds, and other open spaces could be added to enhance the (emergency) urban disaster avoidance ability of central urban areas; (3) the whole central urban area should form an urban mesh disaster avoidance system by combining points, lines, and surfaces with emergency and temporary shelters as first-level disaster avoidance points, fixed and central shelters as second-level disaster avoidance points, urban branch roads and secondary roads as disaster avoidance channels, and expressways and main urban roads as disaster-relief channels.

Anthropogenic and environmental factors associated with koala deaths due to dog attacks and vehicle collisions in South-East Queensland, Australia, 2009–2013

Populations of the iconic Australian koala are under constant decline. Their deaths are associated with rapid and extensive urbanization and the fragmentation of habitat areas. Using citizen science data on reported koala mortalities, we quantified the association between anthropogenic and environmental factors and the two leading causes of koala deaths, dog attacks and vehicle collisions. We achieved this objective using a case–control study design to compare the odds of exposure to a given risk factor for cases (a given cause of death) with the odds of exposure to a given risk factor for controls (all other causes of death). Koala deaths due to dog attacks were positively associated with registered dog population density and negatively associated with lot density whereas koala deaths due to vehicle collisions were positively associated with road density (road length per square kilometer) and negatively associated with human population density and distance to primary and secondary roads. The results of this research can be used to develop strategies to mitigate the risk of deaths due to dog attacks, for example by conducting educational awareness programs, promoting registration of dogs and discouraging free roaming of dogs. In a similar manner, in high-risk areas for vehicle collisions, over- or underpasses can be built to facilitate safe movement of koalas for road crossings or speed limits could be introduced to reduce the likelihood of premature koala deaths due to vehicle collisions.

TRAFFIC ACCIDENTS AND ROAD SAFETY ANALYSIS IN BOGOR CITY, INDONESIA

Bogor City is experiencing rapid development; one of the reasons is its strategic location, which is close to the capital city of Jakarta. As a developing city, transportation is one of the sectors that has an important and strategic role in supporting development in all fields, but in everyday life transportation also has an impact on traffic safety. Therefore, it is felt that there is a need to mitigate traffic accidents to reduce the rate of traffic accidents that occur on highways, especially secondary arterial roads in Bogor City. To make it easier to analyze areas prone to traffic accidents, GIS (Geographic Information System) approach is used, which can integrate various databases, be it spatial data such as maps, photos, and satellite images, or non-spatial or better known as textual data such as attribute and numeric data, thus allowing the location prone to traffic accidents to be described in a two-dimensional form that can be edited and revised quickly so that it can always produce actual information that can be used as a basis for analysts in decision making. Based on the results of the analysis, the highest number and distribution of traffic accidents are in Central Bogor District and North Bogor District, while West Bogor District has a moderate accident rate and South Bogor, North Bogor, and Tanah Sareal Districts have a low accident rate. Most accident-prone points are located in locations that are the center of community activities. With the land use plan in Bogor City in the form of converting green open spaces into residential, trade, and service areas, as well as education and office areas, population growth and the need for transportation will increase. In line with that, the number of accidents will also increase, so it is necessary to mitigate traffic accidents to prevent or suppress the surge in the number of accidents that will occur.

Integrated use of GIS and remote sensing techniques for landscape-scale archaeological analysis: the case study of Metaponto, Basilicata, Italy

The study focuses on the integrated use of multiscale and multisensor remote sensing techniques and big data analysis for the identification of buried archaeological remains or areas of potential archaeological interest. Satellite multispectral data (at very high and high resolution), drone based visible, multispectral, and thermal imagery, and geophysical prospecting (gradiometer) were used. The ancient city of Metaponto was chosen as case study, as it was a very important city in the formative panorama of Italian Magna Graecia and it also is one of the most important and best preserved archaeological sites in southern Italy. The analysis of remote sensing data from different sensors, with different resolutions, and referable to different physical parameters, allowed to deepen archaeological knowledge on a landscape scale, as well as on a site scale, going from the analysis of traces of the ancient landscape (e.g. palaeo-channels, canalisation system, main roads), to the discovery of small features (e.g. secondary roads, houses, facilities).