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Design airport dispatch with ETA uncertainty

Last updated: Mar 29, 2026

Quick Overview

This question evaluates proficiency in online algorithm design under uncertainty, stochastic modeling of ETAs, multi-objective optimization for resource allocation, and streaming data handling relevant to dispatch systems.

  • Medium
  • Uber
  • Coding & Algorithms
  • Data Scientist

Design airport dispatch with ETA uncertainty

Company: Uber

Role: Data Scientist

Category: Coding & Algorithms

Difficulty: Medium

Interview Round: Technical Screen

You control airport pickups with streaming ETAs for arriving flights and live driver locations/queues. Design an online dispatch algorithm that minimizes passenger wait time and deadhead miles under constraints: vehicle capacity, maximum pickup window, staging lot limits, and driver time-on-app. Assume ETA uncertainty is approximately normal with σ ≈ 5 minutes and ETAs update every minute; drivers have heterogeneous distances and service times. Specify the objective function, data structures, and algorithmic approach (e.g., min-cost flow with recourse, online bipartite matching with predicted costs) and how you handle ETA updates, cancellations, and fairness. Analyze complexity, discuss competitive ratio or worst-case bounds, and propose a fallback strategy during demand spikes.

Quick Answer: This question evaluates proficiency in online algorithm design under uncertainty, stochastic modeling of ETAs, multi-objective optimization for resource allocation, and streaming data handling relevant to dispatch systems.

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Uber logo
Uber
Oct 13, 2025, 9:49 PM
Data Scientist
Technical Screen
Coding & Algorithms
11
0

You control airport pickups with streaming ETAs for arriving flights and live driver locations/queues. Design an online dispatch algorithm that minimizes passenger wait time and deadhead miles under constraints: vehicle capacity, maximum pickup window, staging lot limits, and driver time-on-app. Assume ETA uncertainty is approximately normal with σ ≈ 5 minutes and ETAs update every minute; drivers have heterogeneous distances and service times. Specify the objective function, data structures, and algorithmic approach (e.g., min-cost flow with recourse, online bipartite matching with predicted costs) and how you handle ETA updates, cancellations, and fairness. Analyze complexity, discuss competitive ratio or worst-case bounds, and propose a fallback strategy during demand spikes.

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