The Prediction Model of Macro-Road Traffic Accident Basing on Radial Basis Function

The Macro-road traffic accident prediction is an important branch of ITS, which could not only make improving direction, but also improve the traffic operation. The paper based on the analyzing the existing macro prediction model, aiming at the existing shortcomings of prediction models with low accuracy and slow convergence speed, introducing the Radial Basis Function, establishing the accident prediction model between Population, Economic situation, cars, road mileage and the index of accident Statistics, and apply Matlab to Simulation to prove the Feasibility and Practicality of the model.

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TRAFFIC ANALYSIS ZONE LEVEL ROAD TRAFFIC ACCIDENT PREDICTION MODELS BASED ON LAND USE CHARACTERISTICS

International Journal for Traffic and Transport Engineering ◽

10.7708/ijtte.2019.9(4).03 ◽

2019 ◽

Vol 9 (4) ◽

Keyword(s):

Land Use ◽

Road Traffic Accident ◽

Traffic Accident ◽

Prediction Models ◽

Road Traffic ◽

Traffic Analysis ◽

Accident Prediction

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COMPARISON OF ROAD TRAFFIC ACCIDENT PREDICTION MODELS FOR TWO-LANE HIGHWAY INTEGRATING TRAFFIC AND PAVEMENT CONDITION PARAMETERS

Journal of Natural Sciences Engineering and Technology ◽

10.51406/jnset.v16i2.1841 ◽

2019 ◽

Vol 16 (2) ◽

pp. 1-10

Author(s):

O. M. POPOOLA ◽

O. S. ABIOLA ◽

S. O. ODUNFA ◽

S. O. ISMAILA

Keyword(s):

Road Traffic Accident ◽

Traffic Accident ◽

Negative Binomial ◽

Prediction Models ◽

Road Traffic ◽

International Roughness Index ◽

Pavement Condition ◽

Accident Prediction ◽

Explanatory Variables ◽

Roughness Index

In Nigeria, literature on the integration of traffic of pavement condition and traffic characteristics in predicting road traffic accident frequency on 2-lane highways are scanty, hence this article to fill the gap. A comparison of road traffic accident frequency prediction models on IIesha-Akure-Owo road based on the data observed between 2012 and 2014 is presented. Negative Binomial (NB), Ordered Logistic (OL) and Zero Inflated Negative Binomial (ZINB) models were used to model the frequency of road traffic accident occurrence using road traffic accident data from the Federal Road Safety Commission (FRSC) and pavement conditions parameters from pavement evaluation unit of the Federal Ministry of Works, Kaduna. The explanatory variables were: annual average daily traffic (aadt), shoulder factor (sf), rut depth (rd), pavement condition index (pci), and international roughness index (iri). The explanatory variables that were statistically significant for the three models are aadt, sf and iri with the estimated coefficients having the expected signs. The number of road traffic accident on the road increases with the traffic volume and the international roughness index while it decreases with shoulder factor. The systematic variation explained by the models amounts to 87.7, 78.1 and 74.4% for NB, ZINB and OL respectively. The research findings suggest the accident prediction models that should be integrated into pavement rehabilitation. Keywords:

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Subpixel mapping on remote sensing imagery using a prediction model combining wavelet transform and radial basis function neural network

Journal of Applied Remote Sensing ◽

10.1117/1.3277121 ◽

2009 ◽

Vol 3 (1) ◽

pp. 033566 ◽

Cited By ~ 1

Author(s):

Zhongyang Guo

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Wavelet Transform ◽

Prediction Model ◽

Radial Basis Function ◽

Basis Function ◽

Remote Sensing Imagery ◽

Model Combining ◽

Radial Basis ◽

Subpixel Mapping

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The Research Based on RBF Neural Network in the Power of Prediction of Grain Depot

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.182-183.1358 ◽

2012 ◽

Vol 182-183 ◽

pp. 1358-1361

Author(s):

Le Xiao ◽

Min Peng Hu

Keyword(s):

Neural Network ◽

Time Series ◽

Prediction Model ◽

Radial Basis Function ◽

Basis Function ◽

Rbf Neural Network ◽

Electricity Consumption ◽

Nonlinear Time Series ◽

Modeling And Prediction ◽

Radial Basis

According to the fact that the use of electricity in grain depot is nonlinear time series, the article introduces the prediction model of electricity based on Radial Basis Function Neural Network, and conducts the modeling and prediction by adopting the historical electricity consumption of a typical grain depot. As the result of simulation shows, the model obtains better forecasting results in grain depot electricity.

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Software Defect Prediction Using Hybrid Distribution Base Balance Instance Selection and Radial Basis Function Classifier

International Journal of System Dynamics Applications ◽

10.4018/ijsda.2019070103 ◽

2019 ◽

Vol 8 (3) ◽

pp. 53-75 ◽

Cited By ~ 1

Author(s):

Mrutyunjaya Panda

Keyword(s):

Prediction Model ◽

Radial Basis Function ◽

Basis Function ◽

Rapid Development ◽

Defect Prediction ◽

Instance Selection ◽

Software Defect Prediction ◽

Software Defect ◽

Radial Basis ◽

Base Balance

Software is an important part of human life and with the rapid development of software engineering the demands for software to be reliable with low defects is increasingly pressing. The building of a software defect prediction model is proposed in this article by using various software metrics with publicly available historical software defect datasets collected from several projects. Such a prediction model can enable the software engineers to take proactive actions in enhancing software quality from the early stages of the software development cycle. This article introduces a hybrid classification method (DBBRBF) by combining distribution base balance (DBB) based instance selection and radial basis function (RBF) neural network classifier to obtain the best prediction compared to the existing research. The experimental results with post-hoc statistical significance tests shows the effectiveness of the proposed approach.

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