Evaluating alternative methods to estimate bus running times by archived automatic vehicle location data

Benedetto Barabino

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Transportation Planning and Optimization Traffic control and management Traffic Prediction and Management Techniques

Abstract

Bus running times are a key element of time reliability for bus operators and passengers. Hence, their evaluation is crucial in order to build reliable schedules for transit operations. This study analyses three alternative offline methods for estimating bus running times using automatic vehicle location (AVL) data: the average method, the percentile method and an adjusted Kalman filter method, which is amended in order to be implemented for offline use. Experiments are conducted using ∼92,000 real‐world archived AVL records, which are provided by an Italian bus operator. The results can be used to revise scheduled running times along a specific route by these methods.

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2003Transit capacity and quality of service manual 2014A better measure of relative prediction accuracy for model selection and model estimation 2007Public Transit Planning and Operation: Theory, Modeling and Practice 2002Dynamic Bus Arrival Time Prediction with Artificial Neural Networks 2011Bus arrival time prediction at bus stop with multiple routes 2007Public Transit Planning and Operation 2004A Dynamic Bus‐Arrival Time Prediction Model Based on APC Data 2005Bus arrival time prediction using artificial neural network model

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2020Investigating bus travel time and predictive models: a time series-based approach 2025From Raw GPS to GTFS: A Real-World Open Dataset for Bus Travel Time Prediction 2024Real-time bus arrival delays analysis using seemingly unrelated regression model 2022What Do We Know When? Modeling Predictability of Transit Operations 2021An Offline Framework for the Diagnosis of Transfer Reliability Using Automatic Vehicle Location Data

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