Unsupervised approach towards analysing the public transport bunching swings formation phenomenon

Viktoriya Degeler · Léonie Heydenrijk-Ottens · Ding Luo · Niels van Oort · Hans van Lint

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Transportation Planning and Optimization Traffic control and management Railway Systems and Energy Efficiency

Abstract

Abstract We perform an analysis of public transport data from The Hague, the Netherlands, combined from three sources: static network information, automatic vehicles location and automated fare collection data. We highlight the effect of bunching swings, and show that this phenomenon can be extracted using unsupervised machine learning techniques, namely clustering. We also show the correlation between bunching rate and passenger load, and bunching probability patterns for working days and weekends. We present the approach for extracting isolated bunching swings formations (BSF) and show different cases of BSFs, some of which can persist for a considerable time. We applied our approach to the tram line 1 of The Hague, and computed and presented four different patterns of BSFs, which we name “high passenger load”, “whole route”, “evening, end of route”, “long duration”. We analyse each bunching swings formation type in detail.

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References

2011Smart card data use in public transit: A literature review 2009A headway-based approach to eliminate bus bunching: Systematic analysis and comparisons 1972Control Strategies for an Idealized Public Transportation System 2012A self-coördinating bus route to resist bus bunching 1964Maintaining a bus schedule 2015Bus Transit Service Reliability and Improvement Strategies: Integrating the Perspectives of Passengers and Transit Agencies in North America 2017A predictive-control framework to address bus bunching 2016Headway-based bus bunching prediction using transit smart card data

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2023Holding times to maintain quasi-regular headways and reduce real-time bus bunching 2021Zero bunching solution for a local public transport system with multiple-origins bus operation 2022Bus Headways Analysis for Anomaly Detection 2024Unsupervised Learning for Public Transport Delay Pattern Analysis

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