Artificial Society-Oriented Large-Scale Road Path Querying Methods

2014 ◽  
Vol 644-650 ◽  
pp. 2269-2275
Author(s):  
Kai Sheng ◽  
Zhen Li ◽  
Zhi Chao Song ◽  
Hong Duan
Keyword(s):  
Path Planning ◽  
Road Network ◽  
Large Scale ◽  
Network Models ◽  
Simulation System ◽  
The Road ◽  
Simulation Process ◽  
Artificial Society ◽  
Computational Resources ◽  
Simulation Time

In artificial society simulation, each artificial population needs road path planning in the process of travel. However, because of the large amounts of populations in artificial society, road path planning will cost lots computational resources and time, thus this process has terrible efficiency to the performance of the simulation system. In order to solve this problem, this article firstly makes use of CPU to generate the artificial populations, travel logs, and construct the road network models; then computes the shortest road path between each two environments and load the results in RAM for prepare; lastly, sends the ID and its start point and destination of the population who need road path querying to GPU at current simulation time in the simulation process, and then takes advantages of GPU to query the road path and return the results back. In this way, we can obviously reduce the time costs in the process of road path querying and enormously improve the performance of the whole simulation system.

scholarly journals A Parallel-Computing Approach for Vector Road-Network Matching Using GPU Architecture

2018 ◽  
Vol 7 (12) ◽  
pp. 472 ◽  
Author(s):  
Bo Wan ◽  
Lin Yang ◽  
Shunping Zhou ◽  
Run Wang ◽  
Dezhi Wang ◽  
...  
Keyword(s):  
Road Network ◽  
Large Scale ◽  
Graphics Processing Unit ◽  
Road Networks ◽  
Processing Unit ◽  
Data Partition ◽  
Matching Method ◽  
The Road ◽  
Central Processing ◽  
Relaxation Matching

The road-network matching method is an effective tool for map integration, fusion, and update. Due to the complexity of road networks in the real world, matching methods often contain a series of complicated processes to identify homonymous roads and deal with their intricate relationship. However, traditional road-network matching algorithms, which are mainly central processing unit (CPU)-based approaches, may have performance bottleneck problems when facing big data. We developed a particle-swarm optimization (PSO)-based parallel road-network matching method on graphics-processing unit (GPU). Based on the characteristics of the two main stages (similarity computation and matching-relationship identification), data-partition and task-partition strategies were utilized, respectively, to fully use GPU threads. Experiments were conducted on datasets with 14 different scales. Results indicate that the parallel PSO-based matching algorithm (PSOM) could correctly identify most matching relationships with an average accuracy of 84.44%, which was at the same level as the accuracy of a benchmark—the probability-relaxation-matching (PRM) method. The PSOM approach significantly reduced the road-network matching time in dealing with large amounts of data in comparison with the PRM method. This paper provides a common parallel algorithm framework for road-network matching algorithms and contributes to integration and update of large-scale road-networks.

Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network

2022 ◽  
Vol 13 (2) ◽  
pp. 1-25
Author(s):  
Bin Lu ◽  
Xiaoying Gan ◽  
Haiming Jin ◽  
Luoyi Fu ◽  
Xinbing Wang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Large Scale ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Propagation Mechanism ◽  
Urban Road ◽  
The Road ◽  
Traffic Flow Forecasting ◽  
Gating Mechanism

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. Due to the complexity and uncertainty of urban road conditions, how to capture the dynamic spatiotemporal correlation and make accurate predictions is very challenging. In most of existing works, urban road network is often modeled as a fixed graph based on local proximity. However, such modeling is not sufficient to describe the dynamics of the road network and capture the global contextual information. In this paper, we consider constructing the road network as a dynamic weighted graph through attention mechanism. Furthermore, we propose to seek both spatial neighbors and semantic neighbors to make more connections between road nodes. We propose a novel Spatiotemporal Adaptive Gated Graph Convolution Network ( STAG-GCN ) to predict traffic conditions for several time steps ahead. STAG-GCN mainly consists of two major components: (1) multivariate self-attention Temporal Convolution Network ( TCN ) is utilized to capture local and long-range temporal dependencies across recent, daily-periodic and weekly-periodic observations; (2) mix-hop AG-GCN extracts selective spatial and semantic dependencies within multi-layer stacking through adaptive graph gating mechanism and mix-hop propagation mechanism. The output of different components are weighted fused to generate the final prediction results. Extensive experiments on two real-world large scale urban traffic dataset have verified the effectiveness, and the multi-step forecasting performance of our proposed models outperforms the state-of-the-art baselines.

scholarly journals Road Network Vulnerability Analysis Based on Improved Ant Colony Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Yunpeng Wang ◽  
Yuqin Feng ◽  
Wenxiang Li ◽  
William Case Fulcher ◽  
Li Zhang
Keyword(s):  
Road Network ◽  
Large Scale ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Critical Infrastructures ◽  
Traffic Demand ◽  
The Road ◽  
Utility Index ◽  
Decision Supporting ◽  
Improved Ant Colony Algorithm

We present an improved ant colony algorithm-based approach to assess the vulnerability of a road network and identify the critical infrastructures. This approach improves computational efficiency and allows for its applications in large-scale road networks. This research involves defining the vulnerability conception, modeling the traffic utility index and the vulnerability of the road network, and identifying the critical infrastructures of the road network. We apply the approach to a simple test road network and a real road network to verify the methodology. The results show that vulnerability is directly related to traffic demand and increases significantly when the demand approaches capacity. The proposed approach reduces the computational burden and may be applied in large-scale road network analysis. It can be used as a decision-supporting tool for identifying critical infrastructures in transportation planning and management.

Nodes' Adjacency Relation of Video Sensor Network Based on GIS and GPS

2014 ◽  
Vol 687-691 ◽  
pp. 3675-3678
Author(s):  
Guo Liang Tang ◽  
Zhi Jing Liu ◽  
Jing Xiong
Keyword(s):  
Sensor Network ◽  
Road Network ◽  
Large Scale ◽  
Sensor Nodes ◽  
Abnormal Behavior ◽  
Sorting Algorithm ◽  
The Road ◽  
Video Sensors ◽  
Video Sensor ◽  
Relationship Of

As far as the large-scale video surveillance sensor network in urban road and highway, the relay-surveillance on abnormal behavior or particular targets is one of the hot focuses of in recent researches, while the establishment of adjacency relationship of the neighbor sensor nodes is the basis of the sensor scheduling for the relay-surveillance. The topology of a road network is generated according to the road information, which has already existed in the geographic information systems (GIS) regarding the road intersections as nodes and the section between the two intersections as the edge. The initial topology of relay-adjacency relationship between sensors is built by that each video sensor is deployed at the each intersection and the section of the each two intersections is regarded as the basis of adjacency between the each two sensors. When a new video sensor is to be deployed in a section of a road, the related deployed sensors in same section are searched by using the spatial index of GIS based on its GPS information, and then the adjacency relationship between the new sensor and the related ones is generated by using the sorting algorithm according to their GPS information. By using the road network information that has already existed in the GIS system, the algorithm on establishing the relay-adjacency relationship of video sensors is simple and simpler to implement, and it can be used in the construction of sensor relay-surveillance topology such as automatic real-time tracking on abnormal behavior or the analysis of the escape routes and so on in city roads, highway, smarter cities and smarter planet.

scholarly journals Method for Reliable Shortest Path Determination in Stochastic Networks using Parametrically Defined Stable Probability Distributions

2019 ◽  
Vol 18 (3) ◽  
pp. 558-582
Author(s):  
Anton Agafonov ◽  
Vladislav Myasnikov
Keyword(s):  
Travel Time ◽  
Shortest Path ◽  
Road Network ◽  
Large Scale ◽  
Probability Distributions ◽  
Time Budget ◽  
Stochastic Networks ◽  
Computational Time ◽  
Transport Networks ◽  
The Road

An increase in the number of vehicles, especially in large cities, and inability of the existing road infrastructure to distribute transport flows, leads to a higher congestion level in transport networks. This problem makes the solution to navigational problems more and more important. Despite the popularity of these tasks, many existing commercial systems find a route in deterministic networks, not taking into account the time-dependent and stochastic properties of traffic flows, i.e. travel time of road links is considered as constant. This paper addresses the reliable routing problem in stochastic networks using actual information of the traffic flow parameters. We consider the following optimality criterion: maximization of the probability of arriving on time at a destination given a departure time and a time budget. The reliable shortest path takes into account the variance of the travel time of the road network segments, which makes it more applicable for solving routing problems in transport networks compared to standard shortest path search algorithms that take into account only the average travel time of network segments. To describe the travel time of the road network segments, it is proposed to use parametrically defined stable Levy probability distributions. The use of stable distributions allows replacing the operation of calculating convolution to determine the reliability of the path to recalculating the parameters of the distributions density, which significantly reduces the computational time of the algorithm. The proposed method gives a solution in the form of a decision, i.e. the route proposed in the solution is not fixed in advance, but adaptively changes depending on changes in the real state of the network. An experimental analysis of the algorithm carried out on a large-scale transport network of Samara, Russia, showed that the presented algorithm can significantly reduce the computational time of the reliable shortest path algorithm with a slight increase in travel time.

scholarly journals Second Century Defences around Small Towns in Roman Britain Structured by Road Network Connectivity

2021 ◽  
Author(s):  
Joseph Lewis
Keyword(s):  
Roman Empire ◽  
Road Network ◽  
Large Scale ◽  
Network Connectivity ◽  
Small Towns ◽  
Second Century ◽  
Roman Britain ◽  
The Road ◽  
Inference Methods ◽  
Flow Of Information

The large-scale provision of defences around small towns in Roman Britain during the second century is without parallel in the Roman Empire. Whilst the relationship between defended small towns and the Roman road network has been noted previously, provincial-level patterns remain to be explored. Using network analysis and spatial inference methods, this paper shows that defended small towns in the second century are on average better integrated within the road network, as well as located on road segments important for controlling the flow of information, than small towns at random. This research suggests that the fortification of small towns in the second century was structured by the connectivity of the Roman road network and the functioning of the cursus publicus

scholarly journals Template-Based 3D Road Modeling for Generating Large-Scale Virtual Road Network Environment

2019 ◽  
Vol 8 (9) ◽  
pp. 364 ◽  
Author(s):  
Xuequan Zhang ◽  
Ming Zhong ◽  
Shaobo Liu ◽  
Luoheng Zheng ◽  
Yumin Chen
Keyword(s):  
Road Network ◽  
Large Scale ◽  
Road Construction ◽  
Scale Model ◽  
The Road ◽  
Moving Vehicles ◽  
Different Types ◽  
Open Source Software Package ◽  
Block Data ◽  
Construction Algorithms

The 3D road network scene helps to simulate the distribution of road infrastructure and the corresponding traffic conditions. However, the existing road modeling methods have limitations such as inflexibility in different types of road construction, inferior quality in visual effects and poor efficiency for large-scale model rendering. To tackle these challenges, a template-based 3D road modeling method is proposed in this paper. In this method, the road GIS data is first pre-processed before modeling. The road centerlines are analyzed to extract topology information and resampled to improve path accuracy and match the terrain. Meanwhile, the road network is segmented and organized using a hierarchical block data structure. Road elements, including roadbeds, road facilities and moving vehicles are then designed based on templates. These templates define the geometric and semantic information of elements along both the cross-section and road centerline. Finally, the road network scene is built by the construction algorithms, where roads, at-grade intersections, grade separated areas and moving vehicles are modeled and simulated separately. The proposed method is tested by generating large-scale virtual road network scenes in the World Wind, an open source software package. The experimental results demonstrate that the method is flexible and can be used to develop different types of road models and efficiently simulate large-scale road network environments.

scholarly journals A Deep Learning-Based Solution for Large-Scale Extraction of the Secondary Road Network from High-Resolution Aerial Orthoimagery

2020 ◽  
Vol 10 (20) ◽  
pp. 7272 ◽  
Author(s):  
Calimanut-Ionut Cira ◽  
Ramón Alcarria ◽  
Miguel-Ángel Manso-Callejo ◽  
Francisco Serradilla
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Road Network ◽  
Large Scale ◽  
Performance Metrics ◽  
Great Effort ◽  
The Road ◽  
Remotely Sensed Imagery ◽  
Geospatial Databases ◽  
Segmentation Models

Secondary roads represent the largest part of the road network. However, due to the absence of clearly defined edges, presence of occlusions, and differences in widths, monitoring and mapping them represents a great effort for public administration. We believe that recent advancements in machine vision allow the extraction of these types of roads from high-resolution remotely sensed imagery and can enable the automation of the mapping operation. In this work, we leverage these advances and propose a deep learning-based solution capable of efficiently extracting the surface area of secondary roads at a large scale. The solution is based on hybrid segmentation models trained with high-resolution remote sensing imagery divided in tiles of 256 × 256 pixels and their correspondent segmentation masks, resulting in increases in performance metrics of 2.7–3.5% when compared to the original architectures. The best performing model achieved Intersection over Union and F1 scores of maximum 0.5790 and 0.7120, respectively, with a minimum loss of 0.4985 and was integrated on a web platform which handles the evaluation of large areas, the association of the semantic predictions with geographical coordinates, the conversion of the tiles’ format and the generation of geotiff results compatible with geospatial databases.

scholarly journals Automatic Selection Method of Urban Road Network Considering Structural Characteristics

2019 ◽  
Vol 2 ◽  
pp. 1-7
Author(s):  
Wei Wu ◽  
Zhaoting Ma
Keyword(s):  
Road Network ◽  
Large Scale ◽  
Selection Process ◽  
Structural Characteristics ◽  
Selection Effect ◽  
Selection Method ◽  
Topographic Map ◽  
The Road ◽  
Automatic Selection ◽  
Mesh Density

<p><strong>Abstract.</strong> Road network is one of the key elements of map, and its selection effect directly determines the quality of map generalization. In the process of automatic road network selection under a large scale, it is not only necessary to consider the connectivity and integrity of the road itself, but also necessary to take account of the network characteristics and density characteristics of the road network as a whole. However, most of the existing methods cannot take into account the coordination and maintenance of the above-mentioned features, which leads to the result that the spatial distribution characteristics are easily destroyed after the selection. So an automatic selection method of road network considering structural characteristics is proposed in this paper. Firstly, road stroke connection is generated based on road semantics, geometry and topology features, and the end arc, end stroke and end mesh is identified as the objects to be removed in the selection process. Then, road stroke connection is divided into four categories according to the association characteristics of road stroke connection, and the importance of each stroke is evaluated by length, connectivity and betweenness centrality. Finally, the importance threshold (TS) of road stroke connection and the mesh density threshold (TN) are set, and the stroke connection with the least importance is gradually removed to realize the automatic selection of road network. The reliability and superiority of this method are verified by the road topographic map (1&amp;thinsp;:&amp;thinsp;10000) test of a region in Jiangsu Province.</p>

scholarly journals Road Network Connectivity and Freight Transportation for Supporting the Development of the Northern Zone of Aceh

2019 ◽  
Vol 54 (3) ◽  
Author(s):  
Herman Fithra ◽  
Sirojuzilam Hasyim ◽  
Sofyan M. Saleh ◽  
Jumadil Saputra
Keyword(s):  
Structural Equation ◽  
Road Network ◽  
Transportation Network ◽  
Network Connectivity ◽  
Network Models ◽  
Freight Transportation ◽  
Equation Modeling ◽  
The Road ◽  
Northern Zone ◽  
The Government

Road network connectivity significantly affects merchandise, transport, and people’s lives. Freight transportation network models are utilized as frameworks of transport policy decisions to estimate the impacts of infrastructure projects on traffic. The goods supply delivery modes in Aceh, Indonesia, are dominated by road-based transportation, where up to 95% of daily needs, such as food, are carried using trucks, buses, and other vehicles. This condition emerged owing to a lack of infrastructure and facilities. Thus, the main purpose of this study is to analyze the effect of road network connectivity on goods delivery in the northern zone of Aceh. This research adopted a quantitative technique (survey questionnaire) and involved as many as 420 respondents. The data were analyzed using structural equation modeling (SEM) with Analysis of Moment Structures. The result of the study shows the value of regressions weight is 0.375 or 37.5%. It indicates the road network connectivity variable has a significant relationship with the transport of goods. Furthermore, the road network connectivity of the area has the strongest link or significantly influences the activities of regional development. Therefore, the government in the northern zone of Aceh can formulate road network policies oriented towards the development of the area’s new economy and support the implementation of the Arun Lhokseumawe Special Economic Zone, which is the business area and trade laws are different from the rest of the country.

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