HTTE: A Hybrid Technique For Travel Time Estimation In Sparse Data Environments
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Abstract (arXiv)
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.
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References · 5
- 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
- 2015A Simple Baseline for Travel Time Estimation using Large-Scale Trip Datacited by 28
- 2016Latent Space Model for Road Networks to Predict Time-Varying Trafficcited by 22
- 2018DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervisioncited by 21
- 2013Using Incomplete Information for Complete Weight Annotation of Road Networks -- Extended Versioncited by 4
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