基于时空上下文协同过滤的出租车载客点推荐算法

doi:10.11772/j.issn.1001-9081.2015.06.1659

计算机应用 ›› 2015, Vol. 35 ›› Issue (6): 1659-1662.DOI: 10.11772/j.issn.1001-9081.2015.06.1659

基于时空上下文协同过滤的出租车载客点推荐算法

钱文逸¹, 蒋新华², 廖律超^2,3, 邹复民³

1. 中南大学软件学院, 长沙 410075;
2. 中南大学信息科学与工程学院, 长沙 410075;
3. 福建省汽车电子与电驱动技术重点实验室(福建工程学院), 福州 350108

收稿日期:2014-12-18 修回日期:2015-03-19 发布日期:2015-06-12
通讯作者: 钱文逸(1989-),男,湖北黄冈人,硕士研究生,主要研究方向:海量交通数据处理、基于轨迹数据的推荐算法;qwy19891112@126.com
作者简介:蒋新华(1956-),男,湖南长沙人,教授,博士生导师,主要研究方向:下一代移动互联网、交通信息处理、智能控制理论、先进PID控制;廖律超(1980-),男,福建长汀人,副教授,博士研究生,主要研究方向:海量动态信息数据挖掘分析、交通信息处理;邹复民(1976-),男,湖南隆回人,教授,博士,主要研究方向:交通信息处理、无线宽带网络移动应用。
基金资助:
国家自然科学基金资助项目(61304199,41471333);福建省高校杰出青年科研人才计划项目(JA14209);福建省自然科学基金资助项目(2013J01214);福建省科技重大专项(2011HZ0002-1);福建省交通科技计划项目(201318);福建省教育厅B类科研项目(JB3213)。

Recommendation algorithm of taxi passenger-finding locations based on spatio-temporal context collaborative filtering

QIAN Wenyi¹, JIANG Xinhua², LIAO Lyuchao^2,3, ZOU Fumin³

1. School of Software Engineering, Central South University, Changsha Hunan 410075, China;
2. School of Information Science and Engineering, Central South University, Changsha Hunan 410075, China;
3. Fujian Key Laboratory for Automotive Electronics and Electric Drive (Fujian University of Technology), Fuzhou Fujian 350108, China

Received:2014-12-18 Revised:2015-03-19 Published:2015-06-12

摘要/Abstract

摘要：

针对现有出租车载客点推荐算法忽略出租车所处上下文的情况,提出了一种基于时空上下文协同过滤的出租车载客点推荐算法。该算法将载客点信息映射到空间网格,通过在出租车司机驾驶行为相似度的计算中引入时间衰减因子,得到与目标出租车司机驾驶行为最相似的邻居集合,基于地点上下文过滤从相似邻居集合中选取感兴趣程度高的载客点推荐给目标出租车。在基于福州市出租车轨迹数据的实验中,时间衰减因子为0.7时,整体推荐效果最佳,同时该算法在邻居集合的不同大小时推荐准确率均优于传统协同过滤推荐算法。结果表明该算法与传统的协同过滤算法相比有更高的推荐准确度。

Abstract:

Because existing passenger-finding algorithms do not consider taxi's spatio-temporal context, a collaborative filtering recommendation algorithm of taxi passenger-finding based on spatio-temporal context was proposed. The proposed algorithm mapped potential passenger locations to space network, and introduced time delay factor to similarity measure to get the neighbor set which was similar to a target taxi's driving behavior. Based on location context, the proposed algorithm chose the target taxi's most interest potential passenger location from similar neighbor set. The experimental results on Fuzhou taxi trajectory data show that the proposed algorithm can get the best recommendation result when the time delay factor is 0.7. Meanwhile, compared to the traditional collaborative filtering recommendation algorithms, the proposed algorithm obtains better recommendation result under the neighbor sets with different size, which means the proposed algorithm is more accurate than the traditional collaborative filtering algorithms.

Key words: recommendation system, collaborative filtering, spatio-temporal context, Global Positioning System (GPS) trajectory, taxi passenger behavior

中图分类号:

TP181

钱文逸, 蒋新华, 廖律超, 邹复民. 基于时空上下文协同过滤的出租车载客点推荐算法[J]. 计算机应用, 2015, 35(6): 1659-1662.

QIAN Wenyi, JIANG Xinhua, LIAO Lyuchao, ZOU Fumin. Recommendation algorithm of taxi passenger-finding locations based on spatio-temporal context collaborative filtering[J]. Journal of Computer Applications, 2015, 35(6): 1659-1662.

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基于时空上下文协同过滤的出租车载客点推荐算法

Recommendation algorithm of taxi passenger-finding locations based on spatio-temporal context collaborative filtering

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