route recommendation, route clustering, traffic prediction, cloud computing
This paper presents a cloud-based multiple-route recommendation system, xGo, that enables smartphone users to choose suitable routes based on knowledge discovered in real taxi trajectories. In modern cities, GPS-equipped taxicabs report their locations regularly, which generates a huge volume of trajectory data every day. The optimized routes can be learned by mining these massive repositories of spatio-temporal information. We propose a system that can store and manage GPS log files in a cloud-based platform, probe traffic conditions, take advantage of taxi driver route-selection intelligence, and recommend an optimal path or multiple candidates to meet customized requirements. Specifically, we leverage a Hadoop-based distributed route clustering algorithm to distinguish different routes and predict traffic conditions through the latent traffic rhythm. We evaluate our system using a real-world dataset (>100 GB) generated by about 20 000 taxis over a 2-month period in Shenzhen, China. Our experiments reveal that our service can provide appropriate routes in real time and estimate traffic conditions accurately.
Tsinghua University Press
Yaobin He, Fan Zhang, Ye Li et al. Multiple Routes Recommendation System on Massive Taxi Trajectories. Tsinghua Science and Technology 2016, 21(5): 510-520.