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.
The objective of this study was to understand the behavioral differences between various demographic categories in their choice of public transit route. Until now, almost all models have been done for the population as a whole, but this study attempted categorical choice models. The origin-destination survey in the greater Montreal, Quebec, Canada, area was used. For each trip declared, a route choice set was made and then a descriptive analysis was performed. A discrete choice model was applied for six demographic profiles, made up of two genders and three age cohorts. The results showed different coefficients for various profiles. The categorical models could better predict the behavior of individuals compared with the complete model that treated the whole population. Given the issues of aging populations in developed countries, the findings provide a new and vast insight into the future of modeling route choice for public transit.
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