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Customizing services for bus travel can bolster its attractiveness, optimizeusage, alleviate traffic congestion, and diminish carbon emissions. Thispotential is realized by harnessing recent advancements in positioningcommunication facilities, the Internet of Things, and artificial intelligencefor feature mining in public transportation. However, the inherent complexitiesof disorganized and unstructured public transportation data introducesubstantial challenges to travel feature extraction. This study presents a bustravel feature extraction method rooted in Point of Interest (POI) data,employing enhanced P-KMENAS and P-LDA algorithms to overcome these limitations.While the KMEANS algorithm adeptly segments passenger travel paths intodistinct clusters, its outcomes can be influenced by the initial K value. Onthe other hand, Latent Dirichlet Allocation (LDA) excels at featureidentification and probabilistic interpretations yet encounters difficultieswith feature intermingling and nuanced sub-feature interactions. Incorporatingthe POI dimension enhances our understanding of travel behavior, aligning itmore closely with passenger attributes and facilitating easier data analysis.By incorporating POI data, our refined P-KMENAS and P-LDA algorithms grant aholistic insight into travel behaviors and attributes, effectively mitigatingthe limitations above. Consequently, this POI-centric algorithm effectivelyamalgamates diverse POI attributes, delineates varied travel contexts, andimparts probabilistic metrics to feature properties. Our method successfullymines the diverse aspects of bus travel, such as age, occupation, gender,sports, cost, safety, and personality traits. It effectively calculatesrelationships between individual travel behaviors and assigns explanatory andevaluative probabilities to POI labels, thereby enhancing bus traveloptimization.

Optimizing Bus Travel: A Novel Approach to Feature Mining with P-KMEANS and P-LDA Algorithms