# Mobile Crowdsourced Sensors Selection for Journey Services

## TL;DR

This paper introduces a novel method for selecting mobile crowdsourced sensors to enhance journey planning, especially in sensor-scarce areas, by clustering sensor trajectories based on features like speed and direction.

## Contribution

It presents an unsupervised learning model for effective sensor selection and clustering, improving journey service accuracy in sensor-limited environments.

## Key findings

- Efficient sensor selection demonstrated through experiments.
- Clustering based on speed and direction improves journey service accuracy.
- Framework outperforms existing sensor selection methods.

## Abstract

We propose a mobile crowdsourced sensors selection approach to improve the journey planning service especially in areas where no wireless or vehicular sensors are available. We develop a location estimation model of journey services based on an unsupervised learning model to select and cluster the right mobile crowdsourced sensors that are accurately mapped to the right journey service. In our model, the mobile crowdsourced sensors trajectories are clustered based on common features such as speed and direction. Experimental results demonstrate that the proposed framework is efficient in selecting the right crowdsourced sensors.