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 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 Modeling of Artificial Intelligence Based Traffic Flow Prediction with Weather Conditions

2022 ◽  
Vol 71 (2) ◽  
pp. 3953-3968
Author(s):  
Mesfer Al Duhayyim ◽  
Amani Abdulrahman Albraikan ◽  
Fahd N. Al-Wesabi ◽  
Hiba M. Burbur ◽  
Mohammad Alamgeer ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Traffic Flow ◽  
Weather Conditions ◽  
Traffic Flow Prediction ◽  
Flow Prediction

scholarly journals Gap, techniques and evaluation: traffic flow prediction using machine learning and deep learning

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Noor Afiza Mat Razali ◽  
Nuraini Shamsaimon ◽  
Khairul Khalil Ishak ◽  
Suzaimah Ramli ◽  
Mohd Fahmi Mohamad Amran ◽  
...  
Keyword(s):  
Machine Learning ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Intelligent Transportation System ◽  
Short Term Memory ◽  
Smart Cities ◽  
Experimental Studies ◽  
Machine Learning Techniques ◽  
Traffic Flow Prediction ◽  
Flow Prediction

AbstractThe development of the Internet of Things (IoT) has produced new innovative solutions, such as smart cities, which enable humans to have a more efficient, convenient and smarter way of life. The Intelligent Transportation System (ITS) is part of several smart city applications where it enhances the processes of transportation and commutation. ITS aims to solve traffic problems, mainly traffic congestion. In recent years, new models and frameworks for predicting traffic flow have been rapidly developed to enhance the performance of traffic flow prediction, alongside the implementation of Artificial Intelligence (AI) methods such as machine learning (ML). To better understand how ML implementations can enhance traffic flow prediction, it is important to inclusively know the current research that has been conducted. The objective of this paper is to present a comprehensive and systematic review of the literature involving 39 articles published from 2016 onwards and extracted from four main databases: Scopus, ScienceDirect, SpringerLink and Taylor & Francis. The extracted information includes the gaps, approaches, evaluation methods, variables, datasets and results of each reviewed study based on the methodology and algorithms used for the purpose of predicting traffic flow. Based on our findings, the common and frequent machine learning techniques that have been applied for traffic flow prediction are Convolutional Neural Network and Long-Short Term Memory. The performance of their proposed techniques was compared with existing baseline models to determine their effectiveness. This paper is limited to certain literature pertaining to common databases. Through this limitation, the discussion is more focused on (and limited to) the techniques found on the list of reviewed articles. The aim of this paper is to provide a comprehensive understanding of the application of ML and DL techniques for improving traffic flow prediction, contributing to the betterment of ITS in smart cities. For future endeavours, experimental studies that apply the most used techniques in the articles reviewed in this study (such as CNN, LSTM or a combination of both techniques) can be accomplished to enhance traffic flow prediction. The results can be compared with baseline studies to determine the accuracy of these techniques.

Deep Spatio-temporal Adaptive 3D Convolutional Neural Networks for Traffic Flow Prediction

2022 ◽  
Vol 13 (2) ◽  
pp. 1-21
Author(s):  
He Li ◽  
Xuejiao Li ◽  
Liangcai Su ◽  
Duo Jin ◽  
Jianbin Huang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation System ◽  
Historical Data ◽  
Temporal Correlation ◽  
Weather Conditions ◽  
External Factors ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Spatio Temporal ◽  
The Impact

Traffic flow prediction is the upstream problem of path planning, intelligent transportation system, and other tasks. Many studies have been carried out on the traffic flow prediction of the spatio-temporal network, but the effects of spatio-temporal flexibility (historical data of the same type of time intervals in the same location will change flexibly) and spatio-temporal correlation (different road conditions have different effects at different times) have not been considered at the same time. We propose the Deep Spatio-temporal Adaptive 3D Convolution Neural Network (ST-A3DNet), which is a new scheme to solve both spatio-temporal correlation and flexibility, and consider spatio-temporal complexity (complex external factors, such as weather and holidays). Different from other traffic forecasting models, ST-A3DNet captures the spatio-temporal relationship at the same time through the Adaptive 3D convolution module, assigns different weights flexibly according to the influence of historical data, and obtains the impact of external factors on the flow through the ex-mask module. Considering the holidays and weather conditions, we train our model for experiments in Xi’an and Chengdu. We evaluate the ST-A3DNet and the results show that we have better results than the other 11 baselines.

scholarly journals A Novel Traffic Flow Forecasting Method Based on RNN-GCN and BRB

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Hailong Zhu ◽  
Yawen Xie ◽  
Wei He ◽  
Chao Sun ◽  
Kaili Zhu ◽  
...  
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Prediction Method ◽  
Time Correlation ◽  
Rule Base ◽  
Intelligent Transport ◽  
Traffic Flow Prediction ◽  
Traffic Flow Forecasting ◽  
Forecasting Method ◽  
Belief Rule Base

As an important part of a smart city, intelligent transport can effectively reduce energy consumption and environmental pollution. Traffic flow forecasting provides a reliable traffic dispatch basis for intelligent transport, and most of the existing prediction methods only predict a single saturation or speed and do not use the saturation and speed in a unified way. This paper proposes a new traffic flow prediction method based on RNN-GCN and BRB. First, the belief rule base (BRB) is used for data fusion to obtain new traffic flow data, then the recurrent neural network (RNN) and graph convolution neural network (GCN) model is used to obtain the time correlation of the traffic data, and finally, the traffic flow is predicted by the topology graph. The experimental results show that the method has a better performance than ARIMA, LSTM, and GCN.

An Approach for Short Term Traffic Flow Forecasting Based on Fuzzy Logic Control

2013 ◽  
Vol 336-338 ◽  
pp. 438-441
Author(s):  
Wei Dong Dai
Keyword(s):  
Fuzzy Logic ◽  
Traffic Flow ◽  
Fuzzy Logic Control ◽  
Time Correlation ◽  
Output Curve ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Traffic Flow Forecasting ◽  
Logic Control ◽  
Flow Prediction

In this paper, fuzzy logic control is applied to forecast the short-term traffic flow and traffic guidance. Because of the factors of time correlation and spatial correlation, we construct the short-term traffic flow forecasting model using fuzzy logic control that can handle non-linear plant behavior. In order to find a feasibility way of traffic flow prediction, we deal the combination of time correlation traffic value and space correlation traffic value as the input variables. Considering the real condition, we use triangular and trapezoid membership function to design the belongings relationship. Five fuzzy rules are applied in the control. Last, we use fuzzy logic toolbox to simulate the short term traffic flow forecasting basing on the fuzzy logic control. The system input/output curve result shows that this method can have a good performance for short-term traffic flow prediction.

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 ST-AFN: a spatial-temporal attention based fusion network for lane-level traffic flow prediction

2021 ◽  
Vol 7 ◽  
pp. e470
Author(s):  
Guojiang Shen ◽  
Kaifeng Yu ◽  
Meiyu Zhang ◽  
Xiangjie Kong
Keyword(s):  
Traffic Flow ◽  
Smart Cities ◽  
Information Matrix ◽  
Traffic Data ◽  
Temporal Attention ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Proposed Model ◽  
Precise Prediction ◽  
First Time

Traffic flow prediction is the foundation of many applications in smart cities, and the granular precision of traffic flow prediction has to be enhanced with refined applications. However, most of the existing researches cannot meet these requirements. In this paper, we propose a spatial-temporal attention based fusion network (ST-AFN), for lane-level precise prediction. This seq2seq model consists of three parts, namely speed process network, spatial encoder, and temporal decoder. In order to exploit the dynamic dependencies among lanes, attention mechanism blocks are embedded in those networks. The application of deep spatial-temporal information matrix results in progresses in term of reliability. Furthermore, a specific ground lane selection method is also proposed to ST-AFN. To evaluate the proposed model, four months of real-world traffic data are collected in Xiaoshan District, Hangzhou, China. Experimental results demonstrate that ST-AFN can achieve more accurate and stable results than the benchmark models. To the best of our knowledge, this is the first time that a deep learning method has been applied to forecast traffic flow at the lane level on urban ground roads instead of expressways or elevated roads.

Forecasting Website Traffic Using Prophet Time Series Model

2019 ◽  
Vol 1 (1) ◽  
pp. 56-63
Author(s):  
A Subashini ◽  
Sandhiya K ◽  
S Saranya ◽  
U Harsha
Keyword(s):  
Time Series ◽  
Big Data ◽  
Traffic Flow ◽  
Prediction Models ◽  
Internet Traffic ◽  
Time Series Model ◽  
Traffic Data ◽  
Traffic Flow Prediction ◽  
Flow Monitoring ◽  
Flow Prediction

Web traffic is the amount of data sent and received by visitors to a website and it has been the largest portion of Internet traffic. Internet traffic flow prediction heavily depends on historical and real-time traffic data collected from various internet flow monitoring sources. With the widespread traditional traffic sensors and new emerging traffic sensor technologies, traffic data are exploding, and we have entered the era of big data internet traffic. Internet traffic management and control driven by big data is becoming a new trend. Although there have been already many internet traffic flow prediction systems and models, most of which use shallow traffic models and are still somewhat unsatisfying. This inspires us to reconsider the internet traffic flow prediction model based on deep architecture models with such rich amount of internet traffic data. ARIMA is a existing forecasting technique that predicts the future values of a series based entirely on its own inertia. Existing traffic flow prediction methods mainly use simple traffic prediction models and are still unsatisfying for many real-world applications. Now we proposed the prophet time series model to forecasting website traffic.

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