Efficient Top-k Matching for Publish/Subscribe Ride Hitching

With the continued proliferation of mobile Internet and geo-locating technologies, carpooling as a green transport mode is widely accepted and becoming tremendously popular worldwide. In this paper, we focus on a popular carpooling service called ride hitching , which is typically implemented using a publish/subscribe approach. In a ride hitching service, drivers subscribe ride orders published by riders and continuously receive matching ride orders until one is picked. The current systems (e.g., Didi Hitch) adopt a threshold-based approach to filter ride orders. That is, a new ride order will be sent to all subscribing drivers whose planned trips can match the ride order within a pre-defined detour threshold. A limitation of this approach is that it is difficult for drivers to specify a reasonable detour threshold in practice. In addressing this problem, we propose a novel type of top- k subscription queries called Top-kk Ride Subscription (TkRS) query, which continuously returns the best k ride orders that match drivers’ trip plans to them. We propose two efficient algorithms to enable the top- k result maintenance. We also design a novel hybrid grid index and a two-level buffer structure to efficiently track the top- k results for all TkRS queries. Finally, extensive experiments on real-life datasets suggest that our proposed algorithms are capable of achieving desirable performance in practical settings.