Measuring Micromobility - Capastone Project with TfL
LSE MPA Capstone with Transport for London: quantifying micromobility patterns and testing behaviorally informed interventions for equitable, sustainable transport.
Summary
A capstone collaboration with Transport for London (TfL) measuring micromobility usage and accessibility in post-COVID London. We combined platform and survey data with behavioral design to understand who rides, where gaps exist, and what targeted nudges could unlock equitable mode shift.
Problem
Micromobility offers low-carbon options, but adoption is uneven across income and geography. TfL needs reliable measures of usage and exposure, and practical levers to improve accessibility without widening inequities.
Approach
- Built a micromobility measurement framework (exposure, usage, and accessibility proxies).
- Linked area-level indicators (deprivation, density, PTAL) to trip counts and availability.
- Designed behavioral nudges (timing, messaging, incentives) for targeted pilots.
- Compared interventions with a cost–benefit and equity screen.
Methods
- Data assembly: trip/event feeds, borough stats, deprivation indices, station buffers.
- Descriptive + regression analysis for correlates of adoption and access gaps.
- Behavioral experiment design (randomized messaging/incentives; fairness constraints).
- Scenario modeling for uptake under targeted vs. universal interventions.
Results (selected)
- Identified geographic pockets with high exposure but low usage (latent demand).
- Evidence that targeted, time-bound prompts outperform untargeted messaging for first-rides.
- Equity screen flagged areas needing subsidy or infrastructure tweaks before nudges.
My Role
Led data modeling and experimental design: built the measurement framework, engineered datasets, ran the analysis, and authored the recommendations with an equity lens.
Deliverables
- Policy brief and executive deck for TfL.
- Dataset and reproducible analysis notebook.
- Cost–benefit and equity comparison of intervention options.