Context agnostic trajectory prediction based on $\lambda$-architecture

TL;DR

This paper introduces a context-agnostic { extbackslash lambda}-Architecture platform for trajectory prediction that combines batch and stream analytics to improve accuracy across various use cases.

Contribution

The paper presents a novel { extbackslash lambda}-Architecture platform that integrates batch and stream analytics for versatile, context-agnostic trajectory prediction.

Findings

Each component of the { extbackslash lambda}-Architecture performs well on specific targets.

Combining components enhances overall accuracy and performance.

The platform is applicable to any use case with time-stamped geolocation data.

Abstract

Predicting the next position of movable objects has been a problem for at least the last three decades, referred to as trajectory prediction. In our days, the vast amounts of data being continuously produced add the big data dimension to the trajectory prediction problem, which we are trying to tackle by creating a {\lambda}-Architecture based analytics platform. This platform performs both batch and stream analytics tasks and then combines them to perform analytical tasks that cannot be performed by analyzing any of these layers by itself. The biggest benefit of this platform is its context agnostic trait, which allows us to use it for any use case, as long as a time-stamped geolocation stream is provided. The experimental results presented prove that each part of the {\lambda}-Architecture performs well at certain targets, making a combination of these parts a necessity in order to improve the overall accuracy and performance of the platform.