ArticleHeliyon2024
How to maximize the travelers' shift to rail transit in a Chinese valley city after bus fare adjustment.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
Existing studies have not analyzed travelers' travel modal shift behavior after bus fare adjustment for special urban topography. In order to fill this gap and explore the strategies that can effectively encourage the shift of valley city travelers to rail transit after the adjustment of bus fare, the price adjustment perception and topographic space perception are introduced to expand the theory of planned behavior (TPB). On this basis, an integrated model combining structural equation model (SEM) and Mixed Logit model (MLM) is established to analyze the factors of travelers' modal shift behavior of in the valley city after bus fare adjustment, and elastic analysis is carried out. Taking Lanzhou as an example, questionnaire data and traffic data were collected for example analysis. The results show that fare adjustment perception (FAP) and topographic space perception (TSP) have the same significant impact on travel modal shift intention (TMSI) and behavior (TMSB) as subjective norms (SN), shift behavior attitude (SBA) and perceived behavior control (PBC), and the travel characteristics and psychological perception sensitivity of travelers in the valley city are heterogeneous. After the adjustment of bus fares in valley city, the shift to rail transit is the most. When the subjective norms, fare adjustment perception and topographic space perception of travelers in the valley city increase by 50 %. The sharing rate of rail transit is increased by 10.284 %. The effective way to increase the sharing rate of rail transit is to increase the combined factors of subjective norms, topographic space perception and fare adjustment perception. Compared with the Multinomial Logit model and the Logit model combined with structural equation model, the goodness of fit and prediction accuracy of the proposed integrated model are improved.
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