  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.
