[1] Going for algorithm gold. Nature Methods, 5:659, 2008.
[2] Luc Anselin. What is special about spatial data? alternative perspectives on spatial data analysis. Technical Report 89-4, National Center for Geographic Information and Analysis, University of California, Santa Barbara, 1989.
[3] Dominik Bucher, Francesca Mangili, Francesca Cellina, Claudio Bonesana, David Jonietz, and Martin Raubal. From location tracking to personalized eco-feedback: A framework for geographic information collection, processing and visualization to promote sustainable mobility behaviors. Travel behaviour and society, 14:43–56, 2019.
[4] Meng Chen, Xiaohui Yu, and Yang Liu. PCNN: Deep convolutional networks for short- term traffic congestion prediction. IEEE Transactions on Intelligent Transportation Systems, 19(11):3550–3559, 2018.
[5] Stephen Dunne and Bidisha Ghosh. Regime-based short-term multivariate traffic condition forecasting algorithm. Journal of Transportation Engineering, 138(4):455– 466, 2011.
[6] Alireza Ermagun and David Levinson. Spatio-temporal short-term traffic forecasting using the network weight matrix and systematic detrending. In Compendium of papers of Transportation Research Board 97th Annual Meeting, 2018.
[7] Alireza Ermagun and David Levinson. Spatiotemporal traffic forecasting: review and proposed directions. Transport Reviews, 38(6):786–814, 2018.
[8] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In Advances in neural information processing systems, pages 2672–2680, 2014.
[9] Krzysztof Halawa, Andrzej Rusiecki, Tomasz Janiczek, Marek Bazan, Piotr Ciskowski, and Piotr Kozaczewski. Road traffic predictions across major city intersections using multilayer perceptrons and data from multiple intersections located in various places. IET Intelligent Transport Systems, 10(7):469–475, 2016.
[10] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Gu¨nter Klambauer, and Sepp Hochreiter. Gans trained by a two time-scale update rule con- verge to a nash equilibrium. CoRR, abs/1706.08500, 2017.
[11] Kimberly Johnson and Simon Lin. Call to work together on microarray data analysis.
Nature, 411:885, 2001.
[12] David Jonietz, Dominik Bucher, Henry Martin, and Martin Raubal. Identifying and interpreting clusters of persons with similar mobility behaviour change processes. In The Annual International Conference on Geographic Information Science, pages 291–307. Springer, 2018.
[13] Arief Koesdwiady, Ridha Soua, and Fakhreddine Karray. Improving traffic flow prediction with weather information in connected cars: A deep learning approach. IEEE Transactions on Vehicular Technology, 65(12):9508–9517, 2016.
[14] Ourania Kounadi and Bernd Resch. A geoprivacy by design guideline for research campaigns that use participatory sensing data. Journal of Empirical Research on Human Research Ethics, 13(3):203–222, 2018.
[15] Ibai Lana, Javier Del Ser, Manuel Velez, and Eleni I. Vlahogianni. Road traffic fore- casting: Recent advances and new challenges. IEEE Intelligent Transportation Systems Magazine, 10(2):93–109, 2018.
[16] Alex X Lee, Richard Zhang, Frederik Ebert, Pieter Abbeel, Chelsea Finn, and Sergey Levine. Stochastic adversarial video prediction. arXiv preprint arXiv:1804.01523, 2018.
[17] Shanglu; Yang Fan; Zhang Jian; Ran Bin. Li, Linchao; He. Space-mean-speed pre- diction based on the fusion of multi-source data. In TRB 96th Annual Meeting Compendium of Papers, 2017.
[18] Xiaodan Liang, Lisa Lee, Wei Dai, and Eric P Xing. Dual motion gan for future-flow embedded video prediction. In Proceedings of the IEEE International Conference on Computer Vision, pages 1744–1752, 2017.
[19] Yunyi Liang, Zhiyong Cui, Yu Tian, Huimiao Chen, and Yinhai Wang. A deep generative adversarial architecture for network-wide spatial-temporal traffic-state estimation. Transportation Research Record, page 0361198118798737, 2018.
[20] Jianhua; Huang Wei. Liu, Zhao; Guo. Artificial neural networks for short term traf- fic flow forecasting: Effects of training algorithms. In TRB 95th Annual Meeting Compendium of Papers, 2016.
[21] Xiaolei Ma, Zhuang Dai, Zhengbing He, Jihui Ma, Yong Wang, and Yunpeng Wang. Learning traffic as images: A deep convolutional neural network for large-scale transportation network speed prediction. Sensors, 17(4):818, 2017.
[22] Xiaolei Ma, Zhimin Tao, Yinhai Wang, Haiyang Yu, and Yunpeng Wang. Long short- term memory neural network for traffic speed prediction using remote microwave sensor data. Transportation Research Part C: Emerging Technologies, 54:187–197, 2015.
[23] Michael Mathieu, Camille Couprie, and Yann LeCun. Deep multi-scale video prediction beyond mean square error.arXiv preprint arXiv:1511.05440, 2015.
[24] Silvia Nittel. A survey of geosensor networks: Advances in dynamic environmental monitoring. Sensors, 9(7):5664–5678, 2009.
[25] Niklas Christoffer Petersen, Filipe Rodrigues, and Francisco Camara Pereira. Multi-output bus travel time prediction with convolutional LSTM neural network. ExpertSystems with Applications, 120:426–435, 2019.
[26] Huachun Tan, Xuan Xuan, Yuankai Wu, Zhiyu Zhong, and Bin Ran. A comparison of traffic flow prediction methods based on DBN. InCICTP 2016. American Societyof Civil Engineers, 2016.
[27] Claire Tilstone. Vital statistics.Nature, 424:610, 2003.
[28] Jorim Urner, Dominik Bucher, Jing Yang, and David Jonietz. Assessing the influence of spatio-temporal context for next place prediction using different machine learning approaches. ISPRS International Journal of Geo-Information, 7(5):166, 2018.
[29] Carl Vondrick and Antonio Torralba. Generating the future with adversarial transformers. InProceedings of the IEEE Conference on Computer Vision and PatternRecognition, pages 1020–1028, 2017.
[30] Jacob Walker, Carl Doersch, Abhinav Gupta, and Martial Hebert. An uncertain future: Forecasting from static images using variational auto encoders. InEuropeanConference on Computer Vision, pages 835–851. Springer, 2016.
[31] SHI Xingjian, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo. Convolutional lstm network: A machine learning approach for precipitation nowcasting. InAdvances in neural information processing systems, pages 802–810,2015.
[32] Tianfan Xue, Jiajun Wu, Katherine Bouman, and Bill Freeman. Visual dynamics:Probabilistic future frame synthesis via cross convolutional networks. InAdvances inNeural Information Processing Systems, pages 91–99, 2016.17