Predicting Traffic Phases from Car Sensor Data using Machine Learning

Optimization of IoT-Enabled Physical Location Monitoring Using DT and VAR

“…Researchers monitored the home physical location to identify an abnormal condition in the environment to take corrective action to avoid future loss in the premises and optimized person recognition to determine whether the recognized person is valid or invalid with Decision Tree (DT) accuracy of 89.91% using stratified 10 fold cross-validation (Shitole and Devare, 2019). Heyns et al (2019) demonstrated that traffic stages can be ordered utilizing driving conduct. Drivers conduct changes as the traffic stage changes and these progressions can correspond to these traffic stages utilizing machine learning with an accuracy of 95% and precision of 92%.…”

Trend analysis and fatality causes in Kenyan roads: A review of road traffic accident data between 2015 and 2020

“…We will utilize machine learning approaches to explore the data. Machine learning has been applied in various aspects of traffic analysis ranging from infrastructure design to prediction models (Gichaga, 2017;Pakgohar & Kazemi, 2015;Hadji Hosseinlou et al, 2018;Karimzadeh and Shoghli, 2020;Sysoev et al, 2020;Heyns et al, 2019). To our knowledge, there has not been publications on the said dataset.…”

Intelligent Predictive Model for Road Traffic Congestion and Monitoring System: A Systematic Survey

“…The drawbacks of area based approach the limited number of features are used, require more number of data and the ability to recognize new traffic pattern for future research. [E. Heyns, et al,2019][10] present the traffic prediction from driver behavior using signals and Control Area Network (CAN)bus. The predictive model for learning approach using Bagged Trees and RUSBoosted Trees for imbalanced data and the accuracy of the result is 95% achieved and the precision is 92%.…”