Whereas conventional transportation planning tends to be region-centric, we see a shift now to one that is more operator-centric, as transport providers work with different city and regional agencies. As such, understanding service impacts on different communities are more important than ever, particularly for emerging technologies. Microtransit/ridepooling is one such technology, and the limited observations (e.g. dozens) we have alone might not provide a robust statistical picture for understanding deployment needs across a landscape of different market typologies for thousands of cities. Our C2SMART Center study, in collaboration with Via, rectifies this by proposing the use of simulation-based “scenario data upscaling” (in much the same way algorithms can upscale images) to inform on metrics needed to estimate forecast models for managing deployment portfolios. Even with data from only a handful of cities, we were able to generate meaningful forecast models based on hundreds of synthesized scenarios, showing for microtransit deployment how to evaluate different service regions across a portfolio of different cities in the U.S. Should be of interest to federal policymakers, microtransit/ridepooling providers, and other emerging transportation technology providers and local agencies, particularly as we look to revamp the national tools and data we use for planning transport infrastructure.

Paper available here: https://www.sciencedirect.com/science/article/pii/S0965856423000046