Demand and Congestion in Multiplex Transportation Networks

Chodrow, Philip S.; Awwad, Zeyad; Jiang, Shan; González, Marta C.

doi:10.1371/journal.pone.0161738

Cited by 33 publications

(33 citation statements)

References 28 publications

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“…First, the actual topology of a transportation network is usually much more complicated than the topology of a simulated network. Second, the obtained coupling results usually deviate from real situations when using uniformly (randomly) generated travel demands [27]. Third, mobile phone data [30,31] cannot be used to capture the transfers between different network layers either.…”

Section: Introductionmentioning

confidence: 94%

“…An optimal operation speed is determined for London subway [24]. How operation speed of subway influences passenger travel time and road traffic congestion was studied using CDR (Call Details Records) data [27]. The results indicated that travel demand plays crucial quantitative and qualitative roles [27].…”

Section: Introductionmentioning

confidence: 99%

“…How operation speed of subway influences passenger travel time and road traffic congestion was studied using CDR (Call Details Records) data [27]. The results indicated that travel demand plays crucial quantitative and qualitative roles [27]. How interconnections between network layers influence traffic congestion [28] and how speed ratio influences network capacity and travel time [29] were also investigated.…”

Section: Introductionmentioning

confidence: 99%

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Understanding coupling dynamics of public transportation networks

et al. 2018

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Subway and bus networks work as an integrated multiplex transportation system and play an indispensable role in modern big cities. Even though a variety of works have investigated the coupling dynamics of multiplex transportation networks, empirical data that validates the determinant coupling factors are still lacking. In this paper, we employ smartcard data of 2.4 million subway and bus passengers in Shenzhen, China to study the coupling dynamics of subway and bus networks. Surprisingly, the coupling of subway and bus networks is not notably influenced by the time-varying speed ratio of the two network layers but is jointly determined by the distribution of travel demands and transportation facilities. Our findings highlight the important role of real travel demand data in analyzing the coupling dynamics of multiplex transportation networks. They also suggest that the speed ratio of different network layers, which was regarded as a key factor in determining coupling strength, has a negligible effect on travelers' route selections, and thus the coupling dynamics of multiplex transportation networks.

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Understanding coupling dynamics of public transportation networks

et al. 2018

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“…Understanding the emergence of congestion and delays requires, however, a step beyond the static structure of the network. First of all, in both private and public transportation the capacities of lines, roads and vehicles are of wide variety, and need to be taken into account to evaluate congestion and vulnerability [23,28,29]. Secondly, the transportation demand in cities is by no means homogeneous.…”

Section: Introductionmentioning

confidence: 99%

Scaling in the recovery of urban transportation systems from massive events

Bassolas

Gallotti

Lamanna

et al. 2020

Sci Rep

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Public transportation is a fundamental infrastructure for the daily mobility in cities. Although its capacity is prepared for the usual demand, congestion may rise when huge crowds concentrate in special events such as massive demonstrations, concerts or sport events. In this work, we study the resilience and recovery of public transportation networks from massive gatherings by means of a stylized model mimicking the mobility of individuals through the multilayer transportation network. We focus on the delays produced by the congestion in the trips of both event participants and of other citizens doing their usual traveling in the background. Our model can be solved analytically for regular lattices showing that the average delay scales with the number of event participants with an exponent equal to the inverse of the lattice dimension. We then switch to real transportation networks of eight worldwide cities, and observe that there is a whole range of exponents depending on where the event is located. These exponents are distributed around 1/2, which indicates that most of the local structure of the network is two dimensional. Yet, some of the exponents are below (above) that value, implying a local dimension higher (lower) than 2 as a consequence of the multimodality and multifractality of transportation networks. In fact, these exponents can be also obtained from the scaling of the capacity with the distance from the event. Overall, our methodology allows to dynamically probe the local dimensionality of a transportation network and identify the most vulnerable spots in cities for the celebration of massive events.

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“…[5][6][7][8]-and dynamic processes [9], such as mobility on urban multiplexes. The latter-which is the focus of this contribution, see next Section-has been tackled only recently [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Resilience analytics: coverage and robustness in multi-modal transportation networks

2018

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A multi-modal transportation system of a city can be modeled as a multiplex network with different layers corresponding to different transportation modes. These layers include, but are not limited to, bus network, metro network, and road network. Formally, a multiplex network is a multilayer graph in which the same set of nodes are connected by different types of relationships. Intra-layer relationships denote the road segments connecting stations of the same transportation mode, whereas inter-layer relationships represent connections between different transportation modes within the same station. Given a multi-modal transportation system of a city, we are interested in assessing its quality or efficiency by estimating the coverage i.e., a portion of the city that can be covered by a random walker who navigates through it within a given time budget, or steps. We are also interested in the robustness of the whole transportation system which denotes the degree to which the system is able to withstand a random or targeted failure affecting one or more parts of it. Previous approaches proposed a mathematical framework to numerically compute the coverage in multiplex networks. However solutions are usually based on eigenvalue decomposition, known to be time consuming and hard to obtain in the case of large systems. In this work, we propose MUME, an efficient algorithm for Multi-modal Urban Mobility Estimation, that takes advantage of the special structure of the supra-Laplacian matrix of the transportation multiplex, to compute the coverage of the system. We conduct a comprehensive series of experiments to demonstrate the effectiveness and efficiency of MUME on both synthetic and real transportation networks of various cities such as Paris, London, New York and Chicago. A future goal is to use this experience to make projections for a fast growing city like Doha.

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Demand and Congestion in Multiplex Transportation Networks

Cited by 33 publications

References 28 publications

Understanding coupling dynamics of public transportation networks

Understanding coupling dynamics of public transportation networks

Scaling in the recovery of urban transportation systems from massive events

Resilience analytics: coverage and robustness in multi-modal transportation networks

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