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The rapid growth of the vehicle population is a major factor in heavy air pollution and public health issues. Traffic-related air pollutants (TRAPs) on roads are often much higher than ambient values, leading to high exposure levels in vehicles. This research proposes a hybrid forecasting model for early detection and early warning systems (EWS) of road networks during real-world travels. Data is collected from Kannur, Calicut, Palakkad, and Coimbatore using real-time sensors, including surrounding discussion information, activity information, vehicle speed, and stopping events. The study predicts ambient air quality (AAQ) levels on the road network using the Gaussian Dispersion model (GDM) and measures the risk sensitivity of PM10 and PM2.5 in selected regions. This helps formulate powerful prevention strategies and prevent negative health impacts. The air pollution module for predicting concentration has an innovative hybridization model that combines an improved cuckoo search (CS) and differential evolution (DE) algorithm with a stacked LSTM model to increase forecasting accuracy of six major environmental pollution levels. This model predicts the AAQ level and is effective and robust for warning one day before the pollutant event occurs based on the risk level of an ambient air pollutant from the RN. [ABSTRACT FROM AUTHOR]

Recently, the research community focuses on processing various types of location-based queries (or LBQs for short) (e.g., the range and nearest neighbor queries) on spatial objects of the same type in road networks, in which the road distance from objects to the query object is an important metric for determining the query result and needs to be calculated based on the connectivity of the network. The LBQs can be used to find the spatial objects with better spatial closeness (i.e., the shorter road distance to the query object). In many applications, information about the neighboring relationship between different types of objects may be more attractive to the user. As such, the location-based aggregate queries (or LBAggQs for short) are proposed, which consider not only the spatial closeness of objects but also the neighboring relationship between objects. However, the processing techniques for the LBAggQs are mainly based on a centralized paradigm, and hence suffer from highly dynamic environments where a large number of LBAggQs is issued concurrently. In this paper, a distributed processing technique, based on the MapReduce platform, is developed to answer multiple LBAggQs. Three tables T o , o dist , T o , p dist , and T p , p dist are first used to maintain information of the road distances between objects, and then evenly partitioned by a grid structure. With the three tables, a MapReduce-based algorithm, called the MRSAggDQ algorithm, is designed for query processing in a distributed manner. Finally, an extensive set of experiments using two real road networks is conducted to measure the average running time for different numbers of LBAggQs to be processed concurrently. The experimental results demonstrate the efficiency and the scalability of the proposed methods. [ABSTRACT FROM AUTHOR]

Pattern recognition is critical to map data handling and their applications. This study presents a model that combines the Shape Context (SC) descriptor and Graph Convolutional Neural Network (GCNN) to classify the patterns of interchanges, which are indispensable parts of urban road networks. In the SC-GCNN model, an interchange is modeled as a graph, wherein nodes and edges represent the interchange segments and their connections, respectively. Then, a novel SC descriptor is implemented to describe the contextual information of each interchange segment and serve as descriptive features of graph nodes. Finally, a GCNN is designed by combining graph convolution and pooling operations to process the constructed graphs and classify the interchange patterns. The SC-GCNN model was validated using interchange samples obtained from the road networks of 15 cities downloaded from OpenStreetMap. The classification accuracy was 87.06%, which was higher than that of the image-based AlexNet, GoogLeNet, and Random Forest models. [ABSTRACT FROM AUTHOR]

Pattern recognition is critical to map data handling and their applications. This study presents a model that combines the Shape Context (SC) descriptor and Graph Convolutional Neural Network (GCNN) to classify the patterns of interchanges, which are indispensable parts of urban road networks. In the SC-GCNN model, an interchange is modeled as a graph, wherein nodes and edges represent the interchange segments and their connections, respectively. Then, a novel SC descriptor is implemented to describe the contextual information of each interchange segment and serve as descriptive features of graph nodes. Finally, a GCNN is designed by combining graph convolution and pooling operations to process the constructed graphs and classify the interchange patterns. The SC-GCNN model was validated using interchange samples obtained from the road networks of 15 cities downloaded from OpenStreetMap. The classification accuracy was 87.06%, which was higher than that of the image-based AlexNet, GoogLeNet, and Random Forest models.

Landslides are the most common natural hazards and can cause casualties and damage forests, roads, water conservancy, hydropower, and other infrastructure. Road networks are essential components of critical infrastructure networks. Damage from natural disasters significantly impact the production and life in the region. It is important to evaluate the risk of road networks under landslides. In this paper, we investigate the risk of road networks under the influence of landslides in six counties adjacent to Sichuan Province and Yunnan Province by considering landslide susceptibility and road vulnerability. First, according to the characteristics of the study area, six evaluation indicators were selected for landslide susceptibility and road network vulnerability. Second, the combination weighting method is used to assign the weight of each evaluation index. Finally, the risk analysis results combining landslide susceptibility and road network vulnerability are obtained using the landslide risk formula. The research work presented in this paper can help reduce the property loss caused by landslides and realize the timely identification and protection of high-risk road sections.

Building resilient infrastructure is at the core of sustainable development, as evidenced by the UN Sustainable Development Goal 9. In fact, the effective operation of road networks is crucial and strategic for the smooth functioning of a nation's economy. This is also fundamental from a sustainability perspective, as efficient transportation networks reduce traffic, and thus, their environmental impact. However, road networks are constantly at risk of traffic closure and/or limitations due to a plurality of natural hazards. These environmental stressors, among other factors like aging and degradation of structural materials, negatively affect the disaster resilience of both single components and the system of road networks. However, the estimation of such resilience indices requires a broad multidisciplinary vision. In this work, a framework for application to large road networks is delineated. In the proposed methodology, seismic hazard is considered, and its corresponding impacts on road networks are evaluated. The assessment encompasses not only the road network system (including squares, roads, bridges, and viaducts) but also the buildings that are located in the urban area and interact with the network. In this context, the probability that buildings will suffer seismic-induced collapse and produce partial or total obstruction of roads is considered. This scheme is designed for implementation in different geographical contexts using geo-referenced data that include information about specific risks and alternative rerouting options. The proposed methodology is expected to support the mitigation of functionality loss in road networks after disasters, contributing to both the economic and social dimensions of sustainability. To evaluate the methodology, two case studies focusing specifically on hospital-to-hospital connections were conducted in Naples and Turin, Italy. However, the proposed approach is versatile and can be extended to other critical infrastructures, such as theatres, stadiums, and educational facilities. [ABSTRACT FROM AUTHOR]

As the risk of flooding continues to impose on transportation infrastructure systems, enhancing the ability to monitor flood inundations in road networks becomes more significant. This research explores the potential crowdsourced data, specifically 3-1-1 reports, has to complement information from physical flood sensors. Three flooding events in two watersheds in Harris County, Texas were studied. To assess 3-1-1 report's capabilities, a time series analysis between reports and water elevation of physical sensor data was performed to capture channel overflow. Next, a graph-based observability analysis identified multiple combinations of minimum additional sensor locations needed for complete network monitoring in the study area. To find the optimal combination, a principal component analysis assigns a criticality score based on exposure, road importance, neighborhood vulnerability, and frequency of 3-1-1 reports near a node. The results indicate that 3-1-1 reports effectively improve flood monitoring by reducing the need for physical sensors by 32% in areas that lack flood sensors. This approach can help city managers improve flood monitoring by leveraging socially sensed data to supplement physical sensors, especially in blind spots where no flood gauge exists. [ABSTRACT FROM AUTHOR]

Autonomous vehicle (AV) technology is rapidly advancing, leading to a sustainable evolution in transportation. AVs offer the promise of enhanced safety, reduced emissions, improved traffic flow, and increased mobility for passengers. However, the integration of AVs into existing traffic networks presents complex challenges related to ecological sustainability and passenger comfort. This review aims to bridge the gap in the literature by providing a comprehensive evaluation of the influence of AVs on both ecology and passenger comfort within traffic networks. The study synthesizes existing knowledge on AV technology, ecological impacts, and passenger comfort to offer a comprehensive understanding of the challenges and opportunities associated with AVs' integration. The main objectives of this research are to review the current literature on the environmental impact of AVs, analyze studies on passenger comfort in AVs, identify key challenges and opportunities, and propose future research directions. The results highlight the need for a holistic, sustainable approach that considers both ecological and passenger-centric factors in the design and deployment of AVs. Future research directions are proposed to further our understanding of the complex interplay between AVs, the environment, and passenger well-being, and inform policy decisions and technological advancements that promote sustainable and comfortable transportation solutions. [ABSTRACT FROM AUTHOR]

Sillyon was within the ancient region of Pamphylia, and today it is located in the Kocagözler locality of the Yanköy neighborhood of Serik district in Antalya province. The city has preserved its feature of being an important cultural heritage area from past to present with its archaeological, cultural assets, historical values and multi-layered texture. Throughout history, at the point of unification-conflict of cultures and civilizations, Sillyon bears the traces of many civilizations on it. In other words, in light of current data, Sillyon is an example of a multi-layered city that hosts the process of uninterrupted historical settlement from the early Iron Age to the Ottoman period. In this article, the Roman Bridge, which is known as Ayvalıkemer among the people and located on the Koducak Stream, about 1.5 km west of Sillyon, is discussed. The bridge, dated to the Roman Imperial Period, has 3 sections and was built entirely of travertine blocks. The building, which was restored in 2016 with the support of the General Directorate of Highways, is among the strongest examples of bridges in the region. In the study, first of all, the orientation of the building and its entire architectural setup were analyzed. Its historical evaluation was made by comparing it with the contemporary bridges built in Anatolia. Restoration interventions were examined and information about the bridge’s condition before and after restoration was presented. The location of the bridge on the road networks of the Pamphylia Region and its importance for the city were investigated and the connection of Sillyon with the ancient road routes in the region was discussed. It is expected that this article will present a new perspective on both Sillyon research and the ancient transportation network studies in the region.

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In this paper, the proposed work is to design an emergency vehicle (rescue vehicle) equipped with sensors and an Arduino board as an edge device to gather information on approaching vehicle speed and blind spots. To quickly identify potential dangers, the data is transferred to a fog computing device for real-time processing and analysis. The Arduino is used for blind spot identification and proximity monitoring, and the Node MCU is used for real-time data processing, communication, and remote monitoring. This technology improves safety by averting collisions with nearby objects, pedestrians, and bicycles in emergency situations by immediately alerting the emergency vehicle driver. [ABSTRACT FROM AUTHOR]

Persistently, urban regions grapple with the ongoing challenge of vehicular traffic, a predicament fueled by the incessant expansion of the population and the rise in the number of vehicles on the roads. The recurring challenge of vehicular congestion casts a negative influence on urban mobility, thereby diminishing the overall quality of life of residents. It is hypothesized that a dynamic clustering method of vehicle trajectory data can provide an accurate and up-to-date representation of real-time traffic behavior. To evaluate this hypothesis, data were collected from three different cities: San Francisco, Rome, and Guayaquil. A dynamic clustering algorithm was applied to identify traffic congestion patterns, and an indicator was applied to identify and evaluate the congestion conditions of the areas. The findings indicate a heightened level of precision and recall in congestion classification when contrasted with an approach relying on static cells. [ABSTRACT FROM AUTHOR]

With the rising popularity of portable mobile positioning equipment, the volume of mobile trajectory data is increasing. Therefore, trajectory data compression has become an important basis for trajectory data processing, analysis, and mining. According to the literature, it is difficult with trajectory compression methods to balance compression accuracy and efficiency. Among these methods, the one based on spatiotemporal characteristics has low compression accuracy due to its failure to consider the relationship with the road network, while the one based on map matching has low compression efficiency because of the low efficiency of the original method. Therefore, this paper proposes a trajectory segmentation and ranking compression (TSRC) method based on the road network to improve trajectory compression precision and efficiency. The TSRC method first extracts feature points of a trajectory based on road network structural characteristics, splits the trajectory at the feature points, ranks the trajectory points of segmented sub-trajectories based on a binary line generalization (BLG) tree, and finally merges queuing feature points and sub-trajectory points and compresses trajectories. The TSRC method is verified on two taxi trajectory datasets with different levels of sampling frequency. Compared with the classic spatiotemporal compression method, the TSRC method has higher accuracy under different compression degrees and higher overall efficiency. Moreover, when the two methods are combined with the map-matching method, the TSRC method not only has higher accuracy but also can improve the efficiency of map matching. [ABSTRACT FROM AUTHOR]

In the new "gig" economy, a user plays the role of a consumer as well as a service provider. As a service provider, drivers travelling from a source to a destination may opportunistically pickup and drop-off packages along the way if that does not add significantly to their trip distance or time. This gives rise to a new business offering called Package Delivery as a Service (PDaaS) that brokers package pickups and deliveries at one end and connects them to drivers on the other end, thus creating an ecosystem of supply and demand. The dramatic cost savings of such a service model come from the fact that the driver is already en-route to their destination and the package delivery adds a small overhead to an already pre-planned trip. From a technical perspective, this problem introduces new technical challenges that are uncommon in the literature. The driver may want to optimise for distance or time. Furthermore, new packages arrive for delivery all the time and are assigned to various drivers continuously. This means that the algorithm has to work in an environment that is dynamic, thereby precluding most standard road network precomputation efforts. Furthermore, the number of packages that are available for delivery could be in the hundreds of thousands, which has to be quickly pruned down for the algorithm to scale. The paper proposes a variation called dual Dijkstra's that combines a forward and a backward scan in order to find delivery options that satisfy the constraints specified by the driver. The new dual heuristic improves the standard single Dijkstra's approach by narrowing down the search space, thus resulting in significant speed-ups over the standard algorithms. Furthermore, a combination of dual Dijkstra's with a heuristic landmark approach results in a dramatic speed-up compared to the baseline algorithms. Experimental results show that the proposed approach can offer drivers a choice of packages to deliver under specified constraints of time or distance, and providing sub-second response time despite the complexity of the problem involved. As the number of packages in the system increases, the matchmaking process becomes easier resulting in faster response times. The scalability of the PDaaS infrastructure is demonstrated using extensive experimental results. [ABSTRACT FROM AUTHOR]

A large literature suggests that the presence of refugees is associated with greater risk of conflict. We argue that the positive effects of hosting refugees on local conditions have been overlooked. Using global data from 1990 to 2018 on locations of refugee communities and civil conflict at the subnational level, we find no evidence that hosting refugees increases the likelihood of new conflict, prolongs existing conflict, or raises the number of violent events or casualties. Furthermore, we explore conditions where provinces are likely to experience substantively large decreases in conflict risk due to increased development. Analysis examining nighttime lights as a measure of development, coupled with expert interviews, support our claim. To address the possibility of selection bias, we use placebo tests and matching. Our research challenges assertions that refugees are security risks. Instead, we show that in many cases, hosting refugees can encourage local development and even conflict reduction.

Addressing sustainable mobility in urban areas has become a priority in today's society, given the growing population and increasing vehicular flow in these areas. Intelligent Transportation Systems have emerged as innovative and effective technological solutions for addressing these challenges. Research in this area has become crucial, as it contributes not only to improving mobility in urban areas but also to positively impacting the quality of life of their inhabitants. To address this, a dynamic clustering methodology for vehicular trajectory data is proposed which can provide an accurate representation of the traffic state. Data were collected for the city of San Francisco, a dynamic clustering algorithm was applied and then an indicator was applied to identify areas with traffic congestion. Several experiments were also conducted with different parameterizations of the forgetting factor of the clustering algorithm. We observed that there is an inverse relationship between forgetting and accuracy, and the tolerance allows for a flexible margin of error that allows for better results in precision. The results showed in terms of precision that the dynamic clustering methodology achieved high match rates compared to the congestion indicator applied to static cells. [ABSTRACT FROM AUTHOR]

Location-Based Services are often used to find proximal Points of Interest (PoIs)—e.g., nearby restaurants and museums, police stations, hospitals, etc.—in a plethora of applications. An important recently addressed variant of the problem not only considers the distance/proximity aspect, but also desires semantically diverse locations in the answer-set. For instance, rather than picking several close-by attractions with similar features—e.g., restaurants with similar menus; museums with similar art exhibitions—a tourist may be more interested in a result set that could potentially provide more diverse types of experiences, for as long as they are within an acceptable distance from a given (current) location. Towards that goal, in this work we propose a novel approach to efficiently retrieve a path that will maximize the semantic diversity of the visited PoIs that are within distance limits along a given road network. Our approach allows to specify both a start and terminal location to return a (non-necessarily shortest) path that maximizes diversity rather than only minimizing travel cost, thus providing ample applications in tourist route recommendation systems. We introduce a novel indexing structure—the Diversity Aggregated R-tree, based on which we devise efficient algorithms to generate the answer-set—i.e., the recommended locations among a set of given PoIs—relying on a greedy searching strategy. Our experimental evaluations conducted on real datasets demonstrate the benefits of the proposed methodology over the baseline alternative approaches. [ABSTRACT FROM AUTHOR]

Hyrcanian forests are a green belt of deciduous forests in the south of the Caspian Sea whose integrity and survival of its commercial species, such as the chestnut-leaved oak (Quercus castaneifolia), are at risk due to anthropogenic activities. This study performed coupled spatial-statistical modeling to identify the rate and determinants of Q. castaneifolia in parts of the forest landscape (5400 km2) in Mazandaran Province, Northern Iran. According to the Maximum Entropy model (MaxEnt), the species' most suitable habitats were small in area (9.03 % of the total forest cover of the region) and uneven in spatial distribution due to the prevailing effect of bioclimatic variables (Bios). Using object-based segmentation analysis, the oak’s suitable habitats were divided into spectrally and topographically homogenous areas as working units to search for their occurrence and rate thinning operations. The mean annual Landsat-NDVI of each unit was regressed against the year to use its slope as a measure of thinning, indicating that more than 80 % of the units experienced different thinning levels over the past two decades (2002–2022). Using the multiple linear regression model (p

Poverty is a prevailing challenge in the Philippines. Through the small area estimation (SAE) of the Philippine Statistics Authority, the poor cities and municipalities with poor households were identified. Some locations are also vulnerable to natural calamities and have limited resources. The 2018 SAE shows that the region of Eastern Visayas is one of the poorest in the country. Moreover, the region is vulnerable to natural hazards, particularly typhoons. The regional road network is also less connected for the poorer municipalities. Hence, slow economic growth is a concerted outcome. To provide an evidence-based framework on potentially optimized resource allocation of, say, government institutions and humanitarian organizations in countering these poverty concerns exacerbated by natural calamities, this paper used the geographical information system (GIS) for easier visualization and interpretability. Spatial analyses were also applied to [1] determine if clusters of poverty exist in the region across different periods and [2] if hot spots of poverty incidence exist in the latest SAE. The findings reveal that poverty incidence for all four previous periods of SAE has significant non-random clusters. In addition, poverty hot spots, at varying confidence levels, were statistically identified. These hot spots are also vulnerable to frequent typhoons and have limited access to national roads. Additionally, the bootstrap regression shows that economic growth could be boosted by expanding road networks as an indicator of decreased poverty incidence. This study, thus, further emphasizes the importance of data-driven decision-making, particularly in efforts to counter poverty, including some of its aggravating external factors such as natural calamities. The application of empirical methods in formulating and improving policies, especially those related to infrastructure investments and expansions, is also recommended, given the limited resources. It also highlights how road networks in the region could be instrumental in promoting economic progress, particularly in less accessible areas. [ABSTRACT FROM AUTHOR]

In modern smart cities, road networks are becoming more and more complicated, resulting in more complex format of graphs. This brings many challenges to the forecasting of traffic flow in road graphs. Most of traditional traffic flow forecasting methods ignored many implicit relationships inside road graphs. And this cannot be well suitable for modern road networks in smart cities. Besides, the operation of smart cities is accompanied with real-time big data stream. The running efficiency of forecasting methods is another important concern. To handle this issue, this paper proposes a graph deep learning-based fast traffic flow forecasting method in urban road networks. Firstly, the theory about graph convolution operations is deduced and can be used as the basis of a graph convolution network (GCN). Then, the whole road network is viewed as a complex road graph, and the GCN is introduced to establish a novel forecasting method for graph-level traffic flow. With roads being regarded as nodes and their relations being regarded as edges, graph-level forecasting can be realized with the use of the proposed method. Experiments are carried out on a standard real dataset to evaluate the proposal. The experimental results show a proper performance of the proposal.

Beyond its significance in urban planning and traffic engineering, the optimal design of road networks is crucial for the uninterrupted flow of traffic. Traffic congestion results in increased travel times and waiting times as well as higher CO2 emissions. It is therefore imperative to study and optimize the traffic flow through roads and junctions to avoid such conditions. In this paper, a highly congested T-intersection in Narvik, Norway, is analyzed and several alternative plans are tested to reduce the travel time through the junction and the waiting time for cars in queues. The process begins with the collection of traffic data to estimate the current waiting times and understand the queuing problem. The current state is modeled in AnyLogic to simulate the actual conditions at the intersection. Two alternate solutions are then presented and analyzed. One is the use of traffic lights, and the other is the installation of a roundabout. The waiting times and queues associated with the two alternatives are compared with the current state to determine the better alternative. This paper presents the effectiveness of using simulation to solve the traffic congestion problems at T-junctions, and the method can be applied to various similar problems.

Persistently, urban regions grapple with the ongoing challenge of vehicular traffic, a predicament fueled by the incessant expansion of the population and the rise in the number of vehicles on the roads. The recurring challenge of vehicular congestion casts a negative influence on urban mobility, thereby diminishing the overall quality of life of residents. It is hypothesized that a dynamic clustering method of vehicle trajectory data can provide an accurate and up-to-date representation of real-time traffic behavior. To evaluate this hypothesis, data were collected from three different cities: San Francisco, Rome, and Guayaquil. A dynamic clustering algorithm was applied to identify traffic congestion patterns, and an indicator was applied to identify and evaluate the congestion conditions of the areas. The findings indicate a heightened level of precision and recall in congestion classification when contrasted with an approach relying on static cells.

Assessing flood risks on road infrastructures is critical for the definition of mitigation strategies and adaptation processes. Some efforts have been made to conduct a regional flood risk assessment to support the decision-making process of exposed areas. However, these approaches focus on the physical damage of civil infrastructures without considering indirect impacts resulting from social aspects or traffic delays due to the functionality loss of transportation infrastructures. Moreover, existing methodologies do not include a proper assessment of the uncertainties involved in the risk quantification. This work aims to provide a consistent quantitative flood risk estimation and influence factor modelling for road infrastructures. To this end, a Flood Risk Factor (FRF) is computed as a function of hazard, vulnerability, and infrastructure importance factors. A Bayesian Network (BN) is constructed for considering the interdependencies among the selected input factors, as well as accounting for the uncertainties involved in the modelling process. The proposed approach allows weighting the relevant factors differently to compute the FRF and improves the understanding of the causal relations between them. The suggested method is applied to a case study located in the region of Santarem Portugal, allowing the identification of the sub-basins where the road network has the highest risks and illustrating the potential of Bayesian inference techniques for updating the model when new information becomes available.

We introduce a representative dissimilar path (RDP) query, a novel type of path query in road networks. The k representative paths (RPs) between a source and a destination locations have k smallest costs for a feature (e.g., length, number of road intersections, or straightness). Given x features and k, an RDP query returns a set of paths for a sourcedestination pair such that the path set includes at least one of the k RPs for every feature, and the path set's similarity score is minimized. We formulate a novel measure to quantify the similarity of a set of paths. Considering different road features and incorporating the novel similarity measure in the computation of RDPs allow us to accommodate the human movement dynamics between two locations in an effective way. Finding the RDPs is a computational challenge because an RDP query requires computing the RPs for multiple features and then finding the RDPs from an exponential number of path combinations. We develop an efficient solution to answer RDP queries. The underlying ideas behind the efficiency of our algorithms are the refinement of the search space, finding the RPs for multiple features with a single search, and exploiting both the lower and upper bounds of the path set's similarity score while identifying the RDPs. We show the efficacy of the RDP query and the efficiency of our solution to answer the RDP query in extensive experiments using real datasets. [ABSTRACT FROM AUTHOR]

In the context of carbon neutrality, the electric vehicles (EVs) are increasingly used in city logistics service. To utilize EVs in the logistics delivery service in a cost-effective manner, this paper proposes a coordinated operational planning method for EVs considering the real road networks and battery swapping for EVs. Specifically, utilizing the EVs parked in the depot to discharge to power the depot or feed to the grid for an arbitrage, this paper is to minimize the sum of transportation cost and energy cost by determining the optimal number of EVs assigned to delivery goods and their driving route, as well as the charge–discharge operation decisions of all EVs including those without a delivery assignment and those returning to the depot after completing delivery. In simulation study, the effectiveness of the proposed methods is finally validated.

With the rising popularity of portable mobile positioning equipment, the volume of mobile trajectory data is increasing. Therefore, trajectory data compression has become an important basis for trajectory data processing, analysis, and mining. According to the literature, it is difficult with trajectory compression methods to balance compression accuracy and efficiency. Among these methods, the one based on spatiotemporal characteristics has low compression accuracy due to its failure to consider the relationship with the road network, while the one based on map matching has low compression efficiency because of the low efficiency of the original method. Therefore, this paper proposes a trajectory segmentation and ranking compression (TSRC) method based on the road network to improve trajectory compression precision and efficiency. The TSRC method first extracts feature points of a trajectory based on road network structural characteristics, splits the trajectory at the feature points, ranks the trajectory points of segmented sub-trajectories based on a binary line generalization (BLG) tree, and finally merges queuing feature points and sub-trajectory points and compresses trajectories. The TSRC method is verified on two taxi trajectory datasets with different levels of sampling frequency. Compared with the classic spatiotemporal compression method, the TSRC method has higher accuracy under different compression degrees and higher overall efficiency. Moreover, when the two methods are combined with the map-matching method, the TSRC method not only has higher accuracy but also can improve the efficiency of map matching.

Risk Management Systems are a valuable tool for estimating the potential losses of natural events, assessing risk reduction strategies, and increasing the resilience of critical infrastructure. The paper discusses the development of SIGeR-RV, a tool for Risk Management of road networks exposed to multi-hazards developed in Chile. The tool was implemented on a web-based Geographic Information System platform. It is able to display hazard maps, calculate risk levels, prioritize mitigation strategies, estimate direct and indirect losses, and assess the social vulnerability of communities exposed to natural hazards. The article includes an application of SIGeR-RV in a road network exposed to the lahar flows of Villarrica volcano in the south of Chile.

The present article addresses, through the study of two Hellenistic inscriptions, the administrative management of extra-urban roads by Greek city-states. While the organisation of roads within urban areas is relatively well documented by urbanistic inscriptions, the documentation about extra-urban roads is scarce. However, two urbanistic inscriptions contain clauses on the roads of the civic territory: a text from the Macedonian city of Kyrrhos, EKM II, 401 (for which a partial translation is proposed), as well as the famous astynomic inscription from Pergamon OGIS 483. The clauses about the roads in the territory of each city are compared in order to provide perspectives on the public management of road networks in the Hellenistic period.

Abstract Road networks result from a subtle balance between geographical coverage and rapid access to strategic points. An understanding of their structure is fundamental when it comes to evaluating and improving territorial accessibility. This study is designed to provide insight into the progressive structuring of territorial patterns by analyzing the evolution of road networks. Studying road network morphogenesis requires geohistorical data, provided here by historical maps from which earlier road networks can be digitized. A hypergraph is constructed from these networks by combining road segments into “ways” on the basis of a method for defining the continuity of road segments. Next, indicators are computed for these ways based on topological and geometrical features. The road patterns of three cities in the Burgundy Franche-Comte region of France (Dijon, Besançon, and Pontarlier) at three historical periods (the 18th, 19th, and twentieth centuries) are then analyzed. In this manner, their topological features and centrality characteristics can be compared from snapshots at different times and places. The innovative method proposed in this paper helps us to read features of the road patterns accurately and to make simple interpretations. It can be applied to any territory for which data is available. The results highlight the underlying structure of the three cities, reveal information about the history and the functioning of the networks, and give preliminary insights into the morphogenesis of those cities. Prospectively this work aims to identify the mechanisms that drive change in road networks. Detecting stability or variation in indicators over time can help in identifying similar behavior, despite geographic and cultural distances, as well as evolution mechanisms linked to specificities of each city. The study of road network morphogenesis can make a major contribution to understanding how road network structure affects accessibility and mobility.

Protected areas play an active role in protecting natural resources and wildlife habitat. These areas must be accessible within protection-use balance. For this reason, road networks in protected areas are one of the main functions of sustainable infrastructure services. The construction phases of road networks in these sensitive areas should be considered in planning within the balance of protection-use with interdisciplinary studies. Especially during the construction of the road network, it is necessary to pay attention to the construction machinery used, geotextile materials, hydraulic and ecological road structures, plantation of the slopes, fences that increase the visual quality and work schedule. Based on a related literature survey, the issues to be considered during the construction phases of road networks (i.e. road planning, tree felling and removing, excavation and embankment, subgrade finishing, road structures and surfacing) in protected areas were evaluated under nine headings. The implementation phases of these issues are important in reducing the adverse effects that will occur in protected areas. In this regard, during the construction phases of road networks, the issues to be considered were evaluated together with the conceptual indicators in terms of management, technique, economy, ecology, and aesthetics. Matters needing attention according to the sensitivity of conceptual indicators during the construction phases of road networks in and around protected areas that contain sensitive ecosystems have been identified and presented in a framework to further the discussions on this issue. Accordingly, the use of the issues to be considered in the planning and construction of road networks with conceptual indicators will help evaluate the planning phase before and after construction. In particular, it can be expected to lead to the creation of a checklist after the planning phase...

In Latin America, cities are characterized by uncontrolled urban growth and locations in critical geographic areas. This factor affects an individual’s ability to react efficiently to an action known as resilience to disasters caused by natural phenomena or catastrophes that involve human participation. This study proposes a method for detecting vulnerabilities in urban road systems in large, intermediate, and small cities in Latin America, so that resilience can be better supported. Depending on the structural characteristics of a city, measurement techniques can be used to combine the topological, geographic, and spatiotemporal indicators. Several measurements, such as mesh and connectivity, betweenness centrality, closeness centrality, robustness of networks with interruption attacks, location of dead ends, measurement of travel times between origin and destination, elevations and fusion of indicators with clusters on the same map, were applied using network graphs from OpenStreetMap (OSM) to estimate the resilience of road networks. The results are exposed to clusters of fused geospatial maps, which show that intersections and streets tend to be classified as vulnerable, with morphological structures of growth, resistance to degradation of the urban network, marginal urban sectors with high conglomeration, and mobility. The results highlight the importance of applying resilient practices in the region and generating urban management options to strengthen the response capacity of cities in Latin America.

Measuring and attributing greenhouse gas (GHG) emissions remains a challenging problem as the world strives toward meeting emissions reductions targets. As a significant portion of total global emissions, the road transportation sector represents an enormous challenge for estimating and tracking emissions at a global scale. To meet this challenge, we have developed a hybrid approach for estimating road transportation emissions that combines the strengths of machine learning and satellite imagery with localized emissions factors data to create an accurate, globally scalable, and easily configurable GHG monitoring framework.

The basic goal of this research is to find the shortest path of a semidirected graph and apply it to the road network system. In the field of graph theory, networks are described as directed graphs, undirected graphs, or a combination of both. However, in the modern era of computing, several networks, such as social media networks, granular networks, and road transport networks, are not linked to any of the aforementioned network categories and in reality are a hybrid of networks with both directed and undirected interconnections. To better understand the notion of these types of networks, semidirected graphs have been developed to represent such networks. In a semidirect graph, both the directed and undirected edges concepts have been introduced together in a graph. In reality, it has been noted that for every node (source/destination) which is connected to another, some links/connections are directed—i.e., one-way—and some links/connections are undirected—i.e., two-way. Considering that there is no specific direction provided and that two nodes are connected, we have established the concept of an undirected edge as two-way connectivity since this provides for nodes in both ways. In this study, the road network system has been modelled using the concept of a semidirected graph, and the shortest path through it has been determined. For the purposes of illustration, we have used a real transportation road network in this case, and the computed results have been displayed. [ABSTRACT FROM AUTHOR]

Assessing flood risks on road infrastructures is critical for the definition of mitigation strategies and adaptation processes. Some efforts have been made to conduct a regional flood risk assessment to support the decision-making process of exposed areas. However, these approaches focus on the physical damage of civil infrastructures without considering indirect impacts resulting from social aspects or traffic delays due to the functionality loss of transportation infrastructures. Moreover, existing methodologies do not include a proper assessment of the uncertainties involved in the risk quantification. This work aims to provide a consistent quantitative flood risk estimation and influence factor modelling for road infrastructures. To this end, a Flood Risk Factor (FRF) is computed as a function of hazard, vulnerability, and infrastructure importance factors. A Bayesian Network (BN) is constructed for considering the interdependencies among the selected input factors, as well as accounting for the uncertainties involved in the modelling process. The proposed approach allows weighting the relevant factors differently to compute the FRF and improves the understanding of the causal relations between them. The suggested method is applied to a case study located in the region of Santarem Portugal, allowing the identification of the sub-basins where the road network has the highest risks and illustrating the potential of Bayesian inference techniques for updating the model when new information becomes available. [ABSTRACT FROM AUTHOR]

Many modern navigation systems and map-based services do not only provide the fastest route from a source location $s$ s to a target location $t$ t but also provide a few alternative routes to the users as more options to choose from. Consequently, computing alternative paths has received significant research attention. However, it is unclear which of the existing approaches generates alternative routes of better quality because the quality of these alternatives is mostly subjective. Motivated by this, in this paper, we present a user study conducted on the road networks of Melbourne, Dhaka and Copenhagen that compares the quality (as perceived by the users) of the alternative routes generated by four of the most popular existing approaches including the routes provided by Google Maps. We also present a web-based demo system that can be accessed using any internet-enabled device and allows users to see the alternative routes generated by the four approaches for any pair of selected source and target. We report the average ratings received by the four approaches and our statistical analysis shows that there is no credible evidence that the four approaches receive different ratings on average. We also discuss the limitations of this user study and recommend the readers to interpret these results with caution because certain factors may have affected the participants’ ratings. [ABSTRACT FROM AUTHOR]

The impacts of the changing climate have caused extensive disruption to the road network in the United Kingdom in recent years. Roads are vital for economic growth and social wellbeing, and a disruption to the network can have disastrous consequences. Since the impacts of climate change will be felt at regional and local levels, it is the responsibility of local highway authorities to establish effective policies to strengthen the resilience of their section of the road network. This report uses the West Midlands as a case study and aims to evaluate its regional highway network management strategies, to determine the extent to which they promote resilience to climate change. Recommendations and findings from other literature are used to establish a set of evaluation criteria to compare the maturity of highway network management strategies for the West Midlands region. The evaluation of the policy documents is used to rank the maturity of the strategies, and recommendations are made to local authorities to highlight where the strategies could be improved. The analysis highlights the fragmentation and disparity between highways strategies across the region and consequently the vulnerability of the region to climate change. [ABSTRACT FROM AUTHOR]

The ability to quickly calculate or query the shortest path distance between nodes on a road network is essential for many real-world applications. However, the traditional graph traversal shortest path algorithm methods, such as Dijkstra and Floyd–Warshall, cannot be extended to large-scale road networks, or the traversal speed on large-scale networks is very slow, which is computational and memory intensive. Therefore, researchers have developed many approximate methods, such as the landmark method and the embedding method, to speed up the processing time of graphs and the shortest path query. This study proposes a new method based on landmarks and embedding technology, and it proposes a multilayer neural network model to solve this problem. On the one hand, we generate distance-preserving embedding for each node, and on the other hand, we predict the shortest path distance between two nodes of a given embedment. Our approach significantly reduces training time costs and is able to approximate the real distance with a relatively low Mean Absolute Error (MAE). The experimental results on a real road network confirm these advantages. [ABSTRACT FROM AUTHOR]

Congestion emerges when high demand peaks put transportation systems under stress. Understanding the interplay between the spatial organization of demand, the route choices of citizens and the underlying infrastructures is thus crucial to locate congestion hotspots and mitigate the delay. Here we develop a model where links are responsible for the processing of vehicles, which can be solved analytically before and after the onset of congestion, and provide insights into the global and local congestion. We apply our method to synthetic and real transportation networks, observing a strong agreement between the analytical solutions and the Monte Carlo simulations, and a reasonable agreement with the travel times observed in 12 cities under congested phase. Our framework can incorporate any type of routing extracted from real trajectory data to provide a more detailed description of congestion phenomena, and could be used to dynamically adapt the capacity of road segments according to the flow of vehicles, or reduce congestion through hotspot pricing.

Road closure is an effective measure to reduce mobility and prevent the spread of an epidemic in severe public health crises. For instance, during the peak waves of the global COVID-19 pandemic, many countries implemented road closure policies, such as the traffic-calming strategy in the UK. However, it is still not clear how such road closures, if used as a response to different modes of epidemic spreading, affect the resilient performance of large-scale road networks in terms of their efficiency and overall accessibility. In this paper, we propose a simulation-based approach to theoretically investigate two types of spreading mechanisms and evaluate the effectiveness of both static and dynamic response scenarios, including the sporadic epidemic spreading based on network topologies and trajectory-based spreading caused by superspreaders in megacities. The results showed that (1) the road network demonstrates comparatively worse resilient behavior under the trajectory-based spreading mode; (2) the road density and centrality order, as well as the network's regional geographical characteristics, can substantially alter the level of impacts and introduce heterogeneity into the recovery processes; and (3) the resilience lost under static recovery and dynamic recovery scenarios is 8.6 and 6.9%, respectively, which demonstrates the necessity of a dynamic response and the importance of making a systematic and strategic recovery plan. Policy and managerial implications are also discussed. This paper provides new insights for better managing the resilience of urban road networks against public health crises in the post-COVID era.

This study proposes an efficient exact k-flexible aggregate nearest neighbor (k-FANN) search algorithm in road networks using the M-tree. The state-of-the-art IER-kNN algorithm used the R-tree and pruned off unnecessary nodes based on the Euclidean coordinates of objects in road networks. However, IER-kNN made many unnecessary accesses to index nodes since the Euclidean distances between objects are significantly different from the actual shortest-path distances between them. In contrast, our algorithm proposed in this study can greatly reduce unnecessary accesses to index nodes compared with IER-kNN since the M-tree is constructed based on the actual shortest-path distances between objects. To the best of our knowledge, our algorithm is the first exact FANN algorithm that uses the M-tree. We prove that our algorithm does not cause any false drop. In conducting a series of experiments using various real road network datasets, our algorithm consistently outperformed IER-kNN by up to 6.92 times. [ABSTRACT FROM AUTHOR]

VeRoViz is an open-source vehicle routing visualization package consisting of both Python and web-based components. It was developed to streamline the workflow for vehicle routing researchers by simplifying and automating many of the tedious tasks associated with generating realistic test problems. VeRoViz also provides new functionality to produce customizable visualizations of complex vehicle routing problems. These visualization tools—including Gantt charts, static maps, and dynamic 3-D videos—assist researchers in validating models and communicating results. Additionally, a comprehensive collection of utility functions within VeRoViz provides researchers with useful tools to assess features of their problems. This paper provides an overview of VeRoViz and highlights the flexibility and ease-of-use of the toolkit. Summary of Contribution: This paper describes an open-source software package designed to assist vehicle routing researchers. The software package, named VeRoViz (vehicle routing visualization), streamlines the process of collecting and visualizing data relevant to vehicle routing research. This includes capturing road network travel times (and distances) between locations, displaying turn-by-turn vehicle routes, generating Gantt charts of vehicle assignments, and producing 3-D "movies" of vehicle routing solutions. The VeRoViz suite consists of three components. First, a web-based interface allows researchers (and instructors) to quickly "sketch" elements of a vehicle routing problem, providing visual representations of nodes and arcs on a map. Second, the VeRoViz Python package provides an extensive collection of functions that assist operations researchers in collecting data and displaying solutions. Finally, an HTML/JavaScript plugin is available for displaying time-dynamic 3-D visualization of vehicle routes. VeRoViz is not a vehicle routing solver; it is a software suite that complements and supports researchers who are developing solution approaches to increasingly realistic vehicle routing problems (e.g., problems related to drone delivery, electric vehicles, or dynamic delivery requests). [ABSTRACT FROM AUTHOR]

Cost overrun is a common phenomenon observed in construction projects worldwide. It is one of the biggest challenges experienced in the construction industry that leads to overstretched budgets, and directly affects the country's gross domestic product (GDP). This study aims to identify the factors that significantly contribute to cost overrun for road network projects in Egypt during the implementation phase. A survey was conducted to determine the most critical factors affecting the cost overrun for road network construction projects. Results showed that the main factors causing cost overrun are inaccurate cost estimates, design modifications, quantity changes, variation orders, political interference, inflation, specification changes, and change in the scope of work. This study will help decision-makers identify the factors that may affect the costs of future road projects and provide guidelines to mitigate the adverse effects observed during the implementation stage.

Abstract The quantitative study of traffic dynamics is crucial to ensure the efficiency of urban transportation networks. The current work investigates the spatial properties of congestion, that is, we aim to characterize the city areas where traffic bottlenecks occur. The analysis of a large amount of real road networks in previous works showed that congestion points experience spatial abrupt transitions, namely they shift away from the city center as larger urban areas are incorporated. The fundamental ingredient behind this effect is the entanglement of central and arterial roads, embedded in separated geographical regions. In this paper we extend the analysis of the conditions yielding abrupt transitions of congestion location. First, we look into the more realistic situation in which arterial and central roads, rather than lying on sharply separated regions, present spatial overlap. It results that this affects the position of bottlenecks and introduces new possible congestion areas. Secondly, we pay particular attention to the role played by the edge distribution, proving that it allows to smooth the transitions profile, and so to control the congestion displacement. Finally, we show that the aforementioned phenomenology may be recovered also as a consequence of a discontinuity in the node’s density, in a domain with uniform connectivity. Our results provide useful insights for the design and optimization of urban road networks, and the management of the daily traffic.

Dynamically updated and fine-grained flooding maps are critical for situational awareness and decision support. However, traditional methods, such as eyewitness reports, remote sensing, and hydrology models, may fail to correspond with the rapidly changing urban hydrological environment. The presence of crowdsourced data (such as social media data) allows for timely and cost-effective monitoring of flood hazards through collective observations; however, such data can be unreliable due to sample bias and low spatiotemporal resolution. Therefore, new measures to identify flood-affected roads are desirable. In this study, we propose a methodology that leverages taxi GPS data to support post-event flood mapping for the road network. This method can identify whether a significant reduction in the taxi passing rate for a road segment was related to precipitation, and automatically recognize the floodaffected roads based on a logistic regression model. Using taxi GPS data in Shenzhen as an example, we derived the flood map of the road networks, and compared and validated the results. This study demonstrated the usefulness of taxi GPS data in generating high-quality flood maps and the value of incorporating multiple data sources for sensing near real-time flood in the city. [ABSTRACT FROM AUTHOR]

Location privacy protection is an essential but challenging topic in the field of network security. Although the existing research methods, such as ${k}$ -anonymity, mix zone, and differential privacy, show significant success, they usually neglect the location semantic and the proper trade-off between privacy and utility, which may allow attackers to obtain user privacy information by revealing the semantic correlation between the anonymous region and user’s real location, thus causing privacy leakage. To solve this problem, we propose a location privacy protection scheme based on the $k$ -anonymity technique, which provides practical location privacy-preserving through generating an anonymous set. This paper proposes a new location privacy attack strategy termed semantic relativity attack (SRA), which considers the location semantic problem. Correspondingly, a semantic and trade-off aware location privacy protection mechanism (STA-LPPM) is presented to achieve privacy protection with both high-level privacy and utility. To be specific, we model the location privacy protection as a multi-objective optimization problem and propose the Improved Multi-Objective Particle Swarm Optimization (IMOPSO) to generate the optimal anonymous set calculating the well-design fitness functions of the multi-objective optimization problem. In this way, the privacy scheme can provide mobile users with the right balance of privacy protection and service quality. Experiments reveal that our privacy scheme can effectively resist the semantic relativity attack while preventing significant utility degrading.

Container-based sanitation (CBS) is increasingly used to provide safely managed sanitation in low-income urban settlements. However, questions remain around the viability of scaling up the technology, partly because it relies on regular emptying and servicing of containers by a CBS provider. This paper investigates mechanisms by which this process can be achieved efficiently. Three separate collection strategies are evaluated for their routing efficiencies as CBS goes to scale. An open-source route optimisation solver determines the constituent driving and walking distances necessary for each strategy and has been applied in areas of Cape Town, Cap-Haïtien, Lima and Nairobi. The results indicate that with fewer users (e.g. 50) transfer station models offer the shortest driving routes. However, these do require users to carry their containers (e.g. up to 170 m when stations are 100 m apart). As the number of users increases (e.g. to 5,000), visiting individual houses from a neighbourhood depot offers increasingly efficient driving distances. Overall, however, the results suggest that economies in collection distances for scaled CBS will be largely conditional on greater vehicle capacity (rather than any particular provision strategy). This highlights the importance of road access throughout low-income urban settlements in providing a viable CBS service at scale. HIGHLIGHTS Container-based sanitation is a form of road-based faecal sludge management.; Efficient routing of collection vehicles is critical for enabling scaled CBS.; This modelling evaluates three existing provision strategies using Google-OR tools.; Transfer stations and depots within neighbourhoods can reduce the required driving.; However, enlarged vehicle capacity offers greatest reductions in collection routing.;