TY  - JOUR
PY  - 2023//
TI  - A novel approach to avoid road traffic accidents and develop safety rules for traffic using crash prediction model technique
JO  - Lecture notes in networks and systems
A1  - Ahammad, Sk Hasane
A1  - Sukesh, M.
A1  - Narender, Mekala
A1  - Ettyem, Sajjad Ali
A1  - Al-Majdi, Kadhum
A1  - Saikumar, K.
A1  - Sharma, Devendra Kumar
A1  - Peng, Sheng-Lung
A1  - Sharma, Rohit
A1  - Jeon, Gwanggil
SP  - 367
EP  - 377
VL  - 617
IS  - 
N2  - The expansion of nations and communities has resulted in a variety of externalities, such as an increase in traffic accidents. Many attempts have been undertaken to minimize the injuries and fatalities and their intensity. Traffic safety modeling is a most significant technique to motivate harmless mobility because it is capable of the creation of Crash Prediction Models (CPMs) as well as the investigation of the fundamentals that contribute to the incidence of crashes. Statistical modeling has been utilized in this process in the past, regardless of the fact that they are aware of the limits of this sort of strategy which allows you to experiment with other options, such as using machine learning approaches. Machine learning approaches applied to collision datasets can assist researchers in better knowing the features of motorist behavior, highway surroundings, and meteorological circumstances that are linked to varying mortality risk levels. If we build a reliable predictive model capable of automatically classifying the degree of injury in diverse traffic accidents, we may be able to discover patterns involved in severe wrecks. These patterns of behavior and road accidents can be used to design traffic safety rules.<p />  <p>Language: en</p>
LA  - en
SN  - 2367-3370
UR  - http://dx.doi.org/10.1007/978-981-19-9512-5_34
ID  - ref1
ER  -