Optimizing a 20-Hour NYC Subway Speedrun with TSP Algorithms
Trail of Bits researchers optimized an NYC Subway speedrun route using GTFS data and a Christofides Traveling Salesman Problem (TSP) approximation.
Modeling 474 stations as an undirected graph, the team applied combinatorial optimization to solve the TSP in milliseconds.
The proposed 20 h 42 m tour—34 transfers included—trims about 45 minutes off the existing record.
Eigenvector centrality analysis highlights Times Square as the subway network’s true hub.
The NYC Subway speedrun project showcases practical uses of approximation algorithms for complex transit networks and public-transit optimization.
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This article focuses on algorithmic route optimization for the New York subway and makes no reference to cryptocurrencies, blockchain finance, or market conditions. It therefore has no direct impact on crypto trading sentiment. Historically, only news involving regulatory shifts, large token listings, or significant market developments move crypto prices. As such, this subway TSP optimization exercise is neutral for both short-term trader reactions and long-term market trends.