# Destination Prediction by Trajectory Distribution Based Model

arXiv: https://arxiv.org/abs/1605.03027 · HTML: https://mlsanity.com/paper/1605.03027/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2016.

## Built on by (most cited first, 4 of 4)

- [Evaluating the Effectiveness of Large Language Models in Representing and Understanding Movement Trajectories](https://mlsanity.com/paper/2409.00335/) (2024)
- [Potential destination discovery for low predictability individuals based on knowledge graph](https://mlsanity.com/paper/2201.12845/) (2022)
- [An Accurate and Low-Parameter Machine Learning Architecture for Next Location Prediction](https://mlsanity.com/paper/2402.00306/) (2024)
- [A Spatio-Temporal Hybrid Quantum-Classical Graph Convolutional Neural Network Approach for Urban Taxi Destination Prediction](https://mlsanity.com/paper/2512.13745/) (2025)

## Abstract (arXiv)

In this paper we propose a new method to predict the final destination of vehicle trips based on their initial partial trajectories. We first review how we obtained clustering of trajectories that describes user behaviour. Then, we explain how we model main traffic flow patterns by a mixture of 2d Gaussian distributions. This yielded a density based clustering of locations, which produces a data driven grid of similar points within each pattern. We present how this model can be used to predict the final destination of a new trajectory based on their first locations using a two step procedure: We first assign the new trajectory to the clusters it mot likely belongs. Secondly, we use characteristics from trajectories inside these clusters to predict the final destination. Finally, we present experimental results of our methods for classification of trajectories and final destination prediction on datasets of timestamped GPS-Location of taxi trips. We test our methods on two different datasets, to assess the capacity of our method to adapt automatically to different subsets.