On Exploring the Carrying-Charging Demand Balance in Cruising Route Recommendation for Vacant Electric Taxis

As the gasoline taxis are gradually restricted due to the increased environmental awareness, electric taxis (E-taxis) have become a more environmentally friendly choice to provide the transportation service. When some E-taxis are vacant, they typically cruise along roads without any specific destinations, and two major concerns should be considered for vacant E-taxis: In order to increase the business profits of E-taxis, it is vital to recommend the profitable cruising routes along which vacant E-taxis could pick up passengers as early as possible and earn more profits. Besides, the residual electricity of E-taxis is continuously consumed on travels, and E-taxis must be timely charged before their residual electricity is exhausted (i.e. the breakdowns of E-taxis). Thus, the cruising route recommendation for vacant E-taxis should take into account both passenger-carrying demand and charging demand, and the carrying-charging demand balance should be properly made. To this end, we propose a cruising Route Recommendation Method based on Carrying-charging Demand Balance (RRM-CDB) for vacant E-taxis. The passenger-carrying demand and charging demand are first formulated to reflect their changes and interrelationships, and the historical cruising trajectories of vacant E-taxis (with the two types of demand) are locally learned to recommend the future cruising routes, because the historical cruising trajectories contain the distribution of taxi demand of passengers and the trend of vacant E-taxis gradually approaching the charging stations with the decrease of residual electricity. Particularly, in RRM-CDB each vacant E-taxi trains a local learning model in a distributed manner, thus significantly reducing the computational complexity of cruising route recommendation. Extensive simulations and comparisons demonstrate that RRM-CDB can help to increase the business profits of E-taxis and avoid the breakdowns of E-taxis as much as possible.