Fuzzy Model for Safety Evaluation Process of New and Old Roads

Cafiso, Salvatore; Lamm, Ruediger; Cava, Grazia La

doi:10.3141/1881-07

Cited by 20 publications

(14 citation statements)

References 7 publications

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“…In this regard, the prediction power of the ANFIS-PSO model was obtained at 0.51, while those of multiple and Poisson regression were estimated at 0.56 and 0.88, respectively. This finding is in line with those of previous research demonstrating that fuzzy logic systems and artificial intelligence technique produce favorable results for highway safety studies [32]. Furthermore, it is interesting that the output of ANFIS-PSO was relatively different from that of Poisson regression.…”

Section: Discussionsupporting

confidence: 91%

The Importance of Exercise and General Mental Health on Prediction of Property-Damage-Only Accidents among Taxi Drivers in Tehran: A Study Using ANFIS-PSO and Regression Models

Abbasi

Hosseinlou

2019

Journal of Advanced Transportation

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The rate of traffic accidents in Iran is high, and the majority of the causes that must be investigated are human factors. The present study examined the effects of exercise and general health as human factors on the prediction of crash likelihood with the data collected from taxi drivers of Tehran. The data were collected using the general health questionnaire and a form entailing some items regarding the duration of daily exercise and sociodemographic information. The adaptive neurofuzzy inference system and particle swarm optimization (ANFIS-PSO) was used for tuning the parameters of membership function of the fuzzy model applied for this prediction. Thus system was compared with the more conventional methods, such as multiple regression and Poisson regression. To avoid the overfitting issue, the data were divided into 70% for training and 30% for validation. The root-mean-square error (RMSE) was also utilized as a determinant of goodness of fit between ANFIS-PSO and regression methods. The findings indicated that the number of minutes of daily exercise and mental health significantly influence property-damage-only (PDO) accidents of taxi drivers in Tehran, Iran. Furthermore, the results revealed that the hybrid model (ANFIS-PSO) not only had a better fit but also produced different results from those of the traditional regression models, which may be used in policymaking regarding the reduction of PDO accidents. Based on the results, performing daily exercise for more than 10 minutes would substantially reduce the PDO accidents among the taxi drivers in Tehran. The findings showed that ANFIS-PSO could be effectively implemented in the studies addressing accident frequency. Consequently, the policy makers should simply adopt some interventions to encourage the taxi drivers to perform daily exercise that not only improves their wellbeing but also reduces the risk of PDO accidents.

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Section: Discussionsupporting

confidence: 91%

The Importance of Exercise and General Mental Health on Prediction of Property-Damage-Only Accidents among Taxi Drivers in Tehran: A Study Using ANFIS-PSO and Regression Models

Abbasi

Hosseinlou

2019

Journal of Advanced Transportation

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“…We can find the means to resolve these issues by using soft computing or artificial intelligence techniques such as fuzzy logic, neural networks and genetic algorithms (Dorsey, Coovert 2003;Cafiso et al 2004). The soft computing approach, of course, is not always preferable to other methods, but it produces more realistic results when the number of variables involved is considerable and, especially, when their non linear dependence would render other techniques not applicable (Jang 1993 Shanahan et al 2000;Dağdeviren et al 2008).…”

Section: Introductionmentioning

confidence: 98%

A Functional Road Classification With Data Mining Techniques

et al. 2014

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The current international road standards, in order to give organization and safety, promote the classification of roads according to their technical and functional characteristics beyond their administrative membership, but the procedures are yet strongly based on the expertise's judgment. In fact, although this activity has a great importance for the consequences that produces in terms of responsibility and allocation of economic resources, it is solely based on the quantification of some variables without specifying methods or analytical procedures. In this paper, after an instrumental survey of the road environment, we applied data mining techniques that consider the 'vagueness' of the analysed scenario. The type of algorithms used, therefore, permits to quantify a degree of membership (among 0 and 1) of a road to the groupings provided and to prepare any corrective action in order to direct the final result towards a specific class with greater precision. In addition, this method is very flexible and willing to contain new variables or observations at different times with great easiness. Moreover, the geographical location of the individual observations, as it was done also in this research, can be transferred to a GIS system, with a positive impact on maintenance programs.

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“…Recent works have shown some promising results in utilising fuzzy decision-making approaches in road safety planning. Cafiso et al (2004) presented a fuzzy model to classify roadway elements with respect to their actual variation in accident rates with the aim of obtaining a more careful evaluation of highway design inconsistencies. Shi (2009) developed an evaluation index system for city traffic safety development that included safety policy for road traffic, the circumstance of road traffic safety, management of road traffic safety and control levels for road traffic accidents.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy decision-support system in road safety planning

Behnood¹,

Ayati

Brijs

et al. 2017

Proceedings of the Institution of Civil Engineers - Transport

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The objective of this research was to develop a decision-support system to help road safety policy makers make the right choices in road safety planning based on the efficiency of previously implemented safety measures. The measures considered for each region in the study include performance indicators about police operations, treated black spots, freeway and highway facility supplies, speed control cameras, emergency medical services and road lighting projects. To this end, an inefficiency measure is calculated, defined by the proportion of fatality rates in relation to the combined measure of road safety performance indicators, which should be minimised. The relative inefficiency for each region is modelled using the data envelopment analysis (DEA) technique, which follows a benchmarking and target-setting process. In the next step, a fuzzy decision-making system is constructed to convert the information obtained from the DEA into a rule-based system that can be used by policy makers to evaluate the expected outcomes of certain alternative investment strategies in road safety. Using the resultant fuzzy decision-support system, policy makers can analyse alternative strategies in addition to those unique targets suggested by the DEA benchmarking and target-setting process.

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Fuzzy Model for Safety Evaluation Process of New and Old Roads

Cited by 20 publications

References 7 publications

The Importance of Exercise and General Mental Health on Prediction of Property-Damage-Only Accidents among Taxi Drivers in Tehran: A Study Using ANFIS-PSO and Regression Models

The Importance of Exercise and General Mental Health on Prediction of Property-Damage-Only Accidents among Taxi Drivers in Tehran: A Study Using ANFIS-PSO and Regression Models

A Functional Road Classification With Data Mining Techniques

A fuzzy decision-support system in road safety planning

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