An improved radiation model and its applicability for understanding commuting patterns in Hungary

Varga, Levente; Tóth, Géza; Néda, Zoltán

doi:10.15196/rs06202

Cited by 15 publications

(19 citation statements)

References 14 publications

(17 reference statements)

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“…Although previous IO class models are widely used to predict the mobility of people between locations [32][33][34][35][36][37][38][40][41][42], these models can only achieve accurate prediction at specific spatiotemporal scales. In this paper, we developed a UO model to predict human mobility at different spatiotemporal scales.…”

Section: Discussionmentioning

confidence: 99%

“…The radiation model can better predict the commuting behavior between counties. Some researchers improve the radiation model and propose various commuting prediction models, such as the radiation model with selection [33], generalized radiation model [34], the flow and jump model [35], travel cost optimized radiation model [36] and a cost-based radiation model [37]. Yan et al propose a population-weighted opportunities (PWO) model [38] by mining human daily travel data from several cities, such as the GPS trajectories from vehicles and call detail records from mobile phones.…”

Section: Introductionmentioning

confidence: 99%

“…Motivated by the DST model, Liu and Yan propose an opportunity priority selection (OPS) model that assumes that the destination selected by the individual is the location that presents a higher benefit than the benefit of the origin and the benefits of the intervening opportunities [42]. In general, all of the IO class models [32][33][34][35][36][37][38][40][41][42] share two common assumptions: (i) using an agent to represent all of the individuals; (ii) when selecting a destination, the agent will compare the benefits of different locations. The difference between these IO class models is that the rules for comparing benefits of different locations are different.…”

Section: Introductionmentioning

confidence: 99%

“…The difference between these IO class models is that the rules for comparing benefits of different locations are different. Although the radiation class models [32][33][34][35][36][37] can accurately predict commuting behavior and other IO class models [38,[40][41][42] can accurately predict intracity and/or intercity mobility, an IO class model that can simultaneously describe the individual's destination selection behavior at different spatiotemporal scales is still lacking.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A universal opportunity model for human mobility

Liu

Yan

2020

Sci Rep

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Predicting human mobility between locations has practical applications in transportation science, spatial economics, sociology and many other fields. For more than 100 years, many human mobility prediction models have been proposed, among which the gravity model analogous to Newton's law of gravitation is widely used. Another classical model is the intervening opportunity (IO) model, which indicates that an individual selecting a destination is related to both the destination's opportunities and the intervening opportunities between the origin and the destination. The IO model established from the perspective of individual selection behavior has recently triggered the establishment of many new IO class models. Although these IO class models can achieve accurate prediction at specific spatiotemporal scales, an IO class model that can describe an individual's destination selection behavior at different spatiotemporal scales is still lacking. Here, we develop a universal opportunity model that considers two human behavioral tendencies: one is the exploratory tendency, and the other is the cautious tendency. Our model establishes a new framework in IO class models and covers the classical radiation model and opportunity priority selection model. Furthermore, we use various mobility data to demonstrate our model's predictive ability. The results show that our model can better predict human mobility than previous IO class models. Moreover, this model can help us better understand the underlying mechanism of the individual's destination selection behavior in different types of human mobility.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A universal opportunity model for human mobility

Liu

Yan

2020

Sci Rep

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“…To capture the underlying mechanism of human mobility, some models accounting for individuals' decisions on destination choices were proposed, including the intervening opportunities (IO) model [15], the radiation model [16] and the population-weighted opportunity (PWO) model [17,18]. Some recently developed novel variants and extensions of the radiation and the gravity model [19][20][21][22][23][24][25][26][27][28] can more accurately predict commuting, immigration or long distance travel patterns at different spatial scales. However, all these models assume that individuals are independent of each other when selecting destinations, without any interactions.In reality, individuals consider not only the destination attractiveness and the travelling cost, but also the crowding caused by the people who choose the same destination [29][30][31], as well as the congestion brought by the people on the same way to the destination [31,32].…”

mentioning

confidence: 99%

Destination choice game: A spatial interaction theory on human mobility

Yan

Zhou

2019

Sci Rep

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With remarkable significance in migration prediction, global disease mitigation, urban planning and many others, an arresting challenge is to predict human mobility fluxes between any two locations. A number of methods have been proposed against the above challenge, including the gravity model, the intervening opportunity model, the radiation model, the population-weighted opportunity model, and so on. Despite their theoretical elegance, all models ignored an intuitive and important ingredient in individual decision about where to go, that is, the possible congestion on the way and the possible crowding in the destination. Here we propose a microscopic mechanism underlying mobility decisions, named destination choice game (DCG), which takes into account the crowding effects resulted from spatial interactions among individuals. In comparison with the state-of-the-art models, the present one shows more accurate prediction on mobility fluxes across wide scales from intracity trips to intercity travels, and further to internal migrations. The well-known gravity model is proved to be the equilibrium solution of a degenerated DCG neglecting the crowding effects in the destinations.

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The Architecture of Connectivity: A Key to Network Vulnerability, Complexity and Resilience

Reggiani

2022

Netw Spat Econ

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This paper highlights the relevance of connectivity and its architecture as a general conceptual framework which underlies and integrates the concepts of network vulnerability, complexity, and resilience. In particular, it will be pointed out that connectivity architecture can be considered an explicit key element for network vulnerability and shock propagation. While the relevance of the various connectivity configurations is not clearly emphasised in the dynamic complexity models of the space-economy, it appears to play a primary role in network analysis. In this regard, the emerging recognition of connectivity architecture in relation to hubs ‒ and hierarchies of hubs ‒ in a complex network will help the enhancement of network resilience. The paper develops as follows. First, the notion of network vulnerability, which refers not only to the phenomenon of shocks, but also to the propagation of shocks in a network, will be examined. Here it appears that modelling vulnerability and shock propagation, also jointly with cascading disaster models, is strongly based on connectivity issues. The question is: How can conventional (complex) system dynamic modelling, as well as network modelling, take into account these shocks and connectivity dynamics from the methodological viewpoint? A review in this respect shows how connectivity is a ‘hidden’ element in these complexity models, for example, in chaos or (dynamic) competition models, where interaction parameter values might lead to vulnerable domains and chaotic behaviour. On the contrary, connectivity and its various topologies have a distinct, primary role in network analysis. The issue of network resilience appears therefore to be the ‘response’ to vulnerability and chaos, calling for robustness and stability of the network in the presence of shocks and disruptions. Resilience analysis refers to the speed at which a network returns to its equilibrium after a shock, as well as to the perturbations/shocks that can be absorbed before the network is induced into some other equilibrium (adaptivity). Connectivity is relevant here, but not often considered in spatial economics. In order to reach a unified methodological framework, attention will finally be paid to a complementary analysis of the (dynamic) concepts of vulnerability and resilience. In this light, chaos models/properties might be seen in a positive perspective, since small changes can lead to uncertain and unstable effects, but also, thanks to connectivity, to new equilibria which are not necessarily negative. Thus, the architecture of connectivity, in its interdisciplinary insights, can be considered as a fundamental (and analytical) approach for identifying vulnerability and resilience patterns in complex networks.

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An improved radiation model and its applicability for understanding commuting patterns in Hungary

Cited by 15 publications

References 14 publications

A universal opportunity model for human mobility

A universal opportunity model for human mobility

Destination choice game: A spatial interaction theory on human mobility

The Architecture of Connectivity: A Key to Network Vulnerability, Complexity and Resilience

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