  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.
