Figure 1 - uploaded by Simon Hakim
Content may be subject to copyright.
Source publication
Using the results of a unique telephone survey the frequency of consumer flights from airports in a multi-airport region are modeled using a multivariate Poisson framework, the parameters of which were estimated using a latent variable application of the expectation maximization algorithm. This offers a different perspective since other work on air...
Context in source publication
Context 1
... April and May 2000 a phone survey 5 was conducted on behalf of the management of the Philadelphia Interna- tional Airport. Approximately 5000 households in a market region defined by the management of the Phila- delphia International Airport 6 (shown in Figure 1) were contacted regarding their participation in the survey about travel outside the region and modal choice. The phone contacts were selected from one of two sub-populations; those who had previously expressed an interest in travel and those from the general population 7 . ...
Similar publications
Using the results of a unique telephone survey the frequency of consumer flights from airports in a multi-airport region are modeled using a multivariate Poisson framework, the parameters of which were estimated using a latent variable application of the expectation maximization algorithm. This offers a different perspective since other work on air...
Citations
... A multivariate count model may be developed using multivariate versions of the Poisson or negative binomial (NB) discrete distributions (see Buck et al., 2009 andBermúdez andKarlis, 2011 for recent applications of these methods). These multivariate Poisson and NB models have the advantage of a closed form, but they become cumbersome as the number of events increases and can only accommodate a positive correlation in the counts. ...
In the current paper, we propose a new utility-consistent modeling framework to explicitly link a count data model with an event-type multinomial-choice model. The proposed framework uses a multinomial probit kernel for the event-type choice model and introduces unobserved heterogeneity in both the count and discrete-choice components. Additionally, this paper establishes important new results regarding the distribution of the maximum of multivariate normally distributed variables, which form the basis to embed the multinomial probit model within a joint modeling system for multivariate count data. The model is applied to analyzing out-of-home non-work episodes pursued by workers, using data from the National Household Travel Survey. Copyright © 2014 John Wiley & Sons, Ltd.
... There have been two commonly used approaches in the literature to formulate and estimate multivariate count data models. One common approach has been to use multivariate versions of the Poisson or negative binomial discrete distributions (see, for example, Ladrón de Guevara et al., 2004, Buck et al., 2009, and Bermúdez and Karlis, 2011 for applications of these methods). Such multivariate count models have the advantage of a closed form, but they become cumbersome as the number of correlated counts increases (see Herriges et al., 2008 for a discussion). ...
This paper proposes a new spatial multivariate count model to jointly analyze the traffic crash-related counts of pedestrians and bicyclists by injury severity. The modeling framework is applied to predict injury counts at a Census tract level, based on crash data from Manhattan, New York. The results highlight the need to use a multivariate modeling system for the analysis of injury counts by road-user type and injury severity level, while also accommodating spatial dependence effects in injury counts.
... Applications of such count data models abound in the scholarly literature, both in number (a count in and of itself!) as well as diversity of topics. Recent applications include the analysis of (a) the number of doctor visits (Alfo and Maroutti, 2010), the number of homes affected by cholera (Viwatwongkasem et al., 2008), the number of cancer incidents (Czado et al., 2009), and the number of milk formula bottles supplied to infants by breastfeeding mothers (Lee et al., 2006) in the medicine field, (b) the number of crimes (Taniguchi et al., 2011, MacDonald and Lattimore, 2010) and the number of drug possession convictions (Rephann, 2009) in the criminology field, (c) the number of mergers and acquisitions of foreign direct investments (Ho et al., 2009), the number of faults in a bolt (Frühwirth Schnatter et al., 2009), the frequency of contract change orders (Anastasopoulos et al., 2010), and the number of jobs by space unit (Kim et al., 2008) in the economics field, (d) the number of harbor seals hauled out on glacial ice (Ver Hoef and Jansen, 2007) and the count of birds at sanctuaries (Thogmartin and Knutson, 2007) in the ecology field, and (e) roadway crash frequency (see Lord and Mannering, 2010 for a review), counts of flights from airports (Buck et al., 2009), and the number of drinking under intoxication (DUI) infractions (Jackson and Greene, 2011) in the transportation field. ...
... However, this has not been the case for correlated count data, especially for the case of general dependency structures for more than two correlated counts. For instance, one may consider a simple Poisson or negative binomial discrete distribution, and develop multivariate versions of these discrete distributions to accommodate correlated counts (see Buck et al., 2009 and Bermúdez and Karlis, 2011 for applications of these methods). These multivariate Poisson and negative binomial distributions have the advantage of a closed form, but they become cumbersome as the number of correlated counts increases and they also represent the undesirable property that they can only accommodate a positive correlation in the counts. ...
This paper proposes a reformulation of count models as a special case of generalized ordered-response models in which a single latent continuous variable is partitioned into mutually exclusive intervals. Using this equivalent latent variable-based generalized ordered response framework for count data models, we are then able to gainfully and efficiently introduce temporal and spatial dependencies through the latent continuous variables. Our formulation also allows handling excess zeros in correlated count data, a phenomenon that is commonly found in practice. A composite marginal likelihood inference approach is used to estimate model parameters. The modeling framework is applied to predict crash frequency at urban intersections in Arlington, Texas. The sample is drawn from the Texas Department of Transportation (TxDOT) crash incident files between 2003 and 2009, resulting in 1190 intersection-year observations. The results reveal the presence of intersection-specific time-invariant unobserved components influencing crash propensity and a spatial lag structure to characterize spatial dependence. Roadway configuration, approach roadway functional types, traffic control type, total daily entering traffic volumes and the split of volumes between approaches are all important variables in determining crash frequency at intersections.
Bandara merupakan titik penting dalam sistem transportasi udara. Peningkatan pelayanan bandara harus dilakukan untuk memberikan pelayanan maksimal kepada pengguna jasa penerbangan. Mengetahui indikator yang mempengaruhi penumpang dalam pemilihan bandara bermanfaat untuk meningkatkan pelayanan yang bisa diberikan oleh bandara. Pada beberapa bandara yang terletak pada satu wilayah (region), persaingan untuk mendapatkan penumpang sangatlah tinggi. Kondisi dimana dalam suatu wilayah terdapat lebih dari satu bandara disebut Multi-Airport-Region (MAR). Bandara Husein Sastranegara, Bandara Kertajati, dan Bandara Soekarno-Hatta memiliki wilayah cakupan saling berdekatan. Pada MAR, pergantian pemilihan bandara merupakan hal yang umum terjadi dilakukan. Pengolahan data dilakukan menggunakan program software JASP. Pengujian terhadap beberapa indikator yang mempengaruhi penumpang dalam pemilihan bandara dilakukan dengan menggunakan Confirmatory Analysis Factor (CFA). Hasil CFA menunjukkan terdapat tiga faktor utama yang mempengaruhi pemilihan penumpang dalam pemilihan bandara yang akan digunakan yaitu aksesibilitas, availability, dan fasilitas. Nilai loading factor menunjukkan bahwa indikator jarak tempuh ke bandara (X2), frekuensi penerbangan (X4), dan ketersediaan ATM/Money changer (X8) merupakan indikator yang berpengaruh besar terhadap masing-masing faktor.
