Nodes In Road Network Research Articles

The interchange stations siting model of urban rail transit network

The "node anchor weaving method" is commonly used in urban rail transit network planning, which emphasizes identifying the important nodes of the network first, and then using the key nodes as the "anchor nodes" to construct the network. Interchange stations are the most crucial network nodes in terms of both service function and network function. As a result, when planning the network, it is critical to identify the interchanges ahead of time. A key node identification model is initially established in this article, and then a network interchange stations location model is built on top of it. Based on the urban road network, the key node identification model takes into account the location of the road network nodes, the number of neighboring nodes, the influence of the neighboring nodes, and the path information between the nodes, and the key nodes in the network are effectively identified through the improvement of the gravitational model and the neighboring core model, and the model's validity is verified through the SIR contagion model. The online network interchange stations siting model considers the matching degree of residents' traffic demand, the interchange stations coverage to the city, the intensity of land use, and the interchange stations' duplicate coverage to the urban space as evaluation indices for the interchange stations; the objective of solving the interchange stations is the comprehensive optimality of the indexes; in order to avoid the problem of perturbation in the optimization and solution algorithm, this paper presents an improved genetic-simulated annealing optimization algorithm that introduces the concept of community, and avoids the problem of perturbation in the optimization and solution algorithms. The effectiveness of the model is verified by using examples of the Mitte-Center and Friedrichshain networks.

Stochastic Models of Traffic Management

This article discusses the problem of blocking nodes in road networks and percolation thresholds for the transport infrastructure of a modern metropolis. For metropolitan networks, the values of percolation thresholds are calculated and displayed, considering the different density of connections between network nodes. Further, it is shown that the dependence of the values of the percolation thresholds on the network’s density can be described by functional dependencies with a high degree of correlation. The obtained results can be used to assess the reliability of transport infrastructure and to check the increase in the capacity of selected sections of the road network. Further, the article discusses obtaining a description of road infrastructure from open sources (obtaining data from OpenStreetMap using the SUMO - Simulation of Urban MObility package), after which it is possible to build a graph of the road network and determine its percolation properties. For the constructed nodes of the graph described a model of the stochastic dynamics of blocking a single road lane in the transport network. The threshold value L of number of cars that can be placed in the lane (based on the length of the road lane) is used as constraints and the incoming and outgoing flows of cars are also determined as income parameters of a model. The constructed model allows us to obtain the predicted blocking time of a road network line, for a given probability of such blocking, where the probability of blocking a single road network line is taken from the percolation properties of this network discussed earlier. The resulting blocking times of road network nodes make it possible to build an algorithm for controlling traffic light regulation.

Traffic Flow Prediction Research Based on an Interactive Dynamic Spatial–Temporal Graph Convolutional Probabilistic Sparse Attention Mechanism (IDG-PSAtt)

Accurate traffic flow prediction is highly important for relieving road congestion. Due to the intricate spatial–temporal dependence of traffic flows, especially the hidden dynamic correlations among road nodes, and the dynamic spatial–temporal characteristics of traffic flows, a traffic flow prediction model based on an interactive dynamic spatial–temporal graph convolutional probabilistic sparse attention mechanism (IDG-PSAtt) is proposed. Specifically, the IDG-PSAtt model consists of an interactive dynamic graph convolutional network (IL-DGCN) with a spatial–temporal convolution (ST-Conv) block and a probabilistic sparse self-attention (ProbSSAtt) mechanism. The IL-DGCN divides the time series of a traffic flow into intervals and synchronously and interactively shares the captured dynamic spatiotemporal features. The ST-Conv block is utilized to capture the complex dynamic spatial–temporal characteristics of the traffic flow, and the ProbSSAtt block is utilized for medium-to-long-term forecasting. In addition, a dynamic GCN is generated by fusing adaptive and learnable adjacency matrices to learn the hidden dynamic associations among road network nodes. Experimental results demonstrate that the IDG-PSAtt model outperforms the baseline methods in terms of prediction accuracy. Specifically, on METR-LA, the mean absolute error (MAE) and root mean square error (RMSE) induced by IDG-PSAtt for a 60 min forecasting scenario are reduced by 0.75 and 1.31, respectively, compared to those of the state-of-the-art models. This traffic flow prediction improvement will lead to more precise estimates of the emissions produced by mobile sources, resulting in more accurate air quality forecasts. Consequently, this research will greatly support local environmental management efforts.

Defining urban boundaries through DBSCAN and Shannon's entropy: The case of the Mexican National Urban System

A novel method is proposed to define cities' boundaries within a given national urban system. Point Volunteered Geographic Information, such as the OpenStreetMap road network nodes, together with national geospatial information, like economic units, are recursively clustered using DBSCAN at different distance thresholds; clusters smaller than a threshold reflecting the rural settlements pattern are filtered, and then Shannon's entropy for the entire system is calculated. The system of percolated nodes obtained when this entropy reaches its maximum is compared against the current definition of what is officially urban. It is found to be very similar to the actual urban-nonurban classification, and also to have a very high correspondence with the current classification of the Mexican National Urban System (SUN). An additional finding is that the percolated system at the maximum entropy value has a very high correspondence with Zipf's law, while the current Mexican SUN does not.

MGL2Rank: Learning to rank the importance of nodes in road networks based on multi-graph fusion

The identification of important nodes with strong propagation capabilities in road networks is a vital topic in urban planning. Existing methods for evaluating the importance of nodes in traffic networks only consider topological information and traffic volumes, the diversity of the traffic characteristics in road networks, such as the number of lanes and average speed of road segments, is ignored, thus limiting their performance. To solve this problem, we propose a graph learning-based framework (MGL2Rank) that integrates the rich characteristics of road networks to rank the importance of nodes. This framework comprises an embedding module containing a sampling algorithm (MGWalk) and an encoder network to learn the latent representations for each road segment. MGWalk utilizes multi-graph fusion to capture the topology of road networks and establish associations between road segments based on their attributes. The obtained node representation is then used to learn the importance ranking of the road segments. Finally, a synthetic dataset is constructed for ranking tasks based on the regional road network of Shenyang City, and the ranking results on this dataset demonstrate the effectiveness of our method. The data and source code for MGL2Rank are available at https://github.com/ZJ726.

Traffic Flow Prediction Based on Interactive Dynamic Spatio-Temporal Graph Convolution with a Probabilistic Sparse Attention Mechanism

Accurate traffic flow prediction is of great practical significance to alleviate road congestion. Existing methods ignore the hidden dynamic associations between road nodes, and for the problem of difficulty capturing the dynamic spatio-temporal features of traffic flow in the prediction process, a novel model based on the interactive dynamic spatio-temporal graph convolutional probabilistic sparse attention mechanism (IDG-PSAtt) is proposed, which consists of an interactive dynamic graph convolutional network (IDGCN) structure with a spatio-temporal convolutional block (ST-Conv block) and a probabilistic sparse self-attention mechanism block (ProbSSAtt block). Among them, the IDGCN synchronizes the dynamic spatio-temporal features captured by interaction sharing, and the ST-Conv block is combined with the ProbSSAtt block to effectively capture the long short-term temporal features of the traffic flow. In addition, to effectively find the hidden dynamic associations between road network nodes, a dynamic graph convolutional network generated by the fusion of an adaptive neighbor matrix and a learnable neighbor matrix was constructed. Experimental results demonstrate that the prediction performance of the IDG-PSAtt model outperforms the baseline model under the evaluation criteria and experimental settings given in this paper. In the PEMS-BAY dataset, the mean absolute error and root mean square error of the IDG-PSAtt to 60 min are improved by 15.49% and 12.10%, compared with the state-of-the-art model, respectively.

Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection

Traffic time series anomaly detection has been intensively studied for years because of its potential applications in intelligent transportation. However, classical traffic anomaly detection methods often overlook the evolving dynamic associations between road network nodes, which leads to challenges in capturing the long-term temporal correlations, spatial characteristics, and abnormal node behaviors in datasets with high periodicity and trends, such as morning peak travel periods. In this paper, we propose a mirror temporal graph autoencoder (MTGAE) framework to explore anomalies and capture unseen nodes and the spatiotemporal correlation between nodes in the traffic network. Specifically, we propose the mirror temporal convolutional module to enhance feature extraction capabilities and capture hidden node-to-node features in the traffic network. Morever, we propose the graph convolutional gate recurrent unit cell (GCGRU CELL) module. This module uses Gaussian kernel functions to map data into a high-dimensional space, and enables the identification of anomalous information and potential anomalies within the complex interdependencies of the traffic network, based on prior knowledge and input data. We compared our work with several other advanced deep-learning anomaly detection models. Experimental results on the NYC dataset illustrate that our model works best compared to other models for traffic anomaly detection.

Self-Supervised Spatiotemporal Clustering of Vehicle Emissions With Graph Convolutional Network.

Spatiotemporal clustering of vehicle emissions, which reveals the evolution pattern of air pollution from road traffic, is a challenging representation learning task due to the lack of supervision. Some recent work building upon graph convolutional network (GCN) models the intrinsic spatiotemporal correlations among the nodes in road networks as graph representations for clustering. However, these existing methods ignore the interactions between spatial and temporal variations in vehicle emissions, resulting in incomplete descriptions and inaccurate detection of the evolution pattern of air pollution. To address this issue, this article proposes a two-way self-supervised spatiotemporal representation learning scheme, in which the temporal and spatial features are progressively learned in a mutually reinforced manner. Our proposed method is based on the observation that though the variation in vehicle emissions in the road network is consistent in the spatial and temporal domains, its expression is more distinct in temporal sequences. To this end, the input emission data are first projected into an initial temporal representation space spanned by the captured features from a pretrained BiLSTM network. Then the generated distribution of temporal features is used to construct an objective constraint for high-purity clustering through a two-way self-supervised mechanism, which is leveraged as a constraint for the feature clustering of a GCN. Furthermore, to eliminate the initial errors, a joint optimization scheme is presented to generate the decoupled clustering results through the progressive refinement of representation and clustering. Our proposed method is evaluated on the traffic emission dataset of Xian city in 2020, and the experimental results have demonstrated the superiority against the state-of-the-art.

RESEARCH ON SPATIAL CONSISTENCY CHECKING ACCORDING TO NATIONAL TECHNICAL REGULATION ON BASIC GEOGRAPHIC INFORMATION

Spatial consistency checking identifies logical inconsistencies according to pre-specified spatial rules, including data omission or commission, data overlap, and spatial connection. This research experimentally checks the consistency of these components according to national technical regulations on basic geographic information and the construction of checking rules based on GIS analysis methods to determine the relationship between geometric objects within a layer and between two different layers. In experiments using the ArcGIS software suite on some typical feature classes of the transportation group of the uncalibrated and uncorrected geographic database of the Bien Hoa town area at a scale of 1:5,000, it was shown that: the largest percentage of errors is the relationship between the traffic bridge (CauGiaoThong) layer and the road centerline (DoanTimDuongBo), 14.22%; next is the error of duplicate data of the road network node feature class (NutMangDuongBo), at 12.11%; then the relationship between the road surface and the road boundary, at 3.52; and the error of dangles of the road centerline, at 0.97%.

Analysis of Visual Load on Curve and Longitudinal Slope Combination of Extra-Long Underwater Tunnel Based on Visual Sensitive Region

Extra-long underwater tunnels are important traffic nodes in urban road networks and have a high incidence of traffic accidents. The frequent occurrence of curve and longitudinal slope combinations in such tunnels increases crash risk and has a negative impact on traffic safety. Vision is the most important way to obtain traffic information, and visual load is a good measure of driving risk. To explore the impact of curve and longitudinal slope combinations on a driver’s visual load in an extra-long underwater tunnel, eye-movement data and speed data were obtained through a vehicle experiment. Based on driver gaze distribution, the area and location of the visual sensitive region are calculated, and a model of the relationship between the region’s stability and the curve and longitudinal slope at different speeds is established, and applied to the visual-load evaluation. The results show that the curve or longitudinal slope has a significant influence on vehicle speed. High-visual-load areas are mainly located in small-radius curve and large longitudinal slope combination areas and extremely small-radius curve areas. For example, for a combination of left-turn curve and longitudinal slope with a speed of 50–60 km/h, when the curve radius is less than 1150 m and the longitudinal slope is less than −2.4%, or the curve radius is less than 1100 m and the longitudinal slope is greater than 2.2%, the visual load is high and driving safety is poor. This study can provide a theoretical basis for tunnel alignment design and safe operation.

Spatial-Temporal Position-Aware Graph Convolution Networks for Traffic Flow Forecasting

Recent works demonstrate that capturing correlations between road network nodes is crucial to improving traffic flow forecasting accuracy. In general, there are spatial, temporal, and joint spatial-temporal correlations between two nodes, whose strength is related to spatial and temporal position factors. For example, traffic congestion that occurs at a traffic hub has a wider and stronger impact than that at a branch road. Moreover, the above impacts can vary with temporal position. Although spatial-temporal graph convolution networks have become a popular paradigm for modeling those correlations, there are still three problems with existing models: (i) failing to effectively model joint spatial-temporal correlations; (ii) ignoring spatial and temporal position factors when modeling the aforementioned correlations; and (iii) failing to capture distinct spatial-temporal patterns of each node. To cope with the above issues, this paper proposes a novel Spatial-Temporal Position-aware Graph Convolution Network (STPGCN) for traffic flow forecasting. Specifically, a trainable embedding module is constructed to represent the spatial and temporal positions of the nodes. Subsequently, a spatial-temporal position-aware relation inference module is proposed to adaptively infer the correlation weights of the three important spatial-temporal relations. Based on this, the generated spatial-temporal relations are integrated into a graph convolution layer for aggregating and updating node features. Finally, we design a spatial-temporal position-aware gated activation unit in the graph convolution, to capture the node-specific pattern features under the guidance of position embedding. Extensive experiments on six real-world datasets demonstrate the superiority of our model in terms of prediction performance and computational efficiency.

Spatial–Temporal Dynamic Graph Differential Equation Network for Traffic Flow Forecasting

Traffic flow forecasting is the foundation of intelligent transportation systems. Accurate traffic forecasting is crucial for intelligent traffic management and urban development. However, achieving highly accurate traffic flow prediction is challenging due to road networks’ complex dynamic spatial and temporal dependencies. Previous work using predefined static adjacency matrices in graph convolutional networks needs to be revised to reflect the dynamic spatial dependencies in the traffic system. In addition, most current methods ignore the hidden dynamic spatial–temporal correlations between road network nodes as they evolve. We propose a spatial–temporal dynamic graph differential equation network (ST-DGDE) for traffic prediction to address the above problems. First, the model captures the dynamic changes between spatial nodes over time through a dynamic graph learning network. Then, dynamic graph differential equations (DGDE) are used to learn the spatial–temporal dynamic relationships in the global space that change continuously over time. Finally, static adjacency matrices are constructed by static node embedding. The generated dynamic and predefined static graphs are fused and input into a gated temporal causal convolutional network to jointly capture the fixed long-term spatial association patterns and achieve a global receiver domain that facilitates long-term prediction. Experiments of our model on two natural traffic flow datasets show that ST-DGDE outperforms other baselines.

Construction and optimization of the Saihanba ecological network

Since its establishment, the ecosystem of the Saihanba Mechanical Forest Farm has begun to be restored. The extraction of ecological corridors and identification of ecological nodes based on the minimum cumulative resistance model and the gravity model are important for improving regional ecological connectivity and maintaining ecological service functions. This study took the Saihanba Mechanical Forest Farm as the study area. Based on the landscape pattern index analysis of Fragstats and the ecological sensitivity analysis of the minimum resistance surface, the ecological source area was selected according to the patch areas, and the ecological corridors and ecological nodes in the study area were extracted. We combined the road network and ecological nodes to build stepping stones for ecological node construction to promote biological migration, improved forest landscape patch structure through important ecological corridors, and finally built a woodland network structure. The results show that the areas with high ecological sensitivity were concentrated in the road network, water area, and construction land area. Additionally, we identified 20 ecological sources distributed in the central and north–south vertical areas of the Saihanba Forest Farm. We also extracted ninety-six key ecological corridors distributed in the central area, thirty-nine important ecological nodes, and thirteen stepping stones for construction. Through the adjustment of patches along the corridor, the overall connectivity significantly increased. This study can provide a reasonable reference for the optimization of forest landscape in the Saihanba Mechanical Forest Farm.

Optimization of vehicle automatic navigation path based on remote sensing and GIS

ObjectiveTaking a general view of the continuous convenience of modern social life, according to the rapid development of traffic network and communication network, this paper studies the vehicle path optimization problem. MethodsThis paper attempts to design a set of optimal vehicle path from the five aspects of algorithm development stage, algorithm solution idea, data storage mode, analysis data format and algorithm search strategy. According to the relevant theory of urban vehicle path, a mathematical model of vehicle path problem with time window restriction is constructed. Aiming at this model, a*, hierarchical algorithm, ant colony algorithm and genetic algorithm are used Line analysis and discussion, and according to the relevant algorithm examples in the literature to verify the model and algorithm. ResultIt is concluded that the combination of a*, ant colony algorithm and genetic algorithm is more suitable for the optimization of vehicle routing, and the hierarchical algorithm, road quality, congestion and other real-time traffic obstacles are improved to establish the vehicle routing optimization system in the traffic network and communication network. During the experiment, five road networks of different sizes were used successively. The modulus of these road networks increased sequentially, and the ratio of the number of road sections to the number of road network nodes was about 1698. And the conclusion drawn through experimental verification and algorithm verification proves the reliability and effectiveness of the system, has certain application value, and promotes the development of society.

A Grade Identification Method of Critical Node in Urban Road Network Based on Multi-Attribute Evaluation Correction

Accurately identifying the key nodes of the road network and focusing on its management and control is an important means to improve the robustness and invulnerability of the road network. In this paper, a classification and identification method of key nodes in urban road networks based on multi-attribute evaluation and modification was proposed. Firstly, the emergency function guarantee grade of road network nodes was divided by comprehensively considering the importance of road network nodes, the consequences of failure, and the degree of difficulty of recovery. The evaluation indexes were selected according to the local attributes, global attributes, and functional attributes of the road network topology. The spatial distribution patterns of the evaluation indexes of the nodes were analyzed. The dynamic classification method was used to cluster the attributes of the road network nodes, and the TOPSIS method was used to comprehensively evaluate the importance ranking of the road network nodes. Attribute clustering of road network nodes by dynamic classification method (DT) and the TOPSIS method was used to comprehensively evaluate the ranking of the importance of road network nodes. Then, combined with the modification of the comprehensive evaluation and ranking of the importance of the road network nodes, the emergency function support classification results of the road network nodes were obtained. Finally, the method was applied to the road network within the second Ring Road of Beijing. It was compared with the clustering method of self-organizing competitive neural networks. The results show that this method can identify the key nodes of the road network more accurately. The first-grade key nodes are all located at the more important intersections on expressways and trunk roads. The spatial distribution pattern shows a “center-edge” pattern, and the important traffic corridors of the road network show a “five vertical and five horizontal” pattern.

Comparing Methods for Measuring Walkability

Walkability analyses have gained increased attention for economic, environmental and health reasons, but the methods for assessing walkability have yet to be broadly evaluated. In this paper, five methods for calculating walkability scores are described: in-radius, circle buffers, road network node buffers, road network edge buffers and a fully integrated network approach. Unweighted and various weighted versions are analyzed to capture levels of preference for walking longer distances. The methods are evaluated via an application to train stations in central Tokyo in terms of accuracy, similarity and algorithm performance. The fully integrated network method produces the most accurate results in the shortest amount of processing time, but requires a large upfront investment of time and resources. The circle buffer method runs a bit slower, but does not require any network information and when properly weighted yields walkability scores very similar to the integrated network approach.

Mining Travel Time of Airport Ferry Network Based on Historical Trajectory Data

An airport ferry vehicle is a ground service vehicle used to transfer passengers between the far apron and the terminal. The travel time of ferry tasks in the airport ferry network is an important decision-making basis for ferry vehicle scheduling. This paper presents a graph-based method to mine the travel time between nodes in the airport ferry network. Firstly, combined with map and trajectory information, the method takes the terminal boarding gates, parking lots, and remote stands as road network nodes to build a complete airport ferry road network. Then, this paper uses big data processing technology to identify the travel time between regional connection nodes by data fusion through the temporal and spatial relationship between flight schedule and ferry vehicle GPS travel trajectory. Finally, the Floyd shortest path algorithm in graph theory is used to obtain the shortest path and travel time of all OD points. The experimental results show that all the ferry times calculated by the method proposed in this paper can better reflect the actual driving situation. This method saves the manpower, material resources, and time cost of on-site investigation and lays a foundation for the scheduling of ferry vehicles.

Laplacian integration of graph convolutional network with tensor completion for traffic prediction with missing data in inter-city highway network

Traffic prediction on a large-scale road network is of great importance to various applications. However, many factors such as sensor failure and communication errors inevitably resulted in a sparse distribution of effective detection points with missing data, which resulting adversely affects the accuracy of traffic prediction.This study considers the bidirectional connectivity of road networks to construct a two-way network graph topology. Based on the graph representation, the tensor combined temporal similarity revisited graph convolutional gate recurrent unit (T-TRGCGR), ingeniously combining traffic prediction and data completion through the Graph Laplace, is proposed to predict traffic states under partially input data missing circumstances and sparse detector distribution for a large-scale freeway network. Additionally, the proposed model can not only be applicable to traffic data prediction with missing values but also adaptively extract the spatio-temporal characteristics from various traffic periodicities while retaining the topological information of the large-scale network. Experiments on a large intercity network in Jiangsu, China shows that the proposed method outperforms state-of-art baselines on real-world traffic dataset, which can be well adapted to the prediction task of sparse coverage of road network detectors with missing data. Furthermore, through the comprehensive analysis and visualization of model parameters and results, it can be seen that the model adequately identifies the influential road network nodes and automatically learns to determine the importance of past traffic flow.

Analysis of different risk models for the hazardous materials vehicle routing problem in urban areas

The distribution of hazardous materials in urban areas is an important tactical and strategic decision problem. Reducing the transportation risk and increasing the traffic safety is also an important indicator for a sustainable transportation. For the last four decades, a multitude of approaches for risk assessment and modelling have been proposed. However, the majority of these approaches are focusing on different adaptations of shortest path problems. Risk modelling for routing and distributions problems, like the vehicle routing problem (VRP) have received far less attention, especially when the vehicle load is varying during a trip. This paper investigates how different factors are influencing the risk values and the outcomes of the hazardous materials vehicle routing problem (HMVRP). As foundation for the HMVRP, the path generation between the customer and the depot nodes in realistic urban road networks is considered. The influence of the fleet size is investigated. Different risk models are compared, with a special focus on the comparison of classical load-independent risk models with load-dependent risk models. Additionally, the tradeoff between risk minimization and the minimization of travelled distance is investigated. The results show, that these factors can have strong impact on the outcomes and lead to different “optimal” solutions.

ОПТИМИЗАЦИЯ УПРАВЛЕНИЯ ГОРОДСКИМИ СВЕТОФОРАМИ С ПРИМЕНЕНИЕМ НЕЙРОННЫХ СЕТЕЙ

One of the most pressing problems of large cities is the problem of traffic management of vehicles. The reason for this problem is an imperfect way to manage traffic flows. Traffic light regulation is of particular importance in traffic management. Most modern traffic light control systems operate at set time intervals and are not able to cope with the constantly changing situation on the road. A promising direction for solving this problem is to optimize the system using artificial neural networks. The advantage of neural networks is self-learning, which allows the system to adapt to the changing situation on the road. Despite numerous attempts, it has not yet been possible to obtain a high-quality mathematical model of urban traffic management. This model should determine the functional dependence of transport flow parameters on control parameters. Nowadays, traffic flows are regulated everywhere by means of traffic lights. If we can get a fairly accurate mathematical model of traffic flows, we can determine the optimal duration of the traffic signal phases to achieve the maximum capacity of the road network node. A fairly accurate mathematical model of traffic management that works in predictive mode will display an estimate of the optimal control parameters, as well as make correct decisions in emergency situations. Well-known mathematical models of road traffic take into account only the average values of traffic flows, and not the exact number of cars on each road section at a particular time.