Big Data Analytics to Assess Travel Behavior of Shared Micromobility Use

Using Big Data and Machine learning approaches, we proposed a framework for high-resolution analyzing micromobility data based on temporal, spatial, and weather attributes. This study scrutinized over a million e-scooter trips in Nashville, Tennessee to identify five distinct usage patterns over the study period. They are morning work/school, daytime short errand, social, nighttime entertainment district, and utilitarian trips. Among other findings, the most common use of e-scooters in Nashville was daytime short errand trips, contributing to 29% of all e-scooter trips. Only 16% of trips were nighttime entertainment district-related trips. Understanding when and where people use e-scooters can help city governments make data-driven decisions regarding safety, sustainability, and mode substitution of emerging micromobility.

Best Paper Award
Second Place Award