Authors
- Haikal Aiman HartikaCivil Engineering Department, College of Engineering, Universiti Tenaga Nasional, Kajang, Selangor, Malaysia
- Mohd Zakwan RamliCivil Engineering Department, College of Engineering, Universiti Tenaga Nasional, Kajang, Selangor, Malaysia
- Muhamad Zaihafiz Zainal AbidinFaculty of Architecture and Built Environment, Infrastructure University Of Kuala Lumpur, Kajang, Selangor, Malaysia
- Mohd Hafiz ZawawiCivil Engineering Department, College of Engineering, Universiti Tenaga Nasional, Kajang, Selangor, Malaysia
Keywords:
ANN, MLR, prediction model, and road accident.Abstract
This study is focusing in the accident prediction model by using Multiple Linear Regression and Artificial Neural Network, as well as investigating the effectiveness of both predictive analyses. To meet these objectives, the historical data of accident was analysed with the on-site data for the purpose of relating them with the accident occurrence. Also, the accident historical data and the on-site data were analysed using two predictive models to predict the number of accident in current year. After the analysis, it is revealed that the outcomes obtained from both models were different with greater predicted value from the regression analysis due to greater errors being produced. Less predicted value, greater correlation and less error have been shown by the Artificial Neural Network analysis, concluding that it is more suitable for accident prediction.
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Published
2017-08-31
Issue
Volume 3, Issue 5, July-August-2017
Section
Research Articles
License
Copyright (c) IJSRSET
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
[1]
Haikal Aiman Hartika, Mohd Zakwan Ramli, Muhamad Zaihafiz Zainal Abidin, Mohd Hafiz Zawawi, "Study of Road Accident Prediction Model at Accident Blackspot Area: A Case Study at Selangor, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099,Volume 3, Issue 5, pp.466-470, July-August-2017.
