Short Term Traffic Flow Prediction for a Non Urban Highway Using Artificial Neural Network
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TLDR
Results show that Artificial Neural Network has consistent performance even if time interval for traffic flow prediction was increased from 5 minutes to 15 min minutes and produced good results even though speeds of each category of vehicles were considered separately as input variables.About:
This article is published in Procedia - Social and Behavioral Sciences.The article was published on 2013-12-02 and is currently open access. It has received 207 citations till now. The article focuses on the topics: Traffic flow & Traffic generation model.read more
Citations
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Traffic Flow Prediction With Big Data: A Deep Learning Approach
TL;DR: A novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently and is applied for the first time that a deep architecture model is applied using autoencoders as building blocks to represent traffic flow features for prediction.
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Improving Traffic Flow Prediction with Weather Information in Connected Cars: A Deep Learning Approach
TL;DR: The experimental results corroborate the effectiveness of the proposed approach compared with the state of the art, and incorporate deep belief networks for traffic and weather prediction and decision-level data fusion scheme to enhance prediction accuracy using weather conditions.
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Traffic flow prediction using LSTM with feature enhancement
TL;DR: This work proposes an improved approach that connects the high-impact value of remarkably long sequence time steps to the current time step, and these high- impact traffic flow values are captured using the attention mechanism.
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A Hybrid Deep Learning Model With Attention-Based Conv-LSTM Networks for Short-Term Traffic Flow Prediction
TL;DR: A deep learning based model which uses hybrid and multiple-layer architectures to automatically extract inherent features of traffic flow data and achieves better prediction performance compared with other existing approaches is proposed.
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Short-Term Traffic Speed Forecasting Based on Attention Convolutional Neural Network for Arterials
TL;DR: This article proposes an attention CNN to predict traffic speed, which uses three‐dimensional data matrices constructed by traffic flow, speed, and occupancy and which has considerable advantages in predicting tasks compared to other commonly used algorithms.
References
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Journal ArticleDOI
Forecasting with artificial neural networks: the state of the art
TL;DR: In this paper, the authors present a state-of-the-art survey of ANN applications in forecasting and provide a synthesis of published research in this area, insights on ANN modeling issues, and future research directions.
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Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach
TL;DR: Past research is extended by providing an advanced, genetic algorithm based, multilayered structural optimization strategy that can assist both in the proper representation of traffic flow data with temporal and spatial characteristics as well as in the selection of the appropriate neural network structure.
Journal ArticleDOI
Short‐term traffic forecasting: Overview of objectives and methods
TL;DR: This field of research was examined by disaggregating the process of developing short‐term traffic forecasting algorithms into three essential clusters: the determination of the scope, the conceptual process of specifying the output and the process that includes several decisions concerning the selection of the proper methodological approach.
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Short-term freeway traffic flow prediction : Bayesian combined neural network approach
TL;DR: A neural network model is introduced that combines the prediction from single neural network predictors according to an adaptive and heuristic credit assignment algorithm based on the theory of conditional probability and Bayes' rule and is found that most of the time, the combined model outperforms the singular predictors.
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