Short⁃term traffic flow prediction plays a crucial role in the planning of intelligent transportation systems.Nowadays,there is a large amount of traffic flow data generated from the monitoring devices of urban road n...Short⁃term traffic flow prediction plays a crucial role in the planning of intelligent transportation systems.Nowadays,there is a large amount of traffic flow data generated from the monitoring devices of urban road networks,which contains road network traffic information with high application value.In this study,an improved spatio⁃temporal attention transformer model(ISTA⁃transformer model)is proposed to provide a more accurate method for predicting multi⁃step short⁃term traffic flow based on monitoring data.By embedding a temporal attention layer and a spatial attention layer in the model,the model learns the relationship between traffic flows at different time intervals and different geographic locations,and realizes more accurate multi⁃step short⁃time flow prediction.Finally,we validate the superiority of the model with monitoring data spanning 15 days from 620 monitoring points in Qingdao,China.In the four time steps of prediction,the MAPE(Mean Absolute Percentage Error)values of ISTA⁃transformers prediction results are 0.22,0.29,0.37,and 0.38,respectively,and its prediction accuracy is usually better than that of six baseline models(Transformer,GRU,CNN,LSTM,Seq2Seq and LightGBM),which indicates that the proposed model in this paper always has a better ability to explain the prediction results with the time steps in the multi⁃step prediction.展开更多
A solution to compute the optimal path based on a single-line-single-directional（SLSD）road network model is proposed.Unlike the traditional road network model,in the SLSD conceptual model,being single-directional an...A solution to compute the optimal path based on a single-line-single-directional（SLSD）road network model is proposed.Unlike the traditional road network model,in the SLSD conceptual model,being single-directional and single-line style,a road is no longer a linkage of road nodes but abstracted as a network node.Similarly,a road node is abstracted as the linkage of two ordered single-directional roads.This model can describe turn restrictions,circular roads,and other real scenarios usually described using a super-graph.Then a computing framework for optimal path finding（OPF）is presented.It is proved that classical Dijkstra and A algorithms can be directly used for OPF computing of any real-world road networks by transferring a super-graph to an SLSD network.Finally,using Singapore road network data,the proposed conceptual model and its corresponding optimal path finding algorithms are validated using a two-step optimal path finding algorithm with a pre-computing strategy based on the SLSD road network.展开更多
This is the first of a three-part series of pape rs which introduces a general background of building trajectory-oriented road net work data models, including motivation, related works, and basic concepts. The p urpos...This is the first of a three-part series of pape rs which introduces a general background of building trajectory-oriented road net work data models, including motivation, related works, and basic concepts. The p urpose of the series is to develop a trajectory-oriented road network data mode l, namely carriageway-based road network data model (CRNM). Part 1 deals with t he modeling background. Part 2 proposes the principle and architecture of the CR NM. Part 3 investigates the implementation of the CRNM in a case study. In the p resent paper, the challenges of managing trajectory data are discussed. Then, de veloping trajectory-oriented road network data models is proposed as a solution and existing road network data models are reviewed. Basic representation approa ches of a road network are introduced as well as its constitution.展开更多
This is the second of a three-part series of papers which presents the principle and architecture of the CRNM, a trajectory-oriented, carriageway-based road network data model. The first part of the series has introdu...This is the second of a three-part series of papers which presents the principle and architecture of the CRNM, a trajectory-oriented, carriageway-based road network data model. The first part of the series has introduced a general background of building trajectory-oriented road network data models, including motivation, related works, and basic concepts. Based on it, this paper describs the CRNM in detail. At first, the notion of basic roadway entity is proposed and discussed. Secondly, carriageway is selected as the basic roadway entity after compared with other kinds of roadway, and approaches to representing other roadways with carriageways are introduced. At last, an overall architecture of the CRNM is proposed.展开更多
This is the final of a three-part series of papers which mainly discusses the implementation issues of the CRNM. The first two papers in the series have introduced the modeling background and methodology, respectively...This is the final of a three-part series of papers which mainly discusses the implementation issues of the CRNM. The first two papers in the series have introduced the modeling background and methodology, respectively. An overall architecture of the CRNM has been proposed in the last paper. On the basis of the above discusses, a linear reference method (LRM) for providing spatial references for location points of a trajectory is developed. A case study is introduced to illustrate the application of the CRNM for modeling a road network in the real world is given. A comprehensive conclusion is given for the series of papers.展开更多
The technique of incremental updating,which can better guarantee the real-time situation of navigational map,is the developing orientation of navigational road network updating.The data center of vehicle navigation sy...The technique of incremental updating,which can better guarantee the real-time situation of navigational map,is the developing orientation of navigational road network updating.The data center of vehicle navigation system is in charge of storing incremental data,and the spatio-temporal data model for storing incremental data does affect the efficiency of the response of the data center to the requirements of incremental data from the vehicle terminal.According to the analysis on the shortcomings of several typical spatio-temporal data models used in the data center and based on the base map with overlay model,the reverse map with overlay model (RMOM) was put forward for the data center to make rapid response to incremental data request.RMOM supports the data center to store not only the current complete road network data,but also the overlays of incremental data from the time when each road network changed to the current moment.Moreover,the storage mechanism and index structure of the incremental data were designed,and the implementation algorithm of RMOM was developed.Taking navigational road network in Guangzhou City as an example,the simulation test was conducted to validate the efficiency of RMOM.Results show that the navigation database in the data center can response to the requirements of incremental data by only one query with RMOM,and costs less time.Compared with the base map with overlay model,the data center does not need to temporarily overlay incremental data with RMOM,so time-consuming of response is significantly reduced.RMOM greatly improves the efficiency of response and provides strong support for the real-time situation of navigational road network.展开更多
Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transp...Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time complexity.Network modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration 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 ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety.展开更多
In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory...In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework:the deep reinforcement learning output(action) is translated into a set-point to be tracked by the model predictive controller;conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decisionmaking purposes;on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps(links,junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the aim to put in light the main f eatures of the proposed approach. Moreover, it is important to underline that in different operating scenarios the proposed reinforcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors.展开更多
This paper proposes an adaptive agent model with a hybrid routing selection strategy for studying the road-network congestion problem. We focus on improving those severely congested links. Firstly,a multi-agent system...This paper proposes an adaptive agent model with a hybrid routing selection strategy for studying the road-network congestion problem. We focus on improving those severely congested links. Firstly,a multi-agent system is built,where each agent stands for a vehicle,and it makes its routing selection by considering the shortest path and the minimum congested degree of the target link simultaneously. The agent-based model captures the nonlinear feedback between vehicle routing behaviors and road-network congestion status.Secondly,a hybrid routing selection strategy is provided,which guides the vehicle routes adapting to the realtime road-network congestion status. On this basis, we execute simulation experiments and compare the simulation results of network congestion distribution,by Floyd agent with shortest path strategy and our proposed adaptive agent with hybrid strategy. The simulation results show that our proposed model has reduced the congestion degree of those seriously congested links of road-network. Finally,we execute our model on a real road map. The results finds that those seriously congested roads have some common features such as located at the road junction or near the unique road connecting two areas. And,the results also show an effectiveness of our model on reduction of those seriously congested links in this actual road network. Such a bottom-up congestion control approach with a hybrid congestion optimization perspective will have its significance for actual traffic congestion control.展开更多
Awareness of the adverse ecological effects of road and other large construction projects has resulted in a rapidly growing demand to quantitatively predict and evaluate the effects on gene flow among plants and anima...Awareness of the adverse ecological effects of road and other large construction projects has resulted in a rapidly growing demand to quantitatively predict and evaluate the effects on gene flow among plants and animals in natural habitats.In this study,we evaluated the effects of different road construction scenarios on animal species movement by using the methods of ecological landscape connectivity in the typical regions of Yunnan Province,China.The results showed landscape connectivity levels(Probability of Connectivity(PC))decreased after road construction and species with lower dispersal abilities were more affected to the subsequent habitat fragmentation.First level roads affected landscape connectivity most significantly.At a distance of 100 m,the PC index decreased more than 50%.Further analysis suggested that the use of landscape connectivity to assess landscape processes revealed the adverse effects on the species movement more effectively compared with the traditional landscape pattern analysis.In addition,we conducted a graph theory and a least-cost modeling discussion,it is obvious that they are powerful tools to represent and analyze landscape networks with respect to related species movement.Network analysis offers a quantitative and simple but effective tool for ecological process assessment and biological conservation on large scales.展开更多
The post-earthquake emergency period,which is a sensitive time segment just after an event,mainly focuses on saving life and restoring social order.To improve the seismic resilience of city road networks,a resilience ...The post-earthquake emergency period,which is a sensitive time segment just after an event,mainly focuses on saving life and restoring social order.To improve the seismic resilience of city road networks,a resilience evaluation method used in the post-earthquake emergency period is proposed.The road seismic damage index of a city road network can consider the influence of roads,bridges and buildings along the roads,etc.on road capacity after an earthquake.A function index for a city road network is developed,which reflects the connectivity,redundancy,traffic demand and traffic function of the network.An optimization model for improving the road repair order in the post-earthquake emergency period is also developed according to the resilience evaluation,to enable decision support for city emergency management and achieve the best seismic resilience of the city road network.The optimization model is applied to a city road network and the results illustrate the feasibility of the resilience evaluation and optimization method for a city road network in the post-earthquake emergency period.展开更多
Rational planning of agricultural product transport route from initial node to destination node can effectively reduce the cost price of agricultural products,and the calculation of shortest path between any two point...Rational planning of agricultural product transport route from initial node to destination node can effectively reduce the cost price of agricultural products,and the calculation of shortest path between any two points also affects people’s daily travel.Taking Heze Railway Station to Heze College for example,with remote sensing image data as the base map,we conduct vectorization and topological analysis on roads in the target area.With Dijkstra as theoretical basis of shortest path algorithm,we use ArcG IS network analysis method to build road network,and calculate the planning program of the shortest distance path,the shortest path by driving and the shortest path by walking.展开更多
There is a problem of real-time detection difficulty in road surface damage detection. This paper proposes an improved lightweight model based on you only look once version 5(YOLOv5). Firstly, this paper fully utilize...There is a problem of real-time detection difficulty in road surface damage detection. This paper proposes an improved lightweight model based on you only look once version 5(YOLOv5). Firstly, this paper fully utilized the convolutional neural network(CNN) + ghosting bottleneck(G_bneck) architecture to reduce redundant feature maps. Afterwards, we upgraded the original upsampling algorithm to content-aware reassembly of features(CARAFE) and increased the receptive field. Finally, we replaced the spatial pyramid pooling fast(SPPF) module with the basic receptive field block(Basic RFB) pooling module and added dilated convolution. After comparative experiments, we can see that the number of parameters and model size of the improved algorithm in this paper have been reduced by nearly half compared to the YOLOv5s. The frame rate per second(FPS) has been increased by 3.25 times. The mean average precision(m AP@0.5: 0.95) has increased by 8%—17% compared to other lightweight algorithms.展开更多
【目的】随志愿者地理信息系统的快速发展,高现势性众源路网已成为智慧城市建设的重要数据来源,其选取的效率与效果成为影响多尺度数据服务的关键因素。已有的路网选取方法大多基于数据属性信息判断道路重要性,十分合理且有效,但是,实...【目的】随志愿者地理信息系统的快速发展,高现势性众源路网已成为智慧城市建设的重要数据来源,其选取的效率与效果成为影响多尺度数据服务的关键因素。已有的路网选取方法大多基于数据属性信息判断道路重要性,十分合理且有效,但是,实际数据往往存在属性缺失问题,一定程度上限制了方法的适用性。【方法】针对此问题,本文提出一种属性信息缺失条件下的众源路网空间句法自动建模与选取方法。首先,基于开放街道地图(Open Street Map)中心线数据,开发程序自动执行几何化简、拓扑修正与伪节点处理,批量生成整个城市的空间句法线段模型,并基于模型计算整合度、选择度等空间句法指标;随后构建Stroke,并提取几何特征;进一步,创新性地提出2项复合指标:基于路径单元的标准化角度整合度(SNAIN)与基于路径单元的标准化角度选择度(SNACH),以联合刻画道路的拓扑可达性与几何连续性。在此基础上,应用结合熵权法与层次分析法(EW-AHP)的主客观集成赋权方法,确定综合指标的权重,实现道路的重要性排序。最后,通过断头路识别与网格密度修补,进一步提高路网的连通性和完整性。【结果】以兰州(带状道路网)和成都(环形放射状道路网)为案例验证,结果表明:在道路属性信息缺失的条件下,本文方法能够有效识别城市主干路网,其与OSM道路等级匹配准确率分别达到兰州0.9421、成都0.9711;修补后兰州市路网连通率由1.0582提升至1.0864,成都市路网连通率由1.1086提升至1.1198(成都在所选尺度内的断头路完全消除)。消融实验表明,SNAIN更有利于提升全局连通性,SNACH有助于增强几何连续性,二者并用能在连通性与空间覆盖间取得平衡。【结论】本文方法为属性信息不完整情形下的大规模城市路网快速建模与选取提供了新的理论支持和技术路径。展开更多
基金Sponsored by National Key Research and Development Program of China(Grant No.2020YEB1600500).
文摘Short⁃term traffic flow prediction plays a crucial role in the planning of intelligent transportation systems.Nowadays,there is a large amount of traffic flow data generated from the monitoring devices of urban road networks,which contains road network traffic information with high application value.In this study,an improved spatio⁃temporal attention transformer model(ISTA⁃transformer model)is proposed to provide a more accurate method for predicting multi⁃step short⁃term traffic flow based on monitoring data.By embedding a temporal attention layer and a spatial attention layer in the model,the model learns the relationship between traffic flows at different time intervals and different geographic locations,and realizes more accurate multi⁃step short⁃time flow prediction.Finally,we validate the superiority of the model with monitoring data spanning 15 days from 620 monitoring points in Qingdao,China.In the four time steps of prediction,the MAPE(Mean Absolute Percentage Error)values of ISTA⁃transformers prediction results are 0.22,0.29,0.37,and 0.38,respectively,and its prediction accuracy is usually better than that of six baseline models(Transformer,GRU,CNN,LSTM,Seq2Seq and LightGBM),which indicates that the proposed model in this paper always has a better ability to explain the prediction results with the time steps in the multi⁃step prediction.
基金The National Key Technology R&D Program of China during the 11th Five Year Plan Period(No.2008BAJ11B01)
文摘A solution to compute the optimal path based on a single-line-single-directional（SLSD）road network model is proposed.Unlike the traditional road network model,in the SLSD conceptual model,being single-directional and single-line style,a road is no longer a linkage of road nodes but abstracted as a network node.Similarly,a road node is abstracted as the linkage of two ordered single-directional roads.This model can describe turn restrictions,circular roads,and other real scenarios usually described using a super-graph.Then a computing framework for optimal path finding（OPF）is presented.It is proved that classical Dijkstra and A algorithms can be directly used for OPF computing of any real-world road networks by transferring a super-graph to an SLSD network.Finally,using Singapore road network data,the proposed conceptual model and its corresponding optimal path finding algorithms are validated using a two-step optimal path finding algorithm with a pre-computing strategy based on the SLSD road network.
文摘This is the first of a three-part series of pape rs which introduces a general background of building trajectory-oriented road net work data models, including motivation, related works, and basic concepts. The p urpose of the series is to develop a trajectory-oriented road network data mode l, namely carriageway-based road network data model (CRNM). Part 1 deals with t he modeling background. Part 2 proposes the principle and architecture of the CR NM. Part 3 investigates the implementation of the CRNM in a case study. In the p resent paper, the challenges of managing trajectory data are discussed. Then, de veloping trajectory-oriented road network data models is proposed as a solution and existing road network data models are reviewed. Basic representation approa ches of a road network are introduced as well as its constitution.
文摘This is the second of a three-part series of papers which presents the principle and architecture of the CRNM, a trajectory-oriented, carriageway-based road network data model. The first part of the series has introduced a general background of building trajectory-oriented road network data models, including motivation, related works, and basic concepts. Based on it, this paper describs the CRNM in detail. At first, the notion of basic roadway entity is proposed and discussed. Secondly, carriageway is selected as the basic roadway entity after compared with other kinds of roadway, and approaches to representing other roadways with carriageways are introduced. At last, an overall architecture of the CRNM is proposed.
文摘This is the final of a three-part series of papers which mainly discusses the implementation issues of the CRNM. The first two papers in the series have introduced the modeling background and methodology, respectively. An overall architecture of the CRNM has been proposed in the last paper. On the basis of the above discusses, a linear reference method (LRM) for providing spatial references for location points of a trajectory is developed. A case study is introduced to illustrate the application of the CRNM for modeling a road network in the real world is given. A comprehensive conclusion is given for the series of papers.
基金Under the auspices of National High Technology Research and Development Program of China (No.2007AA12Z242)
文摘The technique of incremental updating,which can better guarantee the real-time situation of navigational map,is the developing orientation of navigational road network updating.The data center of vehicle navigation system is in charge of storing incremental data,and the spatio-temporal data model for storing incremental data does affect the efficiency of the response of the data center to the requirements of incremental data from the vehicle terminal.According to the analysis on the shortcomings of several typical spatio-temporal data models used in the data center and based on the base map with overlay model,the reverse map with overlay model (RMOM) was put forward for the data center to make rapid response to incremental data request.RMOM supports the data center to store not only the current complete road network data,but also the overlays of incremental data from the time when each road network changed to the current moment.Moreover,the storage mechanism and index structure of the incremental data were designed,and the implementation algorithm of RMOM was developed.Taking navigational road network in Guangzhou City as an example,the simulation test was conducted to validate the efficiency of RMOM.Results show that the navigation database in the data center can response to the requirements of incremental data by only one query with RMOM,and costs less time.Compared with the base map with overlay model,the data center does not need to temporarily overlay incremental data with RMOM,so time-consuming of response is significantly reduced.RMOM greatly improves the efficiency of response and provides strong support for the real-time situation of navigational road network.
基金the National Natural Science Foundation of China(Nos.62272063,62072056 and 61902041)the Natural Science Foundation of Hunan Province(Nos.2022JJ30617 and 2020JJ2029)+4 种基金Open Research Fund of Key Lab of Broadband Wireless Communication and Sensor Network Technology,Nanjing University of Posts and Telecommunications(No.JZNY202102)the Traffic Science and Technology Project of Hunan Province,China(No.202042)Hunan Provincial Key Research and Development Program(No.2022GK2019)this work was funded by the Researchers Supporting Project Number(RSPD2023R681)King Saud University,Riyadh,Saudi Arabia.
文摘Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time complexity.Network modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration 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 ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety.
文摘In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework:the deep reinforcement learning output(action) is translated into a set-point to be tracked by the model predictive controller;conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decisionmaking purposes;on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps(links,junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the aim to put in light the main f eatures of the proposed approach. Moreover, it is important to underline that in different operating scenarios the proposed reinforcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors.
基金Sponsored by the Natural Science Foundation of Hunan ProvinceChina(Grant No.13JJ3049)the Fundamental Research Funds for the Central Universities(Grant No.2012AA01A301-1)
文摘This paper proposes an adaptive agent model with a hybrid routing selection strategy for studying the road-network congestion problem. We focus on improving those severely congested links. Firstly,a multi-agent system is built,where each agent stands for a vehicle,and it makes its routing selection by considering the shortest path and the minimum congested degree of the target link simultaneously. The agent-based model captures the nonlinear feedback between vehicle routing behaviors and road-network congestion status.Secondly,a hybrid routing selection strategy is provided,which guides the vehicle routes adapting to the realtime road-network congestion status. On this basis, we execute simulation experiments and compare the simulation results of network congestion distribution,by Floyd agent with shortest path strategy and our proposed adaptive agent with hybrid strategy. The simulation results show that our proposed model has reduced the congestion degree of those seriously congested links of road-network. Finally,we execute our model on a real road map. The results finds that those seriously congested roads have some common features such as located at the road junction or near the unique road connecting two areas. And,the results also show an effectiveness of our model on reduction of those seriously congested links in this actual road network. Such a bottom-up congestion control approach with a hybrid congestion optimization perspective will have its significance for actual traffic congestion control.
基金Under the auspices of Nonprofit Environment Protection Specific Project of China(No.201209029-4)
文摘Awareness of the adverse ecological effects of road and other large construction projects has resulted in a rapidly growing demand to quantitatively predict and evaluate the effects on gene flow among plants and animals in natural habitats.In this study,we evaluated the effects of different road construction scenarios on animal species movement by using the methods of ecological landscape connectivity in the typical regions of Yunnan Province,China.The results showed landscape connectivity levels(Probability of Connectivity(PC))decreased after road construction and species with lower dispersal abilities were more affected to the subsequent habitat fragmentation.First level roads affected landscape connectivity most significantly.At a distance of 100 m,the PC index decreased more than 50%.Further analysis suggested that the use of landscape connectivity to assess landscape processes revealed the adverse effects on the species movement more effectively compared with the traditional landscape pattern analysis.In addition,we conducted a graph theory and a least-cost modeling discussion,it is obvious that they are powerful tools to represent and analyze landscape networks with respect to related species movement.Network analysis offers a quantitative and simple but effective tool for ecological process assessment and biological conservation on large scales.
基金National Natural Science Foundation of China under Grant Nos.U1939210 and 51825801。
文摘The post-earthquake emergency period,which is a sensitive time segment just after an event,mainly focuses on saving life and restoring social order.To improve the seismic resilience of city road networks,a resilience evaluation method used in the post-earthquake emergency period is proposed.The road seismic damage index of a city road network can consider the influence of roads,bridges and buildings along the roads,etc.on road capacity after an earthquake.A function index for a city road network is developed,which reflects the connectivity,redundancy,traffic demand and traffic function of the network.An optimization model for improving the road repair order in the post-earthquake emergency period is also developed according to the resilience evaluation,to enable decision support for city emergency management and achieve the best seismic resilience of the city road network.The optimization model is applied to a city road network and the results illustrate the feasibility of the resilience evaluation and optimization method for a city road network in the post-earthquake emergency period.
基金Supported by Science Foundation of Heze University(XY14SK14)
文摘Rational planning of agricultural product transport route from initial node to destination node can effectively reduce the cost price of agricultural products,and the calculation of shortest path between any two points also affects people’s daily travel.Taking Heze Railway Station to Heze College for example,with remote sensing image data as the base map,we conduct vectorization and topological analysis on roads in the target area.With Dijkstra as theoretical basis of shortest path algorithm,we use ArcG IS network analysis method to build road network,and calculate the planning program of the shortest distance path,the shortest path by driving and the shortest path by walking.
基金supported by the Shanghai Sailing Program,China (No.20YF1447600)the Research Start-Up Project of Shanghai Institute of Technology (No.YJ2021-60)+1 种基金the Collaborative Innovation Project of Shanghai Institute of Technology (No.XTCX2020-12)the Science and Technology Talent Development Fund for Young and Middle-Aged Teachers at Shanghai Institute of Technology (No.ZQ2022-6)。
文摘There is a problem of real-time detection difficulty in road surface damage detection. This paper proposes an improved lightweight model based on you only look once version 5(YOLOv5). Firstly, this paper fully utilized the convolutional neural network(CNN) + ghosting bottleneck(G_bneck) architecture to reduce redundant feature maps. Afterwards, we upgraded the original upsampling algorithm to content-aware reassembly of features(CARAFE) and increased the receptive field. Finally, we replaced the spatial pyramid pooling fast(SPPF) module with the basic receptive field block(Basic RFB) pooling module and added dilated convolution. After comparative experiments, we can see that the number of parameters and model size of the improved algorithm in this paper have been reduced by nearly half compared to the YOLOv5s. The frame rate per second(FPS) has been increased by 3.25 times. The mean average precision(m AP@0.5: 0.95) has increased by 8%—17% compared to other lightweight algorithms.
文摘【目的】随志愿者地理信息系统的快速发展,高现势性众源路网已成为智慧城市建设的重要数据来源,其选取的效率与效果成为影响多尺度数据服务的关键因素。已有的路网选取方法大多基于数据属性信息判断道路重要性,十分合理且有效,但是,实际数据往往存在属性缺失问题,一定程度上限制了方法的适用性。【方法】针对此问题,本文提出一种属性信息缺失条件下的众源路网空间句法自动建模与选取方法。首先,基于开放街道地图(Open Street Map)中心线数据,开发程序自动执行几何化简、拓扑修正与伪节点处理,批量生成整个城市的空间句法线段模型,并基于模型计算整合度、选择度等空间句法指标;随后构建Stroke,并提取几何特征;进一步,创新性地提出2项复合指标:基于路径单元的标准化角度整合度(SNAIN)与基于路径单元的标准化角度选择度(SNACH),以联合刻画道路的拓扑可达性与几何连续性。在此基础上,应用结合熵权法与层次分析法(EW-AHP)的主客观集成赋权方法,确定综合指标的权重,实现道路的重要性排序。最后,通过断头路识别与网格密度修补,进一步提高路网的连通性和完整性。【结果】以兰州(带状道路网)和成都(环形放射状道路网)为案例验证,结果表明:在道路属性信息缺失的条件下,本文方法能够有效识别城市主干路网,其与OSM道路等级匹配准确率分别达到兰州0.9421、成都0.9711;修补后兰州市路网连通率由1.0582提升至1.0864,成都市路网连通率由1.1086提升至1.1198(成都在所选尺度内的断头路完全消除)。消融实验表明,SNAIN更有利于提升全局连通性,SNACH有助于增强几何连续性,二者并用能在连通性与空间覆盖间取得平衡。【结论】本文方法为属性信息不完整情形下的大规模城市路网快速建模与选取提供了新的理论支持和技术路径。
