Robust Real-Time Delay Predictions in a Network of High-Frequency Urban Buses

TL;DR

This paper introduces a robust real-time bus delay prediction model using Student-$t$ errors, capturing stochastic traffic variability and providing probabilistic forecasts for improved urban transit reliability.

Contribution

The paper develops a novel Student-$t$ based model for bus delay prediction that outperforms Gaussian models and incorporates Bayesian inference for uncertainty quantification.

Findings

Student-$t$ models outperform Gaussian models in predictive power.

The model captures temporal variability and spatiotemporal effects effectively.

Bayesian inference enables probabilistic delay forecasts and uncertainty quantification.

Abstract

Providing transport users and operators with accurate forecasts on travel times is challenging due to a highly stochastic traffic environment. Public transport users are particularly sensitive to unexpected waiting times, which negatively affect their perception on the system's reliability. In this paper we develop a robust model for real-time bus travel time prediction that depart from Gaussian assumptions by using Student-$t$ errors. The proposed approach uses spatiotemporal characteristics from the route and previous bus trips to model short-term effects, and date/time variables and Gaussian processes for long-run forecasts. The model allows for flexible modeling of mean, variance and kurtosis spaces. We propose algorithms for Bayesian inference and for computing probabilistic forecast distributions. Experiments are performed using data from high-frequency buses in Stockholm, Sweden. Results show that Student-$t$ models outperform Gaussian ones in terms of log-posterior predictive power to forecast bus delays at specific stops, which reveals the importance of accounting for predictive uncertainty in model selection. Estimated Student-$t$ regressions capture typical temporal variability between within-day hours and different weekdays. Strong spatiotemporal effects are detected for incoming buses from immediately previous stops, which is in line with many recently developed models. We finally show how Bayesian inference naturally allows for predictive uncertainty quantification, e.g. by returning the predictive probability that the delay of an incoming bus exceeds a given threshold.