Acceptable Planning: Influencing Individual Behavior to Reduce Transportation Energy Expenditure of a City
This addresses urban transportation energy efficiency for city planners and residents, but is incremental as it builds on existing AI and simulation methods.
The research tackled reducing transportation energy expenditure in a city by influencing individual behavior, introducing COPTER, an intelligent travel assistant that finds acceptable multi-modal travel plans, resulting in a 4% energy reduction and 20% delay reduction in a simulation for Los Angeles.
Our research aims at developing intelligent systems to reduce the transportation-related energy expenditure of a large city by influencing individual behavior. We introduce COPTER - an intelligent travel assistant that evaluates multi-modal travel alternatives to find a plan that is acceptable to a person given their context and preferences. We propose a formulation for acceptable planning that brings together ideas from AI, machine learning, and economics. This formulation has been incorporated in COPTER that produces acceptable plans in real-time. We adopt a novel empirical evaluation framework that combines human decision data with a high fidelity multi-modal transportation simulation to demonstrate a 4\% energy reduction and 20\% delay reduction in a realistic deployment scenario in Los Angeles, California, USA.