The roads of power and the paths of the people: Reconfiguring communities and territories from pre-Hispanic Andean roads to modern Peruvian highways

How to maintain topological relationship's real-time updates and maintain dynamic topological relationship, is an important issue must be taken into account in the network analysis model. The topological relationship is very important for the spatial objects in the algorithm of network analysis, especially when add, delete and modify the spatial object geometry and attribute information dynamically. It influences the efficiency of the network analysis algorithm, especially for the dynamical situation. For road network model, the quantity of data is large and refers to a large region. In practical application, the road network data is often changed. The way that stores a topology document is clearly inappropriate. So the dynamic topology construction for road network is considered to meet the application needs. In this way, only geometry and attribute information of spatial object exist in system. Topological relations between space objects are not permanently stored. Or only very limited basis relation data is preserved. Complex topological relations are dynamically generated in accordance with the need when the system runs. This paper put forward two-layer data structure considering dynamic topology, which are data storage layer and dynamic topology layer. And the rapid dynamic topology construction method of road network is put forward based on this two-layer structure. So the problem of road network dynamic topology construction can be solved.

List of contributors List of tables List of figures Acknowledgements Editor's preface Section I. Theoretical Considerations: 1. An introduction to the study of ancient New World road networks Charles D. Trombold 2. Paths and roads in evolutionary perspective Timothy Earle 3. Roads, routes and ties that bind Ross Hassig 4. Observations about research on prehistoric roads in South America John Hyslop Section II. Methodological Considerations: 5. Photointerpretation of Chaco Roads Margaret S. Obenauf 6. Analysis of prehistoric roadways in Chaco Canyon using remotely sensed digital data Thomas L. Sever and David W. Wagner 7. Prehistoric footpaths in Costa Rica: transportation and communication in a tropical rainforest Payson Sheets and Thomas L. Sever 8. Cross-cutting relationships: the relative dating of ancient roads on the north coast of Peru Colleen M. Beck 9. Network analysis and the study of past regional organisation Larry J. Gorenflo and Thomas L. Bell Section III. Regional Studies: 10. Political, economic, and demographic implications of the Chaco road network Frances Joan Mathien 11. The prehistoric road network at Pueblo Alto, Chaco Canyon, New Mexico Thomas C. Windes 12. The Sonoran connection: road and trail networks in the prehistoric period Carroll L. Riley, and Joni L. Manson 13. Causeways in the context of strategic planning in the La Quemada region, Zacatecas, Mexico Charles D. Trombold 14. Xanhari: protohistoric Tarascan routes Shirley Gorenstien and Helen Perlstein Pollard 15. The influence and legacy of Teotihuacan on regional routes and urban planning Thomas H. Charlton 16. The structure of the Aztec transport network Robert S. Santley 17. Roads, thoroughfares and avenues of power at Xochicalco, Mexico Kenneth Hirth 18. Sacbes of the northern Maya William J. Folan 19. Prehistoric roads and causeways of lowland tropical America William M. Denevan 20. The association between roads and polities: evidence for a Wari road system in Peru Katharina J. Schrieber 21. The Chincha roads: economics and symbolism Dwight T. Wallace.

Analysis of Streets Network Status in Rural Settlements after Performing Guide Plan (Case Study: Rural Settlements of Zanjan County)

With the increase in the road transportation system the safety concerns for the road travels are also increasing. In order to ensure the road safety, various government and non-government efforts are visible to maintain the road quality and transport network system. The maintenance of the road condition is in the verse of getting automated for the quick identification of potholes, cracks and patch works and repair. The automation process is taking place in majority of the counties with the help of ICT enabled frameworks and devices. The primary device used for the purpose is the geo location enabled image capture devices. Regardless to mention the image capture process is always prune to noises and must be removed for better further analysis. Also, the spatial data is collected from the road networks are also prune to various error such as missing values or outliers due to the induced noises in the capture devices. Hence, the demand of the current research is to purpose a complete solution for the noise identification and removal from the spatial road network data for making the automation process highly successful and highly accurate. In the recent time, many parallel research attempts are observed, which resulted into solving the problem of noise reduction in all aspects of spatial data. Nevertheless, all the parallel research outcomes have failed to provide a single solution for all the noise issues. Henceforth, this work proposes three novel algorithms to solve spatial image noise problem using the adaptive moment filtration, missing value noise from the spatial data using adaptive logistic analysis and finally, the outlier noise removal from the same spatial data using corrective logistic machine learning method. The outcome of this work is nearly 70% accuracy in image noise reduction, 90% accuracy for missing value and outlier removal. The work also justifies the information loss reduction by nearly 50%. The final outcome of the work is to ensure higher accuracy for road maintenance automation.

A reliable road network is a vital local asset, connecting communities and unlocking economic growth. Every year landslides cause serious damage and, in some cases, the full disruption of many road networks, which can last from a few days to even months. The identification and monitoring of landslides with conventional methods on an extended and complex road network can be a rather difficult process, as it requires a significant amount of time and resources. The road network of the Chania regional unit on the island of Crete in Greece is a typical example, as it connects, over long distances, many remote mountainous villages with other local communities, as well as with the main urban centers, which are mainly located across the shore. Persistent scatterer interferometry (PSI) is a remote-sensing technique that can provide a reliable and cost-effective solution, as it can be used to identify and monitor slow-moving and ongoing landslides over large and complex areas such as those of the mountainous road networks. This study applied PSI in the Chania regional unit, using the novel parallelized PSI (P-PSI) processing chain, developed by the Operational Unit Center for Earth Observation Research and Satellite Remote Sensing BEYOND of the Institute of Astronomy and Astrophysics, Space Applications and Remote Sensing of the National Observatory of Athens (BEYOND) for the rapid identification of the areas, most critical to landslide in a local road network. The application of P-PSI speeded up the total required processing time by a factor of five and led to the rapid identification and monitoring of 235 new slow-moving landslides. The identified landslides were correlated with a pre-existing landslide inventory and open access visual data to create a complete landslide inventory and a relative landslide inventory map, thus offering a valuable tool to local stakeholders.

In cartography, generalization is a key process used to simplify complex geographic information, making it suitable for display at different scales while maintaining its essential meaning. When representing high-density road networks on a fixed screen area, overcrowding and loss of clarity often occur. To solve these problems, a road selection operation can be applied. However, traditional methods have primarily focused on structured vector road networks, leaving unstructured raster road networks largely unaddressed. This study introduces a novel technique, Adaptive Road Width Selection (ARWS), designed to improve the multiscale visualization of compact road systems using unstructured raster datasets. The ARWS method begins by segmenting the original raster road network into multilevel superpixels of varying sizes, reflecting the road widths, through neighborhood analysis. Next, road superpixel matching and selection are performed based on the minimum angle and maximum distance rules, alongside shortest-path calculations. Finally, redundant intersection pixels are eliminated to generate the selection results. The proposed ARWS method was evaluated using road network data from Shenzhen, China, producing effective multiscale visualization outcomes. Unlike conventional techniques relying on structured vector data, ARWS excels in preserving the semantic attributes, overall structure, local connectivity, and integrity of road networks. It addresses the challenges of multiscale visualization in dense road networks, offering a robust solution for unstructured raster data.

We survey the network properties and response to damage sustained of road networks of cities worldwide, using OpenStreetMap (OSM) data. We find that our primary damage response variable , which is the average shortest time needed to reach all nodes in a road network (which stand in for locations within a metropolitan area) from an initial node (which stands in for the location of a center for disaster relief operations), is strongly linearly–correlated with pd, the fraction of the road network segments damaged. We find that this result, previously reported for a city’s road network as opposed to grid and scale-free idealizations, is widely present across the road networks we have examined regardless of location. Furthermore, we identify three families of road networks according to their damage response, forming a typology by which we can classify city road networks. Using this typology, we identify the family of road networks which may be of most concern from a humanitarian standpoint. We also find that, of the properties of the road networks we examined, the average shortest path length, 〈lmin〉 and the average node degree, 〈k〉, proxies for city road network size and complexity respectively, are very significantly–correlated with damage susceptibility. In addition to forming a damage response typology by which city road networks could be classified, we consider five cities in detail, looking at risks and previous disaster events. Our results offer a generalizable framework in evaluating the feasibility of coursing relief efforts within disaster–affected areas using land–based transportation methods. They also provide, albeit in retrospect, a glimpse of the time difficulties which occurred, and the stakes of life involved in the humanitarian crisis which developed in the Kathmandu area due to the earthquakes of April and May 2015.

Simplified methodology for indirect loss–based prioritization in roadway bridge network risk assessment

The greatest influence of motor vehicles is manifested in the urbanized environment. The city is an indicator of sustainable development or an unfavourable relationship between motor vehicles and the urban environment. The study is based on an assessment of the impact of the vehicles current state on the street and road network and trunk road adjacent areas to substantiate the adopted planning protective measures and to determine the functional purpose of the trunk road adjacent areas proceeding from environmental impact on street and road networks. Anthropogenic air pollution sources are primarily represented by industrial enterprises and vehicles emissions. The main task in determining the assessment of the effectiveness of the protective solutions of trunk road adjacent areas is the correct choice of assessment criteria, according to which the efficiency of solutions will differ. Since the street and road network with all its traffic flows is an integral structural element of the city, its impact on the environmental performance of the urban environment can undoubtedly be called the prevailing one. It is necessary to highlight noise, airborne emissions and air (atmosphere) pollution among the main environmental impacts, the source of which is the functioning of the street and road network. Since the street and road system is the main tool in wastewater collection and disposal, it also has a direct impact on the ecological condition of hydrosphere objects, i.e. groundwater, springs, water bodies. Its environmental impacts on the urban setting's lithosphere are also evident: road surface contamination, lubricant residues and gasoline pollute the soil during the removal of rain and melt wastewater. It is impossible to rule out the harmful effects of electromagnetic loads from rail vehicle operation. According to the State Statistics Service of Ukraine, the quality of atmospheric air in a modern developed city is primarily dependent on the volume of pollutant emissions, the two main sources of pollution being stationary 15…30% and mobile 70…85% (using Kyiv's example).

Starting from the past or medieval period to the present day situation, history clearly indicates that the development of a region was and even today is function of a good transportation network. In the present day, society demands for an efficient and unobstructed road network after experiencing major issues or problems like traffic congestion, delay, pollution, increased vehicle operating cost and road accidents. Keeping in mind the above needs and constraints to traffic movement, an analysis of a digitized road network of the concerned city/town can be one of the best remedy to solve the problems. Such analysis is best suited in ArcGIS, Geographic Information System (GIS) software for creating, analyzing and compiling maps for obtaining information. In the present study an effort was made to prepare a road network map of Guwahati city and to find the shortest route between two places by proper analysis and digitization of its existing road network system in order to solve the traffic related problems to great extent. Network Analyst is a special analysis tool in ArcGIS which not only scrutinize the closest facility available in network of digitized interconnected lines but also facilitates in optimizing route during floods and emergency responses. One of the best models that can be generated through Network Analysis is the shortest route between required origin and destination points. The analysis is done based on input of certain network attributes like traversing distance, time and cost of travel, barriers, vehicle restrictions etc. All the important roads connecting each other within the Guwahati City were digitized in the GIS environment and proceeded further to serve the purpose.

The aim of this contribution is to define the backbone road network of the Vysocina Region and to introduce a system of road management to this network. The determination of the extent of the backbone road network is based on multi-criterion decision-making methods, with traffic as the basic parameter for defining the backbone road network. This is referred to as the number of vehicles passing through that section for a specified period of time. Vehicles are further divided into categories according to their size. A new backbone road network layout is designed to better reflect the current situation. In addition, a road management system is in place for the costs that need to be incurred with regard to the nature of maintenance and repairs. The article does not refer to the division of these costs; it only refers to the problem. It is precisely the efficiency of spending on the infrastructure maintenance of the means of transport that has become a frequent topic of discussion in the assessment of the management of the individual road network owners. Quality, safety, and reliability of transport infrastructure are also a limiting factor for attracting significant investment into the regions and the socio-demographic aspects of the territory. The article analyses the road network from the point of view of traffic intensity. This assessment is most used in patch planning; therefore, a major part of the article is devoted to it. Part of the contribution is also devoted to the issue of public transport on this road network. Public transport is strongly linked to transport infrastructure and is often a crucial factor in planning, especially winter maintenance on lower-class roads. The quality and efficiency of the road network must be seen as a compact unit, which directly or indirectly affects the functioning of all institutions and citizens in the territory concerned.

<p>From the territorial land use planning perspective new urban areas have been safeguarded during the last decades by the Portuguese regulation and practice that consider land use restrictions on landslide hazard prone areas. Nevertheless, the fatalities due to landslides did not reduce in number, mostly due to the occurrence of rapid shallow landslides, affecting people inside buildings and more recently, inside vehicles, as a consequence of the increasing people’s mobility. When the exposure is to deep-seated landslides, structures and infrastructures frequently undergo intense destruction, leading to severe disruption of economic and social activities.</p><p>The present work intends to evaluate actual exposure of road network to landslides in the context of a Landslide Early Warning System (LEWS) prototype, developed upon both soft and low-cost technology. To achieve this main goal, three specific objectives were defined: (i) to evaluate the road network exposure considering different road types; (ii) to evaluate the re-routing of circulation for users, the loss of access to functions or services, due to travel time increase; and (iii) to evaluate the road network connectivity hierarchy and its contribute to define landslide risk hotspots for evacuation and rescue access in case of disastrous landslide events.</p><p>The study area corresponds to four municipalities (Alenquer, Arruda dos Vinhos, Sobral de Monte Agraço and Vila Franca de Xira) that are partially included in the Grande da Pipa River (GPR) basin, which is one of the most landslide prone areas in Portugal. The road network hierarchy data is based on available official road network maps; the road segment connectivity role in regional network is based on graph analysis; and the landslide risk hotspots - are classified by combining the different types of road segments, the re-routing travel time scenarios and the road network hierarchy and network connectivity function with the shallow and deep-seated landslide maps produced in the context of the BeSafeSlide project.</p><p>The final results will be incorporated on the LEWS prototype, which is conceptualized to be people-centred, which means, not specifically focused on hazardous processes, but on reducing exposure and vulnerability, allowing an effective implementation of risk management strategies, contributing for increasing resilience and adaptive capacity building of local communities.</p><p>Acknowledgments: This work was financed by national funds through FCT (Foundation for Science and Technology, I. P.), in the framework of the project BeSafeSlide – Landslide early warning soft technology prototype to improve community resilience and adaptation to environmental change (PTDC/GES-AMB/30052/2017), and the Research Unit UIDB/00295/2020 and UIDP/00295/2020.</p>

The Redes Andinas (Andean Networks) project assesses the complexity of ancient road networks in the archaeological record in the Andes, beyond the Inca roads system. A multiscale methodological approach allows us to characterise the transformation and resilience of the road networks over the past millennium, in the context of the 18°South parallel's vertical transect.

Road maintenance systems (RMS) are crucial for maintaining safe and efficient road networks. The impact of climate change on road maintenance systems is a concern as it makes them more susceptible to weather events and subsequent damages. To tackle this issue, we propose an RMSDC (Road Maintenance Systems Using Deep Learning and Climate Adaptation) technique to improve road maintenance systems based on Deep learning and Climate Adaptation. RMSDC aims to use the multivariate classification technique and divides the dataset into training and test datasets. The RMSDC combines Convolutional Long Short-Term Memory (ConvLSTM) techniques with road weather information and sensor data. However, in emerging nations, the effects of climate change are already apparent, which makes road networks particularly susceptible to extreme weather, floods, and landslides. Therefore, climate adaptation of road networks is essential, especially in developing nations with limited financial resources. To address this issue, we propose an intelligent and effective RMSDC that utilizes deep learning algorithms based on climate change predictions. The ConvLSTM block effectively captures the relationship between input features over time to calculate the root-mean deviation (RMSD). We evaluate RMSDC performance against frameworks for downscaling climate variables using two metrics: root-mean-square error (RMSE) and mean absolute difference. Through real evaluations, RMSDC consistently outperforms approaches with a reduced RMSE of 0.26. These quantitative results highlight how effective RMSDC is in addressing maintenance systems on road networks leading to proactive road maintenance strategies that enhance traffic safety, reduce costs, and improve environmental sustainability.

Portuguese mainland road network safety performance indicator