Artykuły w czasopismach na temat „Trafic spatial”
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Pogorelov, A. V., K. R. Golovan i 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 (1.01.2015): 548–52. http://dx.doi.org/10.24057/2414-9179-2015-1-21-548-552.
Pełny tekst źródłaLiu, Shaohua, Shijun Dai, Jingkai Sun, Tianlu Mao, Junsuo Zhao i 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.
Pełny tekst źródłaZhang, Shen, Jinjun Tang, Hua Wang i 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 (styczeń 2015): 69–77. http://dx.doi.org/10.3141/2528-08.
Pełny tekst źródłaTanner, John. "Urban spatial traffic patterns". Transportation Research Part A: General 24, nr 5 (wrzesień 1990): 397–98. http://dx.doi.org/10.1016/0191-2607(90)90052-8.
Pełny tekst źródłaLi, Tian, Mengmeng Zhang, Haobin Jiang i 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.
Pełny tekst źródłaLiao, Wanying, Hongtao Wang i 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.
Pełny tekst źródłaYAMAGUCHI, Hiromichi, i 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_.
Pełny tekst źródłaBraxmeier, Hans, Volker Schmidt i Evgueni Spodarev. "SPATIAL EXTRAPOLATION OF ANISOTROPIC ROAD TRAFFIC DATA". Image Analysis & Stereology 23, nr 3 (3.05.2011): 185. http://dx.doi.org/10.5566/ias.v23.p185-198.
Pełny tekst źródłaPavlyuk, Dmitry. "Temporal Aggregation Effects in Spatiotemporal Traffic Modelling". Sensors 20, nr 23 (4.12.2020): 6931. http://dx.doi.org/10.3390/s20236931.
Pełny tekst źródłaXiong, Liyan, Weihua Ding, Xiaohui Huang i 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.
Pełny tekst źródłaPavlyuk. "Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting". Algorithms 13, nr 2 (13.02.2020): 39. http://dx.doi.org/10.3390/a13020039.
Pełny tekst źródłaFeng, Jian, Lang Yu i Rui Ma. "AGCN-T: A Traffic Flow Prediction Model for Spatial-Temporal Network Dynamics". Journal of Advanced Transportation 2022 (29.05.2022): 1–12. http://dx.doi.org/10.1155/2022/1217588.
Pełny tekst źródłaTassadit Dial, Rania, i Gabriel Figueiredo De Oliveira. "Accessibilité à l’arrière-pays, connectivité maritime et relations interportuaires : une analyse spatiale". Revue d’Économie Régionale & Urbaine Octobre, nr 4 (19.10.2023): 579–607. http://dx.doi.org/10.3917/reru.234.0579.
Pełny tekst źródłaKumar, Dr T. Senthil. "Video based Traffic Forecasting using Convolution Neural Network Model and Transfer Learning Techniques". Journal of Innovative Image Processing 2, nr 3 (17.06.2020): 128–34. http://dx.doi.org/10.36548/jiip.2020.3.002.
Pełny tekst źródłaGao, Jingqin, Kun Xie i Kaan Ozbay. "Exploring the Spatial Dependence and Selection Bias of Double Parking Citations Data". Transportation Research Record: Journal of the Transportation Research Board 2672, nr 42 (18.08.2018): 159–69. http://dx.doi.org/10.1177/0361198118792323.
Pełny tekst źródłaKošanin, Ivan, Milan Gnjatović, Nemanja Maček i Dušan Joksimović. "A Clustering-Based Approach to Detecting Critical Traffic Road Segments in Urban Areas". Axioms 12, nr 6 (24.05.2023): 509. http://dx.doi.org/10.3390/axioms12060509.
Pełny tekst źródłaAbduljabbar, Rusul, Hussein Dia, Pei-Wei Tsai i Sohani Liyanage. "Short-Term Traffic Forecasting: An LSTM Network for Spatial-Temporal Speed Prediction". Future Transportation 1, nr 1 (30.03.2021): 21–37. http://dx.doi.org/10.3390/futuretransp1010003.
Pełny tekst źródłaChang, Zhihong, Chunsheng Liu i Jianmin Jia. "STA-GCN: Spatial-Temporal Self-Attention Graph Convolutional Networks for Traffic-Flow Prediction". Applied Sciences 13, nr 11 (2.06.2023): 6796. http://dx.doi.org/10.3390/app13116796.
Pełny tekst źródłaHuang, Xiaohui, Yuanchun Lan, Yuming Ye, Junyang Wang i Yuan Jiang. "Traffic Flow Prediction Based on Multi-Mode Spatial-Temporal Convolution of Mixed Hop Diffuse ODE". Electronics 11, nr 19 (22.09.2022): 3012. http://dx.doi.org/10.3390/electronics11193012.
Pełny tekst źródłaGe, Liang, Siyu Li, Yaqian Wang, Feng Chang i Kunyan Wu. "Global Spatial-Temporal Graph Convolutional Network for Urban Traffic Speed Prediction". Applied Sciences 10, nr 4 (22.02.2020): 1509. http://dx.doi.org/10.3390/app10041509.
Pełny tekst źródłaGoścień, Róża. "On the Efficient Flow Restoration in Spectrally-Spatially Flexible Optical Networks". Electronics 10, nr 12 (18.06.2021): 1468. http://dx.doi.org/10.3390/electronics10121468.
Pełny tekst źródłaJiang, Jiawei, Chengkai Han, Wayne Xin Zhao i Jingyuan Wang. "PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 4 (26.06.2023): 4365–73. http://dx.doi.org/10.1609/aaai.v37i4.25556.
Pełny tekst źródłaBarthelemy, Marc, Bernard Gondran i Eric Guichard. "Spatial structure of the internet traffic". Physica A: Statistical Mechanics and its Applications 319 (marzec 2003): 633–42. http://dx.doi.org/10.1016/s0378-4371(02)01382-1.
Pełny tekst źródłaDu, Wen-Bo, Xing-Lian Zhou, Zhen Chen, Kai-Quan Cai i Xian-Bin Cao. "Traffic dynamics on coupled spatial networks". Chaos, Solitons & Fractals 68 (listopad 2014): 72–77. http://dx.doi.org/10.1016/j.chaos.2014.07.009.
Pełny tekst źródłaNewell, Gordon F. "Comments on spatial models of traffic". Transportation Research Part B: Methodological 27, nr 3 (czerwiec 1993): 185–88. http://dx.doi.org/10.1016/0191-2615(93)90028-9.
Pełny tekst źródłaYALÇIN, Güler. "SPATIAL ANALYSIS OF THE TRAFFIC ACCIDENTS FOR URBAN TRAFFIC MANAGEMENT". INTERNATIONAL REFEREED JOURNAL OF ENGINEERING AND SCIENCES 2, nr 3 (30.04.2015): 1. http://dx.doi.org/10.17366/uhmfd.2015310571.
Pełny tekst źródłaXiao, Tianzheng, Huapu Lu, Jianyu Wang i Katrina Wang. "Predicting and Interpreting Spatial Accidents through MDLSTM". International Journal of Environmental Research and Public Health 18, nr 4 (3.02.2021): 1430. http://dx.doi.org/10.3390/ijerph18041430.
Pełny tekst źródłaYang, Yanfang, Jiandong Cao, Yong Qin, Limin Jia, Honghui Dong i Aomuhan Zhang. "Spatial correlation analysis of urban traffic state under a perspective of community detection". International Journal of Modern Physics B 32, nr 12 (3.05.2018): 1850150. http://dx.doi.org/10.1142/s0217979218501503.
Pełny tekst źródłaXu, Chengcheng, Chen Wang, Wei Wang, Jie Bao i Menglin Yang. "Investigating Spatial Interdependence in E-Bike Choice Using Spatially Autoregressive Model". PROMET - Traffic&Transportation 29, nr 4 (28.08.2017): 351–62. http://dx.doi.org/10.7307/ptt.v29i4.2144.
Pełny tekst źródłaGao, Min, Yingmei Wei, Yuxiang Xie i Yitong Zhang. "Traffic Prediction with Self-Supervised Learning: A Heterogeneity-Aware Model for Urban Traffic Flow Prediction Based on Self-Supervised Learning". Mathematics 12, nr 9 (24.04.2024): 1290. http://dx.doi.org/10.3390/math12091290.
Pełny tekst źródłaLac, C., R. P. Donnelly, V. Masson, S. Pal, S. Donier, S. Queguiner, G. Tanguy, L. Ammoura i I. Xueref-Remy. "CO<sub>2</sub> dispersion modelling over Paris region within the CO<sub>2</sub>-MEGAPARIS project". Atmospheric Chemistry and Physics Discussions 12, nr 10 (25.10.2012): 28155–93. http://dx.doi.org/10.5194/acpd-12-28155-2012.
Pełny tekst źródłaYi, Ran, Yang Zhou, Xin Wang, Zhiyuan Liu, Xiaotian Li i Bin Ran. "Spatially Formulated Connected Automated Vehicle Trajectory Optimization with Infrastructure Assistance". Journal of Advanced Transportation 2022 (20.05.2022): 1–15. http://dx.doi.org/10.1155/2022/6184790.
Pełny tekst źródłaGe, Fengjian, Wanxu Chen, Yuanyuan Zeng i Jiangfeng Li. "The Nexus between Urbanization and Traffic Accessibility in the Middle Reaches of the Yangtze River Urban Agglomerations, China". International Journal of Environmental Research and Public Health 18, nr 7 (6.04.2021): 3828. http://dx.doi.org/10.3390/ijerph18073828.
Pełny tekst źródłaYin, Hong Yan. "Study of Traffic Accessibility in Poyang Lake Economic Zone Oriented by High-Speed Railway". Applied Mechanics and Materials 178-181 (maj 2012): 1778–81. http://dx.doi.org/10.4028/www.scientific.net/amm.178-181.1778.
Pełny tekst źródłaLian, Qingyun, Wei Sun i Wei Dong. "Hierarchical Spatial-Temporal Neural Network with Attention Mechanism for Traffic Flow Forecasting". Applied Sciences 13, nr 17 (28.08.2023): 9729. http://dx.doi.org/10.3390/app13179729.
Pełny tekst źródłaJiang, Wenhao, Yunpeng Xiao, Yanbing Liu, Qilie Liu i Zheng Li. "Bi-GRCN: A Spatio-Temporal Traffic Flow Prediction Model Based on Graph Neural Network". Journal of Advanced Transportation 2022 (1.02.2022): 1–12. http://dx.doi.org/10.1155/2022/5221362.
Pełny tekst źródłaXu, Dong-wei, Yong-dong Wang, Li-min Jia, Gui-jun Zhang i Hai-feng Guo. "Compression Algorithm of Road Traffic Spatial Data Based on LZW Encoding". Journal of Advanced Transportation 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/8182690.
Pełny tekst źródłaZhang, Rui, Fei Xie, Jianjun Shi, Jing Zhao, Jiquan Yang i Xu Ling. "Spatial-Temporal Semantic Neural Network for Time Series Forecasting". Journal of Physics: Conference Series 2203, nr 1 (1.02.2022): 012033. http://dx.doi.org/10.1088/1742-6596/2203/1/012033.
Pełny tekst źródłaWu, Xiaoyun, i Cynthia Lum. "The practice of proactive traffic stops". Policing: An International Journal 43, nr 2 (26.11.2019): 229–46. http://dx.doi.org/10.1108/pijpsm-06-2019-0089.
Pełny tekst źródłaHan, Xing, Guowei Zhu, Ling Zhao, Ronghua Du, Yuhan Wang, Zhe Chen, Yang Liu i Silu He. "Ollivier–Ricci Curvature Based Spatio-Temporal Graph Neural Networks for Traffic Flow Forecasting". Symmetry 15, nr 5 (27.04.2023): 995. http://dx.doi.org/10.3390/sym15050995.
Pełny tekst źródłaZhou, Junwei, Xizhong Qin, Yuanfeng Ding i Haodong Ma. "Spatial–Temporal Dynamic Graph Differential Equation Network for Traffic Flow Forecasting". Mathematics 11, nr 13 (26.06.2023): 2867. http://dx.doi.org/10.3390/math11132867.
Pełny tekst źródłaIštoka Otković, Irena, Barbara Karleuša, Aleksandra Deluka-Tibljaš, Sanja Šurdonja i Mario Marušić. "Combining Traffic Microsimulation Modeling and Multi-Criteria Analysis for Sustainable Spatial-Traffic Planning". Land 10, nr 7 (24.06.2021): 666. http://dx.doi.org/10.3390/land10070666.
Pełny tekst źródłaYu, Hongru, Shejun Deng, Caoye Lu, Yucheng Tang, Shijun Yu, Lu Liu i Tao Ji. "Research on the Evolution Mechanism of Congestion in the Entrances and Exits of Parking Facilities Based on the Improved Spatial Autoregressive Model". Journal of Advanced Transportation 2021 (29.08.2021): 1–15. http://dx.doi.org/10.1155/2021/8380247.
Pełny tekst źródłaLi, Y., Q. Zhao i M. Wang. "ANALYSIS THE INFLUENCING FACTORS OF URBAN TRAFFIC FLOWS BY USING NEW AND EMERGING URBAN BIG DATA AND DEEP LEARNING". International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022 (2.06.2022): 537–43. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2022-537-2022.
Pełny tekst źródłaZhou, Shaobo, Xiaodong Zang, Junheng Yang, Wanying Chen, Jiahao Li i Shuyi Chen. "Modelling the Coupling Relationship between Urban Road Spatial Structure and Traffic Flow". Sustainability 15, nr 14 (17.07.2023): 11142. http://dx.doi.org/10.3390/su151411142.
Pełny tekst źródłaChen, Renyi, i Huaxiong Yao. "Hybrid Graph Models for Traffic Prediction". Applied Sciences 13, nr 15 (27.07.2023): 8673. http://dx.doi.org/10.3390/app13158673.
Pełny tekst źródłaZhang, Xiao Na, Ming Yao, Feng Zhu i Jie Ni. "Traffic Image Segmentation Based on Gaussian Mixture Model with Spatial Information and Sampling". Applied Mechanics and Materials 380-384 (sierpień 2013): 3702–5. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3702.
Pełny tekst źródłaZeng, Hui, Chaojie Jiang, Yuanchun Lan, Xiaohui Huang, Junyang Wang i Xinhua Yuan. "Long Short-Term Fusion Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting". Electronics 12, nr 1 (3.01.2023): 238. http://dx.doi.org/10.3390/electronics12010238.
Pełny tekst źródłaWang, Beibei, Youfang Lin, Shengnan Guo i Huaiyu Wan. "GSNet: Learning Spatial-Temporal Correlations from Geographical and Semantic Aspects for Traffic Accident Risk Forecasting". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 5 (18.05.2021): 4402–9. http://dx.doi.org/10.1609/aaai.v35i5.16566.
Pełny tekst źródłaCui, Jiaxing, Ruihao Li, Lingyu Zhang i Ying Jing. "Spatially Illustrating Leisure Agriculture: Empirical Evidence from Picking Orchards in China". Land 10, nr 6 (13.06.2021): 631. http://dx.doi.org/10.3390/land10060631.
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