Fajaruddin Bin Mustakim
Biography
Fajaruddin Bin Mustakim, educational background includes the achievement of a degree in Bachelor of Civil Engineering from Universiti Teknologi Mara (UiTM) (1999), and subsequently receiving an MSc in Construction Management from Universiti Teknologi Malaysia (UTM) (2006). In 2014, he was awarded a PhD Engineering in the field of Scientific and Engineering Simulation from the Nagoya Institute of Technology (NITech), Japan. After completed PhD, continue his service at Universiti Tun Hussien Onn Malaysia (UTHM) as Senior Lecturer. My first post-doctoral position at Institute IR4.0 Universiti Kebangsaan Malaysia (UKM). Simultaneously has been appoint as consultant project entitle: TMR Asynchronous V2V with NLoS Vehicular Sensing (V2V) at MMU?. This was then followed another position as academic collaborator at DatSINI Lab, Institute IR4.0, Universiti Kebangsaan Malaysia. Recently, he works at Malaysia Multimedia University (MMU) as Post-Doctoral Fellow. He has been active for many years in the fields of construction, transportation, and traffic behavior studies. In addition to this, he has authored at least 46 publications comprising of 20 indexed journal articles (ISI), 24 refereed conference articles, 3 edited books. He has supervised 2 PhD, 3 Master, 14 BSc and 2 Diploma. His strong belief is that the application of invention, inspiration and desire is necessary in anything that people want achievement in, and he believes that this is what ensures that academic work has a significant contribution to community and benefit to the mankind.
Research Interest
The Impact of Pedestrian Crossing at Unsignalized Intersection Using
Machine Learning, Binary Logistic Regression, and NARX
Abstract
In 2024, Malaysia experienced a total of 532,125 road accidents and 5,364 fatalities. Meanwhile
pedestrians recorded more than 550 casualties each year and consistently placed third rank after
motorcyclist and passenger car. This study aims to analyze the influence of pedestrian crossing at
selected unsignalized intersections during the vehicle?s manoeuvres. The eleven selected sites were
based on blackspot location and the study focused on comparison between intersections with and
without pedestrian bridge facility. In the early stage the study determined the pedestrian crossing
characteristic throughout the day and the observations were concentrated on three peak hours which is
the morning, midday, and afternoon. Next determine the frequency of traffic patterns involving
pedestrian crossing (PC), traffic conflict (TC) and motorcycle crossing (MC). Traffic characteristic
fluctuation analysis based on PC, TC, MC, and approach speed (AS) were carried out. Finally, this study
manages to develop right-turn motor vehicles (RMV) considering pedestrian crossing and other traffic
variables by adopting Binary Logistic Regression (BLR), Machine Learning base on Neural Net Fitting
and Nonlinear Autoregressive Exogenous model (NARX). The RMV models calibration using 838
datasets and involving eight predictors that influence the vehicle's manoeuvres. The study reveals that
pedestrian crossing, traffic conflict and traffic volume affect the RMV to accept shorter gap and
providing the pedestrian bridge has a positive impact for vehicle manoeuvres. In addition, the result
from machine learning using scale conjugate gradient algorithm achieved mean square error within
10%, in the RMV model. Although the pedestrian bridge has been provided at the intersection, in a few
cases the pedestrian refuses to benefit the facilities. This study recommends the implementation of
autonomous vehicles (AV) and electric vehicles (EV) that are equipped with internet of things (IoV)
and vehicle-to- everything communication (V2X) as part or partial solution in reducing traffic
accidents.
