Evaluating the Promotion of Innovative Mobility Options with an Agent-Based Modeling and Simulation Approach

Abstract

The growing, aging, and urbanizing traveling population of the United States requires different and more personal designed mobility options. The private sector, in association with public firms, has started to test and deploy innovative solutions to address these shifting needs. Unfortunately, the build-first-and-users-will-come theory does not hold much weight. Overnight success stories are often the result of years of hard work behind the scenes. Simply put, start-up marketing is often a unique challenge because of limited resources, whether it is time, money, or talent. This study, therefore, introduced an agent-based model that could be used to simulate the reaction of commuters and travelers on the introduction of an innovative mobility option and business model. The model was backed up with empirical data from an Austin, Texas–based innovative mobility solution, the Metropia app. The results of this study show the importance of how and when promotional activities are conducted, with respect to the characteristics of the concept involved, to achieve appropriate public engagement. Thus, decision makers are provided with a quantitative projection of the effect of different strategies and the associated public reaction. For instance, in the case of introducing innovative mobility options that require high public engagement, such as carpooling or crowdsourcing solutions, it is suggested that strategists focus their attention and marketing budget to make an initial splash.

Exploring Agent-Based Modelling for Car-Based Volunteer Driver Program Planning

“…They have not garnered widespread adoption compared with traditional demand estimation techniques, owing to their complex intricate programming needs. However, a few researchers have utilized them to study transportation problems in the realms of cycling, pedestrian movement, freight, urban demand models, introduction of innovative mobility options and business models, and decision support tools for demand-based transportation (12)(13)(14)(15)(16)(17)(18). There have been rural applications of ABM as well, including investigating how rural merchants select delivery options based on price, delivery time, and their trust of the driver (19); and investigating the operative parallels between demand-responsive transportation and conventional public transit (20).…”

A Systematic Review of the Agent-Based Modelling/Simulation Paradigm in Mobility Transition

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