Policy Impact Analysis of Hierarchical Shift of Travellers’ Public Transport Dependence Under the Pandemic Condition Using Improved Apriori Algorithm

Exploring travellers’ dependence on public transportation (PT) is conducive to understanding individuals’ or groups’ travel choice behaviour and optimizing PT operation organizations. To explore the internal causal relationship between travellers’ dependence on PT and the key influencing factors under the pandemic condition , an online travel survey is designed and implemented in Beijing, China. The individual PT travel chains and travel knowledge graphs are constructed by associating and matching the multisource PT big data and travel survey data. To analyse the heterogeneity of travel behaviour characteristics of different groups, the K -means algorithm is used to identify and classify travellers’ PT dependence levels. Then, an improved Apriori algorithm is developed to mine the frequent association rules of groups under the Corona Virus Disease 2019 (COVID-19 ) epidemic condition. Then, the policy implications of PT dependence hierarchy transfer are developed based on the differences between indicators of association rules. The results show that the travellers are divided into four clusters based on PT dependency levels by clustering the behavioural features. The association rules of travellers’ PT dependence are significantly different among different clusters. The lower the PT dependence level is, the lower the co-occurrence degree and occurrence probability of association rules are. Furthermore, the total distance from origin and destination to PT transit and car availability are the key indicators for each cluster to enhance their PT dependence levels, while whether the routes within high-risk epidemic areas and the support degree of relatives and friends for PT usage are important factors for improving the PT usage behaviour of the clusters with the relatively high dependence level during the epidemic period. The discovered frequent patterns and association rules describe the relationships between key influencing indicators and travellers’ PT dependence. Finally, this study proposes some critical policy suggestions for improving and balancing the proportion of green travel mode in urban travel based on effective strategies under the negative conditions of the major epidemic.