Traffic Dataset and Dynamic Routing Algorithm in Traffic Simulation

Authors

  • Zhemin Zhang School of Computing and Augmented Intelligence, Arizona State University, Tempe, USA
  • Gennaro De Luca Polytechnic School, Arizona State University, Mesa, USA https://orcid.org/0000-0002-0992-4213
  • Brian Archambault School of Computing and Augmented Intelligence, Arizona State University, Tempe, USA https://orcid.org/0000-0002-2114-9838
  • Juan Chavez Andersen Corporation, Arizona, USA
  • Brandon Rice Holcombe Department of Electrical and Computer Engineering, Clemson University, South Carolina, USA https://orcid.org/0000-0002-5775-2890

DOI:

https://doi.org/10.37965/jait.2022.0106

Keywords:

computer science education, dynamic routing, traffic dataset, path planning, traffic simulation

Abstract

The purpose of this research is to create a simulated environment for teaching algorithms, big data processing, and machine learning. The environment is similar to Google Maps, with the capacity of finding the fastest path between two points in dynamic traffic situations. However, the system is significantly simplified for educational purposes. Students can choose different traffic patterns and program a car to navigate through the traffic dynamically based on the changing traffic. The environments used in the project are Visual IoT/Robotics Programming Language Environment (VIPLE) and a traffic simulator developed in the Unity game engine. This paper focuses on creating realistic traffic data for the traffic simulator and implementing dynamic routing algorithms in VIPLE. The traffic data are generated from the recorded real traffic data published on the Arizona Maricopa County website. Based on the generated traffic data, VIPLE programs are developed to implement the traffic simulation with support for dynamic changing data.

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Published

2022-05-12

How to Cite

Zhang, Z., De Luca, G., Archambault, B., Chavez, J., & Rice, B. (2022). Traffic Dataset and Dynamic Routing Algorithm in Traffic Simulation. Journal of Artificial Intelligence and Technology, 2(3), 111–122. https://doi.org/10.37965/jait.2022.0106

Issue

Section

Research Article