Traffic flow modeling and forecasting using cellular automata and neural networks : a thesis presented in partial fulfillment of the requirements for the degree of Master of Science in Computer Science at Massey University, Palmerston North, New Zealand [PDF]
, 2006 In This thesis fine grids are adopted in Cellular Automata (CA) models. The fine-grid models are able to describe traffic flow in detail allowing position, speed, acceleration and deceleration of vehicles simulated in a more realistic way.
Liu, Mingzhe
core
DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting
, 2018 Predicting traffic conditions has been recently explored as a way to relieve traffic congestion. Several pioneering approaches have been proposed based on traffic observations of the target location as well as its adjacent regions, but they obtain ...
Cheng, Xingyi +3 more
core +1 more source
Epilepsy‐Associated Variants of a Single SCN1A Codon Exhibit Divergent Functional Properties
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Pathogenic variants in SCN1A, which encodes the voltage‐gated sodium channel NaV1.1, are associated with multiple epilepsy syndromes exhibiting a range of clinical severity. SCN1A variants are reported in different syndromes, including Dravet syndrome, which is associated with loss‐of‐function, whereas neonatal/infantile‐onset ...
Lanie N. Liebovitz +3 more
wiley +1 more source
Short-term traffic flow prediction at isolated intersections based on parallel multi-task learning
Systems Science & Control EngineeringThis paper proposes a novel phase-based short-term traffic flow prediction method based on parallel multi-task learning for isolated intersections. Different from traditional short-term traffic flow prediction methods, we take the traffic flow of each ...
Bao-Lin Ye +3 more
doaj +1 more source
A deep learning short-term traffic flow prediction method considering spatial-temporal association
智能科学与技术学报, 2021 The short-term traffic flow prediction is too dependent on the time correlation characteristics, which due to the problems that the correlation factors of the spatial correlation characteristics are too complicated and difficult to quantify.In response ...
Yang ZHANG, Yue HU, Dongrong XIN
doaj
NeuTM: A Neural Network-based Framework for Traffic Matrix Prediction in SDN
, 2017 This paper presents NeuTM, a framework for network Traffic Matrix (TM) prediction based on Long Short-Term Memory Recurrent Neural Networks (LSTM RNNs). TM prediction is defined as the problem of estimating future network traffic matrix from the previous
Azzouni, Abdelhadi, Pujolle, Guy
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Hybrid LSTM Neural Network for Short-Term Traffic Flow Prediction [PDF]
Information, 2019 The existing short-term traffic flow prediction models fail to provide precise prediction results and consider the impact of different traffic conditions on the prediction results in an actual traffic network. To solve these problems, a hybrid Long Short–Term Memory (LSTM) neural network is proposed, based on the LSTM model.
Yuelei Xiao 0001, Yang Yin
openaire +2 more sources
Cracking the Code: Genotype–Phenotype Correlation Models in Sarcoglycanopathies
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Sarcoglycanopathies are among the most severe limb‐girdle muscular dystrophies (LGMD), though milder presentations have been described. These diseases are primarily caused by missense variants, but the limited predictability of their effect on protein maturation, complex formation, and transport has hindered reliable genotype ...
Leonela Luce +72 more
wiley +1 more source
An integrated method for short-term prediction of road traffic conditions for intelligent transportation systems applications [PDF]
, 2013 The paper deals with the short-term prediction of road traffic conditions within Intelligent Transportation Systems applications. First, the problem of traffic modeling and the potential of different traffic monitoring technologies are discussed.
COLOMBARONI, CHIARA, FUSCO, Gaetano
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Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
, 2018 Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect ...
Yin, Haoteng, Yu, Bing, Zhu, Zhanxing
core +1 more source