Auswahl der wissenschaftlichen Literatur zum Thema „Trafic spatial“
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Zeitschriftenartikel zum Thema "Trafic spatial"
Pogorelov, A. V., K. R. Golovan und 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, Nr. 21 (01.01.2015): 548–52. http://dx.doi.org/10.24057/2414-9179-2015-1-21-548-552.
Der volle Inhalt der QuelleLiu, Shaohua, Shijun Dai, Jingkai Sun, Tianlu Mao, Junsuo Zhao und Heng Zhang. „Multicomponent Spatial-Temporal Graph Attention Convolution Networks for Traffic Prediction with Spatially Sparse Data“. Computational Intelligence and Neuroscience 2021 (23.12.2021): 1–12. http://dx.doi.org/10.1155/2021/9134942.
Der volle Inhalt der QuelleZhang, Shen, Jinjun Tang, Hua Wang und 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, Nr. 1 (Januar 2015): 69–77. http://dx.doi.org/10.3141/2528-08.
Der volle Inhalt der QuelleTanner, John. „Urban spatial traffic patterns“. Transportation Research Part A: General 24, Nr. 5 (September 1990): 397–98. http://dx.doi.org/10.1016/0191-2607(90)90052-8.
Der volle Inhalt der QuelleLi, Tian, Mengmeng Zhang, Haobin Jiang und 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 (14.09.2022): 1–11. http://dx.doi.org/10.1155/2022/8288248.
Der volle Inhalt der QuelleLiao, Wanying, Hongtao Wang und 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, Nr. 2 (25.06.2023): 388–409. http://dx.doi.org/10.17993/3ctecno.2023.v12n3e45.388-409.
Der volle Inhalt der QuelleYAMAGUCHI, Hiromichi, und 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_.
Der volle Inhalt der QuelleBraxmeier, Hans, Volker Schmidt und Evgueni Spodarev. „SPATIAL EXTRAPOLATION OF ANISOTROPIC ROAD TRAFFIC DATA“. Image Analysis & Stereology 23, Nr. 3 (03.05.2011): 185. http://dx.doi.org/10.5566/ias.v23.p185-198.
Der volle Inhalt der QuellePavlyuk, Dmitry. „Temporal Aggregation Effects in Spatiotemporal Traffic Modelling“. Sensors 20, Nr. 23 (04.12.2020): 6931. http://dx.doi.org/10.3390/s20236931.
Der volle Inhalt der QuelleXiong, Liyan, Weihua Ding, Xiaohui Huang und Weichun Huang. „CLSTAN: ConvLSTM-Based Spatiotemporal Attention Network for Traffic Flow Forecasting“. Mathematical Problems in Engineering 2022 (11.07.2022): 1–13. http://dx.doi.org/10.1155/2022/1604727.
Der volle Inhalt der QuelleDissertationen zum Thema "Trafic spatial"
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.
Der volle Inhalt der QuelleThe 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
Schiper, Nicole. „Traffic data sampling for air pollution estimation at different urban scales“. Thesis, Lyon, 2017. http://www.theses.fr/2017LYSET008/document.
Der volle Inhalt der QuelleRoad 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
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.
Der volle Inhalt der QuelleNowadays, 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
Chen, Guangshuo. „Human Habits Investigation : from Mobility Reconstruction to Mobile Traffic Prediction“. Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLX026/document.
Der volle Inhalt der QuelleThe 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
Fimbel, Amaury. „Origami à base de matériaux électroactifs pour des applications spatiales“. Electronic Thesis or Diss., Lyon, INSA, 2023. http://www.theses.fr/2023ISAL0071.
Der volle Inhalt der QuelleThis 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
Manout, Ouassim. „Spatial aggregation issues in traffic assignment models“. Thesis, Lyon, 2019. http://www.theses.fr/2019LYSE2014/document.
Der volle Inhalt der QuelleCities 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
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.
Der volle Inhalt der QuelleWan, 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.
Der volle Inhalt der QuelleYue, 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.
Der volle Inhalt der QuelleYue, Yang, und 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.
Der volle Inhalt der QuelleBücher zum Thema "Trafic spatial"
Urban spatial traffic patterns. London: Pion, 1987.
Den vollen Inhalt der Quelle findenEliahu, Stern, Salomon Ilan und Bovy, Piet H. L., 1943-, Hrsg. Travel behaviour: Spatial patterns, congestion and modelling. Cheltenham, UK: E. Elgar Pub., 2002.
Den vollen Inhalt der Quelle findenAura, Reggiani, und Nijkamp Peter, Hrsg. Spatial dynamics, networks and modelling. Cheltenham, UK: Edward Elgar, 2006.
Den vollen Inhalt der Quelle findenJohann, Andersen S., Texas. Dept. of Transportation., United States. Federal Highway Administration. und University of Texas at Austin. Center for Transportation Research., Hrsg. 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.
Den vollen Inhalt der Quelle finden1958-, Axhausen K. W., Hrsg. Urban rhythms and travel behaviour: Spatial and temporal phenomena of daily travel. Farnham, England: Ashgate, 2010.
Den vollen Inhalt der Quelle findenStrauss, Tim. Spatial scale of clustering of motor vehicle crash types and appropriate countermeasures. Ames, IA: Midwest Transportation Consortium, Iowa State University, 2009.
Den vollen Inhalt der Quelle findenChristian, Dussault, und Québec (Province). Ministère des ressources naturelles et de la faune., Hrsg. 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.
Den vollen Inhalt der Quelle findenProvinciliens : les voyageurs du quotidien, entre capitale et province. Paris: L'Harmattan, 2001.
Den vollen Inhalt der Quelle findenMathis, Philippe. Graphs and networks: Multilevel modeling. 2. Aufl. London: J. Wiley & Sons, 2010.
Den vollen Inhalt der Quelle findenPhilippe, Mathis, Hrsg. Graphs and networks: Multilevel modeling. 2. Aufl. London: J. Wiley & Sons, 2010.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Trafic spatial"
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.
Der volle Inhalt der QuelleZhuang, Yan, Cheng-Lin Ma, Jin-Yun Xie, Zhui-Ri Li und 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.
Der volle Inhalt der QuelleMeneguzzer, 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.
Der volle Inhalt der QuelleBernstein, David, und 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.
Der volle Inhalt der QuelleEmmerink, 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.
Der volle Inhalt der QuelleNagurney, Anna, und 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.
Der volle Inhalt der QuelleHaynes, Kingsley, Rajendra Kulkarni und 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.
Der volle Inhalt der QuelleDong, Hui, Xiao Pan, Xiao Chen, Jing Sun und 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.
Der volle Inhalt der QuelleMokbel, Mohamed F., Louai Alarabi, Jie Bao, Ahmed Eldawy, Amr Magdy, Mohamed Sarwat, Ethan Waytas und 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.
Der volle Inhalt der QuelleLi, Lutong, Mengmeng Chang, Zhiming Ding, Zunhao Liu und 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.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Trafic spatial"
Hu, Chunchun, Wenzhong Shi, Lingkui Meng und 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, herausgegeben von Yaolin Liu und Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838628.
Der volle Inhalt der QuelleRao, Xuan, Hao Wang, Liang Zhang, Jing Li, Shuo Shang und 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.
Der volle Inhalt der QuelleYu, Bing, Haoteng Yin und 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.
Der volle Inhalt der QuelleZhang, Dongran, Gang Luo und 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.
Der volle Inhalt der QuelleDuarte, Mariana M. G., Marcos V. Pontarolo, Rebeca Schroeder und 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.
Der volle Inhalt der QuelleAldwyish, Abdullah, Egemen Tanin, Hairuo Xie, Shanika Karunasekera und 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.
Der volle Inhalt der QuellePedersen, Kasper F., und 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.
Der volle Inhalt der QuelleHe, Zhiyang, und 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.
Der volle Inhalt der QuellePatroumpas, Kostas, und 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.
Der volle Inhalt der QuelleHan, Shilin, Tian Xu, Tao You und 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.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Trafic spatial"
Crovella, Mark, und Eric Kolaczyk. Graph Wavelets for Spatial Traffic Analysis. Fort Belvoir, VA: Defense Technical Information Center, Juli 2002. http://dx.doi.org/10.21236/ada442573.
Der volle Inhalt der QuelleSchluckebier, Kai. Intersections in contemporary traffic planning. Goethe-Universität, Institut für Humangeographie, August 2021. http://dx.doi.org/10.21248/gups.58866.
Der volle Inhalt der QuelleSalgado, Edgar, und Oscar A. Mitnik. Spatial and Time Spillovers of Driving Restrictions: Causal Evidence from Limas Pico y Placa Policy. Inter-American Development Bank, Dezember 2021. http://dx.doi.org/10.18235/0003849.
Der volle Inhalt der QuelleMathew, Jijo K., Haydn Malackowski, Yerassyl Koshan, Christopher Gartner, Jairaj Desai, Howell Li, Edward Cox, Ayman Habib und 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.
Der volle Inhalt der QuelleMathew, Sonu, Srinivas S. Pulugurtha und Sarvani Duvvuri. Modeling and Predicting Geospatial Teen Crash Frequency. Mineta Transportation Institute, Juni 2022. http://dx.doi.org/10.31979/mti.2022.2119.
Der volle Inhalt der QuelleTarko, Andrew P., Mario A. Romero, Vamsi Krishna Bandaru und 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.
Der volle Inhalt der QuelleKwon, Jaymin, Yushin Ahn und 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.
Der volle Inhalt der QuelleUkkusuri, Satish, Lu Ling, Tho V. Le und Wenbo Zhang. Performance of Right-Turn Lane Designs at Intersections. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317277.
Der volle Inhalt der QuelleTarko, Andrew P., Mario A. Romero, Vamsi Krishna Bandaru und Cristhian Lizarazo. TScan–Stationary LiDAR for Traffic and Safety Applications: Vehicle Interpretation and Tracking. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317402.
Der volle Inhalt der QuelleLi, Howell, Jijo K. Mathew, Woosung Kim und Darcy M. Bullock. Using Crowdsourced Vehicle Braking Data to Identify Roadway Hazards. Purdue University, 2020. http://dx.doi.org/10.5703/1288284317272.
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