Acceptable Planning: Influencing Individual Behavior to Reduce Transportation Energy Expenditure of a City

TL;DR

This paper presents COPTER, an intelligent travel assistant that influences individual transportation choices to reduce city-wide energy use, achieving notable energy and delay reductions through acceptable planning.

Contribution

It introduces a novel formulation for acceptable planning integrating AI, machine learning, and economics, implemented in COPTER for real-time, acceptable multi-modal travel planning.

Findings

Achieved 4% energy reduction in transportation.

Real-time acceptable plans generated for individual travelers.

Demonstrated effectiveness in a realistic Los Angeles scenario.

Abstract

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.