Yiming Xu is conducting research to explore how micromobility is impacting the transportation system and its effectiveness in potentially reducing congestion. Xu is a doctoral student working under the guidance of Dr. Xilei Zhou, an assistant professor in the Department of Civil and Coastal Engineering at the University of Florida.
Micromobility is a type of small, lightweight transportation device such as an e-scooter or dockless bike, which has become very popular in the past two years. It is frequently seen on the streets in 120 cities around the world. A recent study has found that about 70% people view e-scooters positively as they believe that e-scooters can expand transportation options by replacing short trips in automobile and complementing public transit. As most of the short car or ridehailing trips take place in the downtown and its surrounding areas, e-scooters are presenting an opportunity to relieve traffic congestion by replacing automobile trips.
Xu says research is very limited on modeling and analyzing the impacts of micro-mobility on the existing transportation system and quantifying its impacts on congestion mitigation. There are still a lot of questions to be answered he says.
“For example, what market penetration rate of e-scooters can achieve an effective reduction in congestion?” he said. “Or what are the impacts of availability of parking on e-scooter usage and which roads or streets need to build connected bike lanes in order to ensure safety of e-scooters and promote the modal shift from cars to e-scooters?”
The research that Xu and his doctoral adviser, Dr. Zhou, are undertaking look to state-of-the-art, machine-learning techniques and activity-based traffic simulations to understand the potential of micro-mobility to serve as a solution to mitigate congestion. The project is titled Micromobility as a Solution to Reduce Urban Traffic Congestion (Project B3).
In this project, Xu is responsible for gathering raw data and processing it to simulate various conditions where micromobility can have an impact on congestion.
“My role includes raw data collection such as scraping the micromobility usage data, collecting socio-demographic data, and land-use data,” he said. “I will then extract the micromobility trips from the massive and messy raw data, developing machine learning models and interpreting the models to explore relationships between e-scooter travel demand and other important features, including traffic conditions, time of day, availability of bike lanes, etc.”
So far, Xu and his doctoral adviser have found that e-scooter demand is high in downtown areas where congestion is prevalent. They have also found that variables in the built environment such as the quality of the riding environment, parking and hotel densities have great effects on e-scooter travel demand.
“We are still exploring the impact of micromobility usage on congestion,” he said. “But overall, the findings will be able to guide policy intervention for promoting a modal shift from riding a car to using e-scooters or dockless bikes to reduce congestion.”