# A Comparative Study on Traffic Modeling Techniques for Predicting and Simulating Traffic Behavior
## 6 Conclusions  
Our study revealed that no particular non-parametric model outperforms all other methodologies in general. Rather, all these models have some pros and cons over each other. Above all, they are designed to work for specific problems in the traffic domain, such as the prediction of speed, congestion flow, accidents, freeway traffic volume, and multi-scale high-speed traffic. Moreover, the design process of these models largely depends on the data structure and other associated factors such as data dimension reduction, feature extraction, and handling the missing values. All these models can be used for both short-term and long-term prediction by considering the spatiotemporal correlations based on the time interval of each observation in the dataset. We found that in some scenarios, hybrid models that combine different models together provide better prediction accuracy. That combination could be in between parametric and non-parametric models such as ST-Kriging and ANNs, in between different versions of ANNs, or even in between the MCMC and Bayesian approaches. These findings emphasize the need for more hybrid non-parametric spatiotemporal models to predict traffic-related outcomes. In the second part of this survey, numerous state-of-the-art simulation models for traffic have been conducted to outline the differences between different traffic simulation tools. In the literature, most microscopic traffic simulation tools use three main simulation algorithms: car following, gap acceptance, and lane changing. These algorithms were developed to mimic human driving capabilities and were discussed to show how they use real-world driving strategies. Finally, a comparison between different traffic simulator tools was conducted in Table 2 to study SUMO, MATSim, AIMSUN, CORSIM, Paramics, VISSIM, and TRANSIMS. We evaluated these different simulation tools based on their capabilities, availability, features, and implementation flexibility. The user can specify the suitable tool for practice based on the intended outcome, whether simulating traffic flow, analyzing traffic light systems, or simulating vehicle counts, road occupancy, or traffic speed. Defining the purpose of the simulator's usage is an important step in selecting the appropriate simulator. \begin{table}
\begin{tabular}{p{113.8 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt}} \hline \hline
**Feature Simulator** & **MATSim** & **AIMSUN** & **VISSIM** & **SUMO** & **CORSIM** & **Paramics** & **TRANSIMS** \\ \hline
**Open Source** & Yes & No & No & Yes & No & No & Yes \\
**Visualization** & 2D & 2D, 3D & 2D, 3D & 2D, 3D & 2D, 3D & 2D, 3D & 2D, 3D \\
**Output** & Text & Graphs & XML & XML & Text & Graphs & XML \\
**Import Map** & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\
**Programming Language** & C++, Java & Python, C++ & C++, VB, Matlab, Python & C++, VB, Matlab, Python & Python, C++ & C++, VB, Matlab, Python & C++, VB, Matlab, Python \\
**Flexibility in infrastructure Development** & Limited & Flexible & Flexible & Flexible & Flexible & Flexible & Flexible & Flexible \\
**Coding** & Easy & Difficult & Easy & Difficult & Difficult & Easy & Difficult \\
**Objectives** & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \
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\end{table} 
Table 2: Different traffic simulation tools and their main features and capabilities. **Author Contributions:** Conceptualization, T.A., S.M. and G.A. ; methodology, T.A., S.M. and G.A. ; validation, T.A., K.E. and Z.Z. ; investigation, T.A., S.M. and G.A. ; writing--original draft preparation, T.A., S.M. and G.A. ; writing--review and editing, T.A., S.M. and G.A. ; visualization, T.A. ; supervision, K.E.