Disentangling the city traffic rhythms: A longitudinal analysis of MFD patterns over a year
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Monica Menendez | Ludovic Leclercq | Allister Loder | Lukas Ambühl | M. Menéndez | L. Leclercq | Lukas Ambühl | Allister Loder
[1] Nikolas Geroliminis,et al. Modeling and optimization of multimodal urban networks with limited parking and dynamic pricing , 2015 .
[2] Jorge A. Laval,et al. Macroscopic urban dynamics: Analytical and numerical comparisons of existing models , 2017 .
[3] J. H. Ward. Hierarchical Grouping to Optimize an Objective Function , 1963 .
[4] Michelle Y. Merrill,et al. Orangutan Cultures and the Evolution of Material Culture , 2003, Science.
[5] Martin Raubal,et al. Extracting Dynamic Urban Mobility Patterns from Mobile Phone Data , 2012, GIScience.
[6] Nikolaos Geroliminis,et al. On the spatial partitioning of urban transportation networks , 2012 .
[7] Kay W. Axhausen,et al. Empirical macroscopic fundamental diagrams: New insights from loop detector and floating car data , 2016 .
[8] Marta C. González,et al. Understanding individual human mobility patterns , 2008, Nature.
[9] N. Geroliminis,et al. An analytical approximation for the macropscopic fundamental diagram of urban traffic , 2008 .
[10] Donald J. Berndt,et al. Using Dynamic Time Warping to Find Patterns in Time Series , 1994, KDD Workshop.
[11] Monica Menendez,et al. Introducing a Re-Sampling Methodology for the Estimation of Empirical Macroscopic Fundamental Diagrams , 2017, Transportation Research Record: Journal of the Transportation Research Board.
[12] Kay W. Axhausen,et al. A functional form with a physical meaning for the macroscopic fundamental diagram , 2020 .
[13] D. Waddle. Matrix correlation tests support a single origin for modern humans , 1994, Nature.
[14] Rashid A. Waraich,et al. A dynamic cordon pricing scheme combining the Macroscopic Fundamental Diagram and an agent-based traffic model , 2012 .
[15] Xuesong Zhou,et al. Method for investigating intradriver heterogeneity using vehicle trajectory data: A Dynamic Time Warping approach , 2015 .
[16] Nikolaos Geroliminis,et al. Empirical Observations of Congestion Propagation and Dynamic Partitioning with Probe Data for Large-Scale Systems , 2014 .
[17] Monica Menendez,et al. Use of Microsimulation for Examination of Macroscopic Fundamental Diagram Hysteresis Patterns for Hierarchical Urban Street Networks , 2015 .
[18] Serge P. Hoogendoorn,et al. Traffic dynamics: Its impact on the Macroscopic Fundamental Diagram , 2015 .
[19] Dirk Helbing,et al. The spatial variability of vehicle densities as determinant of urban network capacity , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[20] Vikash V. Gayah,et al. Clockwise Hysteresis Loops in the Macroscopic Fundamental Diagram , 2010 .
[21] F. Busch,et al. Evaluation of analytical approximation methods for the macroscopic fundamental diagram , 2020 .
[22] Eamonn J. Keogh,et al. The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances , 2016, Data Mining and Knowledge Discovery.
[23] Kay W. Axhausen,et al. A case study of Zurich’s two-layered perimeter control , 2017 .
[24] Alexander Kowarik,et al. Imputation with the R Package VIM , 2016 .
[25] R. L. Thorndike. Who belongs in the family? , 1953 .
[26] Kay W. Axhausen,et al. Approximative Network Partitioning for MFDs from Stationary Sensor Data , 2019 .
[27] W. Vickrey. Congestion in midtown Manhattan in relation to marginal cost pricing , 2020 .
[28] N. Geroliminis,et al. Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings - eScholarship , 2007 .
[29] Xuesong Zhou,et al. Estimating risk effects of driving distraction: a dynamic errorable car-following model , 2015 .
[30] Marta C. González,et al. Understanding congested travel in urban areas , 2016, Nature Communications.
[31] Nikolas Geroliminis,et al. Dynamics of heterogeneity in urban networks: aggregated traffic modeling and hierarchical control , 2015 .
[32] Toni Giorgino,et al. Matching incomplete time series with dynamic time warping: an algorithm and an application to post-stroke rehabilitation , 2009, Artif. Intell. Medicine.
[33] Nikolaos Geroliminis,et al. Dynamic clustering and propagation of congestion in heterogeneously congested urban traffic networks , 2017 .
[34] Victor L. Knoop,et al. Influence of Road Layout on Network Fundamental Diagram , 2014 .
[35] Ludovic Leclercq,et al. Revealing the day-to-day regularity of urban congestion patterns with 3D speed maps , 2017, Scientific Reports.
[36] Victor L. Knoop,et al. Examining perimeter gating control of urban traffic networkswith locally adaptive traffic signals , 2015 .
[37] Monica Menendez,et al. Understanding traffic capacity of urban networks , 2019, Scientific Reports.
[38] Nan Zheng,et al. Heterogeneity aware urban traffic control in a connected vehicle environment: A joint framework for congestion pricing and perimeter control , 2019, Transportation Research Part C: Emerging Technologies.
[39] Ashish Bhaskar,et al. A pattern recognition algorithm for assessing trajectory completeness , 2018, Transportation Research Part C: Emerging Technologies.
[40] Anil K. Jain,et al. Data clustering: a review , 1999, CSUR.
[41] Mark Hickman,et al. A methodology for identifying critical links and estimating macroscopic fundamental diagram in large-scale urban networks , 2020 .
[42] Peter J. Rousseeuw,et al. Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .
[43] Nikolaos Geroliminis,et al. Clustering of Heterogeneous Networks with Directional Flows Based on “Snake” Similarities , 2016 .
[44] Alexis Sardá-Espinosa,et al. Time-Series Clustering in R Using the dtwclust Package , 2019, R J..
[45] N. Geroliminis,et al. Cordon Pricing Consistent with the Physics of Overcrowding , 2009 .
[46] N. Geroliminis,et al. A three-dimensional macroscopic fundamental diagram for mixed bi-modal urban networks , 2014 .
[47] Nikolaos Geroliminis,et al. Properties of a well-defined Macroscopic Fundamental Diagram for urban traffic , 2011 .
[48] Peter Filzmoser,et al. Outlier identification in high dimensions , 2008, Comput. Stat. Data Anal..
[49] Ludovic Leclercq,et al. Macroscopic Traffic Dynamics with Heterogeneous Route Patterns , 2015 .
[50] Meead Saberi,et al. Urban Network Gridlock: Theory, Characteristics, and Dynamics , 2013 .
[51] Carlos F. Daganzo,et al. Urban Gridlock: Macroscopic Modeling and Mitigation Approaches , 2007 .
[52] Jorge A. Laval,et al. Stochastic Approximations for the Macroscopic Fundamental Diagram of Urban Networks , 2015 .
[53] N. Mantel. The detection of disease clustering and a generalized regression approach. , 1967, Cancer research.
[54] Emilian Necula,et al. Analyzing Traffic Patterns on Street Segments Based on GPS Data Using R , 2015 .
[55] Monica Menendez,et al. Study on the number and location of measurement points for an MFD perimeter control scheme: a case study of Zurich , 2014, EURO J. Transp. Logist..
[56] Claus Bahlmann,et al. The writer independent online handwriting recognition system frog on hand and cluster generative statistical dynamic time warping , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[57] Wei Luo,et al. A Dynamic Time Warping Algorithm Based Analysis of Pedestrian Shockwaves at Bottleneck , 2018 .
[58] Christine Buisson,et al. Exploring the Impact of Homogeneity of Traffic Measurements on the Existence of Macroscopic Fundamental Diagrams , 2009 .
[59] Monica Menendez,et al. Multi-scale perimeter control approach in a connected-vehicle environment , 2016, Transportation Research Part C: Emerging Technologies.
[60] Nikolaos Geroliminis,et al. On the stability of traffic perimeter control in two-region urban cities , 2012 .
[61] S. Ilgin Guler,et al. Providing public transport priority in the perimeter of urban networks: A bimodal strategy , 2019, Transportation Research Part C: Emerging Technologies.
[62] Rob J Hyndman,et al. Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing , 2011 .
[63] Ludovic Leclercq,et al. Validation of Macroscopic Fundamental Diagrams-Based Models with Microscopic Simulations on Real Networks: Importance of Production Hysteresis and Trip Lengths Estimation , 2019, Transportation Research Record: Journal of the Transportation Research Board.
[64] Enrique Frías-Martínez,et al. Uncovering the spatial structure of mobility networks , 2015, Nature Communications.