Abstract
The rise and rapid development of bicycle sharing brings great convenience to residents’ travel and transfer, and also has a profound impact on the travel structure of cities. As college students make up a major share of shared bicycle users, it is necessary to analyze the factors that influence their travel mode and riding frequency choice and to explore how these factors affect their riding behavior. To analyze the bicycle riding characteristics of college students, this paper processes many factors with unknown correlations by using a factor analysis method based on revealed preference (RP) questionnaire data. Then, taking the significant common factors as explanatory variables, a two-layer nested logit (NL) model combining riding frequency and travel mode is established to study college students’ riding behavior. The results suggest that the comprehensive hit rate of the upper and lower levels of the model (riding frequency and travel mode) are, respectively, 76.8% and 83.7%, and the two-layer NL model is applicable. It is also shown that environmental factors (“cheap,” “mixed traffic,” “signal lights at intersection,” and so on) have a significant impact on the choice of travel mode and riding frequency. Also, improving the level of bicycle service can increase the shift from walking to riding. Such findings are meaningful for policy-makers, planners, and others in formulating operational management strategies and policies.
Highlights
Due to urban traffic congestion, environmental pollution, traffic accidents, and other issues, many scholars and policy-makers are paying attention to more sustainable travel modes
The nested logit (NL) model is different from the multinomial logit (MNL) model and binary logit (BL) model, by setting up a multiple or multilayer nest structure, which overcomes the IIA (Independence of irrelevant alternatives) characteristic of the traditional logit model to a certain extent
The common factor with significant influence was selected as the subsequent modeling-explanatory variable, to realize the dimensionality reduction of explanatory variables, and the continuous variation of discrete variables
Summary
Due to urban traffic congestion, environmental pollution, traffic accidents, and other issues, many scholars and policy-makers are paying attention to more sustainable travel modes. Convenient, and low-cost travel mode, bicycle sharing helps to adjust the unbalanced traffic structure and provide an alternative travel mode for short trips, commutes, and transfer trips. It can guide multimodal travel and provide a low-carbon solution for the “last mile” problem. By the end of 2017, China had developed more than 300 service systems, with more than 10 million shared bicycles, more than 100 million registered users, and more than 1 billion passengers, shaping the world’s largest bicycle sharing market [6]. It is necessary to analyze their travel choice behavior under these influencing factors
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