EFFECT OF TOBACCO SMOKING ON DRIVING ACCIDENTS BY USING CLUSTER ANALYSIS

Abstract. The aim of the study is to investigate the influence of tobacco smoking as a risky behavior in driving accidents. In this study, the relationship between consumption and non-consumption of tobacco with the behavior and the information on vehicle drivers from the aspect of traffic safety has been investigated. This relationship was confirmed as a hypothesis using different statistical tests of Chi-square, Mann–Whitney U, and Kruskal–Wallis. Our review has shown that in these tests, there is a high probability of a significant relationship between tobacco smoking and the duality of driving accidents and traffic safety. The traffic safety parameters have been considered with accident status, accident type, average distance, and driving time, brake time length, brake distances, length of overtaking, stop length, maximum speed, and the high bright beam of the car's headlights. A cluster analysis has been used to classify drivers as per this relationship, which resulted in the introduction of three groups of drivers: the first cluster include non-smoker drivers who have a very low accident rate, the second cluster include drivers who do not smoke while driving, and the third cluster include drivers who tend to smoke and their car accident rate is higher. For non-smokers of the first and the second clusters, the probability of an accident type comprises collision with another vehicle, and for drivers who do smoke while driving, probability includes other types besides the following: collision with another vehicle, pedestrian accident, overtaking or exit from the road, and collision with a fixed object.Keywords: tobacco smoking; driving accidents; Chi-square; Mann–Whitney U; Kruskal–Wallis and clustering analysis

Identifying the Most Critical Intersections in Transportation Networks

With the increasing population worldwide, the number of vehicles is increasing day by day. This increase also creates different traffic problems. Various analysis methods are developed to solve traffic problems and to better examine the road transport network. Especially with the development of technology and intelligent transportation systems entering our lives, analyses can be made using computer software and programs. In this study; using the social network analysis method, the use of which has increased in the field of transportation in recent years, the highway network structure of Erzurum Province - which consists of district connection roads has been analyzed. While making the analysis, the network structure was examined through five different centrality concepts and the critical sequence of the intersections in the district connection roads was determined. Accuracy percentages of the concepts of centrality were determined by comparing the sequences obtained with the sequences actually applied. Then, it was determined that the most suitable centrality concept for the study was Bonacich Power

An Artificial Neural Network Model for Highway Accident Prediction: A Case Study of Erzurum, Turkey

This study presents an accident prediction model of Erzurum’s Highways in Turkey using artificial neural network (ANN) approaches. There are many ANN models for predicting the number of accidents on highways that were developed using 8 years with 7,780 complete accident reports of historical data (2005-2012). The best ANN model was chosen for this task and the model parameters included years, highway sections, section length (km), annual average daily traffic (AADT), the degree of horizontal curvature, the degree of vertical curvature, traffic accidents with heavy vehicles (percentage), and traffic accidents that occurred in summer (percentage). In the ANN model development, the sigmoid activation function was employed with Levenberg-Marquardt algorithm. The performance of the developed ANN model was evaluated by mean square error (MSE), the root mean square error (RMSE), and the coefficient of determination (R2). The model results indicate that the degree of vertical curvature is the most important parameter that affects the number of accidents on highways.</p

Forecasting the Accident Frequency and Risk Factors: A Case Study of Erzurum, Turkey

Nowadays, life is intimately associated with transportation, generating several issues on it. Numerous works are available concerning accident prediction techniques depending on independent road and traffic features, while the mix parameters including time, geometry, traffic flow, and weather conditions are still rarely ever taken into consideration. This study aims to predict future accident frequency and the risk factors of traffic accidents. It utilizes the Generalized Linear Model (GLM) and Artificial Neural Networks (ANN) approaches to process and predict traffic data efficiently based on 21500 records of traffic accidents that occurred in Erzurum in Turkey from 2005 to 2019. The results of the comparative evaluation demonstrated that the ANN model outperformed the GLM model. The study revealed that the most effective variable was the number of horizontal curves. The annual average growth rates of accident occurrences based on the ANNꞌs method are predicted to be 11.22% until 2030

Clustering Analysis of Traffic Accident Risk in Turkey

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According to Traffic Accidents Between 1997-2006 Years Clustering Analysis of Provinces in Turkey

In this study, mortality and injury ratios are calculated by using date of interurban road traffic accidents (RTAs) in Turkey occuring in 1997-2006 years. According to the ratios, clustering analysis was done by using both traditional k-measns and fuzzy c-means techniques. Provinces are divided five cluster by clustering analysis are done according to two tecniques . It was definied that provinces has the most highest fatality and injury ratios. Obtained results were compared. It was observed that fuzzy c-means technique gives accurate and consistend results at least k-means technique

Türkiye'deki İllerin 1997-2006 Yılları Arası Trafik Kazalarına Göre Kümeleme Analizi

Bu çalışmada, 1997-2006 yıllarında Türkiye'deki illerde meydana gelen şehir dışı trafik kaza verileri kullanılarak her il için ölüm ve yaralanma oranları hesaplanmıştır. Bu oranlara göre, hem geleneksel k-ortalamalar hem de bulanık c-ortalamalar teknikleri kullanılarak kümeleme analizi yapılmıştır. İki yönteme göre yapılan kümeleme analizi ile iller beş kümeye ayrılmıştır. En yüksek ölüm ve yaralanma oranlarına sahip olan iller belirlenmiştir. Elde edilen sonuçlar karşılaştırılmıştır. Bulanık c-ortalamalar tekniğinin en az geleneksel k-ortalamalar tekniği kadar doğru ve tutarlı sonuçlar verdiği gözlenmiştir

Türkiye'de 1977-2006 Yılları Arasında Meydana Gelen Aylık Trafik Kazalarının Zamansal Analizi

Bu çalışmada 1977-2006 yılları arasında meydana gelen aylık trafik kaza verileri(şehir içi ve şehir dışı toplamı) kullanılarak zaman serisi analiz yöntemi ile modelleme yapılmıştır. Yapılan analizler sonucunda çalışma döneminde kullanılan verilere göre en uygun modelin ARIMA(4,1,4) olduğu belirlenmiştir. Çalışmada en uygun model kullanılarak 2006: 01-2007: 12 dönemi için aylık kaza tahmini yapılmıştır. Tahmin edilen ve gerçek kaza değerleri kullanılarak regresyon eğrisi çizilmiş ve korelasyon katsayısı (r=0,9163) belirlenmiştir. Tahmin değerleri ile gerçek değerler arasında güçlü pozitif ilişki olduğu belirlenmiştir. Ayrıca ARIMA(4,1,4) modelinin başarı ölçütü ortalama karesel hataların karekökü (OKHK) değeri hesaplanarak belirlenmiştir. Çalışma dönemi boyunca en fazla trafik kazası Aralık, Ekim ve Kasım aylarında, en az trafik kazası Şubat, Mart ve Nisan aylarında meydana geldiği belirlenmiştir

Interdisciplinary Evaluation of Intersection Performances—A Microsimulation-Based MCDA

Intersections are the most important regions in terms of urban traffic management. The intersection areas on the corridor should be analyzed together for consistency in traffic engineering. To do so, three intersections on the Vatan Street corridor in İstanbul, the most crowded city of Turkey, were examined. Various geometric and signal designs were performed for intersections and the most suitable corridor design was analyzed. The corridor designs were modeled with the PTV VISSIM microsimulation software. The most suitable corridor design was evaluated by using the results obtained from the microsimulation via analytical hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) from multi criteria decision analysis (MCDA) methods. The evaluation criteria in the study are vehicle delay, queue length, stopped delay, stops, travel time, vehicle safety, CO emission, fuel consumption, and construction cost. As a result, the current and the most suitable alternative corridors were compared according to the comparison parameters and up to 80% improvements were observed. Thus, some advantages were obtained in terms of energy, environment, time, and cost