# A Comparative Study on Traffic Modeling Techniques for Predicting and Simulating Traffic Behavior
###### Abstract  
The significant advancements in intelligent transportation systems (ITS) have contributed to the increased development in traffic modeling. These advancements include prediction and simulation models that are used to simulate and predict traffic behaviors on highway roads and urban networks. These models are capable of precise modeling of the current traffic status and accurate predictions of the future status based on varying traffic conditions. However, selecting the appropriate traffic model for a specific environmental setting is challenging and expensive due to the different requirements that need to be considered, such as accuracy, performance, and efficiency. In this research, we present a comprehensive literature review of the research related to traffic prediction and simulation models. We start by highlighting the challenges in the long-term and short-term prediction of traffic modeling. Then, we review the most common nonparametric prediction models. Lastly, we look into the existing literature on traffic simulation tools and traffic simulation algorithms. We summarize the available traffic models, define the required parameters, and discuss the limitations of each model. We hope that this survey serves as a useful resource for traffic management engineers, researchers, and practitioners in this domain. traffic; modeling; spatiotemporal +
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optimize road design and assess the effects of road network design on vehicle movements. These models provide a geographical environment to plan scenarios of driver behavior in a wide variety of road types, such as urban and freeway networks. Traffic simulation uses mathematical models to demonstrate how vehicles would move in real time using car-following theories. The difference between traffic prediction and traffic simulation models is that in prediction, we estimate the traffic value (volume, speed, density, etc.) at a future time point using historical data under given conditions. In traffic simulation, models estimate the traffic value (volume, speed, density, etc.) under given conditions. In this paper, we examine the implementations of the most common spatiotemporal machine learning models, focusing on nonparametric models that are widely used to estimate and predict traffic status. In addition, we provide a comprehensive comparison of the existing traffic simulators used in smart cities and intelligent transportation applications, specifically for traffic flow modeling. This study aims to explore spatiotemporal nonparametric traffic models and microscopic simulation models. Although there has been a considerable amount of survey papers introduced in traffic modeling, none has been found focusing on nonparametric modeling techniques for traffic prediction or methods for traffic data analysis and the prediction of different time intervals. A number of advanced models will be critically examined and compared in this survey. This paper seeks to address the following questions: What are the suitable models for long-term and short-term predictions in traffic modeling? What are the performance metrics used to evaluate the efficiency of each model? What are the drawbacks of each model, and how can these issues be overcome? In which ways does the nonparametric predictive method differ from the parametric predictive method? What is the required data structure that is suitable to implement the predictive model? The findings of this paper will make an important contribution to the field of spatiotemporal prediction of traffic flows. In addition, it will highlight a number of common nonparametric statistical methods. By demonstrating the challenges in the long-term and short-term prediction of traffic modeling in this survey, abundant room for further development in traffic modeling will be considered.