A Flexible Multi-Modal Multi-User Traffic Simulation for Studying Complex Road Design

Abstract

Abstract While there are many factors that affect traffic accidents, safe road design plays a key role. It is critical to understand traffic flow and driver response to various scenarios. Furthermore, with the inclusion of new traffic trends such as bicycle friendly roads and autonomous vehicles, it is ever more important to study the complex relationships between traffic entities. However, controlled experiments can be difficult to implement in the real world. Traffic simulation has been researched for decades to understand the flow of traffic in a variety of environments and how users interact within it. However, these platforms are limited in simulation and authoring capabilities, do not allow for multiple human agents, or are not accessible by roadway designers. This research explores the technical development of a multi-user multi-modal traffic simulation platform that expands on the capabilities of traditional traffic simulators. Traffic simulation of virtual cars and pedestrian agents were generated through VISSIM and networked into a simulation platform. Multiple human agents can interface with the system through a physical car and bicycle rig featuring both a virtual reality head mounted display and a multi-monitor display. Furthermore, the multi-user nature of the platform allows for a variety of complex research applications including multi-user behavior. By utilizing low-cost commodity hardware and software, the accessibility of this platform is greatly increased. A testbed environment was created to evaluate the technical limitations of the platform and assess where further work is needed. Through this evaluation, the authoring process and networking capabilities were optimized. Additionally, the core functionality of the platform, including the various simulator modes and multi-user functionality, was found to be successful. Through this research, combining commodity hardware with traffic simulation will allow a larger number of researchers to better understand traffic environments and how humans may interact within them.