Manipulating Traffic System Dynamics Using Smartphone Technology for Improving Public Safety, 10-R8126
Principal Investigators
Paul A. Avery
John Whipple
Thomas Dietzel
Inclusive Dates: 01/01/10 – 12/31/10
Background — Modeling traffic system dynamics can be very complex, considering a number of factors that affect the aggregate flow, such as vehicle density, road width, bend radii, obstructions, detours, etc. Approaching traffic system modeling at a macro level and applying averaging assumptions, the model results will only be valid within the bounds of the averaging assumptions and will not give the modeler flexibility to investigate system-level effects that emerge from individual vehicle capabilities. To effectively model traffic system behavior as it relates to the capabilities and behaviors of individual vehicles requires an understanding of how internal dynamics of complex systems interact to produce system-level phenomena. This research modeled traffic system dynamics at the micro level to understand the effect on the system when some vehicles have access to supplemental information through a smartphone application regarding elements of their environment and exit route, and developed the application on physical smartphones for hardware-in-the-loop testing.
Approach — This project developed an agent-based model to simulate traffic flow on urban road networks to understand the traffic system macro-behavior, which is a complex nonlinear problem with adaptive interactions among its elements. The model was constructed by specifying the physical characteristics and the rules of behavior for individual vehicles, the road network in which the vehicles operate, and the goals or objectives for individual vehicles. A smartphone application was also developed using a freely available smartphone emulator, and then moved to actual smartphone platforms. The application subscribed to a notification service and provided specific optimal routing information to the user through a Google Maps interface.
Accomplishments — An agent-based model was developed including vehicle behaviors, such as route selection and following, and vehicle following. The vehicles can also modify their route selections based on local congestion. A road network was constructed based on the San Antonio major highway network using map data from Google Maps. A specific population of vehicles has the capability to receive the evacuation notification, which emulates the smartphone capability. An XMPP communications protocol was implemented to enable the physical smartphones to communicate with the evacuation data server and the agent-based model. Routing information is delivered to the phones using a Google Maps interface, where an origin location is provided by the phone's GPS position, and a destination location is selected based on an effective evacuation route.
