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Markov-Based Time Series Modeling Framework for Traffic-Network State Prediction under Various External Conditions

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s10236-003-0036-9openalex-snapshot

The Ensemble Kalman Filter: theoretical formulation and practical implementation

inferredcites →https://doi.org/10.1016/j.trb.2007.08.005openalex-snapshot

Travel time estimation on a freeway using Discrete Time Markov Chains

inferredcites →https://doi.org/10.1016/j.trb.2012.08.004openalex-snapshot

On the estimation of arterial route travel time distribution with Markov chains

inferredcites →https://doi.org/10.1016/j.trc.2014.02.007openalex-snapshot

A Hidden Markov Model for short term prediction of traffic conditions on freeways

inferredcites →https://doi.org/10.1061/(asce)0733-947x(1997)123:4(261)openalex-snapshot

Traffic Flow Forecasting: Comparison of Modeling Approaches

inferredcites →https://doi.org/10.1061/(asce)0733-947x(2003)129:2(161)openalex-snapshot

Traffic Prediction Using Multivariate Nonparametric Regression

inferredcites →https://doi.org/10.1061/(asce)0733-947x(2003)129:6(664)openalex-snapshot

Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results

inferredcites →https://doi.org/10.1061/(asce)0733-947x(2004)130:4(452)openalex-snapshot

Optimizing Traffic Prediction Performance of Neural Networks under Various Topological, Input, and Traffic Condition Settings

inferredcites →https://doi.org/10.1061/(asce)0887-3801(2005)19:1(94)openalex-snapshot

Short-Term Traffic Prediction on Different Types of Roads with Genetically Designed Regression and Time Delay Neural Network Models

inferredcites →https://doi.org/10.1109/cdc.2008.4739016openalex-snapshot

An ensemble Kalman filtering approach to highway traffic estimation using GPS enabled mobile devices

inferredcites →https://doi.org/10.2307/3008255openalex-snapshot

Time Series Analysis Forecasting and Control.

inferredcites →https://doi.org/10.3141/1836-18openalex-snapshot

Use of Local Linear Regression Model for Short-Term Traffic Forecasting

inferredcites →https://doi.org/10.3141/2421-13openalex-snapshot

Real-Time Prediction of Near-Future Traffic States on Freeways Using a Markov Model

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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A short-term traffic flow prediction network using filter-genetic feature selection method

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A traffic-weather generative adversarial network for traffic flow prediction for road networks under bad weather

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A fundamental diagram based hybrid framework for traffic flow estimation and prediction by combining a Markovian model with deep learning

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The queue dynamics of protected/permissive left turns at pre-timed signalized intersections

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Traffic flow prediction under multiple adverse weather based on self-attention mechanism and deep learning models

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A physics-informed machine learning framework for speed-flow prediction: Integrating an S-shaped traffic stream model with deep learning models

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Variational Autoencoder–Generative Adversarial Network Traffic Prediction with Ramps

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Hybrid Deep Spatio-Temporal Models for Traffic Flow Prediction on Holidays and Under Adverse Weather

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Route Optimization for School Buses Using Real-Time GPS and Geospatial Data

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Foundation Intelligence for Smart Infrastructure Services in Transportation 5.0

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Examine the Prediction Error of Ride-Hailing Travel Demands with Various Ignored Sparse Demand Effects

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Intelligent Short-Term Multiscale Prediction of Parking Space Availability Using an Attention-Enhanced Temporal Convolutional Network

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PFVAE: A Planar Flow-Based Variational Auto-Encoder Prediction Model for Time Series Data

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Impact of High Resolution Radar-Obtained Weather Data on Spatio-Temporal Prediction of Freeway Speed

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Overview of machine learning-based traffic flow prediction

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A Machine Learning-Based CNN–Bilstm Framework for Traffic Congestion and Travel Time Prediction

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