利用实时移动交通数据预测交通密度和兴趣

Yuan Gao, Ao Hong, Quan Zhou, Xiangyang Li, Shasha Liu, B. Shao
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引用次数: 2

摘要

随着移动用户和应用数量的不断增加,数据服务和按需建议逐渐在移动生活中发挥重要作用。本文基于无线个人通信网络,提出了一种基于实时移动流量数据和无线定位信息的用户流量密度和个人兴趣预测新方法。服务提供商可以根据预测向目标用户发送准确的信息,以保证服务质量和推送服务。利用网络服务商提供的清华大学周边地区的真实交通数据进行了仿真,并对预测的准确性进行了评估。仿真结果表明,将交通数据和位置数据结合使用的方法可以显著提高推荐成功率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Traffic Density and Interest Using Real Time Mobile Traffic Data
With the increasing number of mobile users and applications, data services and on-demand suggestions gradually play an important role in mobile life. In this work, based on the wireless personal communication network, we propose a novel prediction method of user traffic density and personal interest based on real time mobile traffic data and wireless positioning information. Service provider could send precise information to target users based on the prediction to ensure the quality and push service. Then we execute a simulation using real traffic data around Tsinghua University obtained from network service provider, and evaluate the accuracy of the prediction. Simulation results indicate that, our proposed method by using the traffic data and position jointly can significantly increase the rate of successful recommendation.
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