Neuro-fuzzy system modelling for the effects of intelligent transportation on road accident fatalities

Rahimi, Amir

doi:10.17559/tv-20151203230220

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…Y=93.1X5 -0.605 X7 -1.408 e 0.00035x1 (7) where: Y = Average yearly number of accidents/zone/year, X1 =Population density, X5 =Percent of bus trips, and X7 =Percentage of street parking.…”

Section: Based On the Regression Analysis The Following Model Was Obtainedmentioning

confidence: 99%

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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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“…Y=93.1X5 -0.605 X7 -1.408 e 0.00035x1 (7) where: Y = Average yearly number of accidents/zone/year, X1 =Population density, X5 =Percent of bus trips, and X7 =Percentage of street parking.…”

Section: Based On the Regression Analysis The Following Model Was Obtainedmentioning

confidence: 99%

“…However, ANFIS was implemented to predict traffic hot spots on rural roads [6]. Also, ANFIS has been used to evaluate the impacts of intelligent transportation systems and technologies on number of road accident fatalities [7]. Moreover, ANFIS was used for prediction of traffic accidents [8].…”

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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show abstract

“…Security has always been the highest priority in traffic, and today a large number of technologies (video surveillance, speed control, etc.) are present within intelligent transport systems, which have the task of raising safety to an even higher level (Rahimi, 2017). Every day, an increasing number of vehicles are in the streets and so are drivers who do not share the same experience and abilities.…”

Section: Safetymentioning

confidence: 99%

Application of the ANFIS model in road traffic and transportation: A literature review from 1993 to 2018

Stojčić

2019

Oper. Res. Eng. Sci. Theor. Appl.

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The paper's focus is on researching the application of the ANFIS (Adaptive Neuro Fuzzy Inference System) model in traffic and transport through a review of relevant papers. The ANFIS, as an element of artificial intelligence, is widely used in intelligent transport systems. All collected papers are divided into 7 sub-areas, namely: 1) vehicle routing, 2) traffic control at intersections with light signaling, 3) vehicle steering and control, 4) safety, 5) modeling of fuel consumption, engine performance and exhaust emissions, 6) traffic congestion prediction, and 7) other applications. For each sub-area, the analysis of the proposed models is performed with a tabular overview of respective input and output variables, while in the third section the discussion of the results is given. It is found that the steering and control of vehicles represent a sub-area with the highest percentage in the total number of examined papers, while the security applications take second place.

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Exploring the Influence of Truck Proportion on Freeway Traffic Safety Using Adaptive Network-Based Fuzzy Inference System

Zhang

Xing

Lü

et al. 2019

Journal of Advanced Transportation

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The truck operation of freeway has an impact on traffic safety. In particular, the gradually increasing in truck proportion will inevitably affect the freeway traffic operation of different traffic volume. In this paper, VISSIM simulation is used to supply the field data and orthogonal experimental is designed for calibrate the simulation data. Then, SSAM modeling is combined to analyze the impact of truck proportion on traffic flow parameters and traffic conflicts. The serious and general conflict prediction model based on the Adaptive Network-based Fuzzy Inference System (ANFIS) is proposed to determine the impact of the truck proportion on freeway traffic safety. The results show that when the truck proportion is around 0.4 under 3200 veh/h and 0.6 under 2600 veh/h, there are more traffic conflicts and the number of serious conflicts is more than the number of general conflicts, which also reflect the relationship between truck proportion and traffic safety. Under 3000 veh/h, travel time and average delay increasing while mean speed and mean speed of small car decreases with truck proportion increases. The mean time headway rises largely with the truck proportion increasing above 3000 veh/h. The speed standard deviation increases initially and then fall with truck proportion increasing. The lane-changing decreases while truck proportion increasing. In addition, ANFIS can accurately determine the impact of truck proportion on traffic conflicts under different traffic volume, and also validate the learning ability of ANFIS.

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Neuro-fuzzy system modelling for the effects of intelligent transportation on road accident fatalities

Cited by 3 publications

References 18 publications

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

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

Application of the ANFIS model in road traffic and transportation: A literature review from 1993 to 2018

Exploring the Influence of Truck Proportion on Freeway Traffic Safety Using Adaptive Network-Based Fuzzy Inference System

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