A method to estimate the historical US air travel demand

Li, Tao; Baik, Hojong; Trani, Antonio

doi:10.1002/atr.1200

Cited by 16 publications

(18 citation statements)

References 33 publications

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“…At each iteration, the solution algorithm maximizes the duality of the original problem sequentially along unit directions, and keeps some optimality conditions satisfied for the original problem. The same idea has been adopted to design solution algorithms for the traffic assignment problem in Bell (1995b) and the demand estimation problems in Bell et al (1997), Nie et al (2005), Chen et al (2010), and Li et al (2013). Especially, the numerical examples in Chen et al (2010), Li et al (2013), and Tang and Zhang (2013) show that this type of solution algorithms is efficient for large-size networks.…”

Section: A Bi-level Model To Estimate the Us Air Travel Demandmentioning

confidence: 95%

“…(1)) seems to have little physical meaning, it is designed in a way that, in the optimal solution, the estimated demand distributes according to a logit model. Using the first-order optimality condition (i.e., Karush−Kuhn−Tucker (KKT) condition) (Bazaraa et al, 2006), Li et al (2013) showed that:…”

Section: ) Andmentioning

confidence: 99%

“…In most of the previous models, route flows can be expressed as an explicit function of the LMs (e.g., linear function (Bell, 1991a) and exponential function (Bell et al, 1997;Li et al, 2013)), which significantly reduces the computational effects needed to update route flows to keep Eq. (16) hold.…”

Section: T LImentioning

confidence: 99%

“…Equation (7) shows that the estimated demand distributes among the routes connecting the OD pair according to a logit model. In Li et al (2013), the RFE is applied to estimate the US travel demand in 1995. Eight route attributes are considered and a summary of them is given in Table 3.…”

Section: ) Andmentioning

confidence: 99%

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A Bi-Level Model to Estimate the US Air Travel Demand

Li¹

2015

Asia Pac. J. Oper. Res.

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A single-level optimization model (i.e., a Route Flow Estimator (RFE)) has been proposed to estimate the historical air travel demand. However, the RFE may require a significant amount of additional data collection effort when applied to estimate travel demand in small or medium-sized networks. We propose a novel bi-level model as an alternative to the RFE to handle demand estimation for small or medium-sized networks. The upper-level model is designed as a constrained least square (LS) model. The lower-level model is designed based on the RFE. The bi-level model estimates travel demand by considering travelers' choice behaviors and some observed data. It requires less data collection effort yet it produces estimation results consistent with those from the RFE. A Gauss-Seidel type (GST) algorithm is proposed to solve the bi-level model. To solve the upper-level model, we propose a heuristic algorithm, which is designed to solve the dual of the upper-level model. The estimation results from the two models are compared using two numerical examples: a small-sized example with one OD pair and a medium-sized example with 400 OD pairs.

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Section: A Bi-level Model To Estimate the Us Air Travel Demandmentioning

confidence: 95%

Section: ) Andmentioning

confidence: 99%

Section: T LImentioning

confidence: 99%

Section: ) Andmentioning

confidence: 99%

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A Bi-Level Model to Estimate the US Air Travel Demand

Li¹

2015

Asia Pac. J. Oper. Res.

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“…These methods could produce relatively accurate and cost-beneficial estimates [10,23]. It is worthwhile to point out that models that utilize posterior observations have also been developed to estimate the historical demand in commercial air transportation (see, e.g., Li et al [24]; Li and Baik [25]; Li [26]; Li et al [27]; and Li [28]) and ground transportation (see, e.g., Maher [29]; Cascetta [30]; Bell [31]; Yang et al [32]; Codina and Barceló [33]; Chootinan et al [34]; Doblas and Benitez [35]; Nie et al [36]; Lundgren and Peterson [37]; Chen et al [38]). …”

Section: Introductionmentioning

confidence: 99%

A Least-Square Model to Estimate Historical Percentages of Itinerant General Aviation Operations by Aircraft Types and Flight Rules at an Airport

Trani

2017

Journal of Advanced Transportation

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Historical airport-level General Aviation (GA) activity plays an important role in airport managerial and operational decisionmaking processes. We developed a model to estimate historical percentages of itinerant GA operations by four aircraft types and two flight rules. The model is formulated as a least-square optimization model and can be applied to both towered and nontowered airports. We applied the model to airports in the Terminal Area Forecast and compared model estimates with the observed statistics. This study provides a method that could be used in the decision-making processes that require detailed historical GA operations.

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Origin–destination synthesis for aviation network data: examining hub operations in the domestic and international US markets

Park

O’Kelly

2016

J of Advced Transportation

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Hub-and-spoke networking is a key feature of current aviation markets in which hubs, as connecting points, function to consolidate and redistribute flows. This indicates that observation of traffic on a segment does not necessarily convey information about the origin to destination routing of passenger journeys because of the unavoidable detours in the system. This paper examines the heterogeneity of the flow composition in domestic and international US markets, which in turn allows us to observe the variation of operations across major hubs. A modified Route Flow Estimator for origin-destination synthesis (or origin-destination matrix estimation) is designed to decompose the segment traffic into itinerary-based passenger trips. Several public and commercial databases, which are easily accessible, are exploited (and reconciled) for the model in order to (i) generate possible trip itineraries using those segment markets, and (ii) link data-driven operational conditions with the underlying segment flows. The results are validated with US domestic trip observations and empirical knowledge related to the air transportation system. Then, the variability of the hub operations is examined based on sensitivity tests using the model parameters. From the resolution of itinerary-based estimates, we observe that major airports' hub operations are spatially uneven, particularly with respect to domestic and international connecting passengers.

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A method to estimate the historical US air travel demand

Cited by 16 publications

References 33 publications

A Bi-Level Model to Estimate the US Air Travel Demand

A Bi-Level Model to Estimate the US Air Travel Demand

A Least-Square Model to Estimate Historical Percentages of Itinerant General Aviation Operations by Aircraft Types and Flight Rules at an Airport

Origin–destination synthesis for aviation network data: examining hub operations in the domestic and international US markets

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