Exploring hierarchy shift of travelers’ public transport dependence using an improved Apriori algorithm

Abstract

Special external environments will lead to significant changes in the use behavior and dependence degree of different PT travellers, but it is difficult to analyze the mechanism of the hierarchy shift of travelers’ public transportation (PT) dependence. Exploring travelers’ dependence on PT is conducive to understanding individuals’ travel choice behavior and optimizing PT operation organizations. Developing methods for analyzing the internal causal relationship between travelers’ dependence on PT and the key influencing factors under the special condition is an issue. Therefore, the individual travel chains are constructed by associating and matching the multisource PT big data and travel survey data. Thereafter, the K-means algorithm and an improved Apriori algorithm are developed to mine the frequent association rules of groups, and a framework of cross-hierarchy policy implications is derived based on the differences in association rules. Finally, the stated preference survey method is used to measure the effectiveness of the policies.