ITSC 2025 Paper Abstract

Paper TH-EA-T30.2

Horn, Alexander (Technische Hochschule Ingolstadt), Adam, Philip-Roman (Technische Hochschule Ingolstadt), Schmidtner, Stefanie (Technische Hochschule Ingolstadt)

A Benchmark Dataset for Bus Travel and Dwell Time Prediction

Scheduled for presentation during the Regular Session "S30b-Intelligent Modeling and Prediction of Traffic Dynamics" (TH-EA-T30), Thursday, November 20, 2025, 13:50−14:10, Gold Coast

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Keywords AI, Machine Learning for Real-time Traffic Flow Prediction and Management, Real-time Passenger Information and Service Optimization in Public Transportation, Testing and Validation of ITS Data for Accuracy and Reliability

Abstract

Accurate predictions of bus travel times and dwell times are essential for public transport. Their prediction using machine learning has been extensively studied, resulting in a wide variety of approaches. However, due to the absence of standardized benchmarks, the field currently lacks a meaningful way to compare model performance. We compile and release a benchmark based on three years of automated vehicle location (AVL) data from the Dutch public transport network. This includes data excerpts representative of different evaluation scenarios (rural, suburban, small-city, and large-city networks) as well as a methodology for calculating metrics in a reproducible manner. The code is available on GitHub and the dataset is available on Zenodo.