Analysis of the impact of population ageing and territorial factors on crosstown roads safety: the Spanish case study

Casado-Sanz, Natalia; Guirao, Begoña

doi:10.1016/j.trpro.2018.10.104

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…This optimal hyperplane is determined by the existing support vectors and constraints. Equations ( 10) and (11) indicate the constraints [87,88].…”

Section: Support Vector Machinementioning

confidence: 99%

“…Therefore, identifying, studying, and evaluating the contributing factors affecting road safety is inevitable to increase the level of safety management. Several parameters affect road safety, and valuable studies have been conducted to investigate road safety, such as Driver Behavior [1][2][3][4][5][6][7], Age of Driver and Vehicle [8][9][10][11][12][13], Weather Conditions [14][15][16][17][18], Road Geometry [19][20][21][22][23], and Lighting Conditions [24][25][26][27][28][29]. Siliquini et al (2010) investigated the link between driving performance and the use of psychoactive drugs in teen drivers who were enrolled at common consuming locations.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Guido

Haghshenas

et al. 2022

Safety

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The evaluation of road safety is a critical issue having to be conducted for successful safety management in road transport systems, whereas safety management is considered in road transportation systems as a challenging task according to the dynamic of this issue and the presence of a large number of effective parameters on road safety. Therefore, the evaluation and analysis of important contributing factors affecting the number of vehicles involved in crashes play a key role in increasing the efficiency of road safety. For this purpose, in this research work, two machine learning algorithms, including the group method of data handling (GMDH)-type neural network and a combination of support vector machine (SVM) and the grasshopper optimization algorithm (GOA), are employed. Hence, the number of vehicles involved in an accident is considered to be the output, and the seven factors affecting transport safety, including Daylight (DL), Weekday (W), Type of accident (TA), Location (L), Speed limit (SL), Average speed (AS), and Annual average daily traffic (AADT) of rural roads in Cosenza, southern Italy, are selected as the inputs. In this study, 564 data sets from rural areas were investigated, and the relevant, effective parameters were measured. In the next stage, several models were developed to investigate the parameters affecting the safety management of road transportation in rural areas. The results obtained demonstrated that the “Type of accident” has the highest level and “Location” has the lowest importance in the investigated rural area. Finally, although the results of both algorithms were the same, the GOA-SVM model showed a better degree of accuracy and robustness than the GMDH model.

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“…This optimal hyperplane is determined by the existing support vectors and constraints. Equations ( 10) and (11) indicate the constraints [87,88].…”

Section: Support Vector Machinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Guido

Haghshenas

et al. 2022

Safety

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“…All these studies mentioned above are based on the factors that most contribute to the severity of the pedestrian injury, with most focusing uniquely on urban areas. As far as it is known, very few studies have looked at crosstown roads [15]. This type of road is considered a hybrid because of its urban and rural characteristics and traffic.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating the Risk Factors Associated with the Severity of the Pedestrians Injured on Spanish Crosstown Roads

et al. 2019

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According to the Spanish General Traffic Accident Directorate, in 2017 a total of 351 pedestrians were killed, and 14,322 pedestrians were injured in motor vehicle crashes in Spain. However, very few studies have been conducted in order to analyse the main factors that contribute to pedestrian injury severity. This study analyses the accidents that involve a single vehicle and a single pedestrian on Spanish crosstown roads from 2006 to 2016 (1535 crashes). The factors that explain these accidents include infractions committed by the pedestrian and the driver, crash profiles, and infrastructure characteristics. As a preliminary tool for the segmentation of 1535 pedestrian crashes, a k-means cluster analysis was applied. In addition, multinomial logit (MNL) models were used for analysing crash data, where possible outcomes were fatalities and severe and minor injured pedestrians. According to the results of these models, the risk factors associated with pedestrian injury severity are as follows: visibility restricted by weather conditions or glare, infractions committed by the pedestrian (such as not using crossings, crossing unlawfully, or walking on the road), infractions committed by the driver (such as distracted driving and not respecting a light or a crossing), and finally, speed infractions committed by drivers (such as inadequate speed). This study proposes the specific safety countermeasures that in turn will improve overall road safety in this particular type of road.

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“…According to the report published by the World Health Organization in 2018, the death rate due to traffic accidents is 18,2 per 100,000 people. Traffic accidents, which rank eighth as the cause of death worldwide, show that it is one of the important issues that should be taken into consideration (Briz-Redón, Martínez-Ruiz and Montes, 2019;Casado-Sanz and Guirao, 2018;Abdolmanafi and Karamad, 2019). According to the report of the World Health Organization, it is predicted that by 2030, traffic accidents, which are the eighth causes of death, will rise to the fifth place (Bhavan, 2019).…”

Section: Introductionmentioning

confidence: 99%

Trafi̇k Kazalarinin Zamansal Ve Mekânsal İncelenmesi̇: Eski̇şehi̇r Şehi̇r Örneği̇

ZERENOĞLU¹,

HAYBAT²

2021

Lnternational Journal of Geography and Geography Education

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Türkiye'de şehir nüfusu her geçen yıl artmaktadır. Nüfusu her yıl artan şehirlerden biri de Eskişehir'dir. Şehirdeki nüfusun artması aynı zamanda araç kullanımını arttırmaktadır. Araç ve sürücü sayısındaki artış trafikte problemlerin ortaya çıkmasına neden olmaktadır. Ortaya çıkan bu problemlerden bir tanesi de trafik kazalarıdır. Gerçekleştirilen bu çalışma ile birlikte Eskişehir'in iki merkez ilçesinde meydana gelen trafik kazaları zamansal ve mekânsal olarak incelenmiştir. Bu merkez ilçelerin seçilmesindeki sebepler ise nüfusun ve trafik akışının bu alan içerisinde yoğunluk göstermesidir. Araştırmada kullanılan veriler; 2016-2017 yılına ait trafik kaza ve 2012 yılına ait arazi kullanım verileridir. Analizlerin ve bulguların tespit edilmesi için ArcGIS 10.6 versiyonunda iki araç kullanılmıştır. Bu araçlar; nokta yoğunluk ve toplam vaka araçlarıdır. Bu araçların kullanılmasındaki amaç trafik kazalarının zamansal ve mekânsal bağlantılarını ortaya çıkarmaktır. Zamansal bağlantılar içerisinde trafik kazaları; iki yıl, mevsim, ay, haftanın günleri ve saat olarak incelenmiştir. Mekânsal bağlantılar içerisinde ise ilçe, mahalle ve karayolları üzerinde gerçekleşen trafik kazaları incelenmiştir. Analizler sonucunda trafik kazalarının; yıl bakımından hangi alanlarda yoğunlaştığı, hangi mevsimlerde artış gösterdiği, hangi aylarda arttığı, haftanın hangi günlerinde artış gösterdiği, saatlere göre dağılımı, ilçelere göre dağılımı, mahallelere göre dağılımı, karayollarına göre dağılımı incelenmiş ve kara noktalar tespit edilmiştir. Zaman bakımından trafik kazaları; ay bazında en fazla temmuz, gün bazında pazartesi ve cuma günleri, saat bazında 12.00 -18.00 saatleri arasında gerçekleştiği çalışma sonucunda ortaya çıkarılmıştır. Mekân bakımından trafik kazaları; Odunpazarı ilçesindeki kazalar Tepebaşı ilçesine göre daha fazla gerçekleşmiştir. Mahalle bazındaki incelemelerde ise trafik kazaları en fazla Merkez Mahallesi'nde meydana geldiği tespit edilmiştir.

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Analysis of the impact of population ageing and territorial factors on crosstown roads safety: the Spanish case study

Cited by 6 publications

References 11 publications

Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Investigating the Risk Factors Associated with the Severity of the Pedestrians Injured on Spanish Crosstown Roads

Trafi̇k Kazalarinin Zamansal Ve Mekânsal İncelenmesi̇: Eski̇şehi̇r Şehi̇r Örneği̇

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