Date of Award
Master of Science (MS)
Cameron J Turner
Connected autonomous intelligent agents (AIA) with enhanced decision making through machine learning can improve intersection performance and resilience for the transportation infrastructure. An agent is an autonomous decision maker whose decision making is determined internally but may be altered by interactions with the environment or other agents. Implementing agent-based modeling techniques to advance communication for more appropriate decision making will provide great benefits to autonomous vehicle technology.
A new algorithm is proposed to improve the decision-making process of autonomous vehicles and intelligent traffic signals, specifically at city like intersections. This is completed by understanding vehicle to vehicle (V2V), vehicle to infrastructure (V2I), and infrastructure to infrastructure (I2I) communication and using gathered data to ensure these agents make more appropriate decisions given the circumstances. These vehicles and signals are modeled to adapt to the common traffic flow of the intersection and ultimately find an optimum flow that will decrease average vehicle time to ultimately reduce inefficiency through each intersection. Considering each light and vehicle as an agent and utilizing communication between these agents will enable opportunity for data transmission. Improving agent-based I2I communication and decision making will provide performance benefits to traffic flow capacities.
Evaluations were completed comparing intersections with fixed, coordinated, and adaptive timing signals. A fixed timing signal is an intersection using a fixed maximum green light time with no opportunity for adjustment. The coordinated signals adapt and change light status based on the current light status of adjacent intersections. Adaptive signals add in a recognition of vehicle load in one direction and adjust their own status either based on the load at the individual intersection or a neighboring light status change with the intent to improve traffic flow.
To compare these scenarios given a specific example of 160 total vehicles present on the road in a 2x2 intersection grid setup, inefficiency was reduced from 50% to 45% given the relationship between ideal average time compared to actual average time for vehicles proceeding through an intersection. Overall tests were run to compare the different light signal options based on the number of vehicles on the road and maximum green light time in one direction. The results were consistent and overall inefficiency was reduced using an adaptive traffic signal to recognize upcoming vehicles combined with the ability to adjust based on adjacent intersection light status changes.
Apostol, Andre Aaron, "Agent-Based Resilient Transportation Infrastructure with Surrogate Adaptive Networks" (2019). All Theses. 3230.