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We propose a mobile crowdsourced sensors selection approach to improve thejourney planning service especially in areas where no wireless or vehicularsensors are available. We develop a location estimation model of journeyservices based on an unsupervised learning model to select and cluster theright mobile crowdsourced sensors that are accurately mapped to the rightjourney service. In our model, the mobile crowdsourced sensors trajectories areclustered based on common features such as speed and direction. Experimentalresults demonstrate that the proposed framework is efficient in selecting theright crowdsourced sensors.

Mobile Crowdsourced Sensors Selection for Journey Services