Understanding characteristics in multivariate traffic flow time series from complex network structure
- 1 July 2017
- journal article
- Published by Elsevier BV in Physica A: Statistical Mechanics and its Applications
- Vol. 477, 149-160
- https://doi.org/10.1016/j.physa.2017.02.040
Abstract
No abstract availableFunding Information
- National Natural Science Foundation of China (51138003, 51329801, G030601)
This publication has 37 references indexed in Scilit:
- An Improved Fuzzy Neural Network for Traffic Speed Prediction Considering Periodic CharacteristicIEEE Transactions on Intelligent Transportation Systems, 2017
- Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference SystemPLOS ONE, 2016
- Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning TheoryPLOS ONE, 2015
- Hybrid Prediction Approach Based on Weekly Similarities of Traffic Flow for Different Temporal ScalesTransportation Research Record: Journal of the Transportation Research Board, 2014
- Phase transition model of non-stationary traffic flow: Definition, properties and solution methodTransportation Research Part B: Methodological, 2013
- Optimizing Coordinated Ramp Metering: A Preemptive Hierarchical Control ApproachComputer-Aided Civil and Infrastructure Engineering, 2012
- Modeling Traffic Flow Dynamics on Managed Lane FacilityTransportation Research Record: Journal of the Transportation Research Board, 2012
- Discontinuous transition from free flow to synchronized flow induced by short-range interaction between vehicles in a three-phase traffic flow modelPhysica A: Statistical Mechanics and its Applications, 2009
- A reserve capacity model of optimal signal control with user-equilibrium route choiceTransportation Research Part B: Methodological, 2002
- Review of Empirical Research on Congested Freeway FlowTransportation Research Record: Journal of the Transportation Research Board, 2002