Spatial dependence in (origin‐destination) air passenger flows

Margaretic, Paula; Thomas‐Agnan, Christine; Doucet, Romain

doi:10.1111/pirs.12189

Cited by 11 publications

(12 citation statements)

References 31 publications

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“…Yang S used taxi GPS data to estimate OD travel time, which helps to analyze the route preference of passengers [14]. Margaretic P applied the spatial economic flow model and characteristic function spatial filtering method to air transportation, and discussed the impact of departure destination on air passenger flow [15]. Hanseler F S proposed a framework for estimating pedestrian demand in railway stations, which considers passenger data and various direct and indirect demand indicators [16].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation Indexes and Correlation Analysis of Origination–Destination Travel Time of Nanjing Metro Based on Complex Network Method

Wei

Yan

et al. 2020

Sustainability

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The information level of the urban public transport system is constantly improving, which promotes the use of smart cards by passengers. The OD (origination–destination) travel time of passengers reflects the temporal and spatial distribution of passenger flow. It is helpful to improve the flow efficiency of passengers and the sustainable development of the city. It is an urgent problem to select appropriate indexes to evaluate OD travel time and analyze the correlation of these indexes. More than one million OD records are generated by the AFC (Auto Fare Collection) system of Nanjing metro every day. A complex network method is proposed to evaluate and analyze OD travel time. Five working days swiping data of Nanjing metro are selected. Firstly, inappropriate data are filtered through data preprocessing. Then, the OD travel time indexes can be divided into three categories: time index, complex network index, and composite index. Time index includes use time probability, passenger flow between stations, average time between stations, and time variance between stations. The complex network index is based on two models: Space P and ride time, including the minimum number of rides, and the shortest ride time. Composite indicators include inter site flow efficiency and network flow efficiency. Based on the complex network model, this research quantitatively analyzes the Pearson correlation of the indexes of OD travel time. This research can be applied to other public transport modes in combination with big data of public smart cards. This will improve the flow efficiency of passengers and optimize the layout of the subway network and urban space.

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Section: Introductionmentioning

confidence: 99%

“…Direction and stations of Nanjing metro lines 15,14,13,12,11,10,9,8,7,6,5,41,42,43,44,45,46,47,48,49,50,. 51, 52, 53, 54, 55] …”

mentioning

confidence: 99%

Evaluation Indexes and Correlation Analysis of Origination–Destination Travel Time of Nanjing Metro Based on Complex Network Method

Wei

Yan

et al. 2020

Sustainability

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“…Second, there are not many empirical examples of the Box-Cox transformation approach ¶ , making it difficult to gauge the prevalence of conditional distance distribution misspecification and assess the magnitude of the associated spatial variation compared to other sources of misspecification. Researchers have often opted instead to choose theoretically motivated functional forms, other selection techniques, to shift focus to travel time or monetary costs, or to include technical, cultural, or political separations that may co-influence flows along with distance (Fischer et al 2006;Chun 2008;Vries et al 2009;Ortúzar S et al 2011 Griffith and Fischer 2013;Fichet de Clairfontaine et al 2015;Margaretic et al 2017).…”

Section: The Box-cox Transformmentioning

confidence: 99%

The spatial structure debate in spatial interaction modeling: 50 years on

Oshan

2020

Progress in Human Geography

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Spatial interaction and spatial structure are foundational geographical abstractions, though there is often variation in how they are conceptualized and deployed in quantitative models. In particular, the last five decades have produced an exceptional diversity regarding the role of spatial structure within spatial interaction models. This is explored by outlining the initiation and development of the notion of spatial structure within spatial interaction modeling and critically reviewing four methodological approaches that emerged from ongoing debate about the topic. The outcome is a comprehensive coverage of the past and a sketch of one potential path forward for advancing this long-standing inquiry.

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“…More recently, Margaretic et al . () used the model of LeSage and Pace () for the analysis air passenger OD flows. With regard to temporal dependence, the auto‐regressive integrated moving average model has been widely used to forecast airport traffic demands (Andreoni and Postorino, ; Abdelghany and Guzhva, ; Tsui et al ., ).…”

Section: Introductionmentioning

confidence: 99%

“…Lesage and Thomas-Agnan (2015) discussed how to interpret coefficient estimates of spatial auto-regressive models for OD flows (LeSage and Pace, 2008). More recently, Margaretic et al (2017) used the model of LeSage and Pace (2008) for the analysis air passenger OD flows. With regard to temporal dependence, the auto-regressive integrated moving average model has been widely used to forecast airport traffic demands (Andreoni and Postorino, 2006;Abdelghany and Guzhva, 2010;Tsui et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Auto-regressive Model for Origin–destination Air Passenger Flows

Kim

2018

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary The growth of the global airline network has increased the importance of modelling origin–destination air passenger flows for better operational planning and scheduling. Origin–destination air passenger flows are correlated both spatially and temporally because of spatial and temporal relationships of human behaviours and environments. However, most existing studies for modelling air passenger flows have assumed that these relationships are independent; few studies have considered either spatial or temporal dependences. To consider both, we develop a spatiotemporal auto‐regressive model for monthly origin–destination air passenger flows. Benefitting from the special structure of a spatiotemporal dependence matrix, the model proposed can be extended to incorporate multi‐dimensional auto‐regressive coefficients for more flexible modelling. Its application to a real open access aviation data set demonstrates the effectiveness of the proposed model in forecasting monthly air passenger flows.

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Spatial dependence in (origin‐destination) air passenger flows

Cited by 11 publications

References 31 publications

Evaluation Indexes and Correlation Analysis of Origination–Destination Travel Time of Nanjing Metro Based on Complex Network Method

Evaluation Indexes and Correlation Analysis of Origination–Destination Travel Time of Nanjing Metro Based on Complex Network Method

The spatial structure debate in spatial interaction modeling: 50 years on

Spatiotemporal Auto-regressive Model for Origin–destination Air Passenger Flows

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