A Study of the Traffic Flow Predictive Model Based on Mathematical Statistics and Stochastic Process

Real-time traffic flow prediction is one of important issues of intelligent transportation system. Based on the theory of stochastic process of the traffic flow data, the prediction methods, such as grey expecting model and neural network, were applied in this paper. Then according to the actual traffic flow data, an improved model was proposed and the fluctuation range of predicted traffic flow was determined due to calculate an accurate result. Finally, the experiment shows that the designed prediction model can be able to achieve a short time prediction accurately for traffic flow.

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.988.715 ◽

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.

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Research on short-term traffic flow prediction based on the tensor decomposition algorithm

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201873 ◽

2020 ◽

pp. 1-11

Author(s):

Mingyu Tong ◽

Huiming Duan ◽

Xilin Luo

Keyword(s):

Traffic Flow ◽

Tensor Decomposition ◽

Decomposition Algorithm ◽

Grey Prediction ◽

Flow Data ◽

Short Term ◽

Traffic Flow Prediction ◽

Verhulst Model ◽

Type Data ◽

Short Time

In view of the uncertainties in short-time traffic flows and the multimode correlation of traffic flow data, a grey prediction model for short-time traffic flows based on tensor decomposition is proposed. First, traffic flow data are expressed as tensors based on the multimode characteristics of traffic flow data, and the principle of the tensor decomposition algorithm is introduced. Second, the Verhulst model is a classic grey prediction model that can effectively predict saturated S-type data, but traffic flow data do not have saturated S-type data. Therefore, the tensor decomposition algorithm is applied to the Verhulst model, and then, the Verhulst model of the tensor decomposition algorithm is established. Finally, the new model is applied to short-term traffic flow prediction, and an instance analysis shows that the model can deeply excavate the multimode correlation of traffic flow data. At the same time, the effect of the new model is superior to five other grey prediction models. The predicted results can provide intelligent transportation system planning, control and optimization with reliable real-time dynamic information in a timely manner.

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Forecasting Model of Short-Term Traffic Flow Based on Cloud Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.94-96.38 ◽

2011 ◽

Vol 94-96 ◽

pp. 38-42

Author(s):

Qin Liu ◽

Jian Min Xu

Keyword(s):

Real Time ◽

Traffic Flow ◽

Cloud Model ◽

Prediction Method ◽

Absolute Error ◽

Short Term ◽

Guangzhou City ◽

Time Prediction ◽

Real Time Traffic ◽

Absolute Percent Error

In order to improve the prediction precision of the short-term traffic flow, a prediction method of short-term traffic flow based on cloud model was proposed. The traffic flow was fit by cloud model. The history cloud and the present cloud were built by historical traffic flow and present traffic flow. The forecast cloud is produced by both clouds. Then, combining with the volume of the short-term traffic flow of an intersection in Guangzhou City, the model was calculated and simulated through programming. Max Absolute Error (MAE) and Mean Absolute percent Error (MAPE) were used to estimate the effect of prediction. The simulation results indicate that this prediction method is effective and advanced. The change of the historical and real time traffic flow is taken into account in this method. Because the short-term traffic flow is dealt with as a whole, the error of prediction is avoided. The prediction precision and real-time prediction are satisfied.

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Short-time Traffic Flow Prediction Method Based on Universal Organic Computing Architecture

Proceedings of the 2012 2nd International Conference on Computer and Information Applications (ICCIA 2012) ◽

10.2991/iccia.2012.303 ◽

2012 ◽

Cited By ~ 1

Author(s):

Shuzhi Nie ◽

Yanhua Zhong ◽

Ming Hu

Keyword(s):

Traffic Flow ◽

Prediction Method ◽

Computing Architecture ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Short Time ◽

Organic Computing

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Modeling Traffic Flows with Fluid Flow Model

International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) ◽

10.34010/injiiscom.v1i1.4860 ◽

2020 ◽

Vol 1 (1) ◽

Author(s):

Paulus Setiawan Suryadjaja ◽

◽

Maclaurin Hutagalung ◽

Herman Yoseph Sutarto ◽

◽

...

Keyword(s):

Fluid Flow ◽

Traffic Flow ◽

Flow Model ◽

Intelligent Transportation System ◽

Macroscopic Model ◽

Chain Process ◽

Data Traffic ◽

Flow Data ◽

Order Markov Chain ◽

Parameter Values

This Research presents a macroscopic model of traffic flow as the basis for making Intelligent Transportation System (ITS). The data used for modeling is The number of passing vehicles per three minutes. The traffic flow model created in The form of Fluid Flow Model (FFM). The parameters in The model are obtained by mixture Gaussian distribution approach. The distribution consists of two Gaussian distributions, each representing the mode of traffic flow. In The distribution, intermode shifting process is illustrated by the first-order Markov chain process. The parameters values are estimated using The Expectation-maximization (EM) algorithm. After The required parameter values are obtained, traffic flow is estimated using the Observation and transition-basedmost likely estimates Tracking Particle Filter (OTPF). To Examine the accuracy of the model has been made, the model estimation results are compared with the actual traffic flow data. Traffic flow data is collected on Monday 20 September 2017 at 06.00 to 10.00 on DipatiukurRoad, Bandung. The proposed model has accuracy with MAPE value below 10%, or falls into highly accurate categories

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Novel Model Based on Wavelet Transform and GA-fuzzy Neural Network Applied to Short Time Traffic Flow Prediction

Information Technology Journal ◽

10.3923/itj.2011.2105.2111 ◽

2011 ◽

Vol 10 (11) ◽

pp. 2105-2111 ◽

Cited By ~ 9

Author(s):

Yuesheng Gu ◽

Yancui Li ◽

Jiucheng Xu ◽

Yanpei Liu

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Traffic Flow ◽

Fuzzy Neural Network ◽

Traffic Flow Prediction ◽

Model Based ◽

Flow Prediction ◽

Fuzzy Neural ◽

Short Time ◽

Novel Model

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

Journal Of Big Data ◽

10.1186/s40537-021-00542-7 ◽

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.

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

PROMET - Traffic&Transportation ◽

10.7307/ptt.v32i6.3394 ◽

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.

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