142 Bibliography
[9] Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez- Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al. Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261, 2018.
[10] Michael Batty. The size, scale, and shape of cities.science, 319(5864):769–771, 2008.
[11] Asma Belhadi, Youcef Djenouri, Djamel Djenouri, and Jerry Chun-Wei Lin.
A recurrent neural network for urban long-term traffic flow forecasting.
Applied Intelligence, 50:3252–3265, 2020.
[12] Yoshua Bengio, Patrice Simard, and Paolo Frasconi. Learning long-term dependencies with gradient descent is difficult. IEEE transactions on neural networks, 5(2):157–166, 1994.
[13] Marcin Bernas, Bartłomiej Płaczek, Wojciech Korski, Piotr Loska, Jarosław Smyła, and Piotr Szymała. A survey and comparison of low-cost sensing technologies for road traffic monitoring. Sensors, 18(10):3243, 2018.
[14] Thierry Blu, Philippe Thévenaz, and Michael Unser. Linear interpolation revitalized. IEEE Transactions on Image Processing, 13(5):710–719, 2004.
[15] S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, and D.-U. Hwang. Complex networks: Structure and dynamics. Physics reports, 424(4):175–308, 2006.
[16] ULRIK BRANDES, GARRY ROBINS, ANN McCRANIE, and STANLEY WASSERMAN. What is network science?Network science (Cambridge Uni- versity Press), 1(1):1–15, 2013.
[17] Álvaro Briz-Redón and Ángel Serrano-Aroca. A spatio-temporal analysis for exploring the effect of temperature on covid-19 early evolution in spain.
Science of the total environment, 728:138811, 2020.
[18] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Ka- plan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901, 2020.
Bibliography 143
[19] Hongyun Cai, Vincent W Zheng, and Kevin Chen-Chuan Chang. A com- prehensive survey of graph embedding: Problems, techniques, and applica- tions. IEEE transactions on knowledge and data engineering, 30(9):1616–1637, 2018.
[20] Wei Cao, Dong Wang, Jian Li, Hao Zhou, Lei Li, and Yitan Li. Brits: Bidirec- tional recurrent imputation for time series. Advances in neural information processing systems, 31, 2018.
[21] Nikita Carney. All lives matter, but so does race: Black lives matter and the evolving role of social media. Humanity & society, 40(2):180–199, 2016.
[22] Sean M Carroll. Spacetime and geometry. Cambridge University Press, 2019.
[23] Pablo Samuel Castro, Daqing Zhang, Chao Chen, Shijian Li, and Gang Pan.
From taxi gps traces to social and community dynamics: A survey. ACM Computing Surveys (CSUR), 46(2):1–34, 2013.
[24] Buru Chang, Gwanghoon Jang, Seoyoon Kim, and Jaewoo Kang. Learning graph-based geographical latent representation for point-of-interest rec- ommendation. InProceedings of the 29th ACM International Conference on Information & Knowledge Management, pages 135–144, 2020.
[25] Chao Chen, Karl Petty, Alexander Skabardonis, Pravin Varaiya, and Zhan- feng Jia. Freeway performance measurement system: mining loop detector data. Transportation Research Record, 1748(1):96–102, 2001.
[26] Jie Chen, Tengfei Ma, and Cao Xiao. Fastgcn: fast learning with graph convo- lutional networks via importance sampling.arXiv preprint arXiv:1801.10247, 2018.
[27] Rong Chen, Chang-Yong Liang, Wei-Chiang Hong, and Dong-Xiao Gu.
Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm. Applied Soft Computing, 26:435–
443, 2015.
[28] Chen Cheng, Haiqin Yang, Michael R Lyu, and Irwin King. Where you like to go next: Successive point-of-interest recommendation. InTwenty-Third international joint conference on Artificial Intelligence, 2013.
144 Bibliography
[29] Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh. Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks. InProceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pages 257–266, 2019.
[30] Steven I-Jy Chien, Yuqing Ding, and Chienhung Wei. Dynamic bus arrival time prediction with artificial neural networks. Journal of transportation engineering, 128(5):429–438, 2002.
[31] Kyunghyun Cho, Bart Van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio. On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259, 2014.
[32] Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bah- danau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078, 2014.
[33] Andrea Cini, Ivan Marisca, and Cesare Alippi. Multivariate time series imputation by graph neural networks.arXiv preprint arXiv:2108.00298, 2021.
[34] Noel Cressie and Christopher K Wikle. Statistics for spatio-temporal data.
John Wiley & Sons, 2015.
[35] Zhiyong Cui, Ruimin Ke, Ziyuan Pu, and Yinhai Wang. Deep bidirectional and unidirectional lstm recurrent neural network for network-wide traffic speed prediction. arXiv preprint arXiv:1801.02143, 2018.
[36] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert:
Pre-training of deep bidirectional transformers for language understanding.
arXiv preprint arXiv:1810.04805, 2018.
[37] Zulong Diao, Xin Wang, Dafang Zhang, Yingru Liu, Kun Xie, and Shaoyao He. Dynamic spatial-temporal graph convolutional neural networks for traffic forecasting. InAAAI, volume 33, pages 890–897, 2019.
[38] Peter J Diggle. Statistical analysis of spatial and spatio-temporal point patterns.
CRC press, 2013.
Bibliography 145
[39] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Trans- formers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.
[40] Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth. From data mining to knowledge discovery in databases. AI magazine, 17(3):37–37, 1996.
[41] Jie Feng, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, and Depeng Jin. Deepmove: Predicting human mobility with attentional recurrent networks. InProceedings of the 2018 world wide web conference, pages 1459–1468, 2018.
[42] Shanshan Feng, Xutao Li, Yifeng Zeng, Gao Cong, Yeow Meng Chee, and Quan Yuan. Personalized ranking metric embedding for next new poi recommendation. InTwenty-Fourth International Joint Conference on Artificial Intelligence, 2015.
[43] Shanshan Feng, Lucas Vinh Tran, Gao Cong, Lisi Chen, Jing Li, and Fan Li. Hme: A hyperbolic metric embedding approach for next-poi recom- mendation. InProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1429–1438, 2020.
[44] Gregory Flato, Jochem Marotzke, Babatunde Abiodun, Pascale Braconnot, Sin Chan Chou, William Collins, Peter Cox, Fatima Driouech, Seita Emori, Veronika Eyring, et al. Evaluation of climate models. InClimate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, pages 741–866. Cambridge University Press, 2014.
[45] Michael F Goodchild. Geographical information science. International journal of geographical information systems, 6(1):31–45, 1992.
[46] Michael F Goodchild. Citizens as sensors: the world of volunteered geogra- phy. GeoJournal, 69:211–221, 2007.
[47] Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016.
146 Bibliography
[48] Marco Gori, Gabriele Monfardini, and Franco Scarselli. A new model for learning in graph domains. InProceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005., volume 2, pages 729–734. IEEE, 2005.
[49] Alex Graves, Navdeep Jaitly, and Abdel-rahman Mohamed. Hybrid speech recognition with deep bidirectional lstm. In2013 IEEE workshop on automatic speech recognition and understanding, pages 273–278. IEEE, 2013.
[50] Alex Graves and Jürgen Schmidhuber. Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks, 18(5-6):602–610, 2005.
[51] David J Griggs and Maria Noguer. Climate change 2001: the scientific basis. contribution of working group i to the third assessment report of the intergovernmental panel on climate change. Weather, 57(8):267–269, 2002.
[52] Bnaya Gross, Zhiguo Zheng, Shiyan Liu, Xiaoqi Chen, Alon Sela, Jianxin Li, Daqing Li, and Shlomo Havlin. Spatio-temporal propagation of covid-19 pandemics. Europhysics Letters, 131(5):58003, 2020.
[53] Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan.
Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. InAAAI, volume 33, pages 922–929, 2019.
[54] Shengnan Guo, Youfang Lin, Huaiyu Wan, Xiucheng Li, and Gao Cong.
Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting. IEEE Transactions on Knowledge and Data Engineering, 34(11):5415–5428, 2021.
[55] Ali Hamdi, Khaled Shaban, Abdelkarim Erradi, Amr Mohamed, Shak- ila Khan Rumi, and Flora D Salim. Spatiotemporal data mining: a survey on challenges and open problems. Artificial Intelligence Review, pages 1–48, 2022.
[56] Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs.Advances in neural information processing systems, 30, 2017.
[57] William L Hamilton, Rex Ying, and Jure Leskovec. Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584, 2017.
Bibliography 147
[58] Jiawei Han, Jian Pei, and Hanghang Tong. Data mining: concepts and tech- niques. Morgan kaufmann, 2022.
[59] Jindong Han, Hao Liu, Hengshu Zhu, Hui Xiong, and Dejing Dou. Joint air quality and weather prediction based on multi-adversarial spatiotemporal networks. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 4081–4089, 2021.
[60] James W Hansen, Andrew Challinor, Amor Ines, Tim Wheeler, and Vincent Moron. Translating climate forecasts into agricultural terms: advances and challenges. Climate research, 33(1):27–41, 2006.
[61] Jane K Hart and Kirk Martinez. Environmental sensor networks: A revo- lution in the earth system science?Earth-Science Reviews, 78(3-4):177–191, 2006.
[62] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory.Neural computation, 9(8):1735–1780, 1997.
[63] Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troc- coli, and Rob J Hyndman. Probabilistic energy forecasting: Global energy forecasting competition 2014 and beyond, 2016.
[64] Wenbing Huang, Yu Rong, Tingyang Xu, Fuchun Sun, and Junzhou Huang.
Tackling over-smoothing for general graph convolutional networks. arXiv preprint arXiv:2008.09864, 2020.
[65] Hosagrahar V Jagadish, Johannes Gehrke, Alexandros Labrinidis, Yannis Papakonstantinou, Jignesh M Patel, Raghu Ramakrishnan, and Cyrus Sha- habi. Big data and its technical challenges. Communications of the ACM, 57(7):86–94, 2014.
[66] Shuiwang Ji, Wei Xu, Ming Yang, and Kai Yu. 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence, 35(1):221–231, 2012.
[67] Shuhui Jiang, Xueming Qian, Jialie Shen, Yun Fu, and Tao Mei. Author topic model-based collaborative filtering for personalized poi recommendations.
IEEE transactions on multimedia, 17(6):907–918, 2015.
148 Bibliography
[68] Hyun Kang. The prevention and handling of the missing data. Korean journal of anesthesiology, 64(5):402–406, 2013.
[69] Charles FF Karney. Algorithms for geodesics.Journal of Geodesy, 87(1):43–55, 2013.
[70] Seyed Mehran Kazemi, Rishab Goel, Sepehr Eghbali, Janahan Ramanan, Jaspreet Sahota, Sanjay Thakur, Stella Wu, Cathal Smyth, Pascal Poupart, and Marcus Brubaker. Time2vec: Learning a vector representation of time.
arXiv preprint arXiv:1907.05321, 2019.
[71] Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016.
[72] Slava Kisilevich, Florian Mansmann, and Daniel Keim. P-dbscan: A density based clustering algorithm for exploration and analysis of attractive areas using collections of geo-tagged photos. InProceedings of the 1st international conference and exhibition on computing for geospatial research & application, pages 1–4, 2010.
[73] Robert Kistler, Eugenia Kalnay, William Collins, Suranjana Saha, Glenn White, John Woollen, Muthuvel Chelliah, Wesley Ebisuzaki, Masao Kana- mitsu, Vernon Kousky, et al. The ncep–ncar 50-year reanalysis: monthly means cd-rom and documentation. Bulletin of the American Meteorological society, 82(2):247–268, 2001.
[74] Yehuda Koren, Robert Bell, and Chris Volinsky. Matrix factorization tech- niques for recommender systems.Computer, 42(8):30–37, 2009.
[75] Mireadili Kuerban, Yizaitiguli Waili, Fan Fan, Ye Liu, Wei Qin, Anthony J Dore, Jingjing Peng, Wen Xu, and Fusuo Zhang. Spatio-temporal patterns of air pollution in china from 2015 to 2018 and implications for health risks.
Environmental Pollution, 258:113659, 2020.
[76] Nikolay Laptev, Jason Yosinski, Li Erran Li, and Slawek Smyl. Time-series extreme event forecasting with neural networks at uber. InInternational conference on machine learning, volume 34, pages 1–5, 2017.
[77] Colin Lea, Michael D Flynn, Rene Vidal, Austin Reiter, and Gregory D Hager.
Temporal convolutional networks for action segmentation and detection. In
Bibliography 149
proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 156–165, 2017.
[78] Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. nature, 521(7553):436–444, 2015.
[79] Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. InICLR, 2018.
[80] Yang Li, Tong Chen, Hongzhi Yin, and Zi Huang. Discovering collaborative signals for next poi recommendation with iterative seq2graph augmenta- tion. InIJCAI, 2021.
[81] Yanshan Li, Rongjie Xia, Qinghua Huang, Weixin Xie, and Xuelong Li.
Survey of spatio-temporal interest point detection algorithms in video.
IEEE Access, 5:10323–10331, 2017.
[82] Bryan Lim, Sercan Ö Arık, Nicolas Loeff, and Tomas Pfister. Temporal fusion transformers for interpretable multi-horizon time series forecasting.
International Journal of Forecasting, 2021.
[83] Ziqian Lin, Jie Feng, Ziyang Lu, Yong Li, and Depeng Jin. Deepstn+:
Context-aware spatial-temporal neural network for crowd flow prediction in metropolis. InProceedings of the AAAI conference on artificial intelligence, volume 33, pages 1020–1027, 2019.
[84] Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. Predicting the next location: A recurrent model with spatial and temporal contexts. InThirtieth AAAI conference on artificial intelligence, 2016.
[85] Yanchi Liu, Chuanren Liu, Bin Liu, Meng Qu, and Hui Xiong. Unified point-of-interest recommendation with temporal interval assessment. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1015–1024, 2016.
[86] Yingtao Luo, Qiang Liu, and Zhaocheng Liu. Stan: Spatio-temporal atten- tion network for next location recommendation. InProceedings of the Web Conference 2021, pages 2177–2185, 2021.
[87] Anne-Katrin Mahlein. Plant disease detection by imaging sensors–parallels and specific demands for precision agriculture and plant phenotyping.
Plant disease, 100(2):241–251, 2016.
150 Bibliography
[88] Yasuko Matsubara, Yasushi Sakurai, Willem G Van Panhuis, and Christos Faloutsos. Funnel: automatic mining of spatially coevolving epidemics. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 105–114, 2014.
[89] Gerald A Meehl, Curt Covey, Thomas Delworth, Mojib Latif, Bryant McA- vaney, John FB Mitchell, Ronald J Stouffer, and Karl E Taylor. The wcrp cmip3 multimodel dataset: A new era in climate change research. Bulletin of the American meteorological society, 88(9):1383–1394, 2007.
[90] Harvey J Miller and Shih-Lung Shaw. Geographic information systems for transportation: principles and applications. Oxford University Press on De- mand, 2001.
[91] Catherine L Muller, Lee Chapman, CSB Grimmond, Duick T Young, and Xiaoming Cai. Sensors and the city: a review of urban meteorological networks. International Journal of Climatology, 33(7):1585–1600, 2013.
[92] Daniel Neimark, Omri Bar, Maya Zohar, and Dotan Asselmann. Video transformer network. arXiv preprint arXiv:2102.00719, 2021.
[93] M. E. J. (Mark E. J.) Newman.Networks. Oxford University Press, Oxford, second edition. edition, 2018.
[94] Qingjian Ni, Yuhui Wang, and Yifei Fang. Ge-stdgn: a novel spatio-temporal weather prediction model based on graph evolution. Applied Intelligence, pages 1–15, 2022.
[95] Xi Ouyang, Chaoyun Zhang, Pan Zhou, Hao Jiang, and Shimin Gong.
Deepspace: An online deep learning framework for mobile big data to understand human mobility patterns. arXiv preprint arXiv:1610.07009, 2016.
[96] Nicholas G Polson and Vadim O Sokolov. Deep learning for short-term traffic flow prediction. Transportation Research Part C: Emerging Technologies, 79:1–17, 2017.
[97] Stephen R Proulx, Daniel EL Promislow, and Patrick C Phillips. Network thinking in ecology and evolution.Trends in ecology & evolution, 20(6):345–
353, 2005.
Bibliography 151
[98] Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al.
Improving language understanding by generative pre-training. 2018.
[99] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language models are unsupervised multitask learners.
OpenAI blog, 1(8):9, 2019.
[100] Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. Fac- torizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th international conference on World wide web, pages 811–
820, 2010.
[101] Arthur H Robinson. Elements of cartography. Soil Science, 90(2):147, 1960.
[102] Frank Rosenblatt. The perceptron: a probabilistic model for information storage and organization in the brain.Psychological review, 65(6):386, 1958.
[103] EG Peter Rowe. Geometrical physics in Minkowski spacetime. Springer Science
& Business Media, 2013.
[104] David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. Learning representations by back-propagating errors.nature, 323(6088):533–536, 1986.
[105] Patrick H Ryan, Grace K LeMasters, Pratim Biswas, Linda Levin, Shaohua Hu, Mark Lindsey, David I Bernstein, James Lockey, Manuel Villareal, Gurjit K Khurana Hershey, et al. A comparison of proximity and land use regression traffic exposure models and wheezing in infants. Environmental health perspectives, 115(2):278–284, 2007.
[106] Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. The graph neural network model.IEEE transactions on neural networks, 20(1):61–80, 2008.
[107] LCoauthors Shaffrey, Ian Stevens, WA Norton, MJ Roberts, Pier-Luigi Vi- dale, JD Harle, Amna Jrrar, DP Stevens, Margaret Jean Woodage, Marie- Estelle Demory, et al. Uk higem: The new uk high-resolution global envi- ronment model—model description and basic evaluation.Journal of Climate, 22(8):1861–1896, 2009.
152 Bibliography
[108] Zezhi Shao, Zhao Zhang, Fei Wang, and Yongjun Xu. Pre-training enhanced spatial-temporal graph neural network for multivariate time series fore- casting. InProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 1567–1577, 2022.
[109] Zezhi Shao, Zhao Zhang, Wei Wei, Fei Wang, Yongjun Xu, Xin Cao, and Christian S Jensen. Decoupled dynamic spatial-temporal graph neural network for traffic forecasting. arXiv preprint arXiv:2206.09112, 2022.
[110] Shashi Shekhar, Zhe Jiang, Reem Y Ali, Emre Eftelioglu, Xun Tang, Venkata MV Gunturi, and Xun Zhou. Spatiotemporal data mining: A computational perspective. ISPRS International Journal of Geo-Information, 4(4):2306–2338, 2015.
[111] Alex Sherstinsky. Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena, 404:132306, 2020.
[112] Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo. Convolutional lstm network: A machine learning approach for precipitation nowcasting. Advances in neural information pro- cessing systems, 28, 2015.
[113] Alessandro Sperduti and Antonina Starita. Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks, 8(3):714–735, 1997.
[114] Yuki Sugiyama, Minoru Fukui, Macoto Kikuchi, Katsuya Hasebe, Akihiro Nakayama, Katsuhiro Nishinari, Shin-ichi Tadaki, and Satoshi Yukawa.
Traffic jams without bottlenecks—experimental evidence for the physical mechanism of the formation of a jam. New journal of physics, 10(3):033001, 2008.
[115] Fangzhou Sun, Abhishek Dubey, and Jules White. Dxnat—deep neural networks for explaining non-recurring traffic congestion. In2017 IEEE International Conference on Big Data (Big Data), pages 2141–2150. IEEE, 2017.
[116] Ke Sun, Tieyun Qian, Tong Chen, Yile Liang, Quoc Viet Hung Nguyen, and Hongzhi Yin. Where to go next: Modeling long-and short-term user preferences for point-of-interest recommendation. InProceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 214–221, 2020.
Bibliography 153
[117] Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. Sequence to sequence learn- ing with neural networks. InNeurIPS, 2014.
[118] Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. Line: Large-scale information network embedding. InProceedings of the 24th international conference on world wide web, pages 1067–1077, 2015.
[119] Jiliang Tang, Yi Chang, and Huan Liu. Mining social media with social theories: a survey. ACM Sigkdd Explorations Newsletter, 15(2):20–29, 2014.
[120] Yusuke Tashiro, Jiaming Song, Yang Song, and Stefano Ermon. Csdi: Condi- tional score-based diffusion models for probabilistic time series imputation.
Advances in Neural Information Processing Systems, 34:24804–24816, 2021.
[121] M Ozan Tezcan, Prakash Ishwar, and Janusz Konrad. Bsuv-net 2.0: Spatio- temporal data augmentations for video-agnostic supervised background subtraction. IEEE Access, 9:53849–53860, 2021.
[122] Tammy M Thompson, Sebastian Rausch, Rebecca K Saari, and Noelle E Selin. A systems approach to evaluating the air quality co-benefits of us carbon policies.Nature Climate Change, 4(10):917–923, 2014.
[123] Lisa Tompson, Shane Johnson, Matthew Ashby, Chloe Perkins, and Phillip Edwards. Uk open source crime data: accuracy and possibilities for research.
Cartography and geographic information science, 42(2):97–111, 2015.
[124] Martin Treiber and Arne Kesting. Traffic flow dynamics. Traffic Flow Dynamics: Data, Models and Simulation, Springer-Verlag Berlin Heidelberg, 2013.
[125] JWC Van Lint and CPIJ Van Hinsbergen. Short-term traffic and travel time prediction models. Artificial Intelligence Applications to Critical Transportation Issues, 22(1):22–41, 2012.
[126] Daksh Varshneya and G Srinivasaraghavan. Human trajectory prediction using spatially aware deep attention models.arXiv preprint arXiv:1705.09436, 2017.
[127] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.
154 Bibliography
[128] Petar Veliˇckovi´c, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. Graph attention networks. arXiv preprint arXiv:1710.10903, 2017.
[129] Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio, et al. Graph attention networks.stat, 1050(20):10–
48550, 2017.
[130] Eleni I Vlahogianni, Matthew G Karlaftis, and John C Golias. Short-term traffic forecasting: Where we are and where we’re going. Transportation Research Part C: Emerging Technologies, 43:3–19, 2014.
[131] Senzhang Wang, Jiannong Cao, and Philip Yu. Deep learning for spatio- temporal data mining: A survey. IEEE transactions on knowledge and data engineering, 2020.
[132] Senzhang Wang, Hao Miao, Jiyue Li, and Jiannong Cao. Spatio-temporal knowledge transfer for urban crowd flow prediction via deep attentive adaptation networks. IEEE Transactions on Intelligent Transportation Systems, 23(5):4695–4705, 2021.
[133] Stanley. Wasserman. Social network analysis : methods and applications. Struc- tural analysis in the social sciences 8. Cambridge University Press, Cam- bridge ;, 1994.
[134] Max Welling and Thomas N Kipf. Semi-supervised classification with graph convolutional networks. In J. International Conference on Learning Representations (ICLR 2017), 2016.
[135] Qingsong Wen, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, and Liang Sun. Transformers in time series: A survey. arXiv preprint arXiv:2202.07125, 2022.
[136] Billy M Williams and Lester A Hoel. Modeling and forecasting vehicular traffic flow as a seasonal arima process: Theoretical basis and empirical results. Journal of transportation engineering, 129(6):664–672, 2003.
[137] Paul D Williams, Thomas WN Haine, and Peter L Read. Inertia–gravity waves emitted from balanced flow: Observations, properties, and conse- quences. Journal of the atmospheric sciences, 65(11):3543–3556, 2008.