HTTE: A Hybrid Technique For Travel Time Estimation In Sparse Data Environments

TL;DR

This paper introduces a hybrid travel time estimation algorithm that combines historical data and sparse real-time trajectories, improving accuracy by modeling segment similarities and correlations.

Contribution

The paper presents a novel hybrid method that effectively integrates historical and real-time data for travel time estimation in sparse data environments.

Findings

The approach outperforms existing methods in accuracy.

Modeling segment similarities improves estimation.

Effective in environments with sparse data.

Abstract

Travel time estimation is a critical task, useful to many urban applications at the individual citizen and the stakeholder level. This paper presents a novel hybrid algorithm for travel time estimation that leverages historical and sparse real-time trajectory data. Given a path and a departure time we estimate the travel time taking into account the historical information, the real-time trajectory data and the correlations among different road segments. We detect similar road segments using historical trajectories, and use a latent representation to model the similarities. Our experimental evaluation demonstrates the effectiveness of our approach.