Estimating travel time in the Helsinki region utilising sequential Bayesian inference

Abstract

This paper explores Bayesian inference's application to dynamic travel time estimation in the Helsinki region. Accurate travel time prediction is crucial in a wide range of fields, including departure time and routing. Limited real-time data challenges modelling accuracy. To address this, this paper utilises Bayesian inference, particularly sequential Bayesian inference for evolving observed values. Incorporating 2018 real-time data and 2015 information as prior knowledge, travel time distribution will be updated. Validation yields a 4.0% mean absolute error between the updated 2018 distribution and actual travel time. Also, using the 2015 posterior distribution as prior via sequential Bayesian inference yields a 4.6% mean absolute error. Results highlight that sequential Bayesian inference is an effective tool for updating distributions. The paper underscores Bayesian inference's potential in addressing data scarcity and enhancing transportation model accuracy. By supplying precise travel time estimates, this approach benefits congestion relief and travel planning. With evolving travel time data, Bayesian inference promises to advance transportation modelling.