Real Time Traffic Congestion Prediction and Mitigation at the City Scale

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[1]  Fang Liu,et al.  Inferring driving trajectories based on probabilistic model from large scale taxi GPS data , 2018, Physica A: Statistical Mechanics and its Applications.

[2]  Xianfeng Tang,et al.  Modeling Spatial-Temporal Dynamics for Traffic Prediction , 2018, ArXiv.

[3]  Jieping Ye,et al.  Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction , 2018, AAAI.

[4]  Linpeng Huang,et al.  Predicting Multi-step Citywide Passenger Demands Using Attention-based Neural Networks , 2018, WSDM.

[5]  Michael I. Jordan,et al.  Ray: A Distributed Framework for Emerging AI Applications , 2017, OSDI.

[6]  Franco Zambonelli,et al.  On Recommending Opportunistic Rides , 2017, IEEE Transactions on Intelligent Transportation Systems.

[7]  John Paul Shen,et al.  Data Driven Analysis of the Potentials of Dynamic Ride Pooling , 2017, IWCTS@SIGSPATIAL.

[8]  John Paul Shen,et al.  On the Real-time Vehicle Placement Problem , 2017, ArXiv.

[9]  Jaakko Lehtinen,et al.  Progressive Growing of GANs for Improved Quality, Stability, and Variation , 2017, ICLR.

[10]  Junhee Seok,et al.  Controllable Generative Adversarial Network , 2017, IEEE Access.

[11]  John Paul Shen,et al.  Space-Time Graph Modeling of Ride Requests Based on Real-World Data , 2017, AAAI Workshops.

[12]  Matthias Grossglauser,et al.  ChoiceRank: Identifying Preferences from Node Traffic in Networks , 2016, ICML.

[13]  Christian Ledig,et al.  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Martin W. P. Savelsbergh,et al.  Enhancing Urban Mobility: Integrating Ride-Sharing and Public Transit , 2016, Comput. Oper. Res..

[15]  Wojciech Zaremba,et al.  Improved Techniques for Training GANs , 2016, NIPS.

[16]  Rob Fergus,et al.  Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks , 2015, NIPS.

[17]  Simon Osindero,et al.  Conditional Generative Adversarial Nets , 2014, ArXiv.

[18]  Jan H. Kietzmann,et al.  Ride On! Mobility Business Models for the Sharing Economy , 2014 .

[19]  Margaret Martonosi,et al.  Human mobility modeling at metropolitan scales , 2012, MobiSys '12.

[20]  C. Cela-Conde,et al.  Predicting beauty: fractal dimension and visual complexity in art. , 2011, British journal of psychology.

[21]  Carlo Ratti,et al.  Taxi-Aware Map: Identifying and Predicting Vacant Taxis in the City , 2010, AmI.

[22]  Christos Faloutsos,et al.  Fast feature selection using fractal dimension , 2010, J. Inf. Data Manag..

[23]  Albert-László Barabási,et al.  Limits of Predictability in Human Mobility , 2010, Science.

[24]  Christos Faloutsos,et al.  Graphs over time: densification laws, shrinking diameters and possible explanations , 2005, KDD '05.

[25]  Christos Faloutsos,et al.  Estimating the Selectivity of Spatial Queries Using the 'Correlation' Fractal Dimension , 1995, VLDB.

[26]  Manfred Schroeder,et al.  Fractals, Chaos, Power Laws: Minutes From an Infinite Paradise , 1992 .

[27]  Robert Hahn,et al.  The Ridesharing Revolution : Economic Survey and Synthesis , 2017 .

[28]  Jon Gauthier.  Conditional generative adversarial nets for convolutional face generation , 2015 .

[29]  Joseph E. Hoag,et al.  Synthetic data generation: theory, techniques and applications , 2008 .