摘要: |
Micro-mobility refers to e-scooters, dockless bikes, and other low-speed modes and is an innovative transportation strategy that has demonstrated a great potential for congestion mitigation. However, the research on micro-mobility is very limited in the field of transportation. This proposal, thus, aims to conduct a comprehensive study to analyze, quantify, and understand the impacts of micro-mobility on congestion reduction and recommend corresponding intervention strategies for stakeholders. Specifically, the research team will first leverage historical e-scooter travel demand data, socio-demographic data, land-use data, and other relevant data to explore travelers’ usage patterns, especially in the congested times and locations. Then, the team will apply interpretable machine learning to model and explain the relationships between e-scooter travel demand and other important features, including traffic conditions, time of day, availability of bike lanes, etc. These results will then feed directly into an activity-based traffic simulator to conduct various scenario analyses and sensitivity analyses to understand whether and when e-scooters can reduce congestion effectively. Lastly, all the findings and insights will be used for identifying needs, opportunities, and potential obstacles for policy and operational cooperation between stakeholders. A set of policy intervention strategies will also be proposed for the promotion of e-scooter usage. |