Development and Evaluation of Aggregate Destination Choice Models for Trip Distribution in Florida

Chow, Lee-Fang; Zhao, Fang; Li, Min-Tang; Li, Shi-Chiang

doi:10.3141/1931-03

Cited by 15 publications

(12 citation statements)

References 2 publications

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“…After calibration of trip distribution models, some goodnessof fit measures are needed. Several measures are presented in the literature (10,24,(26)(27)(28)(29)(30). Formulations of required goodnessoffit measures and other related works are presented in the following sections.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The IOM has been used less than the GM has because of complexities in the calibration procedure (8). How ever, compared with the GM, the IOM is behavioral based (9) and less sensitive to the size and shape of study area (10). The IOM also produces better results in cases where destinations that satisfy the trip purpose are not uniformly distributed, like discrete attraction points, such as for shopping or study purposes (11).…”

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confidence: 99%

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Integrated Intervening Opportunities Model for Public Transit Trip Generation–Distribution

Nazem

Trépanier

Morency

2013

Transportation Research Record

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An integrated intervening opportunities model (IIOM) was developed for public transit (PT) trips. This model is generation–distribution and supply-dependent, with single constraints only on trip production values for work and study PT trips made during morning peak hours (6:00 to 9:00 a.m.) within the Island of Montreal, Quebec, Canada. Several data sets, including the 2008 origin–destination survey of the Greater Montreal Area, 2006 census of Canada, General Transit Feed Specification network data, and school enrollment data, along with the geographical data of the Greater Montreal Area, were used. The IIOM is a nonlinear model with sociodemographic, socioeconomic, and PT supply characteristics, as well as work and study spatial location attributes. Analysis of the modeling performance by means of several goodness-of-fit measures showed that the IIOM was well behaved (i.e., globally it had good prediction capabilities) and more accurate than the classical gravity model. On the basis of explanatory variables used in the IIOM, the study presents a new tool for PT analysts, planners, and policy makers for studying potential changes in PT trip patterns, as a result of changes in sociodemographic and socioeconomic characteristics, PT supply, and so on. Also, this study opens new opportunities for development of more accurate PT demand models with new emergent data such as smartcard entries.

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Section: Literature Reviewmentioning

confidence: 99%

mentioning

confidence: 99%

Integrated Intervening Opportunities Model for Public Transit Trip Generation–Distribution

Nazem

Trépanier

Morency

2013

Transportation Research Record

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“…The KRTM is also believed by the authors to be original in statistically estimating parameters related to the reluctance of travelers to cross major rivers and county lines [although turnpikes were used as a psychological barrier by Chow et al (15), and origin countydestination county k-factors have been common]. The amount of additional impedance represented by these psychological boundaries varied significantly for different stop types.…”

Section: Benefits and Costs Of New Krtmmentioning

confidence: 99%

From Academia to Application

Bernardin¹,

Conger²

2010

Transportation Research Record

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This paper presents an overview of a new architecture for travel demand forecasting models. The new hybrid accessibility-based models are neither traditional trip-based models nor standard activity-based simulation models. Instead, they are a blend of aggregate and disaggregate component models offering much of the sensitivity typical of activity-based models but with more modest development and application costs. This paper presents results from the first full application of the new model design. It documents the experience of the Knoxville (Tennessee) Regional Transportation Planning Organization from the data collection and visioning stage to the stage of investing time and resources to develop the new model. Ultimately, some basic comparisons are made between the new hybrid model and the previous trip-based model. Some key innovations in the new model design are highlighted, making special note of added sensitivity to planning and policy variables. The paper also reports findings related to the effects of residence location on travelers' willingness to travel and the effect of psychological barriers such as river crossings and county lines on stop location choice.

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“…Travel behaviour is one of the areas where GIS has been used for demand modelling of public (Choi and Jang, 2000) and private modes (Choi and Kim, 1996). GIS has been used to model travel choice (Byon et al, 2007;McGowen and McNally, 2007;Bricka and Bhat, 2006;Ogle et al, 2005;Tsui and Shalaby, 2006), destination choice (Chow et al, 2005), location choice (Nicholas et al, 2004;Shelton et al, 2004), mobility (Schlossberg, 2006), and accessibility (Hodge, 1997;Miller and Wu, 2000;Casas, 2003). It has been used for travel time forecasting (You and Kim, 2000), and risk and evacuation models (Church and Cova, 2000;Alexander and Waters, 2000;Horner and Downs, 2007).…”

Section: 3geomodelling Framework In Transportation Researchmentioning

confidence: 99%

Geo-ICT in Transportation Science

Borzacchiello¹,

Casas

Ciuffo³

et al. 2009

Geospatial Technology and the Role of Location in Science

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Since the first appearances of Geographical Information Systems (GIS), transportation science has found in them its natural support, to represent, first, the spatial and, later on, the temporal aspects of transportation networks and infrastructures. As GIS developed so did their use in transportation science, and they became essential not only in visualization but also to facilitate and speed data management, algorithmic operations, and decision making. This paper analyses the contributions and influence of GIS in transport science on the basis of three frameworks: geodatabase, geomapping, and geomodelling, all of which highlight the importance of location.

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Development and Evaluation of Aggregate Destination Choice Models for Trip Distribution in Florida

Cited by 15 publications

References 2 publications

Integrated Intervening Opportunities Model for Public Transit Trip Generation–Distribution

Integrated Intervening Opportunities Model for Public Transit Trip Generation–Distribution

From Academia to Application

Geo-ICT in Transportation Science

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