Dynamical efficiency for multimodal time-varying transportation networks

Leonardo Bellocchi,Vito Latora,Nikolas Geroliminis

doi:10.1038/s41598-021-02418-5

Abstract

Spatial 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.

Highlights

Spatial systems that experience congestion can be modeled as weighted networks whose weights dynamically change over time with the redistribution of flows
We describe a transportation system at time step t as a weighted graph G (t) = G(N, L, T (t)), where N = {n1, n2, . . . , nN } is the set of nodes, L = {l1, l2, . . . , lL} is the set of links and T = {τ1(t), τ2(t), . . . , τL(t)} is a set of positive real numbers associated to the links
Paths and travel times changes dynamically with for each link l(i,j) and time t we can the traffic, define the depending on the weights dynamical efficiency of the that link anse:tEw(olr(ki,jh))at=timeis+2teejp(t )t

Summary

Introduction

Spatial systems that experience congestion can be modeled as weighted networks whose weights dynamically change over time with the redistribution of flows. The accessibility of huge urban sensing data permit to obtain detailed information on human mobility, and to calibrate models and networks analysis in urban transportation as done, for instance in Refs.[19,20,21,22,23] Another interesting study on the evaluation of transportation networks at different spatial scales using information on traffic pattern and on stations topology has been conducted in Ref.[24]. MFD can be utilized to introduce elegant perimeter control or pricing strategies to improve mobility in multi-region and multi-modal networks, like in Refs.[31,32], and others These findings are of great importance because the concept of an MFD can be applied for heterogeneously loaded cities with multiple centers of congestion. We remark that all network structure and node-to-node path choice is lost due to this spatial aggregation, meaning that the granularity of the aggregation does not allow to identify critical links in the network

Objectives

Results

Conclusion