Search

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.

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.

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.

Some urban spaces are associated with disproportionate numbers of criminal events, while other areas are relatively free from disorder and crime. The relationship between urban space and crime concentration has received increased attention in recent years, with the location quotient frequently presented as a tool to identify and quantify such concentration. This measure has several limitations, with one significant concern surrounding the choice of denominator with which to standardize local and global rate calculations. In response, we present a new methodological adaptation to the location quotient, improving the measurement of crime concentration along linear features. To test this adaptation, we measure how crime concentrates by road classification at both a macro and micro level within two Canadian suburban municipalities. Using transportation network data, we identify the road types that are associated with a disproportionate concentration of criminal events, and illustrate how these relationships change alongside the level of aggregation. Results support the use of the adapted location quotient, finding that criminal events concentrate along specific road types, and emphasize the importance of spatial scale in understanding local relationships between crime and the built urban landscape. Key Messages: Traditional calculations of the location quotient can be adjusted to better capture concentrations along road networks.Using this adapted measure, we found that some road types exhibit higher concentrations of specific crimes than others.Measuring differences in concentration along road types assists in understanding urban crime patterns over and above land use type. [ABSTRACT FROM AUTHOR]

Definition of an optimal path in the real-world routing problems is not necessarily the shortest one, because parameters such as travel time, safety, quality, and smoothness also played essential roles in the definition of optimality. In this paper, we use bi-colored graphs for modeling urban and heterogeneous environments and introduce variations of constraint routing problems. Bi-colored graphs are a kind of directed graphs whose vertices are divided into two subsets of white and gray. We consider two criteria, minimizing the length and minimizing the number of gray vertices and present two problems called gray vertices bounded shortest path problem and length bounded shortest path problem on bi-colored graphs. We propose an efficient time label-setting algorithm to solve these problems. Likewise, we simulate the algorithm and compare it with the related path planning methods on random graphs as well as real-world environments. The simulation results show the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

With the development of smart phones and online to offline, spatial platforms, such as TaskRabbit, are getting famous and popular. Tasks on these platforms have three main characters: they are in real-time dynamic scenario, they are on the road networks, and some of them have multiple skills. However, existing studies do not take into account all these three things simultaneously. Therefore, an important issue of spatial crowdsourcing platforms is to assign workers to tasks according to their skills on road networks in a real-time scenario. In this paper, we first propose a practical problem, called online multi-skilled task assignment on road networks (OMTARN) problem, and prove that the OMTARN problem is NP-Hard and no online algorithms can achieve a constant competitive ratio on this problem. Then, we design a framework using batch-based algorithms, including fixed and dynamic batch-based algorithm, and we show that how the algorithms update the batch. After that, we use the hierarchically separated tree structure to accelerate our algorithms. Finally, we implement all the algorithms of the OMTARN problem and clarify their strengths and weaknesses by testing them on both synthetic and real datasets.

Route planning and recommendation are increasingly important in the recent two decades. In this light, we propose and study novel diversified routing (DR) queries that discover the most convenient routes in dynamic road networks, where travel time, congestion probability, and global travel cost are taken into account. Given a dynamic transportation network, a source, a destination, a travel time threshold τt, a congestion probability threshold τp, and a global travel cost threshold Tg, the DR query finds: 1) the route with the minimum travel time whose global travel cost does not exceed τg; 2) the route with the minimum congestion probability whose global travel cost does not exceed τg; and 3) the route with the minimum global travel cost whose travel time does not exceed τt and congestion probability does not exceed τp. Such types of queries are very useful in many mobile applications, such as travel planning and recommendation, urban computing, intelligent transportation, and location-based services in general. The DR queries face two challenges: 1) how to model the dynamic road networks and to define the travel time, congestion probability, and global travel cost practically and 2) how to prune the search space effectively and to return the query results in the real time. To overcome the challenges and to process the DR queries efficiently, we develop a search framework based on network expansion. A series of optimization techniques is developed to further enhance the query efficiency. Finally, we conduct extensive experiments on large datasets to verify the performance of the developed algorithms.

Location search and recommendation have received significant attention in recent years. To protect the users' privacy, we propose and study a novel privacy-protected route-based spatial-textual location (PPRSTL) query in road networks. Given a set of locations O with the textual description, a query route q with a set of query keywords to describe user's preference, the PPRSTL query finds the location with the highest spatial-textual similarity to the query route q. We believe that such type of query is very useful in many mobile applications such as location recommendation and discovery, and location-based services in general. The problem is challenging due to three reasons: (1) how to model the spatial and textual similarity practically; (2) how to constrain the search space in a comparatively small range in the spatial and textual domains, and; (3) how to protect the users' privacy when returning the query results. To overcome these challenges, we define a boolean spatial-textual measure to evaluate the similarity in the spatial and textual domains, and a two-phase search mechanism to protect users' privacy. We develop two expansion search algorithms that follow the filter-and-refinement paradigm, to compute the queries efficiently. Two pairs of upper and lower bounds are defined to prune the search space effectively. In addition, we adopt an expansion center selection method to further enhance the query efficiency. Finally, we conduct extensive experiments on real and synthetic spatial data sets to verify the performance of the developed algorithms.

Haciendo eco de las crónicas coloniales sobre el sistema vial imperial andino, la mayoría de las investigaciones sobre caminos incaicos se han centrado en su planificación y tecnología monumental como las principales características de esta infraestructura prehispánica tardía. Mediante un estudio regional en los valles de Arica (18° S), abordamos, en este trabajo, una investigación del Qhapaq Ñan (sistema vial incaico) desde una perspectiva de redes. Analizamos un palimpsesto de 13.000 km de vías, producido por la acumulación de sistemas viales de distintos períodos cronológicos. Este es relacionado con el patrón de asentamiento prehispánico tardío para modelizar los flujos de movimiento durante los siglos quince y dieciséis: mediante análisis espaciales, delineamos las estructuras más probables de las redes viales local e imperial. Finalmente, consideramos medidas de centralidades y acumulación de flujo para discutir la hipótesis del control del movimiento como estrategia imperial. Los resultados muestran que la hegemonía incaica se pudo relacionar con el control espacial de los flujos de las interacciones, y también que el enfoque de red aplicado a los palimpsestos viales reviste gran potencial para la arqueología del movimiento en regiones desérticas y montañosas. [ABSTRACT FROM AUTHOR]

Effective querying of data in the road networks is an important problem in the Internet of vehicles. Aggregate nearest neighbor queries can return the objects that minimizes an aggregate distance function on road networks considering a set of query points in the Internet of vehicles. And k aggregate nearest neighbor query (k - ANN) is a complicate version for the basic one. The existing k - ANN queries lack effective model to deal with the uncertain data and the existing query methods cannot be applied to solve the problem of (k - ANN) query on uncertain data directly. Therefore, in this paper, a probabilistic threshold k - ANN query method based on uncertain Voronoi diagram is proposed. The method includes three phases: processing phase, pruning phase and refinement phase. The processing phase is to compute the minimum covered circle of the query dataset which is prepared for the pruning phase. In pruning phase, the different pruning algorithms are proposed for the corresponding three aggregate function of aggregate nearest neighbor query. The data points that cannot be the result are pruned and the candidate set is obtained. In refinement phase, the sets composed of k data points in candidate set whose probabilities are not less than the user-specified threshold are stored into the result set and returned to the user. Experiments are performed to evaluate the effectiveness and superiority of these algorithms on probabilistic threshold k-ANN query. [ABSTRACT FROM AUTHOR]

Wildfire is an annual threat for many rural communities in the Pacific Northwest region of the United States. In some severe events, evacuation is one potential course of action to gain safety from an advancing wildfire. Since most evacuations occur in a personal vehicle along the surrounding road network, the quality of this network is a critical component of a community's vulnerability to wildfire. In this paper, we leverage a high-resolution spatial dataset of wildfire burn probability and mean fireline intensity to conduct a regional-scale screening of wildfire evacuation vulnerability for 696 Oregon and Washington rural towns. We characterize each town's surrounding road network to construct four simple road metrics related to the potential to quickly and safely evacuate: (1) the number of paved lanes leaving town that intersect a fixed-distance circular buffer; (2) the variety of lane directions available for egress; (3) the travel area that can be reached within a minimum distance while constrained only to movement along the paved road network; and (4) the sum of connected lanes at each intersection for the road network within a fixed-distance circular buffer. We then combine the road metrics with two metrics characterizing fire hazard of the surrounding landscape through which evacuation will occur: (1) burn probability and (2) mean fireline intensity. By combining the road and fire metrics, we create a composite score for ranking all towns by their overall evacuation vulnerability. The most vulnerable towns are those where poor road networks overlap with high fire hazard. Often, these towns are located in remote, forested, mountainous terrain, where topographic relief constrains the available road network and high fuel loads increase wildfire hazard. An interactive map of all road quality and fire hazard metrics is available at https://www.fs.fed.us/wwetac/brief/evacuation.php. [ABSTRACT FROM AUTHOR]

To develop disaster mitigation measures in coastal regions affected by earthquakes, it is important to consider the effects of both seismic and tsunami hazards on road structures and assess the social impacts associated with the economic loss and reduction in network functionality. In this paper, a risk- and resilience-based assessment framework is established for road networks under both seismic and tsunami hazards. In the proposed methodology, the risk and resilience are quantified by the economic loss and postdisaster functionality of road networks, respectively. Uncertainties associated with estimations of fault movement, hazard intensity and structural vulnerability are considered when estimating the failure probability using Monte Carlo simulation. Moreover, structural vulnerability against tsunamis is estimated considering the effects of ground motion-induced damage on the reduction in tsunami capacity. As an illustrative example, the road networks in two Japanese cities expected to be affected by the anticipated Nankai Trough earthquake, which would cause strong ground motions and a subsequent tsunami, are analyzed. Finally, the retrofitting prioritization for various structures is discussed based on the risk and resilience quantified as performance indicators for social impacts. [ABSTRACT FROM AUTHOR]

Road infrastructures and bridge networks are becoming increasingly complex systems due to the development of technology in transportation engineering and the continual growth of urban communities. Infrastructure disservice due to seismic events may lead to unacceptable discomfort for commuters. Moreover, network downtime results into economic losses for the affected community, to be quantified in monetary terms by user costs. Seismically vulnerable bridges are also affected by aging and deterioration processes that can reduce the bridge structural performance over time. This paper presents a comprehensive life-cycle cost-based probabilistic framework for seismic risk assessment of spatially distributed aging bridge networks. The seismic risk measure is formulated in terms of annual exceedance rate of a target threshold of user costs. The methodology is applied to a road system in Lombardy region, Italy, reproducing network connectivity and daily travel demands among four major cities and smaller neighbouring municipalities. Despite the area of interest is characterized by low seismicity, the results allow to quantify the impact of aging and deterioration in exacerbating the network seismic risk, highlighting the need for a life-cycle-informed approach to optimal management of infrastructure systems. [ABSTRACT FROM AUTHOR]

Road networks are an integral part of any human settlement, facilitating the movement of people, goods, and information. The structural arrangement of a road network can have a profound impact on its performance, mobility and access to critical infrastructure. This study takes advantage of a computational network science approach to examine the structural configuration of road networks using geometric and topological descriptions in districts covering 10 regional capitals in Ghana. The majority of urban road networks were found to follow a radial pattern at the global scale, with either a gridded or a branching configuration at the local scale. Only road networks in Accra and Kumsi are fine grained and of comparable density to other global cities, based on intersection and street densities. Structural indicators such as circuity, clustering, page rank, degree centrality, and betweenness centrality for urban roads in Ghana were found to be comparable to other global cities. Given the lack of adequate infrastructure for traffic monitoring in Ghana, the spatial distribution of betweenness centrality could be part of the critical resource to provide insight for traffic management. These findings provide the basis to inform transportation planning and management on critical issues, particularly, based on the spatial distribution of betweenness centrality it is possible to identify problematic locations within the road network which are most vulnerable to traffic congestion.

Extreme weather-related events are likely to increase in both frequency and intensity if current emission rates continue. Particularly vulnerable to the potential effects of climate change is the transportation sector, which fully relies on the uninterrupted accessibility of the road network. With the Swedish Government adopting a National Adaptation Strategy in 2018, creating a coordinated cross-disciplinary strategy underpinned by international frameworks, new ambition levels regarding climate change adaptation efforts have been established. Identifying particularly vulnerable parts of the road network is an essential step for National Road Authorities (NRAs) in early planning stages to prioritize adaptation efforts around high-risk areas to safeguard the road network. By utilizing both publicly and commercially available climate-related data, as well as applying and assessing new terrain analysis techniques, a large-scale vulnerability assessment is carried out for Halland County in southwestern Sweden by assessing three climate-adjusted extreme weather events (a 100-year pluvial flood event, a 100-year fluvial flood event, and an extreme coastal storm surge event) projected to exacerbate in the region. The study demonstrates how a GIS-based screening method, specifically adapted for NRAs or similar stakeholders, may provide quick overviews of potentially at-risk areas along road networks through a simple vulnerability index assigned to individual road segments. Further studies are recommended to compare modeled results to recorded flood events if available and incorporate updated climate change trajectories from upcoming IPCC reports.

Map generalization is the process of deriving small-scale target maps from a large-scale source map or database while preserving valuable information. In this paper we focus on topographic data, in particular areas of different land-use classes and line features representing the road network. When reducing the map scale, some areas need to be merged to larger composite regions. This process is known as area aggregation. Given a planar partition of areas, one usually aims to build geometrically compact regions of sufficient size while keeping class changes small. Since line features (e.g. roads) are perceived as separating elements in a map, we suggest integrating them into the process of area aggregation. Our aim is that boundaries of regions coincide with line features in such a way that strokes (i.e. chains of line features with small angles of deflection) are not broken into short sections. Complementing the criteria of compact regions and preserving land-use information, we consider this aim as a third criterion. Regarding all three criteria, we formalize an optimization problem and solve it with a heuristic approach using simulated annealing. Our evaluation is based on experiments with different parameter settings. In particular, we compare results of a baseline method that considers two criteria, namely compactness and class changes, with results of our new method that additionally considers our stroke-based criterion. Our results show that this third criterion can be substantially improved while keeping the quality with respect to the original two criteria on a similar level. [ABSTRACT FROM AUTHOR]

Our recent data, collected using remotely sensed imagery and unmanned aerial vehicle surveys, reveal the extremely well-defined patterning of archaeological plaza villages in the Brazilian Acre state in terms of size, layout, chronology, and material culture. The villages comprise various earthen mounds arranged around central plazas and roads that radiate outward from, or converge on, the sites. The roads connected the villages situated 2–10 km from each other in eastern Acre. Our study attests to the existence of large, sedentary, interfluvial populations sharing the same sociocultural identities, as well as structured patterns of movement and spatial planning in relation to operative road networks during the late precolonial period. The plaza villages of Acre show similarity with the well-documented communities organized by road networks in the regions of the Upper Xingu and Llanos de Mojos. Taking into consideration ethnohistorical and ethnographic evidence, as well as the presence of comparable archaeological sites and earthwork features along the southern margin of Amazonia, we suggest that the plaza villages of Acre were linked by an interregional road network to other neighboring territories situated along the southern Amazonian rim and that movement along roads was the primary mode of human transport in Amazonian interfluves. [ABSTRACT FROM AUTHOR]

Forest cover change in the mountainous region is driven by a variety of anthropogenic and natural factors. The Hindu Kush-Himalayan Mountains has experienced a considerable vegetation cover change due to intensive human activities, such as population growth, proximate causes, accessibility, unstable political situations, government policy failure and poverty. The present study seeks to find out the impact of population growth and road network expansion on forest cover of Palas valley based on remotely sensed data and employing geospatial techniques. Changes in forest cover were determined by classifying time-series satellite images of Landsat and Sentinel 2A. The images of October 1980, 2000, 2010 and 2017 were classified into six land cover classes and then the impact of population growth and accessibility on forest cover was analyzed. Furthermore, forest cover and land-use change detection map was prepared using classified images of 1980 and 2017. The data were collected mainly from field visits (ground verification), census reports, Communication and Works Department, Kohistan. Satellite imageries were obtained from the United States Geological Survey's websites and classified in ERDAS imagine 2014 and ESRI ArcGIS 10.2.1 using supervised classification-maximum likelihood algorithm. Result of this study revealed that a substantial reduction in forest cover has taken place mainly in the proximity of human settlements. On the average, during the study period, annually more than 460 hectares of forest area has been converted into other uses. [ABSTRACT FROM AUTHOR]

This paper deals with the Aw-Rascle-Zhang [A. Aw and M. Rascle, SIAM J. Appl. Math., 60 (2000), pp. 916-938; H. M. Zhang, Transp. Res. B Methodol., 36 (2002), pp. 275-290] model for traffic flow on uni-directional road networks. We construct weak solutions to Riemann problems at the junctions, which conserve the mass and the generalized momentum. In particular, we introduce a new approach to approximate the homogenized pressure through an additional equation for the propagation of a reference pressure. The resulting system of coupled conservation laws is then solved using an appropriate numerical scheme of Godunov type. Numerical simulations show that the proposed approach enables us to approximate the homogenized pressure sufficiently well. The features of the new approach are illustrated through a comparative analysis with other methods proposed in the literature for the Aw-Rascle-Zhang second-order traffic model [2, 31] and the Lighthill-Whitham-Richards model [M. J. Lighthill and G. B. Whitman, Proc. A, 229 (1955), pp. 317-345; P. I. Richards, Oper. Res., 4 (1956), pp. 42-51]. [ABSTRACT FROM AUTHOR]

Input to the Most Navigable Path (MNP) problem consists of the following: (a) a road network represented as a directed graph, where each edge is associated with numeric attributes of cost and "navigability score" values; (b) a source and a destination and; (c) a budget value which denotes the maximum permissible cost of the solution. Given the input, MNP aims to determine a path between the source and the destination which maximizes the navigability score while constraining its cost to be within the given budget value. The problem can be modeled as the arc orienteering problem which is known to be NP-hard. The current state-of-the-art for this problem may generate paths having loops, and its adaptation for MNP that yields simple paths, was found to be inefficient. In this paper, we propose five novel algorithms for the MNP problem. Our algorithms first compute a seed path from the source to the destination, and then modify the seed path to improve its navigability. We explore two approaches to compute the seed path. For modification of the seed path, we explore different Dynamic Programming based approaches. We also propose an indexing structure for the MNP problem which helps in reducing the running time of some of our algorithms. Our experimental results indicate that the proposed solutions yield comparable or better solutions while being orders of magnitude faster than the current state-of-the-art for large real road networks. [ABSTRACT FROM AUTHOR]

A common cause of traffic congestions is the concentration of intersecting vehicle routes. It can be difficult to reduce the intersecting routes in existing traffic systems where the routes are decided independently from vehicle to vehicle. The development of connected autonomous vehicles provides the opportunity to address the intersecting route problem as the route of vehicles can be coordinated globally. We prototype a traffic management system for optimizing traffic with connected autonomous vehicles. The system allocates routes to the vehicles based on streaming traffic data. We develop two route assignment algorithms for the system. The algorithms can help to mitigate traffic congestions by reducing intersecting routes. Extensive experiments are conducted to compare the proposed algorithms and two state-of-the-art route assignment algorithms with both synthetic and real road networks in a simulated traffic management system. The experimental results show that the proposed algorithms outperform the competitors in terms of the travel time of the vehicles. [ABSTRACT FROM AUTHOR]

Road networks are an integral part of any human settlement, facilitating the movement of people, goods, and information. The structural arrangement of a road network can have a profound impact on its performance, mobility and access to critical infrastructure. This study takes advantage of a computational network science approach to examine the structural configuration of road networks using geometric and topological descriptions in districts covering 10 regional capitals in Ghana. The majority of urban road networks were found to follow a radial pattern at the global scale, with either a gridded or a branching configuration at the local scale. Only road networks in Accra and Kumsi are fine grained and of comparable density to other global cities, based on intersection and street densities. Structural indicators such as circuity, clustering, page rank, degree centrality, and betweenness centrality for urban roads in Ghana were found to be comparable to other global cities. Given the lack of adequate infrastructure for traffic monitoring in Ghana, the spatial distribution of betweenness centrality could be part of the critical resource to provide insight for traffic management. These findings provide the basis to inform transportation planning and management on critical issues, particularly, based on the spatial distribution of betweenness centrality it is possible to identify problematic locations within the road network which are most vulnerable to traffic congestion. [ABSTRACT FROM AUTHOR]

Sand drifting on road networks in the region of the Lower Algerian Sahara is one of the main problems for the sector. Machines are repeatedly deployed to overcome this phenomenon. The long experience acquired while dealing with the removal of sand from roads pushed us to focus on obstacles called “Draas”. The purpose of this study is to perform an optimization of these special protective structures called “Draas”, using a reduced physical model. Model tests were performed in flow channel. The principle of modeling the wind transport using a reduced model is to simulate the wind using a liquid stream while respecting the laws of hydraulic and sedimentological similarity. The results obtained are extrapolated to make a normal size prototype.

Abstract In this era of mobile application and socialization, location-based services (LBSs) have become unprecedentedly popular and emphasized. By submitting its location to the service provider, the mobile terminal can obtain useful service. As an important technology that can provide location-based services, spatial query processing has become a research hotspot. According to the problems existing in current road network partitioning, first of all, this paper proposes the partition density formula and the optimal road network partitioning method according to the partition density. Based on that, we propose the concepts of partition self-attraction, mutual attraction, and merger factor of partition to effectively merge the partitions with low densities, which can further reduce the number of unnecessary broadcast frames and optimize the index method. Then, we propose the real distributed air index method DIM and kNN spatial query processing algorithm based on the MapReduce platform. According to the DIM index method, the Name Node only stores the prime index, while not storing the data, and each frame consists of defined index and the data in a partition that need to be broadcasted. The mobile user can quickly localize the frame data that they need to obtain according to the main index or the index of current frame, in this way to reduce the tuning time and access latency, which can significantly optimize the query efficiency. Massive experiments have proved the stability and effectiveness of the proposed algorithm.

This paper proposes a dynamic cooperative traffic control framework for multiple intersections based on virtual grids to optimize the throughput and ensure fairness among all traffic flows. The traffic flows are divided by virtual grids; we call it virtual grid-based cooperative control of multiple-intersections (VGCC). The road segment between two intersections has been divided into two parts, which are defined as the reference region and the decision region. When the vehicle arrives at or goes away from any one part, it registers or deregisters itself to the road side unit. The traffic controller at intersection, called intersection control unit, collects the traffic information from all road segments and receives traffic messages from adjacent intersections. The proposed signal-scheduling algorithm considers not only the flows at the local intersection with higher passing rates but also the flows at downstream signalized intersections with higher passing rates. To ensure fairness, the algorithm gives chances to those phases who has a lower passing rate by using the ageing-counter matrix. According to the real-time traffic information, this paper makes signal timing for each phase of a signal cycle one by one. Moreover, a cooperative collision avoidance predictive control algorithm is proposed, which can assist vehicles to pass the next intersection without stopping, by predicting the time conflict. The results indicate that the VGCC algorithm significantly decreases the average number of vehicles on a road segment by 30.77%, reduces the average queuing length by 28.89%, decreases the average time spent on passing intersection by 26.93%, and reduces the average waiting time by 35.21% than the intersection of common road networks algorithm.

The existing continuous nearest neighbor query algorithms of moving objects in road networks do not consider any traffic rule and assume that the speed of moving objects is constant and the topology of road networks never change. However, in real road networks, the object’s speed and the road network’s structure change frequently Hence, these would make the existing methods ineffective when applying to the real-world road network environment To overcome the aforementioned disadvantages, we propose a Data Modeling approach of Road Networks with traffic rules (called DMRNR) and design a novel Expanding Tree-based Continuous k Nearest Neighbors algorithm (abbreviate for ExTCKNN) that can be well adopted to the actual road network environment. The algorithm consists of three steps: 1) it obtains the query results to store using DMRNR in the initial phase; 2) it maintains the data model of road networks by monitoring the real-time change information; and 3) the results are generated according to the submitted query with the updated data model and the latest state of moving objects The merit of the proposed algorithm lies in that it queries the nearest neighbors by taking the movements of the moving object and the variety of the road networks into consideration Extensive experiments are conducted and the experimental results demonstrate a significant improvement of the proposed method when compared with conventional solutions.

Traffic congestions in urban road traffic networks originate from some crowded road segments with crucial locations, and diffuse towards other parts of the urban road network creating further congestions. This behavior of road networks motivates the need to understand the influence of individual road segments on others in terms of congestion. In this paper, we investigate the problems of global influence ranking and local influence ranking of road segments. We propose an algorithm called RoadRank to compute the global influence scores of each road segment from their traffic measures, and rank them based on their overall influence. To identify the locally influential road segments, we also propose an extension called distributed RoadRank, based on road network partitions. We perform extensive experiments on real SCATS datasets of Melbourne. We found that the segments of Batman Avenue, Footscray Road, Punt Road, La Trobe Street, and Victoria Street, are highly influential in the early morning times, which are well known as congestion hotspots for both the network operators and the commuters. Our promising results and detailed insights demonstrate the efficacy of our method. [ABSTRACT FROM AUTHOR]

We study the problem of computing constrained shortest paths for battery electric vehicles. Because battery capacities are limited, fastest routes are often infeasible. Instead, users are interested in fast routes on which the energy consumption does not exceed the battery capacity. For that, drivers can deliberately reduce speed to save energy. Hence, route planning should provide both path and speed recommendations. To tackle the resulting N P -hard optimization problem, previous work trades correctness or accuracy of the underlying model for practical running times. We present a novel framework to compute optimal constrained shortest paths (without charging stops) for electric vehicles that uses more realistic physical models, while taking speed adaptation into account. Careful algorithm engineering makes the approach practical even on large, realistic road networks: We compute optimal solutions in less than a second for typical battery capacities, matching the performance of previous inexact methods. For even faster query times, the approach can easily be extended with heuristics that provide high quality solutions within milliseconds. [ABSTRACT FROM AUTHOR]

Context: The sandy‐savanna ecosystem "Mussununga", a natural ecosystem that occurs as patches throughout the Atlantic Forest domain, is threatened by anthropogenic factors and biological invasions of Australian Acacia species. Habitat degradation in the Atlantic Forest domain and extensive road networks could facilitate Acacia invasion into Mussununga. Objectives: We investigated whether: (a) landscape permeability (measured by effective conductance) facilitates Acacia invasion; (b) forest fragments are barriers, and roads and highways are corridors for invasive spread of Acacia; and (c) size and shape of Mussununga patches play a role in biological invasion. Methods: Acacia invasion was investigated in 32 Mussununga sites within the Atlantic Forest domain. We tested the effect of a set of landscape permeability scenarios based on circuit analysis and nine other metrics of landscape structure on Acacia occurrence using three buffer‐zone sizes (0.5, 1, and 2 km). Results: The likelihood of Acacia invasion significantly increased with landscape permeability. The best‐fitting landscape permeability scenario designated road networks as corridors, intact forests and water surfaces as barriers, and degraded habitats as non‐barriers. We also found that Mussununga areas within a 0.5 km buffer negatively affected the biological invasion by Acacia. Conclusions: Extensive habitat degradation by deforestation and dense road networks facilitate Acacia invasion into sandy‐savanna Mussununga ecosystems. Landscape permeability may be used as a risk‐assessment tool for biological invasion by Acacia species. Mussununga patches can be protected from Acacia invasion through preserving or restoring forest belts around them and disconnecting them from the road network. [ABSTRACT FROM AUTHOR]

In the data mining of road networks, trajectory clustering of moving objects is of particular interest for its practical importance in many applications. Most of the existing approaches to this problem are based on distance measurement, and suffer from several performance limitations including inaccurate clustering, expensive computation, and incompetency to handle high dimensional trajectory data. This paper investigates the complex network theory and explores its application to trajectory clustering in road networks to address these issues. Specifically, we model a road network as a dual graph, which facilitates an effective transformation of the clustering problem from sub-trajectories in the road network to nodes in the complex network. Based on this model, we design a label-based trajectory clustering algorithm, referred to as LBTC, to capture and characterize the essence of similarity between nodes. For the evaluation of clustering performance, we establish a clustering criterion based on the classical Davies-Bouldin Index (DB), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) to maximize inter-cluster separation and intra-cluster homogeneity. The clustering accuracy and performance superiority of the proposed algorithm are illustrated by extensive simulations on both synthetic and real-world dataset in comparison with existing algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the computation of alternative paths between two locations in a road network. More specifically, we study the k-shortest paths with limited overlap ( k SPwLO ) problem that aims at finding a set of k paths such that all paths are sufficiently dissimilar to each other and as short as possible. To compute k SPwLO queries, we propose two exact algorithms, termed OnePass and MultiPass, and we formally prove that MultiPass is optimal in terms of complexity. We also study two classes of heuristic algorithms: (a) performance-oriented heuristic algorithms that trade shortness for performance, i.e., they reduce query processing time, but do not guarantee that the length of each subsequent result is minimum; and (b) completeness-oriented heuristic algorithms that trade dissimilarity for completeness, i.e., they relax the similarity constraint to return a result that contains exactly k paths. An extensive experimental analysis on real road networks demonstrates the efficiency of our proposed solutions in terms of runtime and quality of the result. [ABSTRACT FROM AUTHOR]

Location-to-trajectory matching plays an important role in trajectory data management and analysis. In this paper, we propose and study a novel problem of privacy-preserving spatial keyword location-to-trajectory matching (PPSKLT Matching). Given a set O of locations with keywords, a set T of activity trajectories, a spatial matching threshold θ sp , and a keyword matching threshold θ kw , the PPSKLT matching finds all location-trajectory pairs from O and T while preserving the users' privacy. We believe that the PPSKLT matching may benefit many mobile applications such as trajectory activity analysis, event tracking, and so on. The PPSKLT matching is challenging due to three reasons: (1) how to define the spatial keyword similarity measure between locations and trajectories, (2) how to prune the search space effectively, and (3) how to preserve the users' privacy during query processing. To overcome these challenges and address the problem efficiently, we develop a novel network expansion algorithm (NEA). We define a pair of upper and lower bounds on the spatio-textual similarity to prune the search space. We also define a privacy-preserving mechanism to protect users' privacy. We conduct extensive experiments on spatio-textual data sets to verify the performance of the developed algorithms. [ABSTRACT FROM AUTHOR]

Novel road-network applications often recommend a moving object (e.g., a vehicle) about interesting services or tasks on its way to a destination. A taxi-sharing system, for instance, suggests a new passenger to a taxi while it is serving another one. The traveling cost is then shared among these passengers. A fundamental query is: given two nodes $s$ s and $t$ t , and an area $\mathcal {A}$ A on road network graph $\mathcal {G}$ G , is there a “good” route (e.g., short enough path) $P$ P from $s$ s to $t$ t that crosses $\mathcal {A}$ A in $\mathcal {G}$ G ? In a taxi-sharing system, $s$ s and $t$ t can be a taxi's current and destined locations, and $\mathcal {A}$ A contains all the places to which a person waiting for a taxi is willing to walk. Answering this Route and Area Matching (ROAM) Query allows the application involved to recommend appropriate services to users efficiently. In this paper, we examine efficient ROAM query algorithms. Particularly, we develop solutions for finding a $\rho$ ρ -route, which is an $s$ s - $t$ t path that passes $\mathcal {A}$ A , with a length of at most $(1+\rho)$ (1 + ρ) times the shortest distance between $s$ s and $t$ t . The existence of a $\rho$ ρ -route implies that a service or task located at $\mathcal {A}$ A can be found for a given moving object $m$ m , and that $m$ m only deviates slightly from its current route. We present comprehensive studies on index-free and index-based algorithms for answering ROAM queries. Comprehensive experiments show that our algorithm runs up to 30 times faster than baseline algorithms. [ABSTRACT FROM AUTHOR]

In recent years, the research community has introduced various methods for processing the location-based queries on a single type of objects in road networks. However, in real-life applications user may be interested in obtaining information about different types of objects, in terms of their neighboring relationship. The sets of different types of objects closer to each other are termed the heterogeneous neighboring object sets (or HNO sets for short). To provide users with object information by considering both the spatial closeness of objects to the query object and the neighboring relationship between objects, we present useful and important location-based aggregate queries on finding the HNO sets in road net-works. The location-based aggregate queries are the shortest average distance query (SAvgDQ), the shortest minimal distance query (SMinDQ), the shortest maximal distance query (SMaxDQ), and the shortest sum distance query (SSumDQ). We first utilize a grid index to manage information of data objects and road networks, and then propose the SAvgDQ, SMinDQ, SMaxDQ, and SSumDQ processing algorithms, which are combined with the grid index to efficiently process the four types of location-based aggregate queries, respectively. A comprehensive set of experiments is conducted to demonstrate the efficiency of the proposed processing algorithms using real road network datasets. [ABSTRACT FROM AUTHOR]

A significant proportion of all search volume consists of local searches. As a result, search engines must be capable of finding relevant results combining both spatial proximity and textual relevance with high query throughput. We observe that existing techniques answering these spatial keyword queries use keyword aggregated indexing, which has several disadvantages on road networks. We propose K-SPIN, a versatile framework that instead uses keyword separated indexing to delay and avoid expensive operations. At first glance, this strategy appears to have impractical pre-processing costs. However, by exploiting several useful observations, we make the indexing cost not only viable but also light-weight. For example, we propose a novel $\rho$ ρ -Approximate Network Voronoi Diagram (NVD) with one order of magnitude less space cost than exact NVDs. By carefully exploiting features of the K-SPIN framework, our query algorithms are up to two orders of magnitude more efficient than the state-of-the-art as shown in our experimental investigation on various queries, parameter settings, and real road network and keyword datasets. [ABSTRACT FROM AUTHOR]

This paper analyzed and assessed the pedestrians' mobility issues that are affecting their free movement and safety in the City of Kigali by outlining the major challenges in the City and providing alternative solutions and measures for improving the mobility and safety of pedestrians. The methodology of the paper was designed based on qualitative method with application of structured and unobtrusive. Referring to the paper findings about the mobility challenges of pedestrians within the City of Kigali, it is noted that the mobility of the pedestrians and their safety is still low and typical problems including road crossing viewed as the second challenges about pedestrians mobility, walking along very close to the road networks due to insufficient footpaths, lacking of enough road signs, lacking of information about pedestrian behavior on road networks, and improper functioning of existing traffic signals as the first challenge. The paper found that the majority of road networks in the City of Kigali did not provide walkways, traffics signals designs and availability is very poor and some of them not functioning, zebra crossing facilities were not provided adequately, pedestrians shelter on bus stop are almost absent and ignored, vehicles travelling speed is still high and does not allow pedestrians to move freely, and the mobility of physically challenged people has been forgotten and there is a need of introducing the pedestrian overpass bridges in clouded zones of the city center, Nyabugogo, Kicukiro and Remera-Giporoso areas of the City of Kigali. [ABSTRACT FROM AUTHOR]

Bridge restoration models are essential for assessing the functionality of bridges after extreme events, emergency response planning, and evaluating resilience and indirect losses. Most of the existing restoration models lack explicit evaluation of anticipated bridge functionality in terms of traffic restrictions on bridges, which is necessary for realistic road network modeling. Therefore, this paper proposes a decision tree based approach to determine potential traffic restrictions and their durations using empirical and expert opinion survey data on restrictions imposed on bridges after extreme events. The proposed methodology is applied in this paper to obtain seismic restoration models for bridges. Herein, three separate decision trees are developed to determine various traffic restrictions, anticipated for different levels of damage to bridge components, including bridge closure, lane closure, and speed/load restriction. Corresponding to each of the three traffic restriction decision trees, another decision tree is developed which determines the duration of the traffic restriction. The probabilistic seismic performance of a regional highway network in Memphis, Tennessee, is studied using the proposed decision trees. Additionally, HAZUS bridge restoration functions are also applied and the differences in the time evolving bridge level functionality estimates and their impacts on network performance are highlighted. [ABSTRACT FROM AUTHOR]

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Road networks are susceptible to disruptions. Since disruptions on certain roads have graver impacts, critical link identification is vital in disruption preparedness. To identify critical links, most approaches systematically inject disruptions and evaluate the ensuing after-effects. Since degradation patterns are uncertain, such iterative approaches either presume knowledge of disruption patterns or try numerous patterns but soon escalate to computational impracticality on real-life road networks. This study obviates both, using a novel approach which measures the contribution of each link to a predefined operational limit on the network. It generates a criticality hierarchy on real-life networks, while on hypothetical symmetric networks it rightly identifies all links as critical. [ABSTRACT FROM AUTHOR]