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

Song, Cheng; Li, Quan Yan

doi:10.4028/www.scientific.net/amm.97-98.981

Cited by 5 publications

(2 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These net works are char ac terized by high ap prox i ma tion ac cu racy and high con ver gence speed. An RBF uses a Gaussi an trans fer func tion and stan dard Eu clid ean dis tance to mea sure the dis tance of the in put vec tor from a cen tre vec tor (Song and Li, 2011). A spe cial type of ANN com prises self-or ga niz ing map net works (SOM), which con sist of one in put layer and one out put layer called the "Kohonen" layer (Boniecki et al, 2004).…”

Section: Methods Artificial Neural Network (Anns)mentioning

confidence: 99%

Artificial intelligence methods in water systems research – a literature review

PIOTROWSKA,

DĄBROWSKA

2024

View full text Add to dashboard Cite

We over view se lected ar ti fi cial in tel li gence meth ods used in re search on wa ter sys tems, spe cif i cally ar ti fi cial neu ral net works (ANN), adap tive neuro-fuzzy in fer ence sys tems (ANFIS), ge netic pro gram ming (GP) and sup port vec tor ma chine (SVM) meth ods. Each method is char ac ter ized and the most ef fec tive ways of us ing these meth ods are dis cussed. These meth ods prove widely use ful in fore cast ing changes in se lected sur face and ground wa ter qual ity pa ram e ters, fore cast ing sew age network fail ures, as sess ing wa ter treat ment op tions, cli mate mon i tor ing, drought de tec tion and en vi ron men tal is sues for farmers and pro duc ers. Pub lished stud ies show that ar ti fi cial in tel li gence meth ods should be used in the anal y sis of wa ter sys tems, es pe cially since ar ti fi cial in tel li gence now ap pears in search re sults for over 60,000 en vi ron men tal ar ti cles. Key words: hydrogeology, ma chine learn ing, ur ban wa ter sys tems, ar ti fi cial in tel li gence, sen sors, anal y sis.

show abstract

Section: Methods Artificial Neural Network (Anns)mentioning

confidence: 99%

Artificial intelligence methods in water systems research – a literature review

PIOTROWSKA,

DĄBROWSKA

2024

View full text Add to dashboard Cite

show abstract

“…Most macro models are based on existing mathematical and statistical or network models to analyze the macro-level characteristics of crashes [1][2][3][4]. The conclusions obtained can help to obtain the main factors affecting crashes at the macro level, and then propose traffic safety control measures accordingly.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Traffic Crashes on Urban Network Traffic Flow

et al. 2019

View full text Add to dashboard Cite

This paper aims to investigate the impact of occasional traffic crashes on the urban traffic network flow. Toward this purpose, an extended model of coupled Nagel–Schreckenberg (NaSch) and Biham–Middleton–Levine (BML) models is presented. This extended model not only improves the initial conditions of the coupled models, but also gives the definition of traffic crashes and their spatial/time distribution. Further, we simulated the impact of the number of traffic crashes, their time distribution, and their spatial distribution on urban network traffic flow. This research contributes to the comprehensive understanding of the operational state of urban network traffic flow after traffic crashes, towards mastering the causes and propagation rules of traffic congestion. This work also a theoretical guidance value for the optimization of urban traffic network flow and the prevention and release of traffic crashes.

show abstract

Forecasting road traffic accident using deep artificial neural network approach in case of Oromia Special Zone

et al. 2023

View full text Add to dashboard Cite

Millions of people are dying and billions of properties are damaged by road tra c accidents each year worldwide. In the case of our country Ethiopia, the effect of tra c accidents is even more by causing injuries, death, and property damage. Forecasting Road Tra c Accident and Predicting the severity of Road Tra c accident contributes a role indirectly in reducing road tra c accidents. This Study deals with forecasting the number of accident and prediction of the severity of an accident in the Oromia Special Zone using Deep Arti cial Neural Network models. Around 6170 Road Tra c accidents data are collected from Oromia Police Commission Excel data and Oromia Special zone Tra c Police Department, the dataset consists of accidents in the Special Zone of Oromia Districts (Woredas) from 2005 to 2012 with 15 accidents attributes. 5928 or (80%) of the dataset was used for the training model and 1482 or (20%) of the dataset was used for the testing model. This study proposed Six different Neural Network architectures such as Backpropagation Neural Network (BPNN), Feed Forward Neural Network (FFNN), Multilayer Perceptron Neural Network (MLPNN), Recurrent Neural Networks (RNN), Radial Basis Function Neural Network (RBFNN) and Long Short-Term Memory (LSTM) models for accident severity predictionand The LSTM model for a time serious forecasting of number accidents within speci ed years. The models will take input data, classify accidents, predicts the severity of an accident. Accident predictor GUI has been created using Python Tkinter library for easy Accident Severity prediction. According to the model performance results RNN model showed the best prediction accuracy of 97.18% whereas MLP , LTSM, RBFNN, FFNN, and BPNN models showed the accuracy of 97.13%, 91.00%, 87.00%, 80.56%, 77.26%, respectively. LTSM model forecasted accident for Three years which is 3555 where the actual accident number is 3561. The prediction and forecast result obtained from the model will be helpful in planning and management of road tra c accidents. accident accounted 2.1% of all deaths, this makes them the 11 th leading cause of global deaths.Road tra c injuries place a heavy burden on household nances, not only on global and national economies. Many families are driven deeper into poverty by the loss of breadwinners and the added burden of caring for members disabled by road tra c injuries.The Global status report on road safety, launched by the World Health Organization (WHO) in December 2018, highlights that the number of road tra c deaths has reached 1.35 million annually [2]. And Road tra c crashes cost most countries 3% of their gross domestic product. It is supposed that 30 to 50 million people are exposed to physical disability annually by tra c accidents in the world according to the World Health Organization (WHO) reported in 2012. This report included that more than 600-billiondollar property can be damaged by tra c accidents annually.Road tra c injuries are currently estimated to be the 9 th leading cause of death across all age gro...

show abstract

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

Cited by 5 publications

References 1 publication

Artificial intelligence methods in water systems research – a literature review

Artificial intelligence methods in water systems research – a literature review

The Impact of Traffic Crashes on Urban Network Traffic Flow

Forecasting road traffic accident using deep artificial neural network approach in case of Oromia Special Zone

Contact Info

Product

Resources

About