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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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Dynamical efficiency for multimodal time-varying transportation networks.

Leonardo Bellocchi1, Vito Latora2,3, Nikolas Geroliminis4

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This study introduces "dynamical efficiency" to identify traffic congestion hotspots in urban networks. This new measure helps visualize congestion evolution and assess travel choices in complex transportation systems.

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Area of Science:

  • Complex Systems Science
  • Network Science
  • Urban Mobility Analysis

Background:

  • Urban transportation networks are dynamic systems prone to congestion due to flow redistribution.
  • Existing network measures often provide a myopic view, focusing on individual segments rather than system-wide conditions.

Purpose of the Study:

  • To develop novel network measures for detecting critical congestion zones and bottlenecks in urban transportation systems.
  • To quantify the impact of congestion on travel time and assess the richness of traveler choices.

Main Methods:

  • Proposed a path-based measure, 'dynamical efficiency,' calculating travel time differences under congested and free-flow conditions.
  • Extended the measure to multilayer networks, introducing a centrality index for inter-modal junctions.
  • Defined the 'dilemma factor' to relate travel time increase to the number of available transportation alternatives.

Main Results:

  • Dynamical efficiency effectively detects and visualizes congestion seeds and their temporal evolution as clusters.
  • The multilayer centrality measure quantifies the importance of inter-modal connections.
  • Macroscopic relationships were found between extra travel time, alternative routes, and congestion levels.

Conclusions:

  • Dynamical efficiency offers a robust method for analyzing urban traffic congestion and network performance.
  • The developed measures provide valuable insights into urban mobility, aiding in the planning and management of transportation systems.
  • The study demonstrates the applicability of these methods using real-world traffic data from a megacity.