  Customizing services for bus travel can bolster its attractiveness, optimize
usage, alleviate traffic congestion, and diminish carbon emissions. This
potential is realized by harnessing recent advancements in positioning
communication facilities, the Internet of Things, and artificial intelligence
for feature mining in public transportation. However, the inherent complexities
of disorganized and unstructured public transportation data introduce
substantial challenges to travel feature extraction. This study presents a bus
travel 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 into
distinct clusters, its outcomes can be influenced by the initial K value. On
the other hand, Latent Dirichlet Allocation (LDA) excels at feature
identification and probabilistic interpretations yet encounters difficulties
with feature intermingling and nuanced sub-feature interactions. Incorporating
the POI dimension enhances our understanding of travel behavior, aligning it
more closely with passenger attributes and facilitating easier data analysis.
By incorporating POI data, our refined P-KMENAS and P-LDA algorithms grant a
holistic insight into travel behaviors and attributes, effectively mitigating
the limitations above. Consequently, this POI-centric algorithm effectively
amalgamates diverse POI attributes, delineates varied travel contexts, and
imparts probabilistic metrics to feature properties. Our method successfully
mines the diverse aspects of bus travel, such as age, occupation, gender,
sports, cost, safety, and personality traits. It effectively calculates
relationships between individual travel behaviors and assigns explanatory and
evaluative probabilities to POI labels, thereby enhancing bus travel
optimization.
