**7. Conclusions**

In this study, we improvised machine learning models with hyperparameter tuning optimization for short term TSP. Different schemes offered in parameter tuning were examined by performing the number of simulation iterations incurring different random seeds to ensure that the model worked efficiently under a real-time scenario. To do so, a comprehensive demonstration and the ability of different machine learning models were evaluated using different forecasting time-intervals at distinct time scales. The short-term traffic state was taken as a function of level-of-service (LOS) on a basic freeway segmen<sup>t</sup> along Second Ring Road in Beijing, China. Simulation of a transportation road demonstrated that decision jungles were more efficient and stable at different predicted horizons (time intervals) than the LD-SVM, MLP, and CN2 rule induction. Data utilized in this study was collected from traffic simulator VISSIM. Actual density–flow was captured on freeway segmen<sup>t</sup> via different prediction horizons of 15, 10, and 5 min. The experimental results showed and demonstrated the superior and robust performance of decision jungles compared to the LD-SVM, CN2 rule induction, and MLP. The overall performance of prediction results were improved by over 95 percent on average, which led to an accuracy of 0.982 and 0.975 for the decision jungle and LD-SVM. Moreover, the prediction performance for CN2 rule induction were also observed to be improved based on if–then rules in terms of the traffic patterns for different prediction horizons.

This study has some limitations that must be acknowledged. First, the proposed study was deployed in a developed urban freeway network model, so the simulated data need to be enhanced in future studies. Second, instead of justifying the efficacy of the suggested techniques using microscopic

simulation platform via VISSIM, forthcoming studies may focus on investigating and verifying the performance of proposed methods with an improved model on real tra ffic data.

In the future, studies may focus on long-term tra ffic state prediction (hours, days, weeks), which could also be divided into di fferent LOS groups. The study area can be extended from the basic freeway segmen<sup>t</sup> to weaving, merging, and diverging segments that cover the entire network range of the Second Ring Road. Studies could incorporate temperature, air quality, weather, and other external factors that are likely to a ffect travel demand, thus, enhance prediction accuracy. In addition, it could rely on considering larger and various types of tra ffic datasets to analyze various combinations of flow, occupancy, speed, and other characteristics of road tra ffic to improve the predictive accuracy by using improved machine learning methods for prediction and analytics.

**Author Contributions:** Conceptualization, M.Z. and Y.C.; Methodology, M.Z. and Y.C.; Software, M.Z.; Validation, M.Z. and Y.C.; Formal analysis, M.Z. and Y.C.; Investigation, M.Z. and A.J.; Resources, M.Z. and Y.C.; Writing—original draft preparation, M.Z., Y.C., and M.Q.M.; Writing—review and editing, M.Z., A.J., and M.Q.M.; Visualization, M.Z. and A.J. All authors have read and agreed to the published version of the manuscript.

**Funding:** The work was supported by the National Natural Science Foundation of China (Grant No. 61573030).

**Acknowledgments:** The authors acknowledge the support of the Beijing University of Technology in providing the essential resources for conducting this study.

**Conflicts of Interest:** The authors declare no conflicts of interest.
