Decentralised cooperative cruising of autonomous ride-sourcing fleets

As transportation network companies and automobile manufacturers continue to invest in the development of self-driving vehicles, it can be expected that autonomous taxi (a-taxi) fleets will become a major component of on-demand transport services in the foreseeable future. The ma-jority of existing automated fleet management systems focus on central dispatch strategies that rely on real-time information and communication. This paper proposes a novel decentralised cooperative cruising method for offline operation of a-taxi fleets, which serves as a contingency plan during a full communication shutdown. The proposed method acts as an emergency plan for the system to continue serving passengers with the objective of maximising the total number of served passengers by the fleet. The method uses historical trip data to estimate PageRank cen-tralities of roads as a proxy of long-term likelihood of finding waiting passengers over a series of trips. The proposed method uses this metric to (i) compute weighted shortest paths for vacant a-taxi cruising route planning, and (ii) partition the network into homogeneous regions for effective cruising destination choice (mission planning). The movements of vacant a-taxis between regions are modelled as a Markov chain such that a transition probability matrix is computed to achieve the optimal spatial distribution of vacant a-taxis to maximise the total expected pick-ups by the fleet, estimated based on bilateral meeting functions. Compared to benchmark strategies which select destinations randomly and cruise along the shortest travel time path, the proposed method shows significant improvements in service performances for different fleet sizes.

Automated summary

This paper proposes a novel decentralised cooperative cruising method for offline operation of autonomous taxi fleets, which uses historical trip data to estimate road centralities for route planning in order to maximize service to passengers.