*Article* **Short Term Tra** ffi**c State Prediction via Hyperparameter Optimization Based Classifiers**

#### **Muhammad Zahid 1, Yangzhou Chen 2,\*, Arshad Jamal 3 and Muhammad Qasim Memon 4**


Received: 9 January 2020; Accepted: 23 January 2020; Published: 27 January 2020

**Abstract:** Short-term tra ffic state prediction has become an integral component of an advanced traveler information system (ATIS) in intelligent transportation systems (ITS). Accurate modeling and short-term tra ffic prediction are quite challenging due to its intricate characteristics, stochastic, and dynamic tra ffic processes. Existing works in this area follow di fferent modeling approaches that are focused to fit speed, density, or the volume data. However, the accuracy of such modeling approaches has been frequently questioned, thereby tra ffic state prediction over the short-term from such methods inflicts an overfitting issue. We address this issue to accurately model short-term future tra ffic state prediction using state-of-the-art models via hyperparameter optimization. To do so, we focused on di fferent machine learning classifiers such as local deep support vector machine (LD-SVM), decision jungles, multi-layers perceptron (MLP), and CN2 rule induction. Moreover, tra ffic states are evaluated using tra ffic attributes such as level of service (LOS) horizons and simple if–then rules at di fferent time intervals. Our findings show that hyperparameter optimization via random sweep yielded superior results. The overall prediction performances obtained an average improvement by over 95%, such that the decision jungle and LD-SVM achieved an accuracy of 0.982 and 0.975, respectively. The experimental results show the robustness and superior performances of decision jungles (DJ) over other methods.

**Keywords:** tra ffic state prediction; spatio-temporal tra ffic modeling; simulation; machine learning; hyper parameter optimization; ITS
