Title: A Binary Logit Model for Commute Mode Choice: A Case Study of Hefei
Abstract: Mode choice analysis is an important step in the conventional four-step transportation forecasting model. In this paper, we conducted a survey on self-driving commute and bus commute, and developed a Binary Logit (BL) model for commute mode choice. A questionnaire was designed to investigate the commuting characteristics of commuters in the Hefei area. Based on the disaggregate theory, self-driving commute and bus commute were chosen as the alternatives. Eleven elements - such as gender, age, and character - were determined as the influence factors. The parameters of the BL model were calibrated by the survey data of 280 commuters. Another survey data of another 106 were adopted to verify the model. The results showed the choice of self-driving is more elastic to age, education, and monthly income, while the choice of bus is more elastic to travel time, number of trips, and peak-period congestion. The absolute error between the estimated value and statistical value by the BL model was 3.77%, which implies that the model has high accuracy.
Publication Year: 2014
Publication Date: 2014-06-24
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 3
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