Integrating Personalized Incentives for Enhanced Transportation System Management
Description
As the issue of urbanization accelerates, transportation networks face growing congestion challenges, leading to increased travel delays, environmental impacts, and infrastructure strain. Traditional Transportation System Management (TSM) strategies focus on optimizing traffic flow and enhancing infrastructure efficiency, but demand-side approaches—such as behavior incentives—offer promising alternatives. This research explores the integration of personalized incentives (such as monetary rewards, tokens, and gamification) within TSM to encourage off-peak travel and multimodal transportation choices. Preliminary hypotheses suggest that tailored incentives will significantly improve compliance and reduce peak-hour congestion. Furthermore, we hypothesize that integrating dynamic, user-specific incentives with an existing multimodal trip planner can significantly improve traffic distribution. To test this, we are designing behavioral experiments and developing simulation models to assess user responses. The experiments take into consideration both rational and non- rational user behaviors in decision making. Additionally, we present a survey of existing congestion mitigation strategies to inform our approach. The findings aim to inform the development of adaptive, user-centered solutions for reducing urban congestion while enhancing mobility and sustainability.