Personalized Control for Promoting Sustainable Travel Behaviors

Abstract

Abstract We develop a personalized control system to modify individual travel behaviors by offering personalized incentives. Individual preferences are learned to provide personalized incentives so that the promoted alternative is more likely to be accepted. The work described is based on the integration of two fields (controls and human behavior) that are traditionally separate from each other: we model the travelers’ choice-making behaviors with the random utility theory and responses from the individuals are mined by a particle filter for learning individual preferences to promote sustainable behaviors. The discrete nature of travel behavior naturally leads to limited observability. We overcome this problem by designing a measurement function from which additional information can be solicited. Additionally, the inherent trade-off nature of travel behaviors results in an infinite set of solutions, to which two solutions are proposed: 1) the divide and conquer strategy in which a multi-dimensional conditional probability function is proposed; and 2) use of domain knowledge to restrict that preference values fall in certain ranges and are consistent with certain distributions. The performance of preference learning with these two solutions applied is shown via simulation tests. An online experiment, involving hypothetical scenarios on departure time choices and human subjects, shows that among all the recruited participants, the majority (65% of participants) are more likely to accept the promoted alternatives given personalized incentives. We also show that changes in individual departure time choice can potentially lead to a significant reduction (48%) in total travel time on a simple transportation network.