A Cloud-Edge-End Collaboration Framework for Cruising Route Recommendation of Vacant Taxis

Taxis can provide convenient and flexible transportation services for citizens. The proper cruising routes should be recommended to vacant taxis, so as to help them to pick up passengers as early as possible, and thus increase their business profits. To this end, we propose a Cloud-edge-end Collaboration Framework for the Cruising Route Recommendation of vacant taxis (CCF-CRR). In CCF-CRR, each vacant taxi trains a local model based on its historical cruising route segments, and the local model parameters of the vacant taxis in the same region are periodically uploaded to an edge server for parameter aggregation. Then, the aggregated model parameters are released by the edge server to vacant taxis for their use. In addition, the future waiting time of passengers is predicted by the edge servers in different regions and is uploaded to the cloud server, and then the cloud server can measure the potential taxi demand in regions and dispatch vacant taxis among regions to achieve the taxi demand-supply equilibrium. Extensive simulations and comparisons demonstrate the superior performance of our proposed CCF-CRR, i.e., with the cloud-edge-end collaboration framework, the business profits of taxis can be significantly increased, and the pick-up distance of taxis can be largely shortened.