A Data Mining Approach on Lorry Drivers Overloading in Tehran Urban Roads
The aim of this study is to identify the important factors influencing overloading of commercial vehicles on Tehran’s urban roads. The weight information of commercial freight vehicles was collected using a pair of portable scales besides other information needed including driver information, vehicl...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/6895407 |
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| _version_ | 1832560225739079680 |
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| author | Ehsan Ayazi Abdolreza Sheikholeslami |
| author_facet | Ehsan Ayazi Abdolreza Sheikholeslami |
| author_sort | Ehsan Ayazi |
| collection | DOAJ |
| description | The aim of this study is to identify the important factors influencing overloading of commercial vehicles on Tehran’s urban roads. The weight information of commercial freight vehicles was collected using a pair of portable scales besides other information needed including driver information, vehicle features, load, and travel details by completing a questionnaire. The results showed that the highest probability of overloading is for construction loads. Further, the analysis of the results in the lorry type section shows that the least likely occurrence of overloading is among pickup truck drivers such that this likelihood within this group was one-third among Nissan and small truck drivers. Also, the results of modeling the type of route showed that the highest likelihood of overloading is for internal loads (origin and destination inside Tehran), and the least probability of overloading is for suburban trips (origin and destination outside of Tehran). Considering the type of load packing as a variable, the results of binary regression model analysis showed that the most probability of overloading occurs for packed (boxed) loads. Finally, it was concluded that drivers are 18 times more likely to commit overloading on weekends than on weekdays. |
| format | Article |
| id | doaj-art-0559653d91fb4d7ba7978a906240939b |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-0559653d91fb4d7ba7978a906240939b2025-02-03T01:27:59ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/68954076895407A Data Mining Approach on Lorry Drivers Overloading in Tehran Urban RoadsEhsan Ayazi0Abdolreza Sheikholeslami1Iran University of Science and Technology, Tehran, IranIran University of Science and Technology, Tehran, IranThe aim of this study is to identify the important factors influencing overloading of commercial vehicles on Tehran’s urban roads. The weight information of commercial freight vehicles was collected using a pair of portable scales besides other information needed including driver information, vehicle features, load, and travel details by completing a questionnaire. The results showed that the highest probability of overloading is for construction loads. Further, the analysis of the results in the lorry type section shows that the least likely occurrence of overloading is among pickup truck drivers such that this likelihood within this group was one-third among Nissan and small truck drivers. Also, the results of modeling the type of route showed that the highest likelihood of overloading is for internal loads (origin and destination inside Tehran), and the least probability of overloading is for suburban trips (origin and destination outside of Tehran). Considering the type of load packing as a variable, the results of binary regression model analysis showed that the most probability of overloading occurs for packed (boxed) loads. Finally, it was concluded that drivers are 18 times more likely to commit overloading on weekends than on weekdays.http://dx.doi.org/10.1155/2020/6895407 |
| spellingShingle | Ehsan Ayazi Abdolreza Sheikholeslami A Data Mining Approach on Lorry Drivers Overloading in Tehran Urban Roads Journal of Advanced Transportation |
| title | A Data Mining Approach on Lorry Drivers Overloading in Tehran Urban Roads |
| title_full | A Data Mining Approach on Lorry Drivers Overloading in Tehran Urban Roads |
| title_fullStr | A Data Mining Approach on Lorry Drivers Overloading in Tehran Urban Roads |
| title_full_unstemmed | A Data Mining Approach on Lorry Drivers Overloading in Tehran Urban Roads |
| title_short | A Data Mining Approach on Lorry Drivers Overloading in Tehran Urban Roads |
| title_sort | data mining approach on lorry drivers overloading in tehran urban roads |
| url | http://dx.doi.org/10.1155/2020/6895407 |
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