Efficient Route Planning between Stations in NYC: A Dijkstra Algorithm Approach
Poster Number
3
Faculty Mentor Name
Larry Langley
Research or Creativity Area
Other
Abstract
Efficient public transportation is critical for major cities. The New York City (NYC) Subway is a prime example, transiting on average millions of riderships every week in 2023, and, as such, optimization of transit routes is important. This presentation introduces a methodology for optimizing routes within the NYC Subway system, employing Dijkstra’s algorithm for computational analysis. Each station is represented as vertices in a weighted graph, with weights corresponding to distance between two stations. The results demonstrate significant improvements in route planning efficiency, providing commuters and system planners with a robust tool for enhanced journey planning and network management in one of the world’s largest and busiest urban transit systems. The presentation explains the algorithm thoroughly, accompanied by a small-scale example for better comprehension. Additionally, coding implementations are included to extend the algorithm’s functionality. Limitations of the approach, such as the scaling system used for time and cost, are also discussed.
Location
University of the Pacific, DeRosa University Center
Start Date
26-4-2025 10:00 AM
End Date
26-4-2025 1:00 PM
Efficient Route Planning between Stations in NYC: A Dijkstra Algorithm Approach
University of the Pacific, DeRosa University Center
Efficient public transportation is critical for major cities. The New York City (NYC) Subway is a prime example, transiting on average millions of riderships every week in 2023, and, as such, optimization of transit routes is important. This presentation introduces a methodology for optimizing routes within the NYC Subway system, employing Dijkstra’s algorithm for computational analysis. Each station is represented as vertices in a weighted graph, with weights corresponding to distance between two stations. The results demonstrate significant improvements in route planning efficiency, providing commuters and system planners with a robust tool for enhanced journey planning and network management in one of the world’s largest and busiest urban transit systems. The presentation explains the algorithm thoroughly, accompanied by a small-scale example for better comprehension. Additionally, coding implementations are included to extend the algorithm’s functionality. Limitations of the approach, such as the scaling system used for time and cost, are also discussed.
