Individual Bus Trip Chain Prediction and Pattern Identification Considering Similarities

TL;DR

This paper introduces a similarity-based method for predicting individual bus trip chains by modeling days as nodes in a graph, capturing complex travel patterns, and classifying users into distinct behavior types, achieving state-of-the-art results.

Contribution

It presents a novel similarity function and graph-based semi-supervised classification approach for bus trip chain prediction, offering new insights into travel behavior patterns.

Findings

Achieved state-of-the-art prediction accuracy on real-world data.

Identified three distinct bus user behavior patterns.

Validated the effectiveness of similarity-based modeling for trip prediction.

Abstract

Predicting future bus trip chains for an existing user is of great significance for operators of public transit systems. Existing methods always treat this task as a time-series prediction problem, but the 1-dimensional time series structure cannot express the complex relationship between trips. To better capture the inherent patterns in bus travel behavior, this paper proposes a novel approach that synthesizes future bus trip chains based on those from similar days. Key similarity patterns are defined and tested using real-world data, and a similarity function is then developed to capture these patterns. Afterwards, a graph is constructed where each day is represented as a node and edge weight reflects the similarity between days. Besides, the trips on a given day can be regarded as labels for each node, transferring the bus trip chain prediction problem to a semi-supervised classification problem on a graph. To address this, we propose several methods and validate them on a real-world dataset of 10000 bus users, achieving state-of-the-art prediction results. Analyzing the parameters of similarity function reveals some interesting bus usage patterns, allowing us can to cluster bus users into three types: repeat-dominated, evolve-dominate and repeat-evolve balanced. In summary, our work demonstrates the effectiveness of similarity-based prediction for bus trip chains and provides a new perspective for analyzing individual bus travel patterns. The code for our prediction model is publicly available.