Travel Behavior

Bike lending offers a service that enables individuals to borrow bicycles for short-term use (i.e., ranging from 2 hours to 36 months), typically from designated locations within cities, campuses, or communities. Unlike bikesharing systems that typically rely on automated kiosks and/or undocked and free-floating devices for public access, bike lending involves a managed program with staff, similar to a library model. These programs can be administered by community organizations, bike shops, public libraries, and other local entities. They are typically community- or membership-based, with...

This report analyzes gender issues for female drivers and passengers of transportation network companies (TNCs) and taxis. Between August 2023 and May 2024, the researchers conducted driver and rider focus groups (n=8 drivers and n=8 passengers); a driver survey (n=170); a TNC user/non-user survey (n=440, including n=217/440 taxi non-users, n=7/440 TNC non-users, and a subset of n=5/440 non- users of TNCs and taxis); and expert interviews (n=13). This research finds that both female drivers and passengers experience an array of safety concerns and challenges traveling with young children...

Shared mobility—the shared use of a motor vehicle, bicycle, or other low-speed transportation mode that allows users to obtain short-term access to transportation on an as-needed basis—has the potential to help address some transportation equity challenges. In an effort to categorize the myriad of transportation equity barriers facing transportation system users, this primer proposes a ‘STEPS to Transportation Equity’ framework including: Spatial, Temporal, Economic, Physiological, and Social barriers. For each barrier category, shared mobility opportunities and challenges are explored...

Accurate insights into electric vehicle (EV) driver behavior are essential for long-term infrastructure planning, grid management, and understanding downstream economic impacts, yet individual level data on EV mobility remains limited. Here, we develop a scalable framework to infer EV ownership and charging behavior from passively collected, high-resolution mobility traces covering over 760,000 drivers across four major U.S. metropolitan areas. We identify likely EV drivers based on distinctive visitation patterns to charging stations and gas stations, frequency of visits, and daily travel...