scholarly journals Short-Term Traffic Flow Prediction with Weather Conditions: Based on Deep Learning Algorithms and Data Fusion

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yue Hou ◽  
Zhiyuan Deng ◽  
Hanke Cui
Keyword(s):  
Traffic Flow ◽  
Effective Means ◽  
Weather Conditions ◽  
Weather Data ◽  
Multiple Time ◽  
Traffic Data ◽  
Flow Data ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Flow Prediction

Short-term traffic flow prediction is an effective means for intelligent transportation system (ITS) to mitigate traffic congestion. However, traffic flow data with temporal features and periodic characteristics are vulnerable to weather effects, making short-term traffic flow prediction a challenging issue. However, the existing models do not consider the influence of weather changes on traffic flow, leading to poor performance under some extreme conditions. In view of the rich features of traffic data and the characteristic of being vulnerable to external weather conditions, the prediction model based on traffic data has certain limitations, so it is necessary to conduct research studies on traffic flow prediction driven by both the traffic data and weather data. This paper proposes a combined framework of stacked autoencoder (SAE) and radial basis function (RBF) neural network to predict traffic flow, which can effectively capture the temporal correlation and periodicity of traffic flow data and disturbance of weather factors. Firstly, SAE is used to process the traffic flow data in multiple time slices to acquire a preliminary prediction. Then, RBF is used to capture the relation between weather disturbance and periodicity of traffic flow so as to gain another prediction. Finally, another RBF is used for the fusion of the above two predictions on decision level, obtaining a reconstructed prediction with higher accuracy. The effectiveness and robustness of the proposed model are verified by experiments.

scholarly journals Traffic Flow Prediction Using MI Algorithm and Considering Noisy and Data Loss Conditions: An Application to Minnesota Traffic Flow Prediction

2014 ◽  
Vol 26 (5) ◽  
pp. 393-403 ◽  
Author(s):  
Seyed Hadi Hosseini ◽  
Behzad Moshiri ◽  
Ashkan Rahimi-Kian ◽  
Babak Nadjar Araabi
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Weather Conditions ◽  
Traffic Data ◽  
Data Loss ◽  
Traffic Lights ◽  
Traffic Flow Prediction ◽  
Traffic Flow Forecasting ◽  
Flow Prediction ◽  
Forecasting Method

Traffic flow forecasting is useful for controlling traffic flow, traffic lights, and travel times. This study uses a multi-layer perceptron neural network and the mutual information (MI) technique to forecast traffic flow and compares the prediction results with conventional traffic flow forecasting methods. The MI method is used to calculate the interdependency of historical traffic data and future traffic flow. In numerical case studies, the proposed traffic flow forecasting method was tested against data loss, changes in weather conditions, traffic congestion, and accidents. The outcomes were highly acceptable for all cases and showed the robustness of the proposed flow forecasting method.

scholarly journals Traffic Flow Prediction using Combination of Support Vector Machine and Rough Set

2019 ◽  
Vol 8 (11) ◽  
pp. 3334-3338
Keyword(s):  
Support Vector Machine ◽  
Traffic Flow ◽  
Rough Set ◽  
Traffic Control ◽  
Support Vector ◽  
Traffic Data ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Threshold Variation

Congestion is the primary issue related to traffic flow. Avoiding congestion after getting into is not possible. So the only way is to make the informed decision by knowing the traffic situation in advance. This can be achieved with the help of traffic flow prediction. In the proposed work, short term traffic flow prediction is performed using support vector machine in combination with rough set. Traffic data used for analysis is collected from three adjacent intersections of Nagpur city and traffic flow is predicted at downstream junction. The work has attempted to study the effect of aggregation intervals and past samples on the prediction performance using MSE threshold variation. Rough set is used as a post processor to validate the prediction result. Accurate and timely prediction can provide reliability for optimized traffic control and guidance.

scholarly journals Robust-LSTM: A Novel Approach to Short-Traffic Flow Prediction Based on Signal Decomposition

2021 ◽  
Author(s):  
Erdem Doğan
Keyword(s):  
Traffic Flow ◽  
Short Term Memory ◽  
Performance Measurement System ◽  
Flow Data ◽  
Short Term ◽  
Term Memory ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Novel Approach ◽  
Long Short Term Memory

Abstract Intelligent transport systems need accurate short-term traffic flow forecasts. However, developing a robust short-term traffic flow forecasting approach is a challenge due to the stochastic character of traffic flow. This study proposes a novel approach for short-term traffic flow prediction task namely Robust Long Short Term Memory (R-LSTM) based on Robust Empirical Mode Decomposing (REDM) algorithm and Long Short Term Memory (LSTM). Short-term traffic flow data provided from the Caltrans Performance Measurement System (PeMS) database were used in the training and testing of the model. The dataset was composed of traffic data collected by 25 traffic detectors on different freeways’ main lanes. The time resolution of the dataset was set to 15 minutes, and the Hampel preprocessing algorithm was applied for outlier elimination. The R-LSTM predictions were compared with the state-of-art models, utilizing RMSE, MSE, and MAPE as performance criteria. Performance analyzes for various periods show that R-LSTM is remarkably successful in all time periods. Moreover, developed model performance is significantly higher, especially during mid-day periods when traffic flow fluctuations are high. These results show that R-LSTM is a strong candidate for short-term traffic flow prediction, and can easily adapt to fluctuations in traffic flow. In addition, robust models for short-term predictions can be developed by applying the signal separation method to traffic flow data.

Short-term traffic flow prediction based on 1DCNN-LSTM neural network structure

2020 ◽  
pp. 2150042
Author(s):  
Yihuan Qiao ◽  
Ya Wang ◽  
Changxi Ma ◽  
Ju Yang
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Spatial Information ◽  
Short Term Memory ◽  
Prediction Method ◽  
Traffic Data ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
The Past ◽  
Flow Prediction

In the past decade, the number of cars in China has significantly raised, but the traffic jam spree problem has brought great inconvenience to people’s travel. Accurate and efficient traffic flow prediction, as the core of Intelligent Traffic System (ITS), can effectively solve the problems of traffic travel and management. The existing short-term traffic flow prediction researches mainly use the shallow model method, so they cannot fully reflect the traffic flow characteristics. Therefore, this paper proposed a short-term traffic flow prediction method based on one-dimensional convolution neural network and long short-term memory (1DCNN-LSTM). The spatial information in traffic data is obtained by 1DCNN, and then the time information in traffic data is obtained by LSTM. After that, the space-time features of the traffic flow are used as regression predictions, which are input into the Fully-Connected Layer. In the end, the corresponding prediction results of the current input are calculated. In the past, most of the researches are based on survey data or virtual data, lacking authenticity. In this paper, real data will be used for research. The data are provided by OpenITS open data platform. Finally, the proposed method is compared with other road forecasting models. The results show that the structure of 1DCNN-LSTM can further improve the prediction accuracy.

scholarly journals Research on Short-Term Traffic Flow Prediction Method Based on Similarity Search of Time Series

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Zhaosheng Yang ◽  
Qichun Bing ◽  
Ciyun Lin ◽  
Nan Yang ◽  
Duo Mei
Keyword(s):  
Time Series ◽  
Traffic Flow ◽  
Similarity Search ◽  
Prediction Method ◽  
Flow Data ◽  
Short Term ◽  
Similar Time ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Advanced Traffic Management System

Short-time traffic flow prediction is necessary for advanced traffic management system (ATMS) and advanced traveler information system (ATIS). In order to improve the effect of short-term traffic flow prediction, this paper presents a short-term traffic flow multistep prediction method based on similarity search of time series. Firstly, the landmark model is used to represent time series of traffic flow data. Then the input data of prediction model are determined through searching similar time series. Finally, the echo state networks model is used for traffic flow multistep prediction. The performance of the proposed method is measured with expressway traffic flow data collected from loop detectors in Shanghai, China. The experimental results demonstrate that the proposed method can achieve better multistep prediction performance than conventional methods.

A spatial-temporal approach for traffic status analysis and prediction based on Bi-LSTM structure

2021 ◽  
pp. 2150481
Author(s):  
Linjia Li ◽  
Yang Yang ◽  
Zhenzhou Yuan ◽  
Zhi Chen
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Traffic Control ◽  
Road Traffic ◽  
Performance Measurement System ◽  
Flow Data ◽  
Short Term ◽  
Temporal Differences ◽  
Traffic Flow Prediction ◽  
Flow Prediction

Urban traffic control has become a big issue to help traffic management in recent years. With data explosion, Intelligent Transportation System (ITS) is developing rapidly. ITS is an advanced data-based method for traffic control, which requires timely and effective information supply. This research aims at providing real-time and accurate traffic flow data by intelligent prediction method. Applying multiple road traffic flow data of the Caltrans Performance Measurement System (PeMS) and separating the time series, the mechanism of spatial-temporal differences was taken into consideration. Based on the basic Long Short-Term Memory (LSTM) model, an improved LSTM model with Dropout and Bi-structure (Bi-LSTM) for traffic flow prediction was presented. In the prediction process, we applied three models including the improved Bi-LSTM model, Gated Recurrent Unit (GRU) model and Linear Regression in the experiment, and made a comparison from aspects of model structure complexity, operating efficiency and prediction accuracy. To validate the portability of the prediction model, the features of traffic flow from different datasets were further analyzed. The experimental results show that the improved Bi-LSTM model performs best in traffic flow prediction with comprehensive rationality, which reaches an accuracy of about 92% when considering temporal differences. Particularly, the specific factors of traffic situations and locations which is more applicable to be predicted by the improved Bi-LSTM model are summarized considering spatial differences. This research proposes an advanced and accurate model to provide real-time and short-term traffic flow prediction data, which is of great help to intelligent traffic control. Considering the mechanism between model and road traffic properties, the results suggest that it is more applicable in urban commercial area.

scholarly journals An Incremental Learning Based Convolutional Neural Network Model for Large-Scale and Short-Term Traffic Flow

2021 ◽  
Vol 11 (2) ◽  
pp. 143-151
Author(s):  
Feng Yu ◽  
◽  
Jinglong Fang ◽  
Bin Chen ◽  
Yanli Shao
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Neural Network Model ◽  
Incremental Learning ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Flow Data ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Flow Prediction

Traffic flow prediction is very important for smooth road conditions in cities and convenient travel for residents. With the explosive growth of traffic flow data size, traditional machine learning algorithms cannot fit large-scale training data effectively and the deep learning algorithms do not work well because of the huge training and update costs, and the prediction accuracy may need to be further improved when an emergency affecting traffic occurs. In this study, an incremental learning based convolutional neural network model, TF-net, is proposed to achieve the efficient and accurate prediction of large-scale and short-term traffic flow. The key idea is to introduce the uncertainty features into the model without increasing the training cost to improve the prediction accuracy. Meanwhile, based on the idea of combining incremental learning with active learning, a certain percentage of typical samples in historical traffic flow data are sampled to fine-tune the prediction model, so as to further improve the prediction accuracy for special situations and ensure the real-time requirement. The experimental results show that the proposed traffic flow prediction model has better performance than the existing methods.

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