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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]

Purpose: This study aimed to explore the spatial accessibility dynamics of urban parks and their driving forces from 1901 to 2010 in terms of the dynamic relationships between spatial morphology and road networks, taking Nanjing City as an example. Design/methodology/approach: This study mapped and examined the spatiotemporal distribution of urban parks and road networks in four time points at Nanjing: the 1910s, 1930s, 1960s and 2010s, using the analysis methodology of spatial design network analysis, kernel density estimation and buffer analysis. Two approaches of spatial overlaying and data analysis were adopted to investigate the accessibility dynamics. The spatial overlaying compared the parks' layout and the road networks' core, subcore and noncore accessible areas; the data analysis clarified the average data on the city-wide and local scales of the road networks within the park buffer zone. Findings: The analysis of the changing relationships between urban parks and the spatial morphology of road networks showed that the accessibility of urban parks has generally improved. This was influenced by six main factors: planning implementation, political policies, natural resources, historical heritage and cultural and economic levels. Social implications: The results provide a reference for achieving spatial equity, improving urban park accessibility and supporting sustainable urban park planning. Originality/value: An increasing number of studies have explored the spatial accessibility of urban parks through the relationships between their spatial distribution and road networks. However, few studies have investigated the dynamic changes in accessibility over time. Discussing parks' accessibility over relatively long-time scales has practical, innovative and theoretical values; because it can reveal correlational laws and internal influences not apparent in short term and provide reference and implications for parks' spatial equity. [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.

This research examines the use of Shared Autonomous Electric Vehicles (SAEVs) in 374 U.S. small and medium-sized urban areas, focusing on fleet and infrastructure needs through agent-based simulations. It assesses metrics such as fleet size, trips per vehicle, and charging station requirements, considering two charger types: Level 2 and Level 3. The findings show significant spatial differences in SAEV operations and infrastructure across these cities. Statistical analysis links these variations to regional road networks and travel patterns. The study finds Level 3 chargers more efficient, requiring fewer stations and enabling more trips per vehicle compared to Level 2 chargers. Furthermore, Level 3 chargers exhibit a greater number of trips per SAEV and a higher ratio of vehicles to charging stations. These findings highlight the significance of considering charging infrastructure characteristics to optimize SAEV fleet performance and promote sustainable transportation systems in urban areas. This study significantly contributes by identifying the spatial variation and correlates of the SAEVs' operational and charging infrastructural performance. Policymakers, urban planners, and transportation service providers can leverage these insights to design and implement effective charging infrastructure for SAEV fleets, thereby advancing the transition to cleaner and more efficient mobility solutions. • This study investigates the potential of Shared Autonomous Electric Vehicles (SAEVs). • Agent-based simulations examined critical metrics such as fleet size and charging station needs. • The analysis includes two types of charging stations, Level 2 and Level 3. • Notable spatial variations in SAEV fleet and charging infrastructure performance are identified. • Regional road network characteristics and travel demand patterns contribute to spatial variations. [ABSTRACT FROM AUTHOR]

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]

Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles, people, transportation infrastructure, and networks, thereby realizing a more intelligent and efficient transportation system. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topological structure of IoV to have the high space and time complexity. Network modeling and structure recognition for 3D roads can benefit the description of topological changes for IoV. This paper proposes a 3D general road model based on discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles are analyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed and acceleration are studied. Finally, a general 3D road network model based on road section features is established. This paper also presents intersection and road section recognition methods based on the structural features of the 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted to create the simulation scenario, and the simulation results validate the general 3D road network model and the recognition method. Therefore, this work makes contributions to the field of intelligent transportation by providing a comprehensive approach to modeling the 3D road network and its topological changes in achieving efficient traffic flow and improved road safety. [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.

We present a new type of location-based queries, namely the Budget Range-based All Neighboring Object Group Query (BR-ANOGQ for short), to offer spatial object information while respecting distance and budget range constraints. This query type finds utility in numerous practical scenarios, such as assisting travelers in selecting fitting destinations for their journeys. To support the BR-ANOGQ, we develop data structures for efficient representation of road networks and employ two index structures, the R cC -tree and the grid index, for managing spatial objects based on their locations and costs. We introduce two pruning criteria to filter out object sets that do not meet the specified distance d and budget range [ b g t m , b g t M ] constraints. We also devise a road network traversal method that selectively accesses a small fraction of objects while generating the query result. The BR-ANOGQ algorithm effectively utilizes index structures and pruning criteria for query processing. Through a series of comprehensive experiments, we demonstrate its efficiency in terms of CPU time and index node accesses, providing valuable insights for location-based queries with constraints. [ABSTRACT FROM AUTHOR]

Coverage Centrality is an important metric to evaluate vertex importance in road networks. However, current solutions have to compute the coverage centrality of all the vertices together, which is resource-wasting, especially when only some vertices centrality is required. In addition, they have poor adaption to the dynamic scenario because of the computation inefficiency. In this paper, we focus on the coverage centrality query problem and propose a method that efficiently computes the centrality of single vertices without relying on the underlying graph being static by employing the intra-region pruning, inter-region pruning, and top-down search. We further propose the bottom-up search and mixed search to improve efficiency. Experiments validate the efficiency and effectiveness of our algorithms compared with the state-of-the-art method. [ABSTRACT FROM AUTHOR]

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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]

Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles, people, transportation infrastructure, and networks, thereby realizing a more intelligent and efficient transportation system. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topological structure of IoV to have the high space and time complexity. Network modeling and structure recognition for 3D roads can benefit the description of topological changes for IoV. This paper proposes a 3D general road model based on discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles are analyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed and acceleration are studied. Finally, a general 3D road network model based on road section features is established. This paper also presents intersection and road section recognition methods based on the structural features of the 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted to create the simulation scenario, and the simulation results validate the general 3D road network model and the recognition method. Therefore, this work makes contributions to the field of intelligent transportation by providing a comprehensive approach to modeling the 3D road network and its topological changes in achieving efficient traffic flow and improved road safety. [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]

Spatial similarity plays a critical role in the perception and cognition in capturing information from maps; it can be used as a constraint to automate map generalization. Although measuring similarities seems natural to humans, it can be challenging to quantify them. This is especially true when it comes to calculating spatial similarity degrees between groups of spatial objects at varying scales and quantitatively expressing the relations between spatial similarity and change of map scale in multiscale map spaces. Taking road networks as an example, this paper proposes an approach to measuring spatial similarity between a road network at a large scale and its generalized counterpart at a smaller scale. By fitting a power function to three typical types of road networks, this paper provides a formula for expressing the change in spatial similarity as the map scale changes. The proposed quantitative method lays a foundation for using spatial similarity as a constraint during road network generalization. [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

Navigation choices play an important role in modeling and forecasting traffic flows on road networks. We introduce a macroscopic differential model coupling a conservation law with a Hamilton–Jacobi equation to, respectively, model the nonlinear transportation process and the strategic choices of users. Furthermore, the model is adapted to the multi-population case, where every population differs in the level of traffic information about the system. [ABSTRACT FROM AUTHOR]

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]

Linking trajectories to users who generate them with deep learning techniques has been a popular research topic in recent years, due to the large-scale trajectory data obtained by ubiquitous GPS-enabled devices and the widespread applications served by the study, such as route planning, next location prediction, and destination prediction. To address the TUL (Trajectory User Linking) problem more effectively, we propose a novel semi-supervised model TULRN (Trajectory User Linking on Road Networks) based on GNN (Graph Neural Network) and BiLSTM (Bi-directional Long Short-Term Memory). The main difference between our study and existing ones is that the TUL problem is extended onto road networks in this work, where both the structure of road networks and the sequential characteristics of trajectories will be fully utilized in a unified manner. The reason behind the extension is that many trajectories are usually generated on road networks in real life, and based on which we can model the relationships between trajectories and users more precisely. Specifically, our proposed model TULRN contains four main components: (1) transforming each trajectory into a sequence of road segments and constructing a road network-aware trajectory sequence graph RTSG; (2) learning the representation of a node in RTSG with a weight-aware GNN module; (3) learning the representation of a trajectory with a BiLSTM-based module; (4) linking trajectories to users based on the embedding of each trajectory. The extensive experiments conducted on a real-world dataset demonstrate that the proposed model TULRN performs better than the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Forest roads are an important part of forest management, both in terms of cost and impact on surrounding ecosystems. Existing tools to simulate the construction of forest roads have been designed for tactical or operational planning purposes, for relatively small areas (<10 000 ha) and small-scale topographic information. Hence, no forest road simulation tool properly exists to assist forest landscape ecology and management research. Here, we present the Forest Roads Simulation (FRS) extension for the LANDIS-II model—a spatially explicit landscape simulation model of forest succession and disturbances. The FRS extension simulates forest road networks via a least-cost path algorithm accounting for landscape structure, decision inputs, and forest road types. We demonstrate the accuracy with which the FRS extension reproduces several key characteristics of existing road networks in two managed regions in Quebec, Canada: road density, road position, and fragmentation of the landscape. The FRS extension is easy to parameterize, proposing many options for researchers to simulate forest road networks at a strategic level in managed landscapes. It can tackle new research questions investigating the effects of forest roads within management strategies, such as the cost of road construction and habitat fragmentation, across large management units and long planning horizons. [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.

In this paper we consider the problem of estimating emissions due to vehicular traffic on complex networks, and minimizing their effect by regulating traffic at junctions. For the traffic evolution, we consider a Generic Second Order Model, which encompasses the majority of two-equations (i.e., second-order) models available in the literature, and extend it to road networks with merge and diverge junctions. The dynamics on the whole network is determined by selecting a solution to the Riemann Problems at junctions, i.e., the Cauchy problems with constant initial data on each incident road. The latter are solved by assuming the maximization of the flow and assigning a traffic distribution coefficient for outgoing roads of diverges, and a priority rule for incoming roads of merges. A general emission model is considered and its parameters are tuned to the N O x emission rate. The minimization of emissions is then formulated in terms of the traffic distribution and priority parameters, taking into account travel times. A comparison is provided between roundabouts with optimized parameters and traffic lights, which correspond to time-varying traffic priorities. Our approach can be adapted to manage traffic in complex networks in order to reduce emissions while keeping travel time at acceptable levels. In this paper we consider the problem of estimating emissions due to vehicular traffic on complex networks, and minimizing their effect by regulating traffic at junctions. For the traffic evolution, we consider a Generic Second Order Model, which encompasses the majority of two-equations (i.e., second-order) models available in the literature, and extend it to road networks with merge and diverge junctions. The dynamics on the whole network is determined by selecting a solution to the Riemann Problems at junctions, i.e., the Cauchy problems with constant initial data on each incident road. The latter are solved by assuming the maximization of the flow and assigning a traffic distribution coefficient for outgoing roads of diverges, and a priority rule for incoming roads of merges. A general emission model is considered and its parameters are tuned to the emission rate. The minimization of emissions is then formulated in terms of the traffic distribution and priority parameters, taking into account travel times. A comparison is provided between roundabouts with optimized parameters and traffic lights, which correspond to time-varying traffic priorities. Our approach can be adapted to manage traffic in complex networks in order to reduce emissions while keeping travel time at acceptable levels. [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.

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.

The interrelationships between air quality, land cover change, and road networks in the Lagos megacity have not been explored. Globally, there are knowledge gaps in understanding these dynamics, especially using remote sensing data. This study used multi-temporal and multi-spectral Landsat imageries at four epochs (2002, 2013, 2015, and 2020) to evaluate the aerosol optical thickness (AOT) levels in relation to land cover and road networks in the Lagos megacity. A look-up table (LUT) was generated using Py6S, a python-based 6S module, to simulate the AOT using land surface reflectance and top of atmosphere reflectance. A comparative assessment of the method against in situ measurements of particulate matter (PM) at different locations shows a strong positive correlation between the imagery-derived AOT values and the PMs. The AOT concentration across the land cover and road networks showed an increasing trend from 2002 to 2020, which could be explained by urbanization in the megacity. The higher concentration of AOT along the major roads is attributed to the high air pollutants released from vehicles, including home/office generators and industries along the road corridors. The continuous rise in pollutant values requires urgent intervention and mitigation efforts. Remote sensing-based AOT monitoring is a possible solution. [ABSTRACT FROM AUTHOR]

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]

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. [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.