Dynamic Accessibility Modeling

Overview

This project measures how electric vehicle carsharing improves access to affordable grocery stores in Los Angeles. It focuses on BlueLA, a one-way station-based EV carsharing service designed to expand affordable transportation options in underserved communities. This project links real-time driving times from Mapbox APIs with store operating hours, price levels, and retailer types to produce scenario-specific accessibility maps. Two components of access are modeled: (1) walking time from a household to the nearest carsharing station, and (2) driving time, distance, and rental cost from that station to each grocery store under different traffic conditions. The study is published in Smart Cities.

Business Use

City agencies, mobility operators, and grocery retailers can use these dynamic maps to identify grocery access gaps, site new carsharing stations, and design price or subsidy programs that maximize healthy-food reach. The framework turns raw trip and traffic feeds into actionable insights on where expanded carsharing coverage delivers the greatest social benefit and commercial demand, supporting equity-focused funding decisions and partnership strategies.

Technical Background

The model combines driving times from the Mapbox API, store metadata from the Google Places API, and grocery store data from the U.S. Department of Agriculture (USDA) Supplemental Nutrition Assistance Program (SNAP) Retailer dataset to calculate driving time, distance, and rental cost from each station to each store under different traffic conditions, while also measuring household, carless household, and SNAP-recipient coverage within walking distance of stations to generate a full accessibility profile for each location.