Fuzzy Inspired Deep Belief Network for the Traffic Flow Prediction in Intelligent Transportation System Using Flow Strength Indicators

Big Data ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 291-307
Author(s):  
Shiju George ◽  
Ajit Kumar Santra
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Deep Belief Network ◽  
Flow Strength ◽  
Belief Network ◽  
Traffic Flow Prediction ◽  
Flow Prediction

An New Approach of Real-Time Traffic Flow Prediction Based on Intelligent Transportation Technology

2014 ◽  
Vol 988 ◽  
pp. 715-718
Author(s):  
Jia Yang Li ◽  
Qin Xue ◽  
Jin De Liu
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Short Term ◽  
New Approach ◽  
Traffic Flow Prediction ◽  
Real Time Traffic ◽  
Flow Prediction ◽  
Combination Approach

Short-term traffic flow forecasting is a core problem in Intelligent Transportation System .Considering linear and nonlinear, this paper proposes a short-term traffic flow intelligent combination approach. The weight of four forecasting model is given by the correlation coefficient and standard deviation method. The experimental results show that the new approach of real-time traffic flow prediction is higher precision than single method.

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.

scholarly journals Short-term Traffic Flow Prediction Method in Bayesian Networks Based on Quantile Regression

2020 ◽  
Vol 32 (6) ◽  
pp. 821-835
Author(s):  
Jing Luo
Keyword(s):  
Quantile Regression ◽  
Traffic Flow ◽  
Intelligent Transportation System ◽  
Prediction Method ◽  
Time Data ◽  
Short Term ◽  
Internet Of Vehicles ◽  
Traffic Flow Prediction ◽  
Spatiotemporal Correlation ◽  
Flow Prediction

With the popularization of intelligent transportation system and Internet of vehicles, the traffic flow data on the urban road network can be more easily obtained in large quantities. This provides data support for shortterm traffic flow prediction based on real-time data. Of all the challenges and difficulties faced in the research of short-term traffic flow prediction, this paper intends to address two: one is the difficulty of short-term traffic flow prediction caused by spatiotemporal correlation of traffic flow changes between upstream and downstream intersections; the other is the influence of deviation of traffic flow caused by abnormal conditions on short-term traffic flow prediction. This paper proposes a Bayesian network short-term traffic flow prediction method based on quantile regression. By this method the trouble caused by spatiotemporal correlation of traffic flow prediction could be effectively and efficiently solved. At the same time, the prediction of traffic flow change under abnormal conditions has higher accuracy.

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.

Study on the integration effect of multiple vehicles’ delayed velocities on traffic stability in intelligent transportation system environment

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Geng Zhang ◽  
Qinglu Ma ◽  
Dongbo Pan ◽  
Yu Zhang ◽  
Qiaoli Huang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation System ◽  
Research Result ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Content Type ◽  
Car Following ◽  
Car Following Model ◽  
Multiple Vehicles ◽  
Integration Effect

Purpose In an intelligent transportation system (for short, ITS) environment, a vehicle’s motion is affected by the information in a large scale. The purpose of this paper is to study the integration effect of multiple vehicles’ delayed velocities on traffic flow. Design/methodology/approach This paper constructed a new car-following model to study the integration effect of multiple vehicles’ delayed velocities on traffic flow. The new model is analyzed by linear and nonlinear perturbation method theoretically and also verified by simulation. Findings It is found out that the integration of preceding vehicles’ delayed velocities affect the stability of traffic flow importantly, and three preceding vehicles’ delayed velocities information should be considered in real traffic. Originality/value The new car-following model by considering the integration effect of multiple vehicles’ delayed velocities is firstly proposed in this paper. The research result shows that three preceding vehicles’ delayed velocities information is the best choice to stabilizing traffic flow.

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