Mobility Pattern-Aware Task Recommendation for Taxi Crowdsourcing Delivery

With the emerging of sharing economy, taxi crowdsourcing delivery could be a feasible solution for logistics companies to deliver packages efficiently and securely with a lower cost in the urban area. In this paper, we propose LSTM2V, a novel mobility pattern-aware task recommendation algorithm for taxi crowdsourcing delivery leveraging the long short-term memory and Markov model. Taking the mobility pattern into consideration, LSTM2V leverages both deep learning and probabilistic model to recommend the most suitable tasks to taxis. It mainly consists of two components – the feature window based Long Short-Term Memory neural network (LSTM-w) and SpatioTemporal Markov (STM) model. The taxi mobility pattern is predicted by LSTM-w, and STM is utilized to predict locations which taxis can visit in the future. Extensive evaluations with real taxi trajectory dataset show LSTM2V can predict the mobility pattern precisely, improve the multi-location prediction accuracy, and recommend tasks efficiently.