Work

Bus OD Identification From Incomplete Smart Card Data: A Two-Layer Heterogeneous Decision Tree Approach

Year: 2026

Type: article

Abstract: Accurate bus Origin-Destination (OD) identification based on public transit data is essential for route planning and service optimization. However, current research lacks evaluation metrics specifically designed for the bus OD identification task, often relying on conventional indicators that fail to fully capture the plausibility of identification results. This study proposes an integrated framework encompassing label generation, modeling, and validation. Specifically, we developed a two-layer model that employs a Random Forest for feature selection and a CatBoost classifier for alighting stop identification. To enable validation without ground-truth labels, a novel Trend Consistency Index (TCI) was introduced. A case study using public transit data from Chengdu demonstrates that the proposed method outperforms mainstream algorithms such as XGBoost, achieving the highest scores across all key metrics. Multi-dimensional validation further confirmed its effectiveness, showing high fidelity in reproducing temporal travel patterns, accurately identifying major destination clusters through spatial heatmaps, and yielding an excellent fit for trip distribution curves (R2up to 0.8382). This research provides a robust and practical solution for the bus OD identification problem, offering a reliable quantitative foundation to advance smart public transit management.

Source: IEEE Access

DOI: 10.1109/access.2026.3672633

Authors: Li Tang, Jiayang Li, Jiacheng Yin, Yingying Hu

Institution: Xihua University

Language: English

Topic: Transportation Planning and Optimization

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,075