Measuring accessibility using gravity and radiation models

Piovani, Duccio; Arcaute, Elsa; Uchoa, Gabriela; Wilson, Alan; Batty, Michael

doi:10.1098/rsos.171668

Cited by 49 publications

(46 citation statements)

References 35 publications

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“…Pairwise models suffer from the lack of competition between nodes [30,31]. As such, they tend to work for non-competitive interactions and cannot accurately describe the competitiveness nature of the global air transport industry.…”

Section: Multi-point Entropy Maximization Modelsmentioning

confidence: 99%

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Global air transport complex network: multi-scale analysis

et al. 2019

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Almost half of the world's population is carried by airlines each year, and understanding this mode of transport is important from economic and scientific perspectives. In recent years, the increasing availability of data has led to complex network and agent interaction models which attempt to gain better understanding of the air transport network and develop forecasts. In this case study paper, we review existing research on two key approaches, namely: (1) a top-down multi-scale network science approach, and (2) a bottom-up entropy-maximization interaction network approach. Using simple socioeconomic indicators, we were able to construct a very accurate interaction model that can predict traffic volume, and the model can forward estimate the impact of population growth or fuel cost. Using network science approaches, we were able to identify community structures and relate them to economic outputs. We also saw how hubs evolved over time to become more influential. Looking into the future, using random graph theory, it seems that reduced flight cost will lead to increased hub influence. The disseminated knowledge in this case study paper will provide both academics and industry practitioners with steps forward to co-explore the interesting research landscape.• Bottom-up entropy-maximization interaction model, which considers consumer choice; • Top-down network science analysis, which seeks to uncover common statistical patterns and infer latent knowledge.The former gives a complex and detailed understanding of how spatial networks (i.e., flights) form from spatial processes (i.e., airports) and what the weight of each edge (i.e., passenger volume) is with respect to cost (impedes flow) and benefit (attracts flow) functions that relate to consumer behaviour. The latter approach gives a statistical understanding into the fundamental network properties and how they evolve over time, enabling the application of generalized network scaling laws that can be used to predict the future structure of the network. Both the bottomup and the top-down approach is of fundamental interest to network science and industry.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Section: Multi-point Entropy Maximization Modelsmentioning

confidence: 99%

“…based on established studies), then discovering the correct function form and the parameters is costly. Nonetheless, the BLV model has been applied successfully to complex challenges in urban retail, mobility [30], and policing.…”

Section: Future Scope For Researchmentioning

confidence: 99%

Global air transport complex network: multi-scale analysis

et al. 2019

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“…Moreover, the model can be developed further by taking into account the distinguishability of the trips [35][36][37]. Early after the gravity model introduction, the possibility of defining a potential was discussed [38] but the lack of reliable data prevented ulterior research in this direction.Several works have focused on the comparison between the two families of models and their performance when compared with empirical data [39][40][41][42][43][44][45][46][47]. It is worth mentioning that a fair comparison requires to be carried out over the same type of mobility data (daily or sporadic trips behave differently) and with the same constraints.…”

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confidence: 99%

Field theory for recurrent mobility

et al. 2019

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Understanding human mobility is crucial for applications such as forecasting epidemic spreading, planning transport infrastructure and urbanism in general. While, traditionally, mobility information has been collected via surveys, the pervasive adoption of mobile technologies has brought a wealth of (real time) data. The easy access to this information opens the door to study theoretical questions so far unexplored. In this work, we show for a series of worldwide cities that commuting daily flows can be mapped into a well behaved vector field, fulfilling the divergence theorem and which is, besides, irrotational. This property allows us to define a potential for the field that can become a major instrument to determine separate mobility basins and discern contiguous urban areas. We also show that empirical fluxes and potentials can be well reproduced and analytically characterized using the so-called gravity model, while other models based on intervening opportunities have serious difficulties.

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“…Population-level mobility models characterize the flow of people between locations using local attributes (e.g., their populations or the distance between the locations). They have been used for many applications, such as modeling commuter flow between locations [5], trade flow between countries [6], and traffic flow inside a city [7]. Although these applications are diverse, they are all on large spatial scales, ranging from inter-country level on a scale of thousands of kilometers (e.g., estimating trade flow [6,8]) to city and region levels at the scale of tens of kilometers (e.g., estimating commuting patterns [9]).…”

Section: Introductionmentioning

confidence: 99%

Customer mobility and congestion in supermarkets

et al. 2019

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The analysis and characterization of human mobility using population-level mobility models is important for numerous applications, ranging from the estimation of commuter flows to modeling trade flows. However, almost all of these applications have focused on large spatial scales, typically from intra-city level to inter-country level. In this paper, we investigate population-level human mobility models on a much smaller spatial scale by using them to estimate customer mobility flow between supermarket zones. We use anonymized mobility data of customers in supermarkets to calibrate our models and apply variants of the gravity and intervening-opportunities models to fit this mobility flow and estimate the flow on unseen data. We find that a doubly-constrained gravity model and an extended radiation model (a variant of the intervening-opportunities model) can successfully estimate 65-70% of the flow inside supermarkets. Using the gravity model as a case study, we then investigate how to reduce congestion in supermarkets using mobility models. We model each supermarket zone as a queue; and we use the gravity model to identify store layouts with low congestion, which we measure either by the maximum number of visits to a zone or by the total mean queue size. We then use a simulated-annealing algorithm to find store layouts with lower congestion than the original layout. In these optimized store layouts, popular zones are often in the perimeter of a store. Our research gives insight both into how customers move in supermarkets and into how retailers can arrange stores to reduce congestion. It also provides a case study of human mobility on small spatial scales.

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Measuring accessibility using gravity and radiation models

Cited by 49 publications

References 35 publications

Global air transport complex network: multi-scale analysis

Global air transport complex network: multi-scale analysis

Field theory for recurrent mobility

Customer mobility and congestion in supermarkets

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