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Journal articles on the topic 'Trafic spatial'
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1
Pogorelov, A. V., K. R. Golovan, and M. V. Kuzyakina. "SPATIAL STRUCTURE OF INTERNET-TRAFIC CONSUMPTION IN THE MTS NETWORK IN A LARGE CITY (BASED ON KRASNODAR DATA)." Proceedings of the International conference “InterCarto/InterGIS” 1, no. 21 (January 1, 2015): 548–52. http://dx.doi.org/10.24057/2414-9179-2015-1-21-548-552.
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Liu, Shaohua, Shijun Dai, Jingkai Sun, Tianlu Mao, Junsuo Zhao, and Heng Zhang. "Multicomponent Spatial-Temporal Graph Attention Convolution Networks for Traffic Prediction with Spatially Sparse Data." Computational Intelligence and Neuroscience 2021 (December 23, 2021): 1–12. http://dx.doi.org/10.1155/2021/9134942.
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Predicting traffic data on traffic networks is essential to transportation management. It is a challenging task due to the complicated spatial-temporal dependency. The latest studies mainly focus on capturing temporal and spatial dependencies with spatially dense traffic data. However, when traffic data become spatially sparse, existing methods cannot capture sufficient spatial correlation information and thus fail to learn the temporal periodicity sufficiently. To address these issues, we propose a novel deep learning framework, Multi-component Spatial-Temporal Graph Attention Convolutional Networks (MSTGACN), for traffic prediction, and we successfully apply it to predicting traffic flow and speed with spatially sparse data. MSTGACN mainly consists of three independent components to model three types of periodic information. Each component in MSTGACN combines dilated causal convolution, graph convolution layer, and the weight-shared graph attention layer. Experimental results on three real-world traffic datasets, METR-LA, PeMS-BAY, and PeMSD7-sparse, demonstrate the superior performance of our method in the case of spatially sparse data.
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Zhang, Shen, Jinjun Tang, Hua Wang, and Yinhai Wang. "Enhancing Traffic Incident Detection by Using Spatial Point Pattern Analysis on Social Media." Transportation Research Record: Journal of the Transportation Research Board 2528, no. 1 (January 2015): 69–77. http://dx.doi.org/10.3141/2528-08.
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Expedient incident detection and understanding are important in traffic management and control. Social media as important information venues have immense value for increasing an awareness of traffic incidents. In this paper, an attempt is made to assess the potential of using harvested social media for traffic incident detection. Twitter in Seattle, Washington, was chosen as a representative sample environment for this work. A hybrid mechanism based on latent Dirichlet allocation and document clustering was proposed to model incident-level semantic information, while spatial point pattern analysis was applied to explore the spatial patterns and to assess the spatial dependence between incident-topic tweets and traffic incidents. A global Monte Carlo K-test indicated that the incident-topic tweets were significantly clustered at different scales up to 600 m. The nearest neighbor clutter removal method was used to separate feature tweet points from clutter; then a density-based algorithm successfully detected the clusters of tweets posted spatially close to traffic incidents. In multivariate spatial point pattern analysis, K-cross functions were investigated with Monte Carlo simulation to characterize and model the spatial dependence, and a positive spatial correlation was inferred between incident-topic tweets and traffic incidents up to 800 m. Finally, the tweet intensity as a function of distance from the nearest traffic incident was estimated, and a log-linear model was summarized. The experiments supported the notion that social media feeds acted as sensors, which allowed enhancing awareness of traffic incidents and their potential disturbances.
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Tanner, John. "Urban spatial traffic patterns." Transportation Research Part A: General 24, no. 5 (September 1990): 397–98. http://dx.doi.org/10.1016/0191-2607(90)90052-8.
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Li, Tian, Mengmeng Zhang, Haobin Jiang, and Peng Jing. "Understanding the Modifiable Areal Unit Problem and Identifying Appropriate Spatial Units while Studying the Influence of the Built Environment on the Traffic System State." Journal of Advanced Transportation 2022 (September 14, 2022): 1–11. http://dx.doi.org/10.1155/2022/8288248.
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Spatially aggregated data are prone to the effects of the modifiable areal unit problem (MAUP), which applies to built environments and traffic data. Although various studies have been carried out to explore the impact of built environment factors on traffic systems, few have considered MAUPs, which may result in statistical inconsistency. The purpose of this study is to assess the effects of MAUPs on statistical variables and geographically weighted regression results when evaluating the influence of the built environment on the traffic system state. Fifty sets of spatial configurations were created using the different aggregation criteria. The variance inflation factor and spatial autocorrelation of the variables, as well as the R2 and root mean squared error of the GWR model, were used to assess the MAUP effect. The results show that the index variation is more dependent on the scale of the spatial unit than on zoning type. In the case study presented, based on the available dataset, the optimal spatial unit size for analyzing the influence of the built environment on Jinan’s traffic system was 900 m × 900 m.
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Liao, Wanying, Hongtao Wang, and Jiajun Xu. "The Spatial Structure Characteristic and Road Traffic Accessibility Evaluation of A-Level Tourist Attractions within Wuhan Urban Agglomeration in China." 3C Tecnología_Glosas de innovación aplicadas a la pyme 12, no. 2 (June 25, 2023): 388–409. http://dx.doi.org/10.17993/3ctecno.2023.v12n3e45.388-409.
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Against the backdrop of the post-pandemic COVID-19, regional short-distance tourism has become more prevalent. This paper used Wuhan Urban Agglomeration (WUA) as the research area and explored spatial structure characteristics and road traffic accessibility issues of A-level tourist attractions within WUA. The geospatial analysis methods of Average Nearest Neighbour (ANN) and Kernel Density Estimation (KDE) were used to identify the spatial structure distribution of A-level tourist attractions. Constructing Weighted Network Analysis to measure the traffic access time between tourist attractions and traveler origin and further using Network Analysis to measure the traffic access time between different tourist attractions. The traffic access time results were spatially visualized using Inverse Distance Weight (IDW). The study results were as follows. (1) The spatial structure of A-level tourist attractions in WUA indicated a core-periphery distribution in general. All tourist attractions showed clustering characteristics of the spatial distribution pattern. The spatial clustering degree was highest for human tourist attractions and lowest for nature tourist attractions. (2) Traffic access time results exhibited significant centrality with Wuhan as the core and regional differences in WUA. The road traffic accessibility of human tourist attractions was better than that of natural tourist attractions. (3) The spatial distribution and road traffic accessibility of tourist attractions in WUA indicated a circle structure centered on Wuhan, which aligned with the general rule of regional development. The accessibility of the north-south direction was weaker than the east- west direction in WUA. (4) Human tourist attractions were mainly concentrated in urban areas with high connectivity and intensive road networks. But natural tourist attractions were separated from traveler origin and other different tourist attractions. Most were in mountainous and hilly areas with poor accessibility, which could attract more tourists with better road networks and traffic infrastructure.
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YAMAGUCHI, Hiromichi, and Makoto OKUMURA. "1C33 Temporal and Spatial Differences of Leisure Travel Frequency Distribution in Japan(Traffic Planning)." Proceedings of International Symposium on Seed-up and Service Technology for Railway and Maglev Systems : STECH 2015 (2015): _1C33–1_—_1C33–12_. http://dx.doi.org/10.1299/jsmestech.2015._1c33-1_.
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Braxmeier, Hans, Volker Schmidt, and Evgueni Spodarev. "SPATIAL EXTRAPOLATION OF ANISOTROPIC ROAD TRAFFIC DATA." Image Analysis & Stereology 23, no. 3 (May 3, 2011): 185. http://dx.doi.org/10.5566/ias.v23.p185-198.
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A method of spatial extrapolation of traffic data is proposed. The traffic data is given by GPS signals over downtown Berlin sent by approximately 300 taxis. To reconstruct the traffic situation at a given time spatially, i.e., in the form of traffic maps, kriging with moving neighborhood based on residuals is used. Due to significant anisotropy in directed traffic data, the classical kriging has to be modified in order to include additional information. To verify the extrapolation results, test examples on the basis of a well-known model of stochastic geometry, the Boolean random function are considered.
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Pavlyuk, Dmitry. "Temporal Aggregation Effects in Spatiotemporal Traffic Modelling." Sensors 20, no. 23 (December 4, 2020): 6931. http://dx.doi.org/10.3390/s20236931.
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Spatiotemporal models are a popular tool for urban traffic forecasting, and their correct specification is a challenging task. Temporal aggregation of traffic sensor data series is a critical component of model specification, which determines the spatial structure and affects models’ forecasting accuracy. Through extensive experiments with real-world data, we investigated the effects of the selected temporal aggregation level for forecasting performance of different spatiotemporal model specifications. A set of analysed models include travel-time-based and correlation-based spatially restricted vector autoregressive models, compared to classical univariate and multivariate time series models. Research experiments are executed in several dimensions: temporal aggregation levels, forecasting horizons (one-step and multi-step forecasts), spatial complexity (sequential and complex spatial structures), the spatial restriction approach (unrestricted, travel-time-based and correlation-based), and series transformation (original and detrended traffic volumes). The obtained results demonstrate the crucial role of the temporal aggregation level for identification of the spatiotemporal traffic flow structure and selection of the best model specification. We conclude that the common research practice of an arbitrary selection of the temporal aggregation level could lead to incorrect conclusions on optimal model specification. Thus, we recommend extending the traffic forecasting methodology by validation of existing and newly developed model specifications for different temporal aggregation levels. Additionally, we provide empirical results on the selection of the optimal temporal aggregation level for the discussed spatiotemporal models for different forecasting horizons.
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Xiong, Liyan, Weihua Ding, Xiaohui Huang, and Weichun Huang. "CLSTAN: ConvLSTM-Based Spatiotemporal Attention Network for Traffic Flow Forecasting." Mathematical Problems in Engineering 2022 (July 11, 2022): 1–13. http://dx.doi.org/10.1155/2022/1604727.
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Traffic flow forecasting is the essential part of intelligent transportation sSystem (ITS), which can fully protect traffic safety and improve traffic system management capability. Nevertheless, it is still a challenging problem, which is influenced by many complex factors, including regional distribution and external factors (e.g., holidays and weather). To combine various factors to forecast traffic flow, we presented a novel neural network structure called ConvLSTM-based Spatiotemporal Attention Network (CLSTAN). Specifically, our proposed model is composed of four modules: a preliminary feature extraction module, a spatial attention module, a temporal attention module, and an information fusion module. The spatiotemporal attention module can efficiently learn the complex spatiotemporal patterns of traffic flow through the attention mechanism. The spatial attention module uses a series of initial traffic flow maps as input and obtains the weights of the various regions through a ConvLSTM. The temporal attention module uses the spatially weighted traffic flow map as input and acquires the complex spatiotemporal patterns of traffic flow by a ConvLSTM that introduces an attention mechanism. Finally, the information fusion module integrates spatiotemporal information from multiple time dimensions to forecast future traffic flow. Moreover, to confirm the validity of our method, our experiments were conducted extensively on the TaxiBJ and BikeNYC datasets, and ultimately, CLSTAN performed better than other baseline experiments.
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Pavlyuk. "Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting." Algorithms 13, no. 2 (February 13, 2020): 39. http://dx.doi.org/10.3390/a13020039.
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Transfer learning is a modern concept that focuses on the application of ideas, models, and algorithms, developed in one applied area, for solving a similar problem in another area. In this paper, we identify links between methodologies in two fields: video prediction and spatiotemporal traffic forecasting. The similarities of the video stream and citywide traffic data structures are discovered and analogues between historical development and modern states of the methodologies are presented and discussed. The idea of transferring video prediction models to the urban traffic forecasting domain is validated using a large real-world traffic data set. The list of transferred techniques includes spatial filtering by predefined kernels in combination with time series models and spectral graph convolutional artificial neural networks. The obtained models’ forecasting performance is compared to the baseline traffic forecasting models: non-spatial time series models and spatially regularized vector autoregression models. We conclude that the application of video prediction models and algorithms for urban traffic forecasting is effective both in terms of observed forecasting accuracy and development, and training efforts. Finally, we discuss problems and obstacles of transferring methodologies and present potential directions for further research.
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Feng, Jian, Lang Yu, and Rui Ma. "AGCN-T: A Traffic Flow Prediction Model for Spatial-Temporal Network Dynamics." Journal of Advanced Transportation 2022 (May 29, 2022): 1–12. http://dx.doi.org/10.1155/2022/1217588.
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Traffic prediction is the key for Intelligent Transport Systems (ITS) to achieve traffic control and traffic guidance, and the key challenge is that traffic flow has complex spatial-temporal dependence and nonlinear dynamics. Aiming at the lack of the ability to model complex and dynamic spatial-temporal dependencies in current research, this paper proposes a traffic flow prediction model Attention based Graph Convolution Network (GCN) and Transformer (AGCN-T) to model spatial-temporal network dynamics of traffic flow, which can extract dynamic spatial dependence and long-distance temporal dependence to improve the accuracy of multistep traffic prediction. AGCN-T consists of three modules. In the spatial dependency extraction module, according to the similarity of historical traffic flow sequences of different loop detectors, an adjacency matrix for the road network is constructed based on a sequence similarity calculation method, Predictive Power Score (PPS), to express latent spatial dependency; and then GCN is used on the adjacency matrix to capture the global spatial correlation and Transformer is used to capture dynamic spatial dependency from the most recently flow sequences. And then, the dynamic spatial dependency is merged with the global spatial correlation to obtain the overall spatial dependency pattern. In the temporal dependency extraction module, the temporal dependency pattern of each traffic flow sequence is learned by the temporal Transformer. The prediction module integrates both patterns to form spatial-temporal dependency patterns and performs multistep traffic flow prediction. Four sets of experiments are performed on three actual traffic datasets to show that AGCN-T can effectively capture the dynamic spatial-temporal dependency of the traffic network, and its prediction performance and efficiency are better than existing baselines. AGCN-T can effectively capture the dynamics in traffic flow. In addition to traffic flow prediction, it can also be applied to other spatial-temporal prediction tasks, such as passenger demand prediction and crowd flow prediction.
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Tassadit Dial, Rania, and Gabriel Figueiredo De Oliveira. "Accessibilité à l’arrière-pays, connectivité maritime et relations interportuaires : une analyse spatiale." Revue d’Économie Régionale & Urbaine Octobre, no. 4 (October 19, 2023): 579–607. http://dx.doi.org/10.3917/reru.234.0579.
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L'objectif de cette étude est de comprendre comment les effets spatiaux influencent le trafic conteneurisé des ports européens. Nous utilisons différentes méthodes d’estimation spatiale sur un échantillon de 123 ports européens sur la période 2005-2019. Les principaux résultats sont : (i) l'existence d'effets d'autocorrélation spatiale positifs et significatifs ; (ii) l’augmentation du PIB par habitant de la région impacte positivement les flux de conteneurs sur les terminaux portuaires ; (iii) la hausse du nombre de liaisons maritimes de 10 % pourrait conduire à augmenter le débit de conteneurs d’environ 4 % ; (iv) l’augmentation d'un écart-type de l’indice de spécialisation en hydrocarbures permettrait de diminuer le trafic conteneurisé d’environ 42 %. Ces résultats confirment l’importance pour les autorités portuaires de renforcer la coordination et la coopération entre les ports de la même région.
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Kumar, Dr T. Senthil. "Video based Traffic Forecasting using Convolution Neural Network Model and Transfer Learning Techniques." Journal of Innovative Image Processing 2, no. 3 (June 17, 2020): 128–34. http://dx.doi.org/10.36548/jiip.2020.3.002.
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The ideas, algorithms and models developed for application in one particular domain can be applied for solving similar issues in a different domain using the modern concept termed as transfer learning. The connection between spatiotemporal forecasting of traffic and video prediction is identified in this paper. With the developments in technology, traffic signals are replaced with smart systems and video streaming for analysis and maintenance of the traffic all over the city. Processing of these video streams requires lot of effort due to the amount of data that is generated. This paper proposed a simplified technique for processing such voluminous data. The large data set of real-world traffic is used for prediction and forecasting the urban traffic. A combination of predefined kernels are used for spatial filtering and several such transferred techniques in combination will convolutional artificial neural networks that use spectral graphs and time series models. Spatially regularized vector autoregression models and non‐spatial time series models are the baseline traffic forecasting models that are compared for forecasting the performance. In terms of training efforts, development as well as forecasting accuracy, the efficiency of urban traffic forecasting is high on implementation of video prediction algorithms and models. Further, the potential research directions are presented along the obstacles and problems in transferring schemes.
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Gao, Jingqin, Kun Xie, and Kaan Ozbay. "Exploring the Spatial Dependence and Selection Bias of Double Parking Citations Data." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 42 (August 18, 2018): 159–69. http://dx.doi.org/10.1177/0361198118792323.
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Parking violation citations, often used to identify factors contributing to parking violation behavior, offer one of the most valuable datasets for traffic operation research. However, little has been done to examine their spatial dependence caused by location-specific differences in features such as traffic, land use, etc., and potential selection biases resulting from different effects of traffic enforcement. This study leveraged extensive data on double parking citations in Manhattan, New York City, in 2015, along with other relevant datasets including land use, transportation, and sociodemographic features. Moran’s I statistics confirmed that double parking tickets were spatially correlated so that spatial lag and spatial error models were proposed to account for the spatial dependence of parking tickets to avoid biased estimates. To investigate whether selection bias exists in issuing tickets, we estimated the effects of parking ticket density and police precinct distance, when controlling for variables such as commercial area, truck activity, taxi demand, population, hotels, and restaurants. Parking ticket density and police precinct distance were used as indicators of the enforcement levels and coverage and were found to be statistically significant. This indicated the existence of selection bias due to heterogeneity in enforcement levels or coverage across different regions. Moreover, patrol patterns of traffic enforcement officers revealed that the majority had less than three daily patterns. These findings can assist with proper usage of the citation data by recommending that researchers and agencies consider spatial dependence as well as selection bias, and provide insights for parking violation management strategies.
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Košanin, Ivan, Milan Gnjatović, Nemanja Maček, and Dušan Joksimović. "A Clustering-Based Approach to Detecting Critical Traffic Road Segments in Urban Areas." Axioms 12, no. 6 (May 24, 2023): 509. http://dx.doi.org/10.3390/axioms12060509.
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This paper introduces a parameter-free clustering-based approach to detecting critical traffic road segments in urban areas, i.e., road segments of spatially prolonged and high traffic accident risk. In addition, it proposes a novel domain-specific criterion for evaluating the clustering results, which promotes the stability of the clustering results through time and inter-period accident spatial collocation, and penalizes the size of the selected clusters. To illustrate the proposed approach, it is applied to data on traffic accidents with injuries or death that occurred in three of the largest cities of Serbia over the three-year period.
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Abduljabbar, Rusul, Hussein Dia, Pei-Wei Tsai, and Sohani Liyanage. "Short-Term Traffic Forecasting: An LSTM Network for Spatial-Temporal Speed Prediction." Future Transportation 1, no. 1 (March 30, 2021): 21–37. http://dx.doi.org/10.3390/futuretransp1010003.
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Traffic forecasting remains an active area of research in the transport and data science fields. Decision-makers rely on traffic forecasting models for both policy-making and operational management of transport facilities. The wealth of spatial and temporal real-time data increasingly available from traffic sensors on roads provides a valuable source of information for policymakers. This paper adopts the Long Short-Term Memory (LSTM) recurrent neural network to predict speed by considering both the spatial and temporal characteristics of real-time sensor data. A total of 288,653 real-life traffic measurements were collected from detector stations on the Eastern Freeway in Melbourne/Australia. A comparative performance analysis among different models such as the Recurrent Neural Network (RNN) that has an internal memory that is able to remember its inputs and Deep Learning Backpropagation (DLBP) neural network approaches are also reported. The LSTM results showed average accuracies in the outbound direction ranging between 88 and 99 percent over prediction horizons between 5 and 60 min, and average accuracies between 96 and 98 percent in the inbound direction. The models also showed resilience in accuracies as the prediction horizons increased spatially for distances up to 15 km, providing a remarkable performance compared to other models tested. These results demonstrate the superior performance of LSTM models in capturing the spatial and temporal traffic dynamics, providing decision-makers with robust models to plan and manage transport facilities more effectively.
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Chang, Zhihong, Chunsheng Liu, and Jianmin Jia. "STA-GCN: Spatial-Temporal Self-Attention Graph Convolutional Networks for Traffic-Flow Prediction." Applied Sciences 13, no. 11 (June 2, 2023): 6796. http://dx.doi.org/10.3390/app13116796.
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As an important component of intelligent transportation-management systems, accurate traffic-parameter prediction can help traffic-management departments to conduct effective traffic management. Due to the nonlinearity, complexity, and dynamism of highway-traffic data, traffic-flow prediction is still a challenging issue. Currently, most spatial–temporal traffic-flow-prediction models adopt fixed-structure time convolutional and graph convolutional models, which lack the ability to capture the dynamic characteristics of traffic flow. To address this issue, this paper proposes a spatial–temporal prediction model that can capture the dynamic spatial–temporal characteristics of traffic flow, named the spatial–temporal self-attention graph convolutional network (STA-GCN). In terms of feature engineering, we used the time cosine decomposition and one-hot encoding methods to capture the periodicity and heterogeneity of traffic-flow changes. Additionally, in order to build the model, self-attention mechanisms were incorporated into the spatial–temporal convolution to capture the spatial–temporal dynamic characteristics of traffic flow. The experimental results indicate that the performance of the proposed model on two traffic-volume datasets is superior to those of several baseline models. In particular, in long-term prediction, the prediction error can be reduced by over 5%. Further, the interpretability and robustness of the prediction model are addressed by considering the spatial dynamic changes.
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Huang, Xiaohui, Yuanchun Lan, Yuming Ye, Junyang Wang, and Yuan Jiang. "Traffic Flow Prediction Based on Multi-Mode Spatial-Temporal Convolution of Mixed Hop Diffuse ODE." Electronics 11, no. 19 (September 22, 2022): 3012. http://dx.doi.org/10.3390/electronics11193012.
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In recent years, traffic flow forecasting has attracted the great attention of many researchers with increasing traffic congestion in metropolises. As a hot topic in the field of intelligent city computing, traffic flow forecasting plays a vital role, since predicting the changes in traffic flow can timely alleviate traffic congestion and reduce the occurrence of accidents by vehicle scheduling. The most difficult challenges of traffic flow prediction are the temporal feature extraction and the spatial correlation extraction of nodes. At the same time, graph neural networks (GNNs) show an excellent ability in dealing with spatial dependence. Existing works typically make use of graph neural networks (GNNs) and temporal convolutional networks (TCNs) to model spatial and temporal dependencies respectively. However, how to extract as much valid information as possible from nodes is a challenge for GNNs. Therefore, we propose a multi-mode spatial-temporal convolution of mixed hop diffuse ODE (MHODE) for modeling traffic flow prediction. First, we design an adaptive spatial-temporal convolution module that combines Gate TCN and graph convolution to capture more short-term spatial-temporal dependencies and features. Secondly, we design a mixed hop diffuse ordinary differential equation(ODE) spatial-temporal convolution module to capture long-term spatial-temporal dependencies using the receptive field of the mixed hop diffuse ODE. Finally, we design a multi spatial-temporal fusion module to integrate the different spatial-temporal dependencies extracted from two different spatial-temporal convolutions. To capture more spatial-temporal features of traffic flow, we use the multi-mode spatial-temporal fusion module to get more abundant traffic features by considering short-term and long-term two different features. The experimental results on two public traffic datasets (METR-LA and PEMS-BAY) demonstrate that our proposed algorithm performs better than the existing methods in most of cases.
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Ge, Liang, Siyu Li, Yaqian Wang, Feng Chang, and Kunyan Wu. "Global Spatial-Temporal Graph Convolutional Network for Urban Traffic Speed Prediction." Applied Sciences 10, no. 4 (February 22, 2020): 1509. http://dx.doi.org/10.3390/app10041509.
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Traffic speed prediction plays a significant role in the intelligent traffic system (ITS). However, due to the complex spatial-temporal correlations of traffic data, it is very challenging to predict traffic speed timely and accurately. The traffic speed renders not only short-term neighboring and multiple long-term periodic dependencies in the temporal dimension but also local and global dependencies in the spatial dimension. To address this problem, we propose a novel deep-learning-based model, Global Spatial-Temporal Graph Convolutional Network (GSTGCN), for urban traffic speed prediction. The model consists of three spatial-temporal components with the same structure and an external component. The three spatial-temporal components are used to model the recent, daily-periodic, and weekly-periodic spatial-temporal correlations of the traffic data, respectively. More specifically, each spatial-temporal component consists of a dynamic temporal module and a global correlated spatial module. The former contains multiple residual blocks which are stacked by dilated casual convolutions, while the latter contains a localized graph convolution and a global correlated mechanism. The external component is used to extract the effect of external factors, such as holidays and weather conditions, on the traffic speed. Experimental results on two real-world traffic datasets have demonstrated that the proposed GSTGCN outperforms the state-of-the-art baselines.
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Goścień, Róża. "On the Efficient Flow Restoration in Spectrally-Spatially Flexible Optical Networks." Electronics 10, no. 12 (June 18, 2021): 1468. http://dx.doi.org/10.3390/electronics10121468.
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We focus on the efficient modeling and optimization of the flow restoration in the spectrally-spatially flexible optical networks (SS-FONs) realized using a single mode fiber bundle. To this end, we study a two-phase optimization problem, which consists of: (i) the network planning with respect to the spectrum usage and (ii) the flow restoration after a failure aiming at maximizing the restored bit-rate. Both subproblems we formulate using the integer linear programming with two modeling approaches—the node-link and the link-path. We perform simulations divided into: (i) a comparison of the proposed approaches, (ii) an efficient flow restoration in SS-FONs—case study. The case study focuses on the verification whether the spectral-spatial allocation may improve the restoration process (compared to the spectral allocation) and on the determination of the full restoration cost (the amount of additional resources required to restore whole traffic) in two network topologies. The results show that the spectral-spatial allocation allows us to restore up to 4% more traffic compared to the restoration with only the spectral channels. They also reveal that the cost of the full traffic restoration depends on plenty of factors, including the overall traffic volume and the network size, while the spectral-spatial allocation allows us to reduce its value about 30%.
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Jiang, Jiawei, Chengkai Han, Wayne Xin Zhao, and Jingyuan Wang. "PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (June 26, 2023): 4365–73. http://dx.doi.org/10.1609/aaai.v37i4.25556.
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As a core technology of Intelligent Transportation System, traffic flow prediction has a wide range of applications. The fundamental challenge in traffic flow prediction is to effectively model the complex spatial-temporal dependencies in traffic data. Spatial-temporal Graph Neural Network (GNN) models have emerged as one of the most promising methods to solve this problem. However, GNN-based models have three major limitations for traffic prediction: i) Most methods model spatial dependencies in a static manner, which limits the ability to learn dynamic urban traffic patterns; ii) Most methods only consider short-range spatial information and are unable to capture long-range spatial dependencies; iii) These methods ignore the fact that the propagation of traffic conditions between locations has a time delay in traffic systems. To this end, we propose a novel Propagation Delay-aware dynamic long-range transFormer, namely PDFormer, for accurate traffic flow prediction. Specifically, we design a spatial self-attention module to capture the dynamic spatial dependencies. Then, two graph masking matrices are introduced to highlight spatial dependencies from short- and long-range views. Moreover, a traffic delay-aware feature transformation module is proposed to empower PDFormer with the capability of explicitly modeling the time delay of spatial information propagation. Extensive experimental results on six real-world public traffic datasets show that our method can not only achieve state-of-the-art performance but also exhibit competitive computational efficiency. Moreover, we visualize the learned spatial-temporal attention map to make our model highly interpretable.
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Barthelemy, Marc, Bernard Gondran, and Eric Guichard. "Spatial structure of the internet traffic." Physica A: Statistical Mechanics and its Applications 319 (March 2003): 633–42. http://dx.doi.org/10.1016/s0378-4371(02)01382-1.
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Du, Wen-Bo, Xing-Lian Zhou, Zhen Chen, Kai-Quan Cai, and Xian-Bin Cao. "Traffic dynamics on coupled spatial networks." Chaos, Solitons & Fractals 68 (November 2014): 72–77. http://dx.doi.org/10.1016/j.chaos.2014.07.009.
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Newell, Gordon F. "Comments on spatial models of traffic." Transportation Research Part B: Methodological 27, no. 3 (June 1993): 185–88. http://dx.doi.org/10.1016/0191-2615(93)90028-9.
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YALÇIN, Güler. "SPATIAL ANALYSIS OF THE TRAFFIC ACCIDENTS FOR URBAN TRAFFIC MANAGEMENT." INTERNATIONAL REFEREED JOURNAL OF ENGINEERING AND SCIENCES 2, no. 3 (April 30, 2015): 1. http://dx.doi.org/10.17366/uhmfd.2015310571.
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Xiao, Tianzheng, Huapu Lu, Jianyu Wang, and Katrina Wang. "Predicting and Interpreting Spatial Accidents through MDLSTM." International Journal of Environmental Research and Public Health 18, no. 4 (February 3, 2021): 1430. http://dx.doi.org/10.3390/ijerph18041430.
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Predicting and interpreting the spatial location and causes of traffic accidents is one of the current hot topics in traffic safety. This research purposed a multi-dimensional long-short term memory neural network model (MDLSTM) to fit the non-linear relationships between traffic accident characteristics and land use properties, which are further interpreted to form local and general rules. More variables are taken into account as the input land use properties and the output traffic accident characteristics. Five types of traffic accident characteristics are simultaneously predicted with higher accuracy, and three levels of interpretation, including the hidden factor-traffic potential, the potential-determine factors, which varies between grid cells, and the general rules across the whole study area are analyzed. Based on the model, some interesting insights were revealed including the division line in the potential traffic accidents in Shenyang (China). It is also purposed that the relationship between land use and accidents differ from previous researches in the neighboring and regional aspects. Neighboring grids have strong spatial connections so that the relationship of accidents in a continuous area is relatively similar. In a larger region, the spatial location is found to have a great influence on the traffic accident and has a strong directionality.
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Yang, Yanfang, Jiandong Cao, Yong Qin, Limin Jia, Honghui Dong, and Aomuhan Zhang. "Spatial correlation analysis of urban traffic state under a perspective of community detection." International Journal of Modern Physics B 32, no. 12 (May 3, 2018): 1850150. http://dx.doi.org/10.1142/s0217979218501503.
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Understanding the spatial correlation of urban traffic state is essential for identifying the evolution patterns of urban traffic state. However, the distribution of traffic state always has characteristics of large spatial span and heterogeneity. This paper adapts the concept of community detection to the correlation network of urban traffic state and proposes a new perspective to identify the spatial correlation patterns of traffic state. In the proposed urban traffic network, the nodes represent road segments, and an edge between a pair of nodes is added depending on the result of significance test for the corresponding correlation of traffic state. Further, the process of community detection in the urban traffic network (named GWPA-K-means) is applied to analyze the spatial dependency of traffic state. The proposed method extends the traditional K-means algorithm in two steps: (i) redefines the initial cluster centers by two properties of nodes (the GWPA value and the minimum shortest path length); (ii) utilizes the weight signal propagation process to transfer the topological information of the urban traffic network into a node similarity matrix. Finally, numerical experiments are conducted on a simple network and a real urban road network in Beijing. The results show that GWPA-K-means algorithm is valid in spatial correlation analysis of traffic state. The network science and community structure analysis perform well in describing the spatial heterogeneity of traffic state on a large spatial scale.
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Xu, Chengcheng, Chen Wang, Wei Wang, Jie Bao, and Menglin Yang. "Investigating Spatial Interdependence in E-Bike Choice Using Spatially Autoregressive Model." PROMET - Traffic&Transportation 29, no. 4 (August 28, 2017): 351–62. http://dx.doi.org/10.7307/ptt.v29i4.2144.
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Increased attention has been given to promoting e-bike usage in recent years. However, the research gap still exists in understanding the effects of spatial interdependence on e-bike choice. This study investigated how spatial interdependence affected the e-bike choice. The Moran’s I statistic test showed that spatial interdependence exists in e-bike choice at aggregated level. Bayesian spatial autoregressive logistic analyses were then used to investigate the spatial interdependence at individual level. Separate models were developed for commuting and non-commuting trips. The factors affecting e-bike choice are different between commuting and non-commuting trips. Spatial interdependence exists at both origin and destination sides of commuting and non-commuting trips. Travellers are more likely to choose e-bikes if their neighbours at the trip origin and destination also travel by e-bikes. And the magnitude of this spatial interdependence is different across various traffic analysis zones. The results suggest that, without considering spatial interdependence, the traditional methods may have biased estimation results and make systematic forecasting errors.
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Gao, Min, Yingmei Wei, Yuxiang Xie, and Yitong Zhang. "Traffic Prediction with Self-Supervised Learning: A Heterogeneity-Aware Model for Urban Traffic Flow Prediction Based on Self-Supervised Learning." Mathematics 12, no. 9 (April 24, 2024): 1290. http://dx.doi.org/10.3390/math12091290.
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Accurate traffic prediction is pivotal when constructing intelligent cities to enhance urban mobility and to efficiently manage traffic flows. Traditional deep learning-based traffic prediction models primarily focus on capturing spatial and temporal dependencies, thus overlooking the existence of spatial and temporal heterogeneities. Heterogeneity is a crucial inherent characteristic of traffic data for the practical applications of traffic prediction. Spatial heterogeneities refer to the differences in traffic patterns across different regions, e.g., variations in traffic flow between office and commercial areas. Temporal heterogeneities refer to the changes in traffic patterns across different time steps, e.g., from morning to evening. Although existing models attempt to capture heterogeneities through predefined handcrafted features, multiple sets of parameters, and the fusion of spatial–temporal graphs, there are still some limitations. We propose a self-supervised learning-based traffic prediction framework called Traffic Prediction with Self-Supervised Learning (TPSSL) to address this issue. This framework leverages a spatial–temporal encoder for the prediction task and introduces adaptive data masking to enhance the robustness of the model against noise disturbances. Moreover, we introduce two auxiliary self-supervised learning paradigms to capture spatial heterogeneities and temporal heterogeneities, which also enrich the embeddings of the primary prediction task. We conduct experiments on four widely used traffic flow datasets, and the results demonstrate that TPSSL achieves state-of-the-art performance in traffic prediction tasks.
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Lac, C., R. P. Donnelly, V. Masson, S. Pal, S. Donier, S. Queguiner, G. Tanguy, L. Ammoura, and I. Xueref-Remy. "CO<sub>2</sub> dispersion modelling over Paris region within the CO<sub>2</sub>-MEGAPARIS project." Atmospheric Chemistry and Physics Discussions 12, no. 10 (October 25, 2012): 28155–93. http://dx.doi.org/10.5194/acpd-12-28155-2012.
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Abstract. Accurate simulation of the spatial and temporal variability of tracer mixing ratios over urban areas is challenging, but essential in order to utilize CO2 measurements in an atmospheric inverse framework to better estimate regional CO2 fluxes. This study investigates the ability of a high-resolution model to simulate meteorological and CO2 fields around Paris agglomeration, during the March field campaign of the CO2-MEGAPARIS project. The mesoscale atmospheric model Meso-NH, running at 2 km horizontal resolution, is coupled with the Town-Energy Balance (TEB) urban canopy scheme and with the Interactions between Soil, Biosphere and Atmosphere CO2-reactive (ISBA-A-gs) surface scheme, allowing a full interaction of CO2 between the surface and the atmosphere. Statistical scores show a good representation of the Urban Heat Island (UHI) and urban-rural contrasts. Boundary layer heights (BLH) at urban, sub-urban and rural sites are well captured, especially the onset time of the BLH increase and its growth rate in the morning, that are essential for tall tower CO2 observatories. Only nocturnal BLH at sub-urban sites are slightly underestimated a few nights, with a bias less than 50 m. At Eiffel tower, the observed spikes of CO2 maxima occur every morning exactly at the time at which the Atmospheric Boundary Layer (ABL) growth reaches the measurement height. The timing of the CO2 cycle is well captured by the model, with only small biases on CO2 concentrations, mainly linked to the misrepresentation of anthropogenic emissions, as the Eiffel site is at the heart of trafic emission sources. At sub-urban ground stations, CO2 measurements exhibit maxima at the beginning and at the end of each night, when the ABL is fully contracted, with a very strong spatio-temporal variability. The CO2 cycle at these sites is generally well reproduced by the model, even if some biases on the nocturnal maxima appear in the Paris plume parly due to small errors on the vertical transport, or in the vicinity of airports due to small errors on the horizontal transport (wind direction). A sensitivity test without urban parameterisation removes UHI and underpredicts nighttime BLH over urban and sub-urban sites, leading to large overestimation of nocturnal CO2 concentration at the sub-urban sites. The agreement of daytime and nighttime BLH and CO2 predictions of the reference simulation over Paris agglomeration demonstrates the potential of using the meso-scale system on urban and sub-urban area in the context of inverse modelling.
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Yi, Ran, Yang Zhou, Xin Wang, Zhiyuan Liu, Xiaotian Li, and Bin Ran. "Spatially Formulated Connected Automated Vehicle Trajectory Optimization with Infrastructure Assistance." Journal of Advanced Transportation 2022 (May 20, 2022): 1–15. http://dx.doi.org/10.1155/2022/6184790.
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This paper presents a constrained connected automated vehicles (CAVs) trajectory optimization method on curved roads with infrastructure assistance. Specifically, this paper systematically formulates trajectory optimization problems in a spatial domain and a curvilinear coordinate. As an alternative of temporal domain and Cartesian coordinate formulation, our formulation provides the constrained trajectory optimization flexibility to describe complex road geometries, traffic regulations, and road obstacles, which are usually spatially varying rather than temporal varying, with assistances vehicle to infrastructure (V2I) communication. Based on the formulation, we first conducted a mathematical proof on the controllability of our system, to show that our system can be controlled in the spatial domain and curvilinear coordinate. Further, a multiobjective model predictive control (MPC) approach is designed to optimize the trajectories in a rolling horizon fashion and satisfy the collision avoidances, traffic regulations, and vehicle kinematics constraints simultaneously. To verify the control efficiency of our method, multiscenario numerical simulations are conducted. Suggested by the results, our proposed method can provide smooth vehicular trajectories, avoid road obstacles, and simultaneously follow traffic regulations in different scenarios. Moreover, our method is robust to the spatial change of road geometries and other potential disturbances by the road curvature, work zone, and speed limit change.
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Ge, Fengjian, Wanxu Chen, Yuanyuan Zeng, and Jiangfeng Li. "The Nexus between Urbanization and Traffic Accessibility in the Middle Reaches of the Yangtze River Urban Agglomerations, China." International Journal of Environmental Research and Public Health 18, no. 7 (April 6, 2021): 3828. http://dx.doi.org/10.3390/ijerph18073828.
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China has entered the stage where urban agglomerations underpin and spearhead the county’s urbanization. Urban agglomerations in China have become economic growth poles, and the constantly improving transport networks in these agglomerations bring about opportunities for redistributing labor forces and promoting regional economic development, trade, and social progress for all. This is the foundation and fuel for urban development. However, lack of knowledge of the spatial features of, and the interrelationship between, regional urbanization and traffic accessibility constrains effective urban planning and decision-making. To fill this gap, this study attempted to evaluate the spatiotemporal distribution characteristics of urbanization levels and traffic accessibility in 1995, 2005, and 2015 in the Middle Reaches of the Yangtze River Urban Agglomerations (MRYRUA), China. The spatial interaction, spatial dependence effect, and spatial spillover effect between urbanization and traffic accessibility were tested by employing the bivariate spatial autocorrelation model and spatial regression models. The results showed that the urbanization level and traffic accessibility in the MRYRUA shot up over time and manifested similar spatial distribution characteristics. The global bivariate spatial autocorrelation coefficients were positive and significant during the period studied, and the main relationship types were the high urbanization and high traffic accessibility types and low urbanization and low traffic accessibility types. The spatial regression results showed that there was a significant positive association between urbanization and traffic accessibility, but with a significant scale effect. Urbanization is not only affected by the traffic accessibility of the individual grid unit but also by those in the adjacent or further grid units. The findings in this study provide important implications for urbanization development and transportation planning. The spatial dependence effect and spatial spillover effect between urbanization and traffic accessibility should be considered in future urban planning and transportation planning. The rational allocation of resources and inter-regional joint management can be an effective path toward regional sustainability.
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Yin, Hong Yan. "Study of Traffic Accessibility in Poyang Lake Economic Zone Oriented by High-Speed Railway." Applied Mechanics and Materials 178-181 (May 2012): 1778–81. http://dx.doi.org/10.4028/www.scientific.net/amm.178-181.1778.
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The high-speed railway framework can promote the harmonious development in the unbalanced regions. Based on construction experiences and spatial development of high-speed railway, the spatial effects were analyzed in traffic economic belt, regional balance, spatial organization reconfiguration and urban environment improvement. According to the high-speed rail plan of China, the traffic accessibility of spatial organization in Poyang lake economic zone were discussed in internal traffic accessibility and external traffic accessibility. The study results showed that the hub-and-spoke patterns of the spatial traffic organization reconfiguration in Poyang Lake agglomeration can realize the regional balance oriented by the high-speed railway. The patterns can be also applied to other underdeveloped and unbalanced regions.
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Lian, Qingyun, Wei Sun, and Wei Dong. "Hierarchical Spatial-Temporal Neural Network with Attention Mechanism for Traffic Flow Forecasting." Applied Sciences 13, no. 17 (August 28, 2023): 9729. http://dx.doi.org/10.3390/app13179729.
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Accurate traffic flow forecasting is pivotal for intelligent traffic control and guidance. Manually capturing the intricate dependencies between spatial and temporal dimensions in traffic data presents a significant challenge. Prior methods have primarily employed Recurrent Neural Networks or Graph Convolutional Networks, without fully accounting for the interdependency between spatial and temporal factors. To address this, we introduce a novel Hierarchical Spatial-Temporal Neural Networks with Attention Mechanism model (HSTAN). This model concurrently captures temporal correlations and spatial dependencies using a multi-headed self-attention mechanism in both temporal and spatial terms. It also integrates global spatial-temporal correlations through a hierarchical structure with residuals. Moreover, the analysis of attention weight matrices can depict complex spatial-temporal correlations, thereby enhancing our traffic forecasting capabilities. We conducted experiments on two publicly available traffic datasets, and the results demonstrated that the HSTAN model’s prediction accuracy surpassed that of several benchmark methods.
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Jiang, Wenhao, Yunpeng Xiao, Yanbing Liu, Qilie Liu, and Zheng Li. "Bi-GRCN: A Spatio-Temporal Traffic Flow Prediction Model Based on Graph Neural Network." Journal of Advanced Transportation 2022 (February 1, 2022): 1–12. http://dx.doi.org/10.1155/2022/5221362.
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Because traffic flow data has complex spatial dependence and temporal correlation, it is a challenging problem for researchers in the field of Intelligent Transportation to accurately predict traffic flow by analyzing spatio-temporal traffic data. Based on the idea of spatio-temporal data fusion, fully considering the correlation of traffic flow data in the time dimension and the dependence of spatial structure, this paper proposes a new spatio-temporal traffic flow prediction model based on Graph Neural Network (GNN), which is called Bidirectional-Graph Recurrent Convolutional Network (Bi-GRCN). First, aiming at the spatial dependence between traffic flow data and traffic roads, Graph Convolution Network (GCN) which can directly analyze complex non-Euclidean space data is selected for spatial dependence modeling, to extract the spatial dependence characteristics. Second, considering the temporal dependence of traffic flow data on historical data and future data in its time-series period, Bidirectional-Gate Recurrent Unit (Bi-GRU) is used to process historical data and future data at the same time, to learn the temporal correlation characteristics of data in the bidirectional time dimension from the input data. Finally, the full connection layer is used to fuse the extracted spatial features and the learned temporal features to optimize the prediction results so that the Bi-GRCN model can better extract the spatial dependence and temporal correlation of traffic flow data. The experimental results show that the model can not only effectively predict the short-term traffic flow but also get a good prediction effect in the medium- and long-term traffic flow prediction.
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Xu, Dong-wei, Yong-dong Wang, Li-min Jia, Gui-jun Zhang, and Hai-feng Guo. "Compression Algorithm of Road Traffic Spatial Data Based on LZW Encoding." Journal of Advanced Transportation 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/8182690.
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Wide-ranging applications of road traffic detection technology in road traffic state data acquisition have introduced new challenges for transportation and storage of road traffic big data. In this paper, a compression method for road traffic spatial data based on LZW encoding is proposed. First, the spatial correlation of road segments was analyzed by principal component analysis. Then, the road traffic spatial data compression based on LZW encoding is presented. The parameters determination is also discussed. Finally, six typical road segments in Beijing are adopted for case studies. The final results are listed and prove that the road traffic spatial data compression method based on LZW encoding is feasible, and the reconstructed data can achieve high accuracy.
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Zhang, Rui, Fei Xie, Jianjun Shi, Jing Zhao, Jiquan Yang, and Xu Ling. "Spatial-Temporal Semantic Neural Network for Time Series Forecasting." Journal of Physics: Conference Series 2203, no. 1 (February 1, 2022): 012033. http://dx.doi.org/10.1088/1742-6596/2203/1/012033.
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Abstract Time series forecasting play an important role in many applications, and combining neural networks to forecast time series features can uncover many potential application situations. For example, forecasting traffic flow time series data is important for urban traffic planning and traffic management. Accurate forecasting of traffic flow provides the basis for road traffic control, which in turn improves traffic efficiency and reduces congestion. In this paper, STSeNN is proposed, which combines graph neural network and TCN to dynamically capture the spatial correlation, temporal correlation and semantic correlation of data. Experiments show that the prediction performance of STSeNN on the traffic flow datasets is more accurate compared with existing methods.
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Wu, Xiaoyun, and Cynthia Lum. "The practice of proactive traffic stops." Policing: An International Journal 43, no. 2 (November 26, 2019): 229–46. http://dx.doi.org/10.1108/pijpsm-06-2019-0089.
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Purpose Empirical research suggests that traffic enforcement is the most common type of proactive activity police officers engage in on a daily basis. Further, agencies often use traffic enforcement to achieve both traffic safety and crime control. Given these goals, the purpose of this paper is to investigate whether (and to what extent) officers are accurately targeting their proactive traffic enforcement with crime and vehicle crashes in two agencies. Design/methodology/approach The study examines traffic enforcement patterns in two agencies to see whether proactive traffic enforcement aligns spatially with crime and vehicle crashes. This study employs negative binomial regression models with clustered standard errors to investigate this alignment at the micro-spatial level. Key variables of interest are measured with police calls for service data, traffic citation data and vehicle crash data from two law enforcement jurisdictions. Findings High levels of spatial association are observed between traffic accidents and crime in both agencies, lending empirical support to the underlying theories of traffic enforcement programs that also try to reduce crime (i.e. “DDACTS”). In both agencies, traffic accidents also appear to be the most prominent predictor of police proactive traffic enforcement activities, even across different times of day. However, when vehicle crashes are accounted for, the association between crime and traffic stops is weaker, even during times of day when agencies believe they are using proactive traffic enforcement as a crime deterrent. Originality/value No prior study to authors knowledge has examined the empirical association between police proactive traffic activities and crime and traffic accidents in practice. The current study seeks to fill that void by investigating the realities of traffic stops as practiced daily by police officers, and their alignment with crime and vehicle crashes. Such empirical inquiry is especially important given the prevalent use of traffic enforcement as a common proactive policing tool by police agencies to control both traffic and crime problems.
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Han, Xing, Guowei Zhu, Ling Zhao, Ronghua Du, Yuhan Wang, Zhe Chen, Yang Liu, and Silu He. "Ollivier–Ricci Curvature Based Spatio-Temporal Graph Neural Networks for Traffic Flow Forecasting." Symmetry 15, no. 5 (April 27, 2023): 995. http://dx.doi.org/10.3390/sym15050995.
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Traffic flow forecasting is a basic function of intelligent transportation systems, and the accuracy of prediction is of great significance for traffic management and urban planning. The main difficulty of traffic flow predictions is that there is complex underlying spatiotemporal dependence in traffic flow; thus, the existing spatiotemporal graph neural network (STGNN) models need to model both temporal dependence and spatial dependence. Graph neural networks (GNNs) are adopted to capture the spatial dependence in traffic flow, which can model the symmetric or asymmetric spatial relations between nodes in the traffic network. The transmission process of traffic features in GNNs is guided by the node-to-node relationship (e.g., adjacency or spatial distance) between nodes, ignoring the spatial dependence caused by local topological constraints in the road network. To further consider the influence of local topology on the spatial dependence of road networks, in this paper, we introduce Ollivier–Ricci curvature information between connected edges in the road network, which is based on optimal transport theory and makes comprehensive use of the neighborhood-to-neighborhood relationship to guide the transmission process of traffic features between nodes in STGNNs. Experiments on real-world traffic datasets show that the models with Ollivier–Ricci curvature information outperforms those based on only node-to-node relationships between nodes by ten percent on average in the RMSE metric. This study indicates that by utilizing complex topological features in road networks, spatial dependence can be captured more sufficiently, further improving the predictive ability of traffic forecasting models.
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Zhou, Junwei, Xizhong Qin, Yuanfeng Ding, and Haodong Ma. "Spatial–Temporal Dynamic Graph Differential Equation Network for Traffic Flow Forecasting." Mathematics 11, no. 13 (June 26, 2023): 2867. http://dx.doi.org/10.3390/math11132867.
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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.
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Ištoka Otković, Irena, Barbara Karleuša, Aleksandra Deluka-Tibljaš, Sanja Šurdonja, and Mario Marušić. "Combining Traffic Microsimulation Modeling and Multi-Criteria Analysis for Sustainable Spatial-Traffic Planning." Land 10, no. 7 (June 24, 2021): 666. http://dx.doi.org/10.3390/land10070666.
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Spatial and traffic planning is important in order to achieve a quality, safe, functional, and integrated urban environment. Different tools and expert models were developed that are aimed at a more objective view of the consequences of reconstruction in different spatial and temporal ranges while respecting selection criteria. In this paper we analyze the application of the multi-criteria analysis method when choosing sustainable traffic solutions in the center of a small town, in this case Belišće, Croatia. The goal of this paper is to examine the possibility of improving the methodology for selecting an optimal spatial–traffic solution by combining the quantifiable results of the traffic microsimulation and the method of multi-criteria optimization. Socially sensitive design should include psychological and social evaluation criteria that are included in this paper as qualitative spatial–urban criteria. In the optimization process, different stakeholder groups (experts, students, and citizens) were actively involved in evaluating the importance of selected criteria. The analysis of stakeholders’ survey results showed statistically significant differences in criteria preference among three groups. The AHP (Analytic Hierarchy Process) multi-criteria analysis method was used; a total of five criteria groups (functional, safety, economic, environmental, and spatial–urban) were developed, which contain 21 criteria and 7 sub-criteria; and the weights of criteria groups were varied based on stakeholders’ preferences. The application of the developed methodology enabled the selection of an optimal solution for the improvement of traffic conditions in a small city with the potential to also be applied to other types of traffic–spatial problems and assure sustainable traffic planning.
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Yu, Hongru, Shejun Deng, Caoye Lu, Yucheng Tang, Shijun Yu, Lu Liu, and Tao Ji. "Research on the Evolution Mechanism of Congestion in the Entrances and Exits of Parking Facilities Based on the Improved Spatial Autoregressive Model." Journal of Advanced Transportation 2021 (August 29, 2021): 1–15. http://dx.doi.org/10.1155/2021/8380247.
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The entrance and exit area of parking facilities has the characteristics of high concentration of urban traffic and prominent traffic intertwining phenomenon, which easily induces rapid congestion of mixed heterogeneous traffic at specific times and local locations and quickly spreads to the entire road section or even a larger area. In order to better understand the congestion distribution characteristics and propagation effects of access section of the parking entrance and exit from the mid and microperspective, a 5 m ∗ lane width pixel grid is used to divide the frontage road research. It also proposes a spatially robust autoregressive model and complex network tools suitable for analysis of local traffic flow to analyze it. The results show that as spatial scale increases, the congestion propagation decreases sharply and spatial adjacency within the fourth order can account for more than 90% of the propagation; the frontage road to the entrance and exit is the place where the congestion first happens, and the congestion gradually attenuates as it propagates to the inner lane and the upstream of the road segments; the lateral congestion propagation attenuates faster, so the area affected by congestion is mainly distributed in the outermost lane. This paper can provide theoretical guidance for alleviating traffic congestion in the entrance and exit areas of parking facilities and has theoretical and empirical significance.
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Li, Y., Q. Zhao, and M. Wang. "ANALYSIS THE INFLUENCING FACTORS OF URBAN TRAFFIC FLOWS BY USING NEW AND EMERGING URBAN BIG DATA AND DEEP LEARNING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022 (June 2, 2022): 537–43. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2022-537-2022.
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Abstract. Urban traffic analysis has acted an important role in the process of urban development, which can provide insights for urban planning, traffic management and resource allocation. Meanwhile, the advancement of Intelligent Transportation Systems has produced a variety of traffic-related data from sensors and cameras to monitor urban traffic conditions in high spatio-temporal resolution. This research applies spatial regression models combined with computer vision and deep learning to analyse traffic flow distributions via various factors in the urban areas and traffic flow data. We include road characteristics and surrounding environments such as land use/cover, nearby points of interest (POI) and Google Street View images. The results show that the daily average traffic flow on main roads is much higher than smaller roads, and nearby POIs numbers have positive effect on traffic flows. The impact of land cover type is insignificant in the linear regression model, while demonstrates significant contribution to traffic flows in spatial regression models. Although the spatial autocorrelation still exists after the spatial regression, the spatial error model generates a better fit on the dataset. Further analysis will focus on extend the current model with the time parameters and understand what influence the changes of traffic flow in the different spatio-temporal scales.
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Zhou, Shaobo, Xiaodong Zang, Junheng Yang, Wanying Chen, Jiahao Li, and Shuyi Chen. "Modelling the Coupling Relationship between Urban Road Spatial Structure and Traffic Flow." Sustainability 15, no. 14 (July 17, 2023): 11142. http://dx.doi.org/10.3390/su151411142.
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In order to promote the sustainable development of urban traffic systems, improve the accuracy of traffic system analysis in the urban planning stage and reduce the possibility of traffic congestion in the operation stage of road networks, the coupling relationship and evolution mechanism between urban road spatial structure and traffic flow were studied, and a model of the relationship between the metrics was established in this study based on real road network and traffic flow data. First, the road spatial structure model of the study area was established from the perspective of road space, and the spatial syntax method was applied to verify the rationality of the spatial structure of the road network. Secondly, the initial OD matrix was determined by OD backpropagation based on the measured traffic flow data. Thirdly, the coupling rule between the spatial structure and the traffic flow of the road network was explored by loading the increment in the OD matrix to the initial OD matrix step by step based on a simulation experiment. Finally, the relationship between the degree of integration of the spatial syntactic feature parameter and the saturation of the traffic flow feature parameter was modelled on the basis of experimental results and verified by an example. This research shows that the spatial structure of urban roads has a significant impact on the characterisation of the traffic flow distribution of road networks, and a strong correlation can be found between the integration degree and saturation degree. An optimal fit, which can be used as a reference for the design of road spatial structure, was explored in this research.
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Chen, Renyi, and Huaxiong Yao. "Hybrid Graph Models for Traffic Prediction." Applied Sciences 13, no. 15 (July 27, 2023): 8673. http://dx.doi.org/10.3390/app13158673.
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Obtaining accurate road conditions is crucial for traffic management, dynamic route planning, and intelligent guidance services. The complex spatial correlation and nonlinear temporal dependence pose great challenges to obtaining accurate road conditions. Existing graph-based methods use a static adjacency matrix or a dynamic adjacency matrix to aggregate spatial information between nodes, which cannot fully represent the topological information. In this paper, we propose a Hybrid Graph Model (HGM) for accurate traffic prediction. The HGM constructs a static graph and a dynamic graph to represent the topological information of the traffic network, which is beneficial for mining potential and obvious spatial correlations. The proposed method combines a graph neural network, convolutional neural network, and attention mechanism to jointly extract complex spatial–temporal features. The HGM consists of two different sub-modules, called spatial–temporal attention module and dynamic graph convolutional network, to fuse complex spatial–temporal information. Furthermore, the proposed method designs a novel gated function to adaptively fuse the results from spatial–temporal attention and dynamic graph convolutional network to improve prediction performance. Extensive experiments on two real datasets show that the HGM outperforms comparable state-of-the-art methods.
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Zhang, Xiao Na, Ming Yao, Feng Zhu, and Jie Ni. "Traffic Image Segmentation Based on Gaussian Mixture Model with Spatial Information and Sampling." Applied Mechanics and Materials 380-384 (August 2013): 3702–5. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3702.
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The application of classical gaussian mixture model to image segmentation has highly computer complexiton and have not taking into account spatial information except intensity values. A image segmentation based on Gaussian mixture model with sampling and spatially information is proposed in order to solve this problem. First, a spatial information function is defined as the neighbour information weighted class probabilities of very pixels; Secondly, the sampling theorem is given in this paper,and the size of the minimum sample has been derived according to the smallest cluster and cluster number; Finally, image pixels are sampled based on the size of the minimum sample to estimate the parameter of model , which are classifed to different clusters according to bayesian rules. The experimental results show the effectiveness of the algorithm.
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Zeng, Hui, Chaojie Jiang, Yuanchun Lan, Xiaohui Huang, Junyang Wang, and Xinhua Yuan. "Long Short-Term Fusion Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting." Electronics 12, no. 1 (January 3, 2023): 238. http://dx.doi.org/10.3390/electronics12010238.
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Traffic flow forecasting, as one of the important components of intelligent transport systems (ITS), plays an indispensable role in a wide range of applications such as traffic management and city planning. However, complex spatial dependencies and dynamic changes in temporal patterns exist between different routes, and obtaining as many spatial-temporal features and dependencies as possible from node data has been a challenging task in traffic flow prediction. Current approaches typically use independent modules to treat temporal and spatial correlations separately without synchronously capturing such spatial-temporal correlations, or focus only on local spatial-temporal dependencies, thereby ignoring the implied long-term spatial-temporal periodicity. With this in mind, this paper proposes a long-term spatial-temporal graph convolutional fusion network (LSTFGCN) for traffic flow prediction modeling. First, we designed a synchronous spatial-temporal feature capture module, which can fruitfully extract the complex local spatial-temporal dependence of nodes. Second, we designed an ordinary differential equation graph convolution (ODEGCN) to capture more long-term spatial-temporal dependence using the spatial-temporal graph convolution of ordinary differential equation. At the same time, by integrating in parallel the ODEGCN, the spatial-temporal graph convolution attention module (GCAM), and the gated convolution module, we can effectively make the model learn more long short-term spatial-temporal dependencies in the processing of spatial-temporal sequences.Our experimental results on multiple public traffic datasets show that our method consistently obtained the optimal performance compared to the other baselines.
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Wang, Beibei, Youfang Lin, Shengnan Guo, and Huaiyu Wan. "GSNet: Learning Spatial-Temporal Correlations from Geographical and Semantic Aspects for Traffic Accident Risk Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (May 18, 2021): 4402–9. http://dx.doi.org/10.1609/aaai.v35i5.16566.
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Traffic accident forecasting is of great importance to urban public safety, emergency treatment, and construction planning. However, it is very challenging since traffic accidents are affected by multiple factors, and have multi-scale dependencies on both spatial and temporal dimensional features. Meanwhile, traffic accidents are rare events, which leads to the zero-inflated issue. Existing traffic accident forecasting methods cannot deal with all above problems simultaneously. In this paper, we propose a novel model, named GSNet, to learn the spatial-temporal correlations from geographical and semantic aspects for traffic accident risk forecasting. In the model, a Spatial-Temporal Geographical Module is designed to capture the geographical spatial-temporal correlations among regions, while a Spatial-Temporal Semantic Module is proposed to model the semantic spatial-temporal correlations among regions. In addition, a weighted loss function is designed to solve the zero-inflated issue. Extensive experiments on two real-world datasets demonstrate the superiority of GSNet against the state-of-the-art baseline methods.
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Cui, Jiaxing, Ruihao Li, Lingyu Zhang, and Ying Jing. "Spatially Illustrating Leisure Agriculture: Empirical Evidence from Picking Orchards in China." Land 10, no. 6 (June 13, 2021): 631. http://dx.doi.org/10.3390/land10060631.
Full textAbstract:
In the context of rural revitalization strategies and humans’ increasing leisure pursuit, leisure agriculture starts to act as a new engine of rural economic growth and industrial upgradation. Unraveling the agri-leisure developmental regularity from a spatial perspective facilitates urban-rural integration and poverty alleviation in rural regions. Given the lack of spatially analyzing agri-leisure (e.g., sightseeing picking orchards) especially at the macro-spatial scale (e.g., the national scale), this study aims to explore the spatiality of leisure agriculture and its fundamental driving mechanisms based on geo-visual (spatially visualizing) analytical tools looking at 20,778 picking orchards in China. Results show that: (1) Picking orchards are distributed in the form of clusters with striking disparity at multiple spatial scales; (2) Five spatial agglomerations are found involving the regions around Beijing and Tianjin, Shandong hinterland, Henan hinterland, the core district of the Yangtze Delta, and the core district of the Pearl River Delta; (3) The driving mechanisms are revealed, and the spatial pattern of picking orchards is found to be largely influenced by morphology, distance to central cities, traffic conditions, economic level, and tourism resources. This study is conducive to optimizing the spatial planning of rural eco-tourism towards sustainable agro-development.