SMU Data Science Review


Traffic simulations are often used by city planners as a basis for predicting the impact of policies, plans, and operations. The complexities underpinning traffic simulations are often not described in detail yet can significantly impact the simulation outcome. Conflating underlying data for simulations is complex and hinders the interest in this type of exploration. This paper aims to elucidate critical features of traffic simulations that drive the generated metrics of the modeled urban environment. Specifically, this paper examines differences in two road graph networks for the metropolitan region of Houston, TX: a reduced network composed of 45,675 road links and an expanded network consisting of 729,753 road links. This paper will also cover collecting, refining, the feature extracting, and mapping matching real-world data to the simulated data. The traffic dynamics are generated by a simulator called Mobiliti. Two scenarios are explored: a baseline shortest travel time with 50% of the vehicles enabled to dynamically route to reduce travel time (B50), and a User Equilibrium Travel time (UET) scenario that results from a quasi-dynamic traffic assignment optimization. The resultant dynamics of these routing algorithms generate speeds and flows on the road graph links. The demand model trips are characterized by key features like travel times, delay times, and vehicle miles traveled. Validation with real-world data is presented using open-source Texas Department of Transportation data. The validation results of the various simulations provided evidence that the expanded network resulted in a more accurate simulation.

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License