A data mining approach to investigate the factors influencing the crash severity of motorcycle pillion passengers

2014 ◽  
Vol 51 ◽  
pp. 93-98 ◽  
Author(s):  
Ali Tavakoli Kashani ◽  
Rahim Rabieyan ◽  
Mohammad Mehdi Besharati
Keyword(s):  
Data Mining ◽  
Crash Severity ◽  
Data Mining Approach ◽  
Factors Influencing

scholarly journals Data mining approach to model bus crash severity in Australia

2020 ◽  
Author(s):  
Seyed Alireza Samerei ◽  
Kayvan Aghabayk ◽  
Amin Mohammadi ◽  
Nirajan Shiwakoti
Keyword(s):  
Data Mining ◽  
Crash Severity ◽  
Data Mining Approach

scholarly journals A Data Mining Approach on Lorry Drivers Overloading in Tehran Urban Roads

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Ehsan Ayazi ◽  
Abdolreza Sheikholeslami
Keyword(s):  
Data Mining ◽  
Regression Model ◽  
Truck Drivers ◽  
Commercial Vehicles ◽  
Binary Regression ◽  
Pickup Truck ◽  
Data Mining Approach ◽  
Factors Influencing ◽  
Other Information ◽  
Construction Loads

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.

Data mining approach to construction productivity prediction for cutter suction dredgers

2019 ◽  
Vol 105 ◽  
pp. 102833 ◽  
Author(s):  
Shuo Bai ◽  
Mingchao Li ◽  
Rui Kong ◽  
Shuai Han ◽  
Heng Li ◽  
...  
Keyword(s):  
Data Mining ◽  
Construction Productivity ◽  
Data Mining Approach ◽  
Productivity Prediction
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