Predicting Stochastic Travel Times based on High-Volume Floating Car Data

TL;DR

This paper presents a method for stochastic travel time prediction using high-volume floating car data, analyzing how data aggregation and route dependence affect prediction accuracy.

Contribution

It introduces a novel approach to stochastic travel time prediction leveraging floating car data and examines the impact of data aggregation and route dependence.

Findings

Floating car data can effectively predict stochastic travel times.

Data aggregation level influences prediction accuracy.

Route dependence affects the reliability of travel time estimates.

Abstract

Transportation planning depends on predictions of the travel times between loading and unloading locations. While accurate techniques exist for making deterministic predictions of travel times based on real-world data, making stochastic predictions remains an open issue. This paper aims to fill this gap by showing how floating car data from TomTom can be used to make stochastic predictions of travel times. It also shows how these predictions are affected by choices that can be made with respect to the level of aggregation of the data in space and time, and by choices regarding the dependence between travel times on different parts of the route.