An Efficient Passenger-Hunting Recommendation Framework With Multitask Deep Learning

Using large-scale GPS trajectory data to improve taxi services has recently attracted much attention in Internet of Things and smart city communities. In this paper, we use a large-scale GPS trajectory dataset generated by over 12 000 taxis in a period of three months in Shanghai, China, and present an efficient passenger-hunting recommendation framework with the multitask deep learning paradigm. This framework contains two modules: 1) offline training of passenger-hunting recommendation model (OT-PHRM) and 2) online application of passenger-hunting recommendation model (OA-PHRM). The module OT-PHRM mainly includes two deep convolutional neural networks (DCNNs) and uses the multitask learning strategy. The first DCNN realizes the region prediction for picking up passengers, while the second DCNN uses the weight-sharing structure to predict the levels of road congestion and earnings of carrying passengers. In particular, for the input of two DCNNs, we not only consider contextual features of taxi driving, region features and valuable statistical features, but also combine individual features into meaningful ones. In the module OA-PHRM, we propose DL-PHRec, which calculates three prediction values using two trained DCNNs in OT-PHRM in real time, and then recommends a personal ranking-list of regions to each taxi driver according to their scores. The experimental results show the feasibility and effectiveness of our recommendation framework.