Results 251 to 260 of about 11,119,065 (317)

Adaptive Multi-Kernel SVM With Spatial–Temporal Correlation for Short-Term Traffic Flow Prediction

IEEE Transactions on Intelligent Transportation Systems, 2019
Accurate estimation of the traffic state can help to address the issue of urban traffic congestion, providing guiding advices for people’s travel and traffic regulation. In this paper, we propose a novel short-term traffic flow prediction algorithm based
Haifeng Zheng, Yiwen Xu, Xinxin Feng
exaly   +2 more sources

A Survey of Traffic Flow Prediction Methods Based on Long Short-Term Memory Networks

IEEE Intelligent Transportation Systems Magazine
It is generally recognized that accurate and timely prediction of future traffic flow information is one of the important conditions for improving the utilization rate of road networks and relieving traffic congestion. Since long short-term memory (LSTM)
Weimin Wu, Chunyuan Liu, Lingxi Li
exaly   +2 more sources

An Evaluation of HTM and LSTM for Short-Term Arterial Traffic Flow Prediction

IEEE Transactions on Intelligent Transportation Systems, 2019
Recent years have seen the emergence of two significant technologies: big data systems capable of storing, retrieving, and processing large amounts of data, and machine learning algorithms capable of learning and predicting complex sequences. In combination, these technologies present new opportunities to leverage the increasingly large amounts of ...
John F Roddick, Rocco Zito, Jonathan Mackenzie   +2 more
exaly   +2 more sources

Short-Term Traffic Flow Prediction for Urban Road Sections Based on Time Series Analysis and LSTM_BILSTM Method

IEEE transactions on intelligent transportation systems (Print), 2021
The real-time performance and accuracy of traffic flow prediction directly affect the efficiency of traffic flow guidance systems, and traffic flow prediction is a hotspot in the field of intelligent transportation.
Chang-Xi Ma, G. Dai, Ji-Biao Zhou
semanticscholar   +1 more source

Short-Term Traffic Flow Prediction Based on Graph Convolutional Networks and Federated Learning

IEEE transactions on intelligent transportation systems (Print), 2023
This study proposes a short-term traffic flow prediction model that combines community detection-based federated learning with a graph convolutional network (GCN) to alleviate the time-consuming training, higher communication costs, and data privacy ...
Mengran Xia, Dawei Jin, Jingyu Chen
semanticscholar   +1 more source

Short-term traffic flow prediction in bike-sharing networks

Journal of Intelligent Transportation Systems, 2021
For station-based bike-sharing systems, the balance between user demand and bike allocation is critical for the operation.
Bo Wang 0121, Hai Le Vu, Inhi Kim, Chen Cai   +3 more
openaire   +1 more source

Approach for Short-Term Traffic Flow Prediction Based on Empirical Mode Decomposition and Combination Model Fusion

IEEE Transactions on Intelligent Transportation Systems, 2021
Zhongda Tian
exaly   +2 more sources

A Short-Term Traffic Flow Prediction Model Based on an Improved Gate Recurrent Unit Neural Network

IEEE transactions on intelligent transportation systems (Print), 2021
With the increasing demand for intelligent transportation systems, short-term traffic flow prediction has become an important research direction. The memory unit of a Long Short-Term Memory (LSTM) neural network can store data characteristics over a ...
Wanneng Shu, Ken Cai, N. Xiong
semanticscholar   +1 more source

Application of LSTM in Short-term Traffic Flow Prediction

2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE), 2020
As urbanization intensifies, the status of the traffic situation predict is becoming more and more prominent. The urban traffic flow is influenced by many factors and is characterized by strong randomness. This paper combines MSE and Adam to construct a linear LSTM to realize the prediction of short-term traffic flow based on time series.
Chuanli Kang, Zhenyu Zhang
openaire   +2 more sources

A Hybrid Method for Short-Term Traffic Flow Prediction

2020 12th International Conference on Advanced Computational Intelligence (ICACI), 2020
We analyze the problem of short-term traffic flow prediction using a hybrid approach composed of regression and optimization. First, the data preprocessing method is described for the scenario of traffic flow prediction. Second, extreme gradient boosting is used to generate boosted trees that are then used to transform the input of each record. Finally,
Wei Song 0004, Taolin Yin
openaire   +1 more source