The Urban Taxi Fairness Problem

Taxi services in cities like Shenzhen exhibit significant spatial inequality. Certain areas — often wealthier, centrally-located neighborhoods — receive disproportionately more service relative to demand, while other areas — frequently lower-income or peripheral neighborhoods — are systematically underserved.

How Inequality Emerges

This inequality arises naturally from expert driver behavior: drivers learn to optimize for personal revenue, gravitating toward high-demand, high-tip areas and avoiding less profitable zones. Over time, this creates a self-reinforcing cycle:

• High-service areas attract more taxis → shorter wait times → more riders → more revenue
• Low-service areas have fewer taxis → longer wait times → fewer riders → less incentive for drivers

When this spatial inequality correlates with socioeconomic factors — such as neighborhood income levels — it raises a systemic fairness issue in urban mobility.

The Central Question

Can we modify how taxi drivers serve a city to make the distribution of service more equitable — without compromising the realism of driver behavior?

Why This Matters

Transportation equity is a growing concern in urban planning and policy. Access to reliable taxi service affects:

• Economic participation — residents in underserved areas face longer commutes and reduced mobility
• Equity of opportunity — service patterns may reinforce existing socioeconomic divides
• Policy design — understanding the mechanisms of inequality enables targeted interventions

FAMAIL addresses this challenge by developing algorithmic tools that can reshape the spatial distribution of taxi service while preserving realistic driving patterns.

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