# A Comparative Study of Ensemble Models for Predicting Road Traffic Congestion
###### Abstract  
Increased road traffic congestion is due to different factors, such as population and economic growth, in different cities globally. On the other hand, many households afford personal vehicles, contributing to the high volume of cars. The primary purpose of this study is to perform a comparative analysis of ensemble methods using road traffic congestion data. Ensemble methods are capable of enhancing the performance of weak classifiers. The comparative analysis was conducted using a real-world dataset and bagging, boosting, stacking and random forest ensemble models to compare the predictive performance of the methods. The ensemble prediction models are developed to predict road traffic congestion. The models are evaluated using the following performance metrics: accuracy, precision, recall, f1-score, and the misclassification cost viewed as a penalty for errors incurred during the classification process. The combination of AdaBoost with decision trees exhibited the best performance in terms of all performance metrics. Additionally, the results showed that the variables that included travel time, traffic volume, and average speed helped predict vehicle traffic flow on the roads. Thus, the model was developed to benefit transport planners, researchers, and transport stakeholders to allocate resources accordingly. Furthermore, adopting this model would benefit commuters and businesses in tandem with other interventions proffered by the transport authorities. ensemble methods; misclassification cost; machine learning; road traffic congestion +
Footnote †: journal: _Sci._  
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emission. They also produce nitrogen oxides (NOx), carbon oxides (CO), sulphur oxides (SOx), and particle filters. According to [3], the transport sector accounts for three-quarters of global CO2 emissions, adding that the emissions can originate from trucks, cars, planes, and trains. Furthermore, the report by [2] has stated that the main sources of air pollution are from road traffic congestion-related issues, causing diseases such as cardiovascular and respiratory failure. Traffic congestion is harmful to drivers and passengers sitting in traffic and people living near the affected highways. According to [4], adults living next to busy roadways might end up having a disease like dementia, and when it comes to children, it might risk them developing long-term chronic diseases. In addition, other research studies like [5; 6; 7; 8; 9; 10] have investigated the negative impacts of RTCs in detail. Road traffic congestion prediction presented in this paper can be defined as predicting traffic state at a specific time [11]. This study focuses more on traffic flow and road traffic congestion. Furthermore, the study considers traffic volume (density), average speed (speed flow/velocity) and travel time parameters of the fundamental diagram. The fundamental diagram graphs from [12] consist of freeflow \((\mathrm{V_{f}}\sim\mathrm{flow}\) velocity), bound flow \((\mathrm{V_{c}}\sim\mathrm{critical}\) velocity), and congested (traffic density) vectors. The fundamental analysis of the study is as follows: freeflow refers to the traffic being stable with vehicles moving freely, and bound flow is the bistable transition from a stable traffic state. Then, the congested state refers to the unstable traffic state, which means vehicles velocity is reduced and the volume of vehicles increases gradually for the freeway capacity [12; 13; 14]. For this study, the vectors (classes) were defined as freeflow, moderate, and congested, where the bound flow vector is referred to as moderate. These three classes correspond based on the obtained data. This study considered supervised ensemble methods to design predictive models for road traffic congestion. Ensemble methods are implemented in machine learning to boost several classifiers and to improve their overall performance. The main aim of ensemble methods is to perform best compared to the traditional machine learning methods called base learners [15].