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

TL;DR

This paper presents a spatial clustering and local modeling framework for bus ridership prediction, improving accuracy by capturing localized urban dynamics using diverse data sources.

Contribution

It introduces a novel localized forecasting approach that integrates spatial clustering with multi-dimensional features, outperforming traditional global models.

Findings

Localized models achieve accuracy comparable to global models.

Spatial clustering effectively captures regional ridership variations.

Incorporating diverse open source data improves prediction performance.

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.