The development of models to determine the bunning speed of motorcycles and analyze riders' compliance to speed limit
1
Issued Date
2024
Copyright Date
2017
Resource Type
Language
eng
File Type
application/pdf
No. of Pages/File Size
xiii, 143 leaves : ill. (some col.)
Access Rights
open access
Rights
ผลงานนี้เป็นลิขสิทธิ์ของมหาวิทยาลัยมหิดล ขอสงวนไว้สำหรับเพื่อการศึกษาเท่านั้น ต้องอ้างอิงแหล่งที่มา ห้ามดัดแปลงเนื้อหา และห้ามนำไปใช้เพื่อการค้า
Rights Holder(s)
Mahidol University
Bibliographic Citation
Thesis (M.Eng. (Civil Engineering))--Mahidol University, 2017
Suggested Citation
Chea, Sovann, 1990- The development of models to determine the bunning speed of motorcycles and analyze riders' compliance to speed limit. Thesis (M.Eng. (Civil Engineering))--Mahidol University, 2017. Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/92450
Title
The development of models to determine the bunning speed of motorcycles and analyze riders' compliance to speed limit
Author(s)
Abstract
This research analyzed riding behaviour of motorcyclists under free flow or near free flow conditions on a highway section in Thailand. Traffic flow data were recorded from roadside by using video cameras and then subsequently analyzed by image processing technique using Autoscope traffic analysis tool. The analysis results showed that on average a motorcyclist traveled at the speed of 71.57 km/h. There was a significant difference of mean motorcycle speeds due to helmet use, lane position, and engine power of motorcycles. Furthermore, the multiple regression model and binary logistic model were estimated to explain the running speed of motorcycles and the likelihood of riders' compliance to speed limit, respectively. Three common variables were significant in both models namely, distance between sidewalk and the subjected motorcycle, total number of riders and passengers, and engine power. The use of helmet (dummy variable) was also significant in the multiple linear regression. The adjusted Rsquared was 50.08%. The accuracy test using 167 testing samples showed mean absolute error (MAE) and mean absolute percentage error (MAPE) of 8.22 km/h and 12.84%, respectively. For binary logistic regression, the receiver operating characteristics (ROC) curve was analyzed for model accuracy. The area under curve (AUC) was 0.925 and the optimal cut off value for the best classification was 0.523. Finally, several policy implications could be derived based on the research findings.
Description
Civil Engineering (Mahidol University 2017)
Degree Name
Master of Engineering
Degree Level
Master's degree
Degree Department
Faculty of Engineering
Degree Discipline
Civil Engineering
Degree Grantor(s)
Mahidol University