Academic literature on the topic 'Trafic spatial'
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Journal articles on the topic "Trafic spatial"
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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.
Dissertations / Theses on the topic "Trafic spatial"
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Tanzi, Tullio. "Systeme spatial temps réel d'aide a la décision, application aux risques autoroutiers : D.E.S.T.IN : dispositif d'études et de surveillance du trafic et des incidents." Lyon, INSA, 1998. http://www.theses.fr/1998ISAL0058.
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L'objectif de ces travaux est de concevoir un système temps réel d'analyse de risque afin de compléter les systèmes autoroutiers d'aide à l'exploitation. Plutôt que de s'attacher à la révision d'accidents, le système repose sur l'analyse de l'évolution des conditions de trafic, afin de caractériser les situations à haut risque. Cette anticipation des accidents potentiels doit permettre la mise en œuvre d'actions préventives et une meilleure gestion de la crise. Ces travaux ont permis la définition d'indicateurs de risque dans le contexte routier. Un prototype original a été développé et testé en vraie grandeur. Les indicateurs ont été testés sur un jeu de données de l'autoroute du réseau ESCOT A. Grace à notre approche, les techniques classiques d'analyses spatiales, telles que nous les connaissons dans le monde des systèmes d'information géographiques permettront à la fois de produire des informations quantitatives en temps réel, tels que des distances, des temps prévus d'arrivée, ou encore des périmètres de sécurité, mais aussi de mieux maîtriser l'événement en permettant la simulation de ses phénomènes. Cela nécessite la prise en compte de l'acheminement de l'information au sein du système d'information global. L'originalité de ces travaux peut être résumée en deux points principaux : • Un nouveau concept de système d'information temps réel pour l'analyse des risques. A ce niveau il s'agit de nouveaux indicateurs, • Un nouveau concept qui émerge : la TéléGéomatique, terme fédérateur de la géomatique et des télécommunications, dont l'importance est justifiée par ces travauxThe objective of these works is to specify a real-time system of risk analysis in order to complete systems for motorways to help the exploitation of motorways. Instead of focusing on accidents, the system relies on the analysis of the evolution of traffic conditions, in order to characterise high risk situations. The aim of this potential accident anticipation is to elaborate preventive actions and to allow a better management of the crisis. These works have permitted the definition of risk indicators in the road context. An original prototype has been developed and has been tested in real situations. Indicators have been tested on samples ofdata ofthe freeway of the ESCOT A network. Thanks to our approach, the classic techniques of spatial analyses, as we know them in the world of the geographical information systems will permit to produce sorne quantitative information in real-time, as distances, predicted times of arrivai or security perimeters, but also to better manage the event using phenomena simulations. It requires to take into ac-count the routing of information within the global information system. The originality ofthese works can be summarised in two main points: • A new concept of real-time information system for the analysis of risks (it is about new indicators) A new emerging concept: TéléGéomatique, term based on geomatics and telecommunications, whose importance is justified by these works
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Schiper, Nicole. "Traffic data sampling for air pollution estimation at different urban scales." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSET008/document.
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La circulation routière est une source majeure de pollution atmosphérique dans les zones urbaines. Les décideurs insistent pour qu’on leur propose de nouvelles solutions, y compris de nouvelles stratégies de management qui pourraient directement faire baisser les émissions de polluants. Pour évaluer les performances de ces stratégies, le calcul des émissions de pollution devrait tenir compte de la dynamique spatiale et temporelle du trafic. L’utilisation de capteurs traditionnels sur route (par exemple, capteurs inductifs ou boucles de comptage) pour collecter des données en temps réel est nécessaire mais pas suffisante en raison de leur coût de mise en oeuvre très élevé. Le fait que de telles technologies, pour des raisons pratiques, ne fournissent que des informations locales est un inconvénient. Certaines méthodes devraient ensuite être appliquées pour étendre cette information locale à une grande échelle. Ces méthodes souffrent actuellement des limites suivantes : (i) la relation entre les données manquantes et la précision de l’estimation ne peut être facilement déterminée et (ii) les calculs à grande échelle sont énormément coûteux, principalement lorsque les phénomènes de congestion sont considérés. Compte tenu d’une simulation microscopique du trafic couplée à un modèle d’émission, une approche innovante de ce problème est mise en oeuvre. Elle consiste à appliquer des techniques de sélection statistique qui permettent d’identifier les emplacements les plus pertinents pour estimer les émissions des véhicules du réseau à différentes échelles spatiales et temporelles. Ce travail explore l’utilisation de méthodes statistiques intelligentes et naïves, comme outil pour sélectionner l’information la plus pertinente sur le trafic et les émissions sur un réseau afin de déterminer les valeurs totales à plusieurs échelles. Ce travail met également en évidence quelques précautions à prendre en compte quand on calcul les émissions à large échelle à partir des données trafic et d’un modèle d’émission. L’utilisation des facteurs d’émission COPERT IV à différentes échelles spatio-temporelles induit un biais en fonction des conditions de circulation par rapport à l’échelle d’origine (cycles de conduite). Ce biais observé sur nos simulations a été quantifié en fonction des indicateurs de trafic (vitesse moyenne). Il a également été démontré qu’il avait une double origine : la convexité des fonctions d’émission et la covariance des variables de traficRoad traffic is a major source of air pollution in urban areas. Policy makers are pushing for different solutions including new traffic management strategies that can directly lower pollutants emissions. To assess the performances of such strategies, the calculation of pollution emission should consider spatial and temporal dynamic of the traffic. The use of traditional on-road sensors (e.g. inductive sensors) for collecting real-time data is necessary but not sufficient because of their expensive cost of implementation. It is also a disadvantage that such technologies, for practical reasons, only provide local information. Some methods should then be applied to expand this local information to large spatial extent. These methods currently suffer from the following limitations: (i) the relationship between missing data and the estimation accuracy, both cannot be easily determined and (ii) the calculations on large area is computationally expensive in particular when time evolution is considered. Given a dynamic traffic simulation coupled with an emission model, a novel approach to this problem is taken by applying selection techniques that can identify the most relevant locations to estimate the network vehicle emissions in various spatial and temporal scales. This work explores the use of different statistical methods both naïve and smart, as tools for selecting the most relevant traffic and emission information on a network to determine the total values at any scale. This work also highlights some cautions when such traffic-emission coupled method is used to quantify emissions due the traffic. Using the COPERT IV emission functions at various spatial-temporal scales induces a bias depending on traffic conditions, in comparison to the original scale (driving cycles). This bias observed in our simulations, has been quantified in function of traffic indicators (mean speed). It also has been demonstrated to have a double origin: the emission functions’ convexity and the traffic variables covariance
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Tchappi, haman Igor. "Dynamic Multilevel and Holonic Model for the Simulation of a Large-Scale Complex System with Spatial Environment : Application to Road Traffic Simulation." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCA004.
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De nos jours, avec l’émergence d’objets et de voitures connectés, les systèmes de trafic routier deviennent de plus en plus complexes et présentent des comportements hiérarchiques à plusieurs niveaux de détail. L'approche de modélisation multiniveaux est une approche appropriée pour représenter le trafic sous plusieurs perspectives. Les modèles multiniveaux constituent également une approche appropriée pour modéliser des systèmes complexes à grande échelle comme le trafic routier. Cependant, la plupart des modèles multiniveaux de trafic proposés dans la littérature sont statiques car ils utilisent un ensemble de niveaux de détail prédéfinis et ces représentations ne peuvent pas commuter pendant la simulation. De plus ces modèles multiniveaux considèrent généralement seulement deux niveaux de détail. Très peu de travaux se sont intéressés à la modélisation dynamique multiniveau de trafic.Cette thèse propose un modèle holonique multiniveau et dynamique du trafic à grande échelle.La commutation dynamique des niveaux de détail lors de l’exécution de la simulation permet d’adapter le modèle aux contraintes liées à la qualité des résultats ou aux ressources de calcul disponibles.La proposition étend l'algorithme DBSCAN dans le contexte des systèmes multi-agents holoniques. De plus, une méthodologie permettant la commutation dynamique entre les différents niveaux de détail est proposée. Des indicateurs multiniveaux basés sur l'écart type sont aussi proposés afin d'évaluer la cohérence des résultats de la simulationNowadays, with the emergence of connected objects and cars, road traffic systems become more and more complex and exhibit hierarchical behaviours at several levels of detail. The multilevel modeling approach is an appropriate approach to represent traffic from several perspectives. Multilevel models are also an appropriate approach to model large-scale complex systems such as road traffic. However, most of the multilevel models of traffic proposed in the literature are static because they use a set of predefined levels of detail and these representations cannot change during simulation. Moreover, these multilevel models generally consider only two levels of detail. Few works have been interested on the dynamic multilevel traffic modeling.This thesis proposes a holonic multilevel and dynamic traffic model for large scale traffic systems. The dynamic switching of the levels of detail during the execution of the simulation allows to adapt the model to the constraints related to the quality of the results or to the available computing resources.The proposal extends the DBSCAN algorithm in the context of holonic multi-agent systems. In addition, a methodology allowing a dynamic transition between the different levels of detail is proposed. Multilevel indicators based on standard deviation are also proposed in order to assess the consistency of the simulation results
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Chen, Guangshuo. "Human Habits Investigation : from Mobility Reconstruction to Mobile Traffic Prediction." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLX026/document.
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La capacité à prévoir les activités humaines a des implications essentielles dans de nombreux aspects des réseaux cellulaires. En particulier, la haute disponibilité de la prédiction de la mobilité peut permettre différents scénarios d'application; une meilleure compréhension de la demande de trafic de données mobiles peut aider à améliorer la conception de solutions pour l'équilibrage de la charge du réseau. Bien que de nombreux chercheurs aient étudié le sujet de la prédiction de la mobilité humaine, il y a eu peu de discussions sur l'anticipation du trafic de données mobiles dans les réseaux cellulaires.Pour comprendre la mobilité humaine, les ensembles de données de téléphones mobiles, consistant en des enregistrements de données de taxation (CDR), constituent un choix pratique d'empreintes humaines. Comme le déploiement du réseau cellulaire est très irrégulier et que les fréquences d'interaction sont généralement faibles, les données CDR sont souvent caractérisées par une parcimonie spatio-temporelle qui, à son tour, peut biaiser les analyses de mobilité basées sur de telles données et provoquer la perte de trajectoires individuelles.Dans cette thèse, nous présentons de nouvelles solutions de reconstruction de trajectoires individuelles et de prédiction de trafic de données mobiles individuelles. Nos contributions abordent les problèmes de (1) surmonter l'incomplétude des informations de mobilité pour l'utilisation des ensembles de données de téléphonie mobile et (2) prédire la future demande de trafic de données mobiles pour le support des applications de gestion de réseau.Premièrement, nous nous concentrons sur la faille de l'information sur la mobilité dans les ensembles de données de téléphones mobiles. Nous rapportons une analyse en profondeur de son effet sur la mesure des caractéristiques de mobilité individuelles et l'exhaustivité des trajectoires individuelles. En particulier, (1) nous fournissons une confirmation des résultats antérieurs concernant les biais dans les mesures de mobilité causées par la rareté temporelle de la CDR; (2) nous évaluons le décalage géographique provoqué par la cartographie des emplacements des utilisateurs vers les tours cellulaires et révélons le biais causé par la rareté spatiale de la CDR; (3) nous fournissons une estimation empirique de l'exhaustivité des données des trajectoires CDR individuelles. (4) nous proposons de nouvelles solutions de complétion CDR pour reconstruire incomplète. Nos solutions tirent parti de la nature des modèles de mouvements humains répétitifs et des techniques d'inférence de données de pointe et surpassent les approches précédentes illustrées par des simulations axées sur les données.Deuxièmement, nous abordons la prédiction des demandes de trafic de données mobiles générées par les abonnés individuels du réseau mobile. Sur la base de trajectoires complétées par nos solutions développées et nos historiques de consommation de données extraites d'un ensemble de données de téléphonie mobile à grande échelle, (1) nous étudions les limites de prévisibilité en mesurant la prévisibilité maximale que tout algorithme peut atteindre. les approches de prédiction du trafic de données mobiles qui utilisent les résultats de l'analyse théorique de la prévisibilité. Notre analyse théorique montre qu'il est théoriquement possible d'anticiper la demande individuelle avec une précision typique de 75% malgré l'hétérogénéité des utilisateurs et avec une précision améliorée de 80% en utilisant la prédiction conjointe avec des informations de mobilité. Notre pratique basée sur des techniques d'apprentissage automatique peut atteindre une précision typique de 65% et avoir un degré d'amélioration de 1% à 5% en considérant les déplacements individuels.En résumé, les contributions mentionnées ci-dessus vont dans le sens de l'utilisation des ensembles de données de téléphonie mobile et de la gestion des opérateurs de réseau et de leurs abonnésThe understanding of human behaviors is a central question in multi-disciplinary research and has contributed to a wide range of applications. The ability to foresee human activities has essential implications in many aspects of cellular networks. In particular, the high availability of mobility prediction can enable various application scenarios such as location-based recommendation, home automation, and location-related data dissemination; the better understanding of mobile data traffic demand can help to improve the design of solutions for network load balancing, aiming at improving the quality of Internet-based mobile services. Although a large and growing body of literature has investigated the topic of predicting human mobility, there has been little discussion in anticipating mobile data traffic in cellular networks, especially in spatiotemporal view of individuals.For understanding human mobility, mobile phone datasets, consisting of Charging Data Records (CDRs), are a practical choice of human footprints because of the large-scale user populations and the vast diversity of individual movement patterns. The accuracy of mobility information granted by CDR depends on the network infrastructure and the frequency of user communication events. As cellular network deployment is highly irregular and interaction frequencies are typically low, CDR data is often characterized by spatial and temporal sparsity, which, in turn, can bias mobility analyses based on such data and cause the loss of whereabouts in individual trajectories.In this thesis, we present novel solutions of the reconstruction of individual trajectories and the prediction of individual mobile data traffic. Our contributions address the problems of (1) overcoming the incompleteness of mobility information for the use of mobile phone datasets and (2) predicting future mobile data traffic demand for the support of network management applications.First, we focus on the flaw of mobility information in mobile phone datasets. We report on an in-depth analysis of its effect on the measurement of individual mobility features and the completeness of individual trajectories. In particular, (1) we provide a confirmation of previous findings regarding the biases in mobility measurements caused by the temporal sparsity of CDR; (2) we evaluate the geographical shift caused by the mapping of user locations to cell towers and reveal the bias caused by the spatial sparsity of CDR; (3) we provide an empirical estimation of the data completeness of individual CDR-based trajectories. (4) we propose novel solutions of CDR completion to reconstruct incomplete. Our solutions leverage the nature of repetitive human movement patterns and the state-of-the-art data inference techniques and outperform previous approaches shown by data-driven simulations.Second, we address the prediction of mobile data traffic demands generated by individual mobile network subscribers. Building on trajectories completed by our developed solutions and data consumption histories extracted from a large-scale mobile phone dataset, (1) we investigate the limits of predictability by measuring the maximum predictability that any algorithm has potential to achieve and (2) we propose practical mobile data traffic prediction approaches that utilize the findings of the theoretical predictability analysis. Our theoretical analysis shows that it is theoretically possible to anticipate the individual demand with a typical accuracy of 75% despite the heterogeneity of users and with an improved accuracy of 80% using joint prediction with mobility information. Our practical based on machine learning techniques can achieve a typical accuracy of 65% and have a 1%~5% degree of improvement by considering individual whereabouts.In summary, the contributions mentioned above provide a step further towards supporting the use of mobile phone datasets and the management of network operators and their subscribers
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Fimbel, Amaury. "Origami à base de matériaux électroactifs pour des applications spatiales." Electronic Thesis or Diss., Lyon, INSA, 2023. http://www.theses.fr/2023ISAL0071.
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Ce projet de thèse s’inscrit dans le cadre d’une collaboration Cifre entre le LGEF et l’entreprise ArianeGroup. La fluctuation de forme de structures complexes à l'aide de polymères électroactifs est le sujet principal de cette étude. Les matériaux électroactifs, qui, de par leurs structures peuvent réaliser une conversion électromécanique de l’énergie, prouvent progressivement leur potentiel de percée technologique dans de nombreux domaines. En plus de l'hypothèse qu'ils pourraient éventuellement remplacer les capteurs et actionneurs actuellement utilisés, les nouvelles capacités de ces matériaux tant au niveau des performances que des capacités de couplage multiphysique sont une sérieuse source d’espoir pour aborder et résoudre des problèmes issus de secteurs émergents. Ces innovations technologiques potentielles peuvent affecter particulièrement le domaine de l'aérospatial. La combinaison d'une faible masse volumique et d'une densité d'énergie mécanique considérable pour un polymère semble apporter une réponse attrayante à la mise au point de dispositifs innovants, compacts et modulables. Mais certains points restent à explorer pour démontrer tout le potentiel applicatif de cette technologie et aboutir au développement de systèmes intelligents. Une grande partie de ce travail de recherche va donc se concentrer sur cette problématique. Ce projet se focalise ainsi sur l'élaboration et la caractérisation d'un composite à haute performance pour l'actionnement électrostatique et sa tenue en vieillissement en milieu spatial. Les objectifs de l'étude mécanique des structures origami sont de trouver des solutions concernant la compréhension et le développement de systèmes complexes et modulables. L’association de ces deux axes ouvre la voie à la création de structures mécaniques très légères pilotables par un champ électrique. Cette thèse concerne les applications spatiales mais peut tout à fait s’ancrer dans un enjeu sociétal plus large comme par exemple le médical, la robotique ou encore le domaine des transportsThis thesis project is part of a Cifre collaboration between the Electrical Engineering and Ferro Electricity Laboratory and ArianeGroup. The main subject of this study is the shape shifting of complex structures by using electroactive polymers. Electroactive materials, whose internal conformations are capable of electromechanical energy conversion, are gradually proving their potential for technological breakthroughs in many fields. In addition to the hypothesis that they could eventually replace actual sensors and actuators, the new capabilities of these materials in terms of both performance and multiphysics coupling capacities are a serious source of hope for tackling and solving problems in emerging fields. These potential technological innovations may be of particular interest for aerospace industry. Combination of low density and high mechanical energy density in a polymer seems to offer an attractive answer to the development of innovative, compact and modular devices. However, some parts remain to be explored in order to demonstrate the full application potential of this technology and lead to the development of smart systems. A large part of this research work will focus on this issue. This project will deal with the development and characterization of a high-performance composite for electrostatic actuation and its resistance to ageing in a space environment. The objectives of the mechanical study of origami structures are to find solutions for understanding and developing complex, modular systems. The combination of these two lines opens the way to the creation of very light mechanical structures that can be controlled by an electric field. This thesis concerns space applications, but can also be applied to a wider societal issue, such as medical, robotics or transport sectors
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Manout, Ouassim. "Spatial aggregation issues in traffic assignment models." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSE2014/document.
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Les villes sont des systèmes complexes que les modèles urbains peuvent aider à comprendre. Des modèles les plus simplistes aux modèles les plus sophistiqués, la modélisation urbaine a permis de mieux comprendre la question urbaine et ses implications sociétales. Dans ce contexte, les modèles peuvent avoir une valeur-ajoutée appréciable dans le processus de décision publique. Encore faut-il que ces modèles deviennent pratiques et répondent aux contraintes opérationnelles de la chaîne de décision. Dans ce sens, peu de recherches s’est intéressée à la question de praticité des modèles urbains et leur utilisation en situation opérationnelle. À ce jour, les modèles urbains standard qui reposent sur une description agrégée de l’espace sont parmi les approches de modélisation les plus opérationnelles et aussi les plus répandues. De par sa relative praticité, cette approche standard est attractive et simple à mettre en oeuvre. Toutefois, l’agrégation spatiale peut aussi être une source de biais statistiques préjudiciables à la qualité de la modélisation. C’est en particulier, le cas des modèles intégrés Transport-Urbanisme ou des modèles de transport à quatre étapes.La présente thèse a pour objectif d’étudier la question de l’agrégation spatiale dans les modèles transport et plus particulièrement dans les modèles d’affectation des déplacements. Les modèles d’affectation servent à calculer les temps de parcours et les conditions de déplacement sous congestion, présents et futurs, des personnes et des marchandises. Ils servent aussi à calculer les accessibilités nécessaires aux modèles d’usage des sols dont les modèles de choix de localisation des ménages et des entreprises. Toute erreur ou biais dans l’affectation des déplacements peut compromettre la validité et la qualité globales de la modélisation. Dans ce cadre, une attention particulière doit être allouée au problème d’agrégation spatiale dans les modèles d’affectation. Dans ces modèles, l’agrégation spatiale consiste à regrouper les observations individuelles enutilisant une description agrégée de l’espace, i.e. des zones. Par nature, l’utilisation d’une description agrégée à la place d’une représentation continue engendre une omission de l’information et de sa variabilité et donc un biais statistique dans la modélisation. C’est le cas par exemple avec l’utilisation des connecteurs de zones ou avec l’omission des trafics intrazones dans les modèles d’affectation.En reposant sur les zones comme unité spatiale de base, les modèles de transport recourent à l’utilisation des connecteurs de zones pour relier les centroïdes de zones au réseau de transport. Les connecteurs sont des liens fictifs qui modélisent les conditions moyennes d’entrée et de sortie du réseau de transport. Pour ce faire, la majorité des modèles de transport reposent sur une méthode simpliste sujette au problème d’agrégation spatiale. La présente thèse examine en détail l’impact de cette description simpliste sur les résultats et la qualité d’un modèle d’affectation des déplacements en transports en commun. Cette thèse propose aussi une nouvelle méthode de modélisation des connecteurs de zones afin de s’affranchir partiellement du biaisd’agrégation spatiale dans la modélisation des conditions d’accès au réseau des transports en commun.L’utilisation des zones comme unité spatiale de base a aussi pour conséquence l’omission des trafics intrazones de l’affectation des déplacements. Les trafics intrazones ont pour origine et pour destination la même zone et de ce fait ne sont pas pris en compte par les modèles standard d’affectation. Cette omission a souvent été ignorée et son impact sur la qualité de la modélisation demeure non évalué. Cette thèse développe une méthode stochastique pour l’évaluation de cet impactCities are complex systems that urban models can help to comprehend. From simplistic models to more sophisticated ones, urban models have pushed forward our understanding the urban phenomenon and its intricacies. In this context, models can be of great value to policy makers providing that these tools become practical. In this regard, research has put little emphasis on the practicality of urban models and their use under operational conditions.To date, urban models which rely on spatial aggregation are the closest possibility to come to practical models. For this reason, the spatially aggregated modeling framework is widely used. This framework is relatively practical when compared to other modeling frameworks like microsimulation. Nevertheless, spatial aggregation is a serious source of bias in these models. This is especially the case of Land-Use and Transport Interaction (LUTI) models and more particularly of Four Step Models.The current PhD is committed to the study of spatial aggregation issues in traffic assignment models. Traffic assignment is responsable for the computation of travel times and travel conditions of present and future travel demand. Accessibility measurement, which is at the core of LUTI models, is tightly dependent on traffic assignment modeling and outcomes. Any bias in traffic assignment is likely to corrupt the overall modeling framework. In this context, a special attention is to be paid to spatial aggregation in traffic assignment models.In traffic assignment, spatial aggregation consists in grouping observations using zones or traffic analysis zones instead of using a continuous representation of space. By design, aggregation bears an implicit omission in data variability and thus a potential bias if this omission is not random. This is the case with the definition of centroid connectors and the omission of intrazonal demand in traffic assignment. With the use of zones as the basic spatial units, transport models require the use of centroid connectors to attach zones to the transportation network. Centroid connectors are introduced to model average access and egress conditions to and from the network. Nevertheless, average accessibility conditions are found to be too crude to render accurately accessibility conditions as encountered by trip makers. The current PhD explores the extent of the impact of this spatial aggregation bias in the case of transit models and suggests a new modeling strategy to overcome such modeling errors.The use of zones as spatial units induces a loss of intrazonal data. The omission of intrazonal trips in traffic assignment models is an example of such omission. This research introduces an uncertainty framework to study the statistical impact of ignoring intrazonal trips in traffic assignment models. Findings from this research are used to design new assignment strategies that are more robust towards the omission bias and more generally towards the spatial aggregation bias
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Lenkei, Zsolt. "Crowdsourced traffic information in traffic management : Evaluation of traffic information from Waze." Thesis, KTH, Transportplanering, ekonomi och teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239178.
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The early observation and elimination of non-recurring incidents is a crucial task in trafficmanagement. The performance of the conventional incident detection methods (trafficcameras and other sensory technologies) is limited and there are still challenges inobtaining an accurate picture of the traffic conditions in real time. During the last decade,the technical development of mobile platforms and the growing online connectivity made itpossible to obtain traffic information from social media and applications based on spatialcrowdsourcing. Utilizing the benefits of crowdsourcing, traffic authorities can receiveinformation about a more comprehensive number of incidents and can monitor areaswhich are not covered by the conventional incident detection systems. The crowdsourcedtraffic data can provide supplementary information for incidents already reported throughother sources and it can contribute to earlier detection of incidents, which can lead tofaster response and clearance time. Furthermore, spatial crowdsourcing can help to detectincident types, which are not collected systematically yet (e.g. potholes, traffic light faults,missing road signs). However, before exploiting crowdsourced traffic data in trafficmanagement, numerous challenges need to be resolved, such as verification of the incidentreports, predicting the severity of the crowdsourced incidents and integration with trafficdata obtained from other sources.During this thesis, the possibilities and challenges of utilizing spatial crowdsourcingtechnologies to detect non-recurring incidents were examined in form of a case study.Traffic incident alerts obtained from Waze, a navigation application using the concept ofcrowdsourcing, were analyzed and compared with officially verified incident reports inStockholm. The thesis provides insight into the spatial and temporal characteristics of theWaze data. Moreover, a method to identify related Waze alerts and to determine matchingincident reports from different sources is presented. The results showed that the number ofreported incidents in Waze is 4,5 times higher than the number of registered incidents bythe Swedish authorities. Furthermore, 27,5 % of the incidents could have been detectedfaster by using the traffic alerts from Waze. In addition, the severity of Waze alerts isexamined depending on the attributes of the alerts.
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Wan, Kin-yung. "Biham-middleton-levine traffic model in different spatial dimensions /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B20128538.
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Yue, Yang. "Spatial-temporal dependency of traffic flow and its implications for short-term traffic forecasting." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B35507366.
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Yue, Yang, and 樂陽. "Spatial-temporal dependency of traffic flow and its implications for short-term traffic forecasting." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B35507366.
Full textBooks on the topic "Trafic spatial"
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Urban spatial traffic patterns. London: Pion, 1987.
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Eliahu, Stern, Salomon Ilan, and Bovy, Piet H. L., 1943-, eds. Travel behaviour: Spatial patterns, congestion and modelling. Cheltenham, UK: E. Elgar Pub., 2002.
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Aura, Reggiani, and Nijkamp Peter, eds. Spatial dynamics, networks and modelling. Cheltenham, UK: Edward Elgar, 2006.
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Johann, Andersen S., Texas. Dept. of Transportation., United States. Federal Highway Administration., and University of Texas at Austin. Center for Transportation Research., eds. Traffic and spatial impacts and the classification of small highway-bypassed cities. Austin, Tex. (Center for Transportation Research, University of Texas at Austin, Austin 78712-1075): The Center, 1992.
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1958-, Axhausen K. W., ed. Urban rhythms and travel behaviour: Spatial and temporal phenomena of daily travel. Farnham, England: Ashgate, 2010.
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Strauss, Tim. Spatial scale of clustering of motor vehicle crash types and appropriate countermeasures. Ames, IA: Midwest Transportation Consortium, Iowa State University, 2009.
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Christian, Dussault, and Québec (Province). Ministère des ressources naturelles et de la faune., eds. Répartition temporelle et spatiale des accidents routiers impliquant l'orignal dans la Réserve faunique des Laurentides de 1990 à 2002. Québec: Ministère des ressources naturelles et de la faune, 2004.
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Provinciliens : les voyageurs du quotidien, entre capitale et province. Paris: L'Harmattan, 2001.
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Mathis, Philippe. Graphs and networks: Multilevel modeling. 2nd ed. London: J. Wiley & Sons, 2010.
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Philippe, Mathis, ed. Graphs and networks: Multilevel modeling. 2nd ed. London: J. Wiley & Sons, 2010.
Find full textBook chapters on the topic "Trafic spatial"
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Brinkhoff, Thomas. "Requirements of Traffic Telematics to Spatial Databases." In Advances in Spatial Databases, 365–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48482-5_23.
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Zhuang, Yan, Cheng-Lin Ma, Jin-Yun Xie, Zhui-Ri Li, and Yang Yue. "A Fast Clustering Approach for Identifying Traffic Congestions." In Spatial Data and Intelligence, 3–13. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69873-7_1.
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Meneguzzer, Claudio. "Stochastic User Equilibrium Assignment with Traffic-Responsive Signal Control." In Advances in Spatial Science, 382–400. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-59787-9_18.
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Bernstein, David, and Terry L. Friesz. "Infinite Dimensional Formulations of Some Dynamic Traffic Assignment Models." In Advances in Spatial Science, 112–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72242-4_7.
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Emmerink, Richard H. M. "Radio Traffic and Variable Message Sign Information; An Empirical Analysis." In Advances in Spatial Science, 235–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72143-4_13.
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Nagurney, Anna, and Ding Zhang. "Introduction to Projected Dynamical Systems for Traffic Network Equilibrium Problems." In Advances in Spatial Science, 125–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72242-4_8.
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Haynes, Kingsley, Rajendra Kulkarni, and Roger Stough. "Hidden Order in Traffic Flows Using Approximate Entropy: An Illustration." In Advances in Spatial Science, 143–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01017-0_9.
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Dong, Hui, Xiao Pan, Xiao Chen, Jing Sun, and Shuhai Wang. "DyAdapTransformer: Dynamic Adaptive Spatial-Temporal Graph Transformer for Traffic Prediction." In Spatial Data and Intelligence, 228–41. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2966-1_17.
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Mokbel, Mohamed F., Louai Alarabi, Jie Bao, Ahmed Eldawy, Amr Magdy, Mohamed Sarwat, Ethan Waytas, and Steven Yackel. "MNTG: An Extensible Web-Based Traffic Generator." In Advances in Spatial and Temporal Databases, 38–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40235-7_3.
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Li, Lutong, Mengmeng Chang, Zhiming Ding, Zunhao Liu, and Nannan Jia. "A Dynamic Traffic Community Prediction Model Based on Hierarchical Graph Attention Network." In Spatial Data and Intelligence, 15–26. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85462-1_2.
Full textConference papers on the topic "Trafic spatial"
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Hu, Chunchun, Wenzhong Shi, Lingkui Meng, and Min Liu. "Applying fuzzy clustering optimization algorithm to extracting traffic spatial pattern." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838628.
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Rao, Xuan, Hao Wang, Liang Zhang, Jing Li, Shuo Shang, and Peng Han. "FOGS: First-Order Gradient Supervision with Learning-based Graph for Traffic Flow Forecasting." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/545.
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Traffic flow forecasting plays a vital role in the transportation domain. Existing studies usually manually construct correlation graphs and design sophisticated models for learning spatial and temporal features to predict future traffic states. However, manually constructed correlation graphs cannot accurately extract the complex patterns hidden in the traffic data. In addition, it is challenging for the prediction model to fit traffic data due to its irregularly-shaped distribution. To solve the above-mentioned problems, in this paper, we propose a novel learning-based method to learn a spatial-temporal correlation graph, which could make good use of the traffic flow data. Moreover, we propose First-Order Gradient Supervision (FOGS), a novel method for traffic flow forecasting. FOGS utilizes first-order gradients, rather than specific flows, to train prediction model, which effectively avoids the problem of fitting irregularly-shaped distributions. Comprehensive numerical evaluations on four real-world datasets reveal that the proposed methods achieve state-of-the-art performance and significantly outperform the benchmarks.
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Yu, Bing, Haoteng Yin, and Zhanxing Zhu. "Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/505.
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Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect spatial and temporal dependencies. In this paper, we propose a novel deep learning framework, Spatio-Temporal Graph Convolutional Networks (STGCN), to tackle the time series prediction problem in traffic domain. Instead of applying regular convolutional and recurrent units, we formulate the problem on graphs and build the model with complete convolutional structures, which enable much faster training speed with fewer parameters. Experiments show that our model STGCN effectively captures comprehensive spatio-temporal correlations through modeling multi-scale traffic networks and consistently outperforms state-of-the-art baselines on various real-world traffic datasets.
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Zhang, Dongran, Gang Luo, and Jun Li. "Traffic Spatial-Temporal Prediction Based on Neural Architecture Search." In SSTD '23: Symposium on Spatial and Temporal Data. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3609956.3609962.
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Duarte, Mariana M. G., Marcos V. Pontarolo, Rebeca Schroeder, and Carmem S. Hara. "MIDET: A Method for Indexing Traffic Events." In Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/sbbd.2021.17879.
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Traffic events announcements such as jams and road closures are continuously reported by mobile and Web applications. This collection of spatio-temporal data is an important source of information for urban planning, and can be used to orchestrate a number of actions to mprove the mobility, such as traffic control, traffic lights synchronization and preventive maintenance. Such analysis usually involves computation of spatial relationships among data, and may involve location of landmarks, roads and different types of events. In this paper, we propose a Method for Indexing Traffic Events (MIDET) for querying spatio-temporal data, whose location can be represented as a point or collection of points. MIDET is based on a fixed-grid space-oriented partitioning. In order to tackle the data skew, each grid cell is associated with a set of blocks containing event records. Moreover, a bitmap index is used for filtering out blocks without retrieving the actual data. MIDET provides the following benefits: adoption of a simple bulk loading process to manage dynamic insertion streams, and in-memory spatial joins. We conducted an experimental study using real data obtained from Waze. MIDET’s query performance was compared with Postgis, which adopts an R-tree index structure.
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Aldwyish, Abdullah, Egemen Tanin, Hairuo Xie, Shanika Karunasekera, and Kotagiri Ramamohanarao. "Effective Traffic Forecasting with Multi-Resolution Learning." In SSTD '21: 17th International Symposium on Spatial and Temporal Databases. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3469830.3470904.
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Pedersen, Kasper F., and Kristian Torp. "Geolocating Traffic Signs using Large Imagery Datasets." In SSTD '21: 17th International Symposium on Spatial and Temporal Databases. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3469830.3470900.
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He, Zhiyang, and Ye Ding. "Traffic Spatial-Temporal Transformer for Traffic Prediction." In 2023 4th International Symposium on Computer Engineering and Intelligent Communications (ISCEIC). IEEE, 2023. http://dx.doi.org/10.1109/isceic59030.2023.10271152.
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Patroumpas, Kostas, and Serafeim Papadias. "Trajectory-aware Load Adaption for Continuous Traffic Analytics." In SSTD '19: 16th International Symposium on Spatial and Temporal Databases. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3340964.3340967.
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Han, Shilin, Tian Xu, Tao You, and Liping Zhao. "Ports Spatial Structure Analytical Method and Case Study." In Seventh International Conference on Traffic and Transportation Studies (ICTTS) 2010. Reston, VA: American Society of Civil Engineers, 2010. http://dx.doi.org/10.1061/41123(383)12.
Full textReports on the topic "Trafic spatial"
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Crovella, Mark, and Eric Kolaczyk. Graph Wavelets for Spatial Traffic Analysis. Fort Belvoir, VA: Defense Technical Information Center, July 2002. http://dx.doi.org/10.21236/ada442573.
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Schluckebier, Kai. Intersections in contemporary traffic planning. Goethe-Universität, Institut für Humangeographie, August 2021. http://dx.doi.org/10.21248/gups.58866.
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In Germany, traffic planning still follows the tradition of modernist urban planning theory from the beginning of the 1930s and car-oriented city planning during the post-war period in West Germany. From a methodological perspective, the prevailing narrative is that traffic can be abstracted and modelled under laboratory conditions (in vitro) as a spatial movement process of individual neutral particles. The use of these laboratory experiments in traffic planning cannot be understood as a neutral application of experimental results, assumed to be true, in a variety of spatial contexts. Rather, it is an active practice of staging traffic according to a particular social interactionist paradigm. According to this, traffic is staged through interventions in planning authorities as well as the practices of people on the streets. In order to describe these staging conduits, traffic is ontologically thought of as a social order that is continuously reproduced situationally through interactions, following Erving Goffman and Harold Garfinkel. To investigate the staging conduits empirically, an ethnographic-inspired field study was conducted at Willy-Brandt-Platz in Frankfurt am Main in May and June 2020. Through situational mapping and observation of social interactions (in situ), knowledge about the staging of social orders was generated. These empirical findings are further embedded in debates that discuss traffic not only as a staging but also as an enactment of certain realities. Understanding planning practice as a political enactment, through which realities are not only described but also made, makes it possible for us to think and design alternative realities.
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Salgado, Edgar, and Oscar A. Mitnik. Spatial and Time Spillovers of Driving Restrictions: Causal Evidence from Limas Pico y Placa Policy. Inter-American Development Bank, December 2021. http://dx.doi.org/10.18235/0003849.
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Driving restrictions are popular interventions in rapidly urbanizing developing countries. Their relatively inexpensive implementation appeals to the pressing need to reduce traffic congestion and pollution. Their effectiveness however, remains contested. Using high frequency data from the community-based driving directions app Waze, we evaluate the causal effect on traffic congestion of Lima's Pico y Placa driving restriction policy introduced in 2019. We find small improvements in traffic congestion for the policy's directly targeted areas. However, those improvements are offset by time and spatial spillovers in the opposite direction in the aggregate. Speed improved by 2 percent during the early weeks of the intervention, but this effect disappeared 16 weeks after the start of the policy. Moreover, traffic conditions worsened in adjacent areas and in hours outside the time schedule of the policy. In the aggregate, accounting for time and spatial spillovers, a simulation exercise suggests that overall welfare declined by 2 percent, mostly driven by the extensive margin (more roads becoming congested) outside the direct areas and hours targeted by the policy. The policy seems not only to have failed to achieve its intended benefits in terms of congestion, but also probably caused increases in traffic-related pollution. These results highlight the need for policy makers to take into account the overall impacts of driving restrictions policies before implementing them.
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Mathew, Jijo K., Haydn Malackowski, Yerassyl Koshan, Christopher Gartner, Jairaj Desai, Howell Li, Edward Cox, Ayman Habib, and Darcy M. Bullock. Development of Latitude/Longitude (and Route/Milepost) Model for Positioning Traffic Management Cameras. Purdue University, 2024. http://dx.doi.org/10.5703/1288284317720.
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Traffic Incident Management (TIM) is a FHWA Every Day Counts initiative with the objective of reducing secondary crashes, improving travel reliability, and ensuring the safety of responders. Agency roadside cameras play a critical role in TIM by helping dispatchers quickly identify the precise location of incidents when receiving reports from motorists with varying levels of spatial accuracy. Reconciling position reports that are often mile-marker based with cameras that operate in a Pan-Tilt-Zoom (PTZ) coordinate system relies on dispatchers having detailed knowledge of hundreds of cameras and perhaps some presets. During real-time incident dispatching, reducing the time it takes to identify the most relevant cameras and view the incident improves incident management dispatch times. This research developed a camera-to-mile marker mapping technique that automatically sets the camera view to a specified mile marker within the field-of-view of the camera. A new performance metric on verification time (TEYE) that captures the time it takes for TMC operators to have the first visual on roadside cameras is proposed for integration into the FHWA TIM event sequence. Performance metrics that summarize spatial camera coverage and image quality for use in both dispatch and long-term statewide planning for camera deployments were also developed. Using mobile mapping and LiDAR geospatial data to automate the mapping of mile markers to camera PTZ settings, and the integration of connected vehicle trajectory data to detect incidents and set the nearest camera view on the incident are both discussed for future studies.
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Mathew, Sonu, Srinivas S. Pulugurtha, and Sarvani Duvvuri. Modeling and Predicting Geospatial Teen Crash Frequency. Mineta Transportation Institute, June 2022. http://dx.doi.org/10.31979/mti.2022.2119.
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This research project 1) evaluates the effect of road network, demographic, and land use characteristics on road crashes involving teen drivers, and, 2) develops and compares the predictability of local and global regression models in estimating teen crash frequency. The team considered data for 201 spatially distributed road segments in Mecklenburg County, North Carolina, USA for the evaluation and obtained data related to teen crashes from the Highway Safety Information System (HSIS) database. The team extracted demographic and land use characteristics using two different buffer widths (0.25 miles and 0.5 miles) at each selected road segment, with the number of crashes on each road segment used as the dependent variable. The generalized linear models with negative binomial distribution (GLM-based NB model) as well as the geographically weighted negative binomial regression (GWNBR) and geographically weighted negative binomial regression model with global dispersion (GWNBRg) were developed and compared. This research relied on data for 147 geographically distributed road segments for modeling and data for 49 segments for validation. The annual average daily traffic (AADT), light commercial land use, light industrial land use, number of household units, and number of pupils enrolled in public or private high schools are significant explanatory variables influencing the teen crash frequency. Both methods have good predictive capabilities and can be used to estimate the teen crash frequency. However, the GWNBR and GWNBRg better capture the spatial dependency and spatial heterogeneity among road teen crashes and the associated risk factors.
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Tarko, Andrew P., Mario A. Romero, Vamsi Krishna Bandaru, and Xueqian Shi. Guidelines for Evaluating Safety Using Traffic Encounters: Proactive Crash Estimation on Roadways with Conventional and Autonomous Vehicle Scenarios. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317587.
Full textAbstract:
With the expected arrival of autonomous vehicles, and the ever-increasing levels of automation in today’s human driven vehicles, road safety is changing at a rapid pace. This project aimed to address the need for an efficient and rapid method of safety evaluation and countermeasure identification via traffic encounters, specifically traffic conflicts that are considered useful surrogates of crashes. Recent research-delivered methods for estimating crash frequencies based on these events were observed in the field. In this project we developed a method for observing traffic encounters with two LiDAR-based traffic monitoring units, called TScan, which were recently developed in JTRP-funded projects SPR-3831 and SPR-4102. The TScan units were deployed in the field for several hours to collect data at selected intersections. These large data sets were used to improve object detection and tracking algorithms in order to better assist in detecting traffic encounters and conflicts. Consequently, the software of the TScan trailer-based units was improved and the results generated with the upgraded system include a list of potential encounters for further analysis. We developed an engineering application for analyzing the trajectories of vehicles involved in the pre-selected encounters to identify final traffic encounters and conflicts. Another module of the engineering application visualized the traffic encounters and conflicts to inspect the spatial patterns of these events and to estimate the number of crashes for the observation period. Furthermore, a significant modeling effort resulted in a method of producing factors that expand the conflict-based crash estimates in short observation periods to an entire year. This report provides guidelines for traffic encounters and conflicts, the user manuals for setting up and operating the TScan research unit. and manuals for the engineering applications mentioned above.
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Kwon, Jaymin, Yushin Ahn, and Steve Chung. Spatio-Temporal Analysis of the Roadside Transportation Related Air Quality (STARTRAQ) and Neighborhood Characterization. Mineta Transportation Institute, August 2021. http://dx.doi.org/10.31979/mti.2021.2010.
Full textAbstract:
To promote active transportation modes (such as bike ride and walking), and to create safer communities for easier access to transit, it is essential to provide consolidated data-driven transportation information to the public. The relevant and timely information from data facilitates the improvement of decision-making processes for the establishment of public policy and urban planning for sustainable growth, and for promoting public health in the region. For the characterization of the spatial variation of transportation-emitted air pollution in the Fresno/Clovis neighborhood in California, various species of particulate matters emitted from traffic sources were measured using real-time monitors and GPS loggers at over 100 neighborhood walking routes within 58 census tracts from the previous research, Children’s Health to Air Pollution Study - San Joaquin Valley (CHAPS-SJV). Roadside air pollution data show that PM2.5, black carbon, and PAHs were significantly elevated in the neighborhood walking air samples compared to indoor air or the ambient monitoring station in the Central Fresno area due to the immediate source proximity. The simultaneous parallel measurements in two neighborhoods which are distinctively different areas (High diesel High poverty vs. Low diesel Low poverty) showed that the higher pollution levels were observed when more frequent vehicular activities were occurring around the neighborhoods. Elevated PM2.5 concentrations near the roadways were evident with a high volume of traffic and in regions with more unpaved areas. Neighborhood walking air samples were influenced by immediate roadway traffic conditions, such as encounters with diesel trucks, approaching in close proximity to freeways and/or busy roadways, passing cigarette smokers, and gardening activity. The elevated black carbon concentrations occur near the highway corridors and regions with high diesel traffic and high industry. This project provides consolidated data-driven transportation information to the public including: 1. Transportation-related particle pollution data 2. Spatial analyses of geocoded vehicle emissions 3. Neighborhood characterization for the built environment such as cities, buildings, roads, parks, walkways, etc.
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Ukkusuri, Satish, Lu Ling, Tho V. Le, and Wenbo Zhang. Performance of Right-Turn Lane Designs at Intersections. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317277.
Full textAbstract:
Right-turn lane (RTL) crashes are among the most key contributors to intersection crashes in the US. Different right turn lanes based on their design, traffic volume, and location have varying levels of crash risk. Therefore, engineers and researchers have been looking for alternative ways to improve the safety and operations for right-turn traffic. This study investigates the traffic safety performance of the RTL in Indiana state based on multi-sources, including official crash reports, official database, and field study. To understand the RTL crashes' influencing factors, we introduce a random effect negative binomial model and log-linear model to estimate the impact of influencing factors on the crash frequency and severity and adopt the robustness test to verify the reliability of estimations. In addition to the environmental factors, spatial and temporal factors, intersection, and RTL geometric factors, we propose build environment factors such as the RTL geometrics and intersection characteristics to address the endogeneity issues, which is rarely addressed in the accident-related research literature. Last, we develop a case study with the help of the Indiana Department of Transportation (INDOT). The empirical analyses indicate that RTL crash frequency and severity is mainly influenced by turn radius, traffic control, and other intersection related factors such as right-turn type and speed limit, channelized type, and AADT, acceleration lane and AADT. In particular, the effects of these factors are different among counties and right turn lane roadway types.
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Tarko, Andrew P., Mario A. Romero, Vamsi Krishna Bandaru, and Cristhian Lizarazo. TScan–Stationary LiDAR for Traffic and Safety Applications: Vehicle Interpretation and Tracking. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317402.
Full textAbstract:
To improve traffic performance and safety, the ability to measure traffic accurately and effectively, including motorists and other vulnerable road users, at road intersections is needed. A past study conducted by the Center for Road Safety has demonstrated that it is feasible to detect and track various types of road users using a LiDAR-based system called TScan. This project aimed to progress towards a real-world implementation of TScan by building two trailer-based prototypes with full end-user documentation. The previously developed detection and tracking algorithms have been modified and converted from the research code to its implementational version written in the C++ programming language. Two trailer-based TScan units have been built. The design of the prototype was iterated multiple times to account for component placement, ease of maintenance, etc. The expansion of the TScan system from a one single-sensor unit to multiple units with multiple LiDAR sensors necessitated transforming all the measurements into a common spatial and temporal reference frame. Engineering applications for performing traffic counts, analyzing speeds at intersections, and visualizing pedestrian presence data were developed. The limitations of the existing SSAM for traffic conflicts analysis with computer simulation prompted the research team to develop and implement their own traffic conflicts detection and analysis technique that is applicable to real-world data. Efficient use of the development system requires proper training of its end users. An INDOT-CRS collaborative process was developed and its execution planned to gradually transfer the two TScan prototypes to INDOT’s full control. This period will be also an opportunity for collecting feedback from the end user and making limited modifications to the system and documentation as needed.
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Li, Howell, Jijo K. Mathew, Woosung Kim, and Darcy M. Bullock. Using Crowdsourced Vehicle Braking Data to Identify Roadway Hazards. Purdue University, 2020. http://dx.doi.org/10.5703/1288284317272.
Full textAbstract:
Modern vehicles know more about the road conditions than transportation agencies. Enhanced vehicle data that provides information on “close calls” such as hard braking events or road conditions during winter such as wheel slips and traction control will be critical for improving safety and traffic operations. This research applied conflict analyses techniques to process approximately 1.5 million hard braking events that occurred in the state of Indiana over a period of one week in August 2019. The study looked at work zones, signalized intersections, interchanges and entry/exit ramps. Qualitative spatial frequency analysis of hard-braking events on the interstate demonstrated the ability to quickly identify temporary and long-term construction zones that warrant further investigation to improve geometry and advance warning signs. The study concludes by recommending the frequency of hard-braking events across different interstate routes to identify roadway locations that have abnormally high numbers of “close calls” for further engineering assessment.