详情

R2Deep: Recharging Recommendation System for Electric Taxis based on Deep Learning

项目名称：	R2Deep: Recharging Recommendation System for Electric Taxis based on Deep Learning
摘要：	Electric taxi (eTaxi) often face an inherent long waiting and recharging time (e.g., in hours) at charging stations. Therefore, where to charge and how long to charge an eTaxi has already emerged as an urgent and crucial problem to be solved for wide deployment of eTaxis. In this project, the research team proposes to develop a recharging recommendation system based on deep learning, called R2Deep, for eTaxi drivers to improve their operational efficiency as well as increase the revenue of both eTaxi drivers and companies. The project has three tasks: 1) analyze the existing eTaxi global positioning system (GPS) trajectory data and convert them into information on the grid maps, which will then be directly fed into deep learning models; 2) utilize deep learning techniques including both Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) to learn latent patterns behind eTaxi data sets and provide real-time suggestions on recharging time and charging stations to eTaxis drivers; 3) evaluate the R2Deep model, analyze its performance (e.g., the recommendation accuracy, increase in the eTaxi drivers and companies� revenue, average reduction of waiting time at charging stations, etc.) with real world data.
状态：	Active
资金：	74638
资助组织：	Office of the Assistant Secretary for Research and Technology
管理组织：	Office of the Assistant Secretary for Research and Technology
项目负责人：	Qiao, Chunming
执行机构：	State University of New York, Buffalo
主要研究人员：	Dong, Wen
开始时间：	20170901
预计完成日期：	20180831
实际结束时间：	0