01593604

Component

https://doi.org/10.3141/2563-14

To improve the level of service of traffic and predict travel demand, it is essential to analyze the behavioral factors that affect transportation mode choice and route choice at the individual level. Such analysis requires detailed data on the behavior of people in the selection of different modes and routes. This paper presents a unique data collection endeavor, intended to observe the formation of the choice set from which the final alternative is selected. The revealed preference information about the routes people considered and used for their last work or study trip was targeted. Google Maps API (application programming interface), which has the capacity to calculate and return car and public transportation routes, was employed to program the survey and adaptively show the respondents the routes according to the reported origin and destination. A pilot survey was conducted with this survey tool at the University of New South Wales, Sydney, Australia, with a sample of 200 respondents. A preliminary analysis was carried out to analyze the effectiveness of the survey tool and the specification of the choice set. Three modeling structures—multinomial logit (MNL), nested logit, and mixed MNL—were used to estimate the parameters of the preliminary analysis. The results were fairly intuitive as far as the signs of the parameters were concerned, and travel time significantly influenced route choice.

01595160

16-6923

English

pp 96–104

2016

Transportation Research Record: Journal of the Transportation Research Board

9780309441223

Print

Figures (2) ; References (35) ; Tables (4)

Logits; Mode choice; Multinomial logits; Revealed preferences; Route choice

Google Maps

Nested logit models

Data and Information Technology; Highways; Planning and Forecasting; Public Transportation

TRIS, TRB, ATRI

Jan 12 2016 7:01PM

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