Hybrid Learning Framework-Based Profit-Maximizing Personalized Route Recommendation for Vacant Taxis

As a complement of public transportation system in modern cities, taxis can provide flexible transportation services for citizens. To increase the profits of taxis, proper cruising routes should be recommended to help vacant taxis find passengers faster. Besides, the cruising tendencies of taxi drivers differ due to the personal cognitions. To this end, we propose a hybrid learning framework-based profit-maximizing personalized route (PMPR) recommendation method for vacant taxis (HL-PPRRM) to obtain and recommend the PMPRs for vacant taxis. HL-PPRRM consists of the local learning on vacant taxis and the global learning on cloud server. Specifically, vacant taxis locally learn the historical cruising routes to predict the personalized routes, while the cloud server globally learns the occupied records to predict the future taxi demand. The obtained PMPRs tally with the cruising tendencies of taxi drivers roughly, and the occupied durations of taxis can be effectively prolonged when vacant taxis cruise along PMPRs. Extensive simulations and comparisons demonstrate the superior performance of our proposed HL-PPRRM, i.e., with the hybrid learning framework, the profits of taxis can be significantly increased, and the average displacement error of PMPRs is very small.