Research ArticleNETWORK SCIENCE

Resilience and efficiency in transportation networks

See allHide authors and affiliations

Science Advances  20 Dec 2017:
Vol. 3, no. 12, e1701079
DOI: 10.1126/sciadv.1701079
  • Fig. 1 Definition of urban areas and assignment of nodes’ population.

    (A) Boston, MA-NH-RI urban area as defined by the U.S. Census Bureau shapefiles (gray background). To simplify the model and the algorithms calculating the distance from network nodes to the city boundary, we approximate each of the urban areas shapefiles with a coarse manually drawn polygon (pink outline). (B) Assignment of the number of people departing from each of the network nodes. Population distribution (color polygons; red corresponds to higher population density), Voronoi polygons (black outline), and network nodes (dots) in Downtown Boston.

  • Fig. 2 Model details.

    (A) Distance factor P(xod) (Eq. 2) of trips given the distance between nodes (solid line) and the statistical data (bars). (B) Dependency of speed on density for V = 100 km/hour.

  • Fig. 3 Modeled and observed delays in 40 urban areas.

    Pearson correlation coefficients and P values between observed and modeled delays are (0.91, 2.17 × 10−8) for the 20 cities used to calibrate the model and (0.63, 3.00 × 10−3) for the 20 cities used to validate the model. Observed delays were taken from the Texas A&M Transportation Institute Urban Mobility Scorecard (11).

  • Fig. 4 Traffic distributions.

    Typical congestion at 8 a.m. for Los Angeles (top) and San Francisco (bottom) as given by Google Maps (A and D), modeled with no disruptions (B and E), and modeled with a 5% link disruption (C and F). Notably, in Los Angeles, the disruption results in traffic redistribution to smaller roads, whereas in San Francisco, it results in increased congestion along the major highways.

  • Fig. 5 Dependency of the additional delay on the severity of the links disruption for six representative urban areas.

    Error bars show mean values ± SD. The inset shows distribution densities for two selected urban areas for 1000 realizations of 5% disruption. Note that San Francisco’s unique topology makes it susceptible to failures of a small number of discrete roadways, and this produces an anomalous impact at 5 to 15% disruption.

  • Fig. 6 Comparison of resilience and efficiency metrics.

    Annual impact of 5% disruption (additional delay) has a low correlation with normal annual delay per peak-period auto commuter (delay). Pearson R = 0.49, P = 1.18 × 10−3.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/3/12/e1701079/DC1

    Alternative approaches to model transportation

    Mapping from OSM Foundation shapefiles to network nodes and links

    Population assignment algorithm

    Distance factor of the likelihood of travel between nodes

    Estimation of the traffic speed from the density of vehicles

    Model calibration procedure

    Sensitivity of the model to ramp speeds

    Additional delay as a function of the severity of link disruption

    table S1. Mapping original OSM types to network link types and assignment of the number of lanes.

    table S2. The algorithm of the node population assignment.

    table S3. Distance factor P(xod) of the likelihood of travel between nodes.

    table S4. Model sensitivity to ramp speed coefficient.

    fig. S1. Effects of the removal of nodes of degree 2.

    fig. S2. Density-flow relationship in the Daganzo traffic model.

    fig. S3. Model calibration.

    fig. S4. Modeled delays for ramp speed coefficients of 1/3 and 1/2.

    fig. S5. Dependency of the additional delay on the severity of the link disruption for all 40 urban areas.

  • Supplementary Materials

    This PDF file includes:

    • Alternative approaches to model transportation
    • Mapping from OSM Foundation shapefiles to network nodes and links
    • Population assignment algorithm
    • Distance factor of the likelihood of travel between nodes
    • Estimation of the traffic speed from the density of vehicles
    • Model calibration procedure
    • Sensitivity of the model to ramp speeds
    • Additional delay as a function of the severity of link disruption
    • table S1. Mapping original OSM types to network link types and assignment of the number of lanes.
    • table S2. The algorithm of the node population assignment.
    • table S3. Distance factor P(xod) of the likelihood of travel between nodes.
    • table S4. Model sensitivity to ramp speed coefficient.
    • fig. S1. Effects of the removal of nodes of degree 2.
    • fig. S2. Density-flow relationship in the Daganzo traffic model.
    • fig. S3. Model calibration.
    • fig. S4. Modeled delays for ramp speed coefficients of 1/3 and ½.
    • fig. S5. Dependency of the additional delay on the severity of the link disruption for all 40 urban areas.

    Download PDF

    Files in this Data Supplement:

Stay Connected to Science Advances

Navigate This Article