Revealing the day-to-day regularity of urban congestion patterns with 3D speed maps

[1]  Tharam S. Dillon,et al.  Optimized Structure of the Traffic Flow Forecasting Model With a Deep Learning Approach , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[2]  Lelitha Vanajakshi,et al.  Bus travel time prediction using a time-space discretization approach , 2017 .

[3]  Lelitha Vanajakshi,et al.  Arterial Path-Level Travel-Time Estimation Using Machine-Learning Techniques , 2017, J. Comput. Civ. Eng..

[4]  Fan Yang,et al.  Cluster ensemble selection with constraints , 2017, Neurocomputing.

[5]  Satish Chandra,et al.  Kriging-Based Approach for Estimation of Vehicular Speed and Passenger Car Units on an Urban Arterial , 2017 .

[6]  Gaetano Fusco,et al.  Short-term speed predictions exploiting big data on large urban road networks , 2016 .

[7]  Nikolaos Geroliminis,et al.  Clustering of Heterogeneous Networks with Directional Flows Based on “Snake” Similarities , 2016 .

[8]  Jiaqiu Wang,et al.  A space-time delay neural network model for travel time prediction , 2016, Eng. Appl. Artif. Intell..

[9]  Hui Chen,et al.  Urban traffic flow prediction: a spatio‐temporal variable selection‐based approach , 2016 .

[10]  Ashish Bhaskar,et al.  Real-time traffic state estimation in urban corridors from heterogeneous data , 2016 .

[11]  Li Li,et al.  Robust causal dependence mining in big data network and its application to traffic flow predictions , 2015 .

[12]  Maxime Lenormand,et al.  Systematic comparison of trip distribution laws and models , 2015, 1506.04889.

[13]  Fei-Yue Wang,et al.  Traffic Flow Prediction With Big Data: A Deep Learning Approach , 2015, IEEE Transactions on Intelligent Transportation Systems.

[14]  Alexander Mendiburu,et al.  A Review of Travel Time Estimation and Forecasting for Advanced Traveler Information Systems , 2012 .

[15]  Romain Billot,et al.  Network-Wide Traffic State Prediction Using Bluetooth Data , 2015 .

[16]  Le Minh Kieu,et al.  Urban traffic state estimation: Fusing point and zone based data , 2014 .

[17]  Mete Ozay,et al.  Semi-supervised Segmentation Fusion of Multi-spectral and Aerial Images , 2014, 2014 22nd International Conference on Pattern Recognition.

[18]  Sasu Tarkoma,et al.  Explaining the power-law distribution of human mobility through transportation modality decomposition , 2014, Scientific Reports.

[19]  Nathan Eagle,et al.  Limits of Predictability in Commuting Flows in the Absence of Data for Calibration , 2014, Scientific Reports.

[20]  Eleni I. Vlahogianni,et al.  Short-term traffic forecasting: Where we are and where we’re going , 2014 .

[21]  Wenhao Huang,et al.  Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning , 2014, IEEE Transactions on Intelligent Transportation Systems.

[22]  Alexandre M. Bayen,et al.  Understanding Road Usage Patterns in Urban Areas , 2012, Scientific Reports.

[23]  Nikolaos Geroliminis,et al.  On the spatial partitioning of urban transportation networks , 2012 .

[24]  Pietro Liò,et al.  Collective Human Mobility Pattern from Taxi Trips in Urban Area , 2012, PloS one.

[25]  Chaoming Song,et al.  Modelling the scaling properties of human mobility , 2010, 1010.0436.

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

[27]  Ilya Safro,et al.  Comparison of Coarsening Schemes for Multilevel Graph Partitioning , 2009, LION.

[28]  Peter Sanders,et al.  Contraction Hierarchies: Faster and Simpler Hierarchical Routing in Road Networks , 2008, WEA.

[29]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[30]  J. W. C. van Lint,et al.  Online Learning Solutions for Freeway Travel Time Prediction , 2008, IEEE Transactions on Intelligent Transportation Systems.

[31]  Markos Papageorgiou,et al.  RENAISSANCE – A Unified Macroscopic Model-Based Approach to Real-Time Freeway Network Traffic Surveillance , 2006 .

[32]  T. Geisel,et al.  The scaling laws of human travel , 2006, Nature.

[33]  Erik van Zwet,et al.  A simple and effective method for predicting travel times on freeways , 2004, IEEE Transactions on Intelligent Transportation Systems.

[34]  Steven Skiena,et al.  Integrating microarray data by consensus clustering , 2003, Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence.

[35]  Xiaoyan Zhang,et al.  Short-term travel time prediction , 2003 .

[36]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[37]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[38]  A. Anas Discrete choice theory, information theory and the multinomial logit and gravity models , 1983 .

[39]  J-M Choukroun A general framework for the development of gravity-type trip distribution models , 1975 .

[40]  Ludovic Leclercq,et al.  Spatiotemporal Partitioning of Transportation Network Using Travel Time Data , 2017 .

[41]  Alan Wilson LAND-USE/TRANSPORT INTERACTION MODELS: PAST AND FUTURE. , 1998 .

[42]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .