Predicting the Collisions of Heavy Vehicle Drivers in Iran by Investigating the Effective Human Factors
Traffic collisions are one of the most important challenges threatening the general health of the world. Iran’s crash statistics demonstrate that approximately 16,500 people lose their lives every year due to road collisions. According to the traffic police of Iran, heavy vehicles (including trailer...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/5532998 |
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| author | Hossein Naderi Habibollah Nassiri Farnaz Zahedieh |
| author_facet | Hossein Naderi Habibollah Nassiri Farnaz Zahedieh |
| author_sort | Hossein Naderi |
| collection | DOAJ |
| description | Traffic collisions are one of the most important challenges threatening the general health of the world. Iran’s crash statistics demonstrate that approximately 16,500 people lose their lives every year due to road collisions. According to the traffic police of Iran, heavy vehicles (including trailers, trucks, and panel trucks) contributed to 20.5% of the fatal road traffic collisions in the year 2013. This highlights the need for devoting special attention to heavy vehicle drivers to further explore their driving characteristics. In this research, the effect of heavy vehicle drivers’ behavior on at-fault collisions over three years has been investigated with an innovative approach of structural equation modeling (SEM) and Bayesian Network (BN). The database utilized in this research was collected using a questionnaire. For this purpose, 474 heavy vehicle drivers have been questioned in the Parviz Khan Border Market, located on the border of Iran and Iraq. The response rate of the survey was 80%. The participants answered the questions on Driver Behavior Questionnaire (DBQ) and a sleep assessing questionnaire named Global Dissatisfaction with Sleep (GSD). In this research, human factors affecting at-fault collisions of heavy vehicles were identified and their relationships with other variables were determined using the SEM approach. Then the descriptive model constructed by the SEM method was used as the basis of the BN, and the conditional probabilities of each node in the BN were calculated by the database collected by the field survey. SEM indicates that other attributes including GSD, mobile usage, daily fatigue, exposure, and education level have an indirect relation with heavy vehicle drivers’ at-fault collisions. According to the BN, if there is no information about the characteristics of a heavy vehicle driver, the driver will likely have at least one collision during the next three years with the probability of 0.17. Also, it was indicated that the minimum probability of the at-fault collision occurrence for a heavy vehicle is 0.08. |
| format | Article |
| id | doaj-art-1b07dbe4ac0f48f287dc0065e81a1588 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-1b07dbe4ac0f48f287dc0065e81a15882025-02-03T01:08:52ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/55329985532998Predicting the Collisions of Heavy Vehicle Drivers in Iran by Investigating the Effective Human FactorsHossein Naderi0Habibollah Nassiri1Farnaz Zahedieh2Civil Engineering Department, Sharif University of Technology, Tehran, IranCivil Engineering Department, Sharif University of Technology, Tehran, IranCivil Engineering Department, Sharif University of Technology, Tehran, IranTraffic collisions are one of the most important challenges threatening the general health of the world. Iran’s crash statistics demonstrate that approximately 16,500 people lose their lives every year due to road collisions. According to the traffic police of Iran, heavy vehicles (including trailers, trucks, and panel trucks) contributed to 20.5% of the fatal road traffic collisions in the year 2013. This highlights the need for devoting special attention to heavy vehicle drivers to further explore their driving characteristics. In this research, the effect of heavy vehicle drivers’ behavior on at-fault collisions over three years has been investigated with an innovative approach of structural equation modeling (SEM) and Bayesian Network (BN). The database utilized in this research was collected using a questionnaire. For this purpose, 474 heavy vehicle drivers have been questioned in the Parviz Khan Border Market, located on the border of Iran and Iraq. The response rate of the survey was 80%. The participants answered the questions on Driver Behavior Questionnaire (DBQ) and a sleep assessing questionnaire named Global Dissatisfaction with Sleep (GSD). In this research, human factors affecting at-fault collisions of heavy vehicles were identified and their relationships with other variables were determined using the SEM approach. Then the descriptive model constructed by the SEM method was used as the basis of the BN, and the conditional probabilities of each node in the BN were calculated by the database collected by the field survey. SEM indicates that other attributes including GSD, mobile usage, daily fatigue, exposure, and education level have an indirect relation with heavy vehicle drivers’ at-fault collisions. According to the BN, if there is no information about the characteristics of a heavy vehicle driver, the driver will likely have at least one collision during the next three years with the probability of 0.17. Also, it was indicated that the minimum probability of the at-fault collision occurrence for a heavy vehicle is 0.08.http://dx.doi.org/10.1155/2021/5532998 |
| spellingShingle | Hossein Naderi Habibollah Nassiri Farnaz Zahedieh Predicting the Collisions of Heavy Vehicle Drivers in Iran by Investigating the Effective Human Factors Journal of Advanced Transportation |
| title | Predicting the Collisions of Heavy Vehicle Drivers in Iran by Investigating the Effective Human Factors |
| title_full | Predicting the Collisions of Heavy Vehicle Drivers in Iran by Investigating the Effective Human Factors |
| title_fullStr | Predicting the Collisions of Heavy Vehicle Drivers in Iran by Investigating the Effective Human Factors |
| title_full_unstemmed | Predicting the Collisions of Heavy Vehicle Drivers in Iran by Investigating the Effective Human Factors |
| title_short | Predicting the Collisions of Heavy Vehicle Drivers in Iran by Investigating the Effective Human Factors |
| title_sort | predicting the collisions of heavy vehicle drivers in iran by investigating the effective human factors |
| url | http://dx.doi.org/10.1155/2021/5532998 |
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