# A Comparative Study of Ensemble Models for Predicting Road Traffic Congestion
## 6 Conclusions  
This paper addressed the determination of vehicle traffic flow well in advance using a dataset from a highway in the Gauteng Province, South Africa. Variables such as travel time, traffic volume, and average speed contributed to a good description of road traffic state regarding its prediction. These were useful for predicting the status of vehicle traffic flow. Results also suggested that the AdaBoost (DT) ensemble model performed better when compared to RF, DT, SVM, bagging, other AdaBoost combination models, and stacking ensemble learning.  
This model will complement the current interventions by transport authorities already in place, including e-toll gates, cycle tracks, bus rapid transit, road expansion, and corridors of freedom (well-planned transport routes), among other interventions. Adopting this model will benefit commuters and businesses in the province and potentially make the province an attractive destination for investors. This method is better than the available methods, which have been used to date for addressing these challenges in the Gauteng Province. Although the results here are specific to the dataset used and cannot be generalised, they afford the validation of the presented framework, which may be applied on different road traffic datasets. The predictive model, when implemented well, could directly and indirectly decrease the number of road traffic collisions and risks of poor health in different communities. Furthermore, commuters wishing to travel on the highway will receive helpful information on the traffic flow state well in advance, thanks to the developed prediction model. The proposed prediction model will enable commuters to plan travel time properly. The model can also benefit the authorities to plan the distribution of resources. Businesses will also see improvements due to the timely delivery of goods and staff reporting to work on time, positively impacting the economy of different cities.  
For future work, authors are planning to pay more attention to deep learning and various missing data imputation methods as promising emerging approaches, and the authors see this as a future area worth exploring.  
T.B.: conceptualisation, data curation, formal analysis, methodology, experiments, writing, editing and results validation. W.D. and B.S.P.: project administration, supervision, conceptualisation and critical revision. All authors have read and agreed to the published version of the manuscript.  
This research did not receive external funding.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data are not publicly available due to restrictions from the subjects' agreement.  
The authors would like to thank the Department of Applied Information Systems, the Institute for Intelligent Systems, and the University of Johannesburg for the resources used. The authors also wish to thank Mikros Traffic Monitoring (MTM) (Pty) Ltd. for providing the road traffic dataset used during experiments, and we would also like to thank [PERSON] and [PERSON] for their useful comments.  
The authors do not have any conflict of interest with other entities or researchers.