Road Safety Evaluation from Traffic Information Based on ANFIS

Ma, Zengqiang; Pan, Cunzhi; Wang, Yongqiang

doi:10.1109/chicc.2008.4605560

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…The ANN model does not obtain the output data from making decision. Although it has automatic learning capability and the fuzzy logic is inverse of ANN model [108], the ANFIS algorithm has the advantages of both the ANN and fuzzy logic models on producing input and output data in one framework [109], and it is known as a commonly used data-driven model for solving nonlinear issues [110] in the fields of data processing and fuzzy control [111].…”

Section: Anfismentioning

confidence: 99%

Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)

et al. 2019

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The most dangerous landslide disasters always cause serious economic losses and human deaths. The contribution of this work is to present an integrated landslide modelling framework, in which an adaptive neuro-fuzzy inference system (ANFIS) is combined with the two optimization algorithms of whale optimization algorithm (WOA) and grey wolf optimizer (GWO) at Anyuan County, China. It means that WOA and GWO are used as two meta-heuristic algorithms to improve the prediction performance of the ANFIS-based methods. In addition, the step-wise weight assessment ratio analysis (SWARA) method is used to obtain the initial weight of each class of landslide influencing factors. To validate the effectiveness of the proposed framework, 315 landslide events in history were selected for our experiments and were randomly divided into the training and verification sets. To perform landslide susceptibility mapping, fifteen geological, hydrological, geomorphological, land cover, and other factors are considered for the modelling construction. The landslide susceptibility maps by SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-WOA, and SWARA-ANFIS-GWO models are assessed using the measures of the receiver operating characteristic (ROC) curve and root-mean-square error (RMSE). The experiments demonstrated that the obtained results of modelling process from the SWARA to the SAWRA-ANFIS-GWO model were more accurate and that the proposed methods have satisfactory prediction ability. Specifically, prediction accuracy by area under the curve (AUC) of SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-GWO, and SWARA-ANFIS-WOA models were 0.831, 0.831, 0.850, 0.856, and 0.869, respectively. Due to adaptability and usability, the proposed prediction methods can be applied to other areas for landslide management and mitigation as well as prevention throughout the world.

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Section: Anfismentioning

confidence: 99%

Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)

et al. 2019

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“…Moreover, the highest accidents potential hot spots were determined using GIS tool. The used parameters were number of accidents, fatalities and injured cases per accident [17]. The importance of this study is its ability to investigate Istanbul's traffic accidents by means of different statistical, graphical and mathematical methods.…”

Section: Introductionmentioning

confidence: 99%

Effect of Transportation Parameters on Traffic Accident in Urban Areas Comparison study of ANFIS With Statistical Analysis

Suleiman¹,

Younes²,

Ergun³

et al. 2021

IJSSE

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Traffic accidents present a serious problem for both developed and developing countries and have become an urgent matter to tackle in all large metropolitan areas. This study aims to perform a deep comprehensive analysis of the traffic accidents issue in Istanbul, one of the world’s most populous cities. The accidents were classified and its intensities were presented on Istanbul map using a GIS tool. Furthermore, the performance of Negative Binomial Regression analysis and Adaptive Neuro-Fuzzy Inference System (ANFIS) model was assessed. Data collection of independent variables included distribution of trips, percentage of street parking, rate of car ownership, street density and population density. Trips were divided into three categories, passenger car, minibus and bus trips. The results showed that four legs intersection got the highest proportion of accidents among the other types with (40%). It also demonstrated that increasing both the percentage of bus trips and the percentage of street parking will decrease the traffic accident rate. Furthermore, the implementation of ANFIS model increased the accuracy of forecasts and reduced errors more than the regression model.

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“…In addition, Adaptive Neural Fuzzy Inference System (ANFIS) was implemented to predict the traffic flow over a short period using 104 changeable parameters [8]. ANFIS has been implemented to determine the level of safety on roads in relation with traffic density, speed and road plane visibility [9]. The results have shown ANFIS ability to enhance safety levels on roads; ANFIS was used to improve the work of Traffic Controllers in decreasing traffic congestion.…”

Section: Introductionmentioning

confidence: 99%

Modelling CO<sub>2</sub> and NO<sub>x</sub> on signalized roundabout using modified adaptive neural fuzzy inference system model

Sulaiman¹,

Younes

Al-Dulaimi

2017

Environmental Engineering Research

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The relative abilities of 2, dimensioned statistics -the root-mean-square error (RMSE) and the mean absolute error (MAE) -to describe average model-performance error are examined. The RMSE is of special interest because it is widely reported in the climatic and environmental literature; nevertheless, it is an inappropriate and misinterpreted measure of average error. RMSE is inappropriate because it is a function of 3 characteristics of a set of errors, rather than of one (the average error). RMSE varies with the variability within the distribution of error magnitudes and with the square root of the number of errors (n 1/2 ), as well as with the average-error magnitude (MAE). Our findings indicate that MAE is a more natural measure of average error, and (unlike RMSE) is unambiguous. Dimensioned evaluations and inter-comparisons of average model-performance error, therefore, should be based on MAE.KEY WORDS: Model-performance measures · Root-mean-square error · Mean absolute errorResale or republication not permitted without written consent of the publisher

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Road Safety Evaluation from Traffic Information Based on ANFIS

Cited by 5 publications

References 4 publications

Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)

Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)

Effect of Transportation Parameters on Traffic Accident in Urban Areas Comparison study of ANFIS With Statistical Analysis

Modelling CO<sub>2</sub> and NO<sub>x</sub> on signalized roundabout using modified adaptive neural fuzzy inference system model

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