Author:
Bellocchi Leonardo,Latora Vito,Geroliminis Nikolas
Abstract
AbstractSpatial systems that experience congestion can be modeled as weighted networks whose weights dynamically change over time with the redistribution of flows. This is particularly true for urban transportation networks. The aim of this work is to find appropriate network measures that are able to detect critical zones for traffic congestion and bottlenecks in a transportation system. We propose for both single and multi-layered networks a path-based measure, called dynamical efficiency, which computes the travel time differences under congested and free-flow conditions. The dynamical efficiency quantifies the reachability of a location embedded in the whole urban traffic condition, in lieu of a myopic description based on the average speed of single road segments. In this way, we are able to detect the formation of congestion seeds and visualize their evolution in time as well-defined clusters. Moreover, the extension to multilayer networks allows us to introduce a novel measure of centrality, which estimates the expected usage of inter-modal junctions between two different transportation means. Finally, we define the so-called dilemma factor in terms of number of alternatives that an interconnected transportation system offers to the travelers in exchange for a small increase in travel time. We find macroscopic relations between the percentage of extra-time, number of alternatives and level of congestion, useful to quantify the richness of trip choices that a city offers. As an illustrative example, we show how our methods work to study the real network of a megacity with probe traffic data.
Publisher
Springer Science and Business Media LLC
Reference53 articles.
1. Mazloumian, A., Geroliminis, N. & Helbing, D. The spatial variability of vehicle densities as determinant of urban network capacity. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 368, 4627–4647 (2010).
2. Saeedmanesh, M. & Geroliminis, N. Dynamic clustering and propagation of congestion in heterogeneously congested urban traffic networks. Transp. Res. Proc. 23, 962–979 (2017).
3. Loder, A., Ambühl, L., Menendez, M. & Axhausen, K. W. Understanding traffic capacity of urban networks. Sci. Rep. 9, 1–10 (2019).
4. Bellocchi, L. & Geroliminis, N. Unraveling reaction–diffusion-like dynamics in urban congestion propagation: insights from a large-scale road network. Sci. Rep. 10, 1–11 (2020).
5. Saberi, M. et al. A simple contagion process describes spreading of traffic jams in urban networks. Nat. Commun. 11, 1–9 (2020).
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