Dynamical efficiency for multimodal time-varying transportation networks

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.

Framework for constructing multimodal transport networks and routing using a graph database: A case study in London

“…Interconnected metropolitan transport systems include private travel (e.g., cars, bikes and walking), along with public transit modes, such as subways and buses (Bellocchi et al, 2021). The most basic mode of travel is by foot, which can cover short trips (Orozco et al, 2021).…”

“…Transfer actions were reflected with time costs in their model; however, they did not provide a detailed description of transfer routes. Bellocchi et al (2021) reflected the temporal effects of configured transport networks by setting variable weights with time. In their approach, a transportation system was represented with a multilayered network including car, walking, bus, and metro modes.…”

A Global Feature-Rich Network Dataset of Cities and Dashboard for Comprehensive Urban Analyses

“…For urban and regional planning, primal planar graphs supplemented with contextual built environment data can support various applications and use cases. These networks are essential in transportation planning for assessing traffic flow, identifying bottlenecks, and optimizing road networks [36][37][38] . Network population estimates can also help evaluate the accessibility of public facilities, such as schools, hospitals, or parks, empowering planners to pinpoint underserved areas and prioritise infrastructure investments [39][40][41] .…”

A Pricing Mechanism for Balancing the Charging of Ride-Hailing Electric Vehicle Fleets

“…We consider two scenarios of a case study in the smart mobility domain previously introduced in [25,27]. In particular, we analyze a market model depicted in Figure 3, where ride-hailing companies I = {I 1 , I 2 , I 3 } compete to meet demand requests that are distributed heterogeneously across the city of Shenzhen [2]. The city is inherently partitioned into four Voronoi-based regions by the available charging infrastructure that consists of stations M = {M 1 , M 2 , M 3 , M 4 } and is controlled by the central authority L through adjustable electricity prices π ∈ [p min , p max ] 4 .…”

“…{marko.maljkovic, gustav.nilsson, nikolas.geroliminis}@epfl.ch. 2 This work was supported by the Swiss National Science Foundation under NCCR Automation, grant agreement 51NF40 180545. 3 Some preliminary results of this work were presented in [27].…”