Efficiency of ETA Prediction

TL;DR

This paper provides the first theoretical comparison of ETA prediction methods, emphasizing that segment-level models often outperform route-based approaches due to their spatial granularity.

Contribution

It offers a rigorous theoretical analysis demonstrating the importance of spatial granularity in ETA prediction accuracy, a topic previously explored mainly through empirical studies.

Findings

Segment-level models generally achieve higher accuracy.

Spatial granularity significantly impacts ETA prediction performance.

Theoretical insights clarify conflicting empirical results.

Abstract

Modern mobile applications such as navigation services and ride-sharing platforms rely heavily on geospatial technologies, most critically predictions of the time required for a vehicle to traverse a particular route, or the so-called estimated time of arrival (ETA). There are various methods used in practice, which differ in terms of the geographic granularity at which the predictive model is trained -- e.g., segment-based methods predict travel time at the level of road segments (or a combination of several adjacent road segments) and then aggregate across the route, whereas route-based methods use generic information about the trip, such as origin and destination, to predict travel time. Though various forms of these methods have been developed, there has been no rigorous theoretical comparison regarding their accuracies, and empirical studies have, in many cases, drawn opposite conclusions. We provide the first theoretical analysis of the predictive accuracy of various ETA prediction methods and argue that maintaining a segment-level architecture in predicting travel time is often of first-order importance. Our work highlights that the accuracy of ETA prediction is driven not just by the sophistication of the model but also by the spatial granularity at which those methods are applied.