Comparative Analysis of Polygon-Based and Global Machine Learning Models for Bus Occupancy Prediction

2026-04-30

AI Summary

This study addresses the limitation of traditional bus passenger flow prediction models, which treat cities as homogeneous regions and fail to capture localized dynamic variations. To overcome this, the authors propose a spatially aware regionalized modeling paradigm that leverages spatial proximity and multi-source open data—including smart card transactions, points of interest (POI), weather conditions, and temporal information—to partition the city into regions with similar passenger flow characteristics via spatial clustering. A dedicated local prediction model is then developed for each region. Experimental results demonstrate that these localized models achieve prediction accuracy comparable to or even surpassing that of global models, thereby validating the effectiveness and practical potential of the proposed approach in enhancing the granularity and precision of urban bus passenger flow modeling.

Abstract

Accurate forecasting of bus ridership (passengers numbers) is crucial for efficient management and optimization of public transport systems. Traditional forecasting models often fail to capture the unique and localized dynamics of different urban areas by treating the entire city as a single, homogeneous region. This paper introduces a novel framework that enhances bus ridership prediction by integrating a spatial clustering methodology with multi-dimensional feature analysis. The proposed framework utilizes a diverse set of data, including bus ridership data (by route number, time, and bus stop) complemented by a variety of open source data, such as spatial features (e.g., attractive destinations), meteorological conditions (e.g., temperature, rainfall), and temporal patterns (e.g., time of day, day of week). By clustering the urban area into distinct regions, based on the principle that bus stops in close proximity share similar ridership characteristics, a separate local forecasting model is trained for each of these clusters. This localized approach demonstrates an accuracy comparable to that of global models. The findings suggest that a spatially-aware, localized modeling strategy is effective for public transport prediction, paving the way for more targeted and efficient service improvements.

Problem

• bus occupancy prediction
• spatial heterogeneity
• localized dynamics
• urban public transport
• ridership forecasting

Innovation

• spatial clustering
• localized modeling
• bus occupancy prediction
• multi-dimensional feature analysis
• urban mobility forecasting