Prediction of pedal cyclists and pedestrian fatalities from total monthly accidents and registered private car numbers
Accident prevention is relatively a complex issue considering the effectiveness of the injury prevention technologies as well as more detailed assessment of the complex interactions between the road condition, vehicle and human factor. For many years, highway agencies and vehicle manufacturers showe...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Faculty of Transport, Warsaw University of Technology
2015-06-01
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| Series: | Archives of Transport |
| Subjects: | |
| Online Access: | http://aot.publisherspanel.com/gicid/01.3001.0010.1643 |
| _version_ | 1818946616352047104 |
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| author | Kiarash Ghasemlou Metin Mutlu Aydin Mehmet Sinan Yıldırım |
| author_facet | Kiarash Ghasemlou Metin Mutlu Aydin Mehmet Sinan Yıldırım |
| author_sort | Kiarash Ghasemlou |
| collection | DOAJ |
| description | Accident prevention is relatively a complex issue considering the effectiveness of the injury prevention technologies as well as more detailed assessment of the complex interactions between the road condition, vehicle and human factor. For many years, highway agencies and vehicle manufacturers showed great efforts to reduce the injuries resulting from the vehicle crashes. Many researchers used a broad range of methods to evaluate the impact of several factors on traffic accidents and injuries. Recent developments lead up to capable for determining the effects of these factors. According to World Health Organization (WHO), cyclists and pedestrians comprise respectively 1.6% and 16.3% in traffic crash fatalities in 2013. Also in Turkey crash fatalities for pedestrian and cyclists are respectively 20.6% and 3% according to Turkish Statistical Instıtute data in 2013. The relationship between cycling and pedestrian rates and injury rates over time is also unknown. This paper aims to predict the crash severity with the traffic injury data of the Konya City in Turkey by implementing the Artificial Neural Networks (ANN), Regression Trees (RT) and Multiple Linear Regression modelling (MLRM) method. |
| first_indexed | 2024-12-20T08:17:50Z |
| format | Article |
| id | doaj.art-4881c0c36a7a4b35a4ec22050bc4a589 |
| institution | Directory Open Access Journal |
| issn | 0866-9546 2300-8830 |
| language | English |
| last_indexed | 2024-12-20T08:17:50Z |
| publishDate | 2015-06-01 |
| publisher | Faculty of Transport, Warsaw University of Technology |
| record_format | Article |
| series | Archives of Transport |
| spelling | doaj.art-4881c0c36a7a4b35a4ec22050bc4a5892022-12-21T19:47:04ZengFaculty of Transport, Warsaw University of TechnologyArchives of Transport0866-95462300-88302015-06-01342293510.5604/08669546.116920901.3001.0010.1643Prediction of pedal cyclists and pedestrian fatalities from total monthly accidents and registered private car numbersKiarash Ghasemlou0Metin Mutlu Aydin1Mehmet Sinan Yıldırım2Gümüşhane University, Department of Civil Engineering, Gümüşhane, TurkeyGümüşhane University, Department of Civil Engineering, Gümüşhane, TurkeyCelal Bayar University, Department of Civil Engineering, Manisa, TurkeyAccident prevention is relatively a complex issue considering the effectiveness of the injury prevention technologies as well as more detailed assessment of the complex interactions between the road condition, vehicle and human factor. For many years, highway agencies and vehicle manufacturers showed great efforts to reduce the injuries resulting from the vehicle crashes. Many researchers used a broad range of methods to evaluate the impact of several factors on traffic accidents and injuries. Recent developments lead up to capable for determining the effects of these factors. According to World Health Organization (WHO), cyclists and pedestrians comprise respectively 1.6% and 16.3% in traffic crash fatalities in 2013. Also in Turkey crash fatalities for pedestrian and cyclists are respectively 20.6% and 3% according to Turkish Statistical Instıtute data in 2013. The relationship between cycling and pedestrian rates and injury rates over time is also unknown. This paper aims to predict the crash severity with the traffic injury data of the Konya City in Turkey by implementing the Artificial Neural Networks (ANN), Regression Trees (RT) and Multiple Linear Regression modelling (MLRM) method.http://aot.publisherspanel.com/gicid/01.3001.0010.1643traffic accidentcyclistpedestriansartificial linear networkregression treesmultiple linear regression |
| spellingShingle | Kiarash Ghasemlou Metin Mutlu Aydin Mehmet Sinan Yıldırım Prediction of pedal cyclists and pedestrian fatalities from total monthly accidents and registered private car numbers Archives of Transport traffic accident cyclist pedestrians artificial linear network regression trees multiple linear regression |
| title | Prediction of pedal cyclists and pedestrian fatalities from total monthly accidents and registered private car numbers |
| title_full | Prediction of pedal cyclists and pedestrian fatalities from total monthly accidents and registered private car numbers |
| title_fullStr | Prediction of pedal cyclists and pedestrian fatalities from total monthly accidents and registered private car numbers |
| title_full_unstemmed | Prediction of pedal cyclists and pedestrian fatalities from total monthly accidents and registered private car numbers |
| title_short | Prediction of pedal cyclists and pedestrian fatalities from total monthly accidents and registered private car numbers |
| title_sort | prediction of pedal cyclists and pedestrian fatalities from total monthly accidents and registered private car numbers |
| topic | traffic accident cyclist pedestrians artificial linear network regression trees multiple linear regression |
| url | http://aot.publisherspanel.com/gicid/01.3001.0010.1643 |
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