Awesome

Vulnerability analysis for transportation networks 2.0

Montivation

Public transport plays a critical role in cities by providing access and mobility. Disruptions can lead to major delays with far-reaching impacts on urban economies, security, and quality of life. In this study, we examined different levels of subway disruption impacts in London, Singapore, Taipei and Washington, DC. (This repository included results for London subway system)

Data

• Oyster card travel data (entry/exit times)
• Subway timetables
• Station locations (geocoded KML)

Steps

• Simulated London Tube as network graph (390 nodes, 2274 edges) weighted by travel time from GTFS schedule data with Python NetworkX, Pandas, GeoPandas.
• Used Dijkstra algorithm to calculate shortest path.
• Created the criticality score (total time loss) to measure the impacts of single and pair node disruption and identified most vulnerable stations.
• Analyzed the positive synergy effects of stations shutting down simultaneously considering network topology.

Results

• All five cities exhibit positive synergies. (they seem to be inherent to subway networks!)
• Pairs with high synergies often differ from those with high pair-node scores which usually are large transfer stations. By evaluating stations with high synergies can help us find the hidden pair of stations that have huge impacts when shutting down simultaneously.
• Most (but not all) synergies can be observed through the network topology alone, independent of ridership and time delays.
• Typical characteristics of pairs exhibiting high positive synergy:
  • from delay path to broken path
  • between 5 to 15 network distance
  • exhibit low disruptive score
• Webpage
• Report

Practical Applications and Next Steps

• City Planning: Use as a quantitative framework for planning of new routes, making systems more resilient in the face of disaster. Create strategies for emergency situations, as well as maintenance projects to ensure minimal overall network disruption.
• Future Research: Gather data for more cities; will increase confidence in universality of effects, and new universalities may emerge.

Codes in this repository

• Data cleaning and preparation for network construction <br />Prepare_data_for_nodes_and_links.ipynb
• Network construction <br />network_construction_criticality.ipynb
• Visualization for station activity & criticality <br />criticality_plot.ipynb
• Single node disruption <br />one_node_criticality.ipynb
• Paired node disruption <br />two_nodes_criticality.ipynb
• Synergistic effects <br />synergy_effect.ipynb
• Investigate synergies considering network distance and topology <br />network_distance_plot.ipynb <br />calculate_broken.ipynb <br />calculate_delay.ipynb <br />broken_delay_analysis.ipynb