基于出租车数据的载客热点与打车热点研究
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摘要
面对城市出租车高空载率和乘客打车难问题,本文针对出租车司机端和乘客端分别进行载客热点和打车热点的分析研究,提出了一种基于DBSCAN算法的数据处理模型.利用这个模型对北京市182辆出租车的GPS轨迹数据进行处理,提高了数据精度;对于不同的受众,采用K-means算法对数据进行聚类分析,得到相关热点.实验表明,划分目标用户进行各热点的推荐不仅可以有效地为出租车司机提供高概率的载客热点,乘客打车难问题也有了一种可行的解决方法.
Faced with the problem of high no-load rate of urban taxi and taxi difficulty of passengers, this study analyzes the passenger hotspots for taxi drivers and taxi hotspots for passengers, and proposes a data processing model based on DBSCAN algorithm. Using this model, the GPS trajectory data of 182 taxis in Beijing are processed, and the data precision is improved. For different audiences, K-means algorithm is used to cluster the data and get the relevant hotspots.Experiments show that the proposed method can not only effectively provide taxi drivers with high probability of passenger hotspots, but also provide a feasible solution to the problem of taxi difficulty of passengers.
Faced with the problem of high no-load rate of urban taxi and taxi difficulty of passengers, this study analyzes the passenger hotspots for taxi drivers and taxi hotspots for passengers, and proposes a data processing model based on DBSCAN algorithm. Using this model, the GPS trajectory data of 182 taxis in Beijing are processed, and the data precision is improved. For different audiences, K-means algorithm is used to cluster the data and get the relevant hotspots.Experiments show that the proposed method can not only effectively provide taxi drivers with high probability of passenger hotspots, but also provide a feasible solution to the problem of taxi difficulty of passengers.
引文
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17姬波,叶阳东,肖煜.基于信息瓶颈方法的出租车空载聚集区聚类算法.小型微型计算机系统,2013,34(9):2139-2143.[doi:10.3969/j.issn.1000-1220.2013.09.035]
18张明月.基于出租车轨迹的载客点与热点区域推荐[硕士学位论文].湘潭:湖南科技大学,2013.
2 Yuan J,Zheng Y,Xie X,et al.T-drive:Enhancing driving directions with taxi drivers’intelligence.IEEE Transactions on Knowledge and Data Engineering,2013,25(1):220-232.[doi:10.1109/TKDE.2011.200]
3连德福.基于位置社交网络的数据挖掘[博士学位论文].合肥:中国科学技术大学,2014.
4 Yuan W,Deng P,Table T,et al.An unlicensed taxi identification model based on big data analysis.IEEETransactions on Intelligent Transportation Systems,2016,17(6):1703-1713.[doi:10.1109/TITS.2015.2498180]
5 Zheng Y,Liu Y C,Yuan J,et al.Urban computing with taxicabs.Proceeding of the 13th International Conference on Ubiquitous Computing.Beijing,China.2011.89-98.
6 Pan G,Qi GD,Wu ZH,et al.Land-use classification using taxi GPS traces.IEEE Transactions on Intelligent Transportation Systems,2013,14(1):113-123.[doi:10.1109/TITS.2012.2209201]
7 Zheng Y,Capra L,Wolfson O,et al.Urban computing:Concepts,methodologies,and application.ACMTransactions on Intelligent Systens and Technology,2014,5(3):38.
8 Atmaji FTD,Sig KY.Mining the GPS big data to optimize the taxi dispatching management.Proceedings of the 4th International Conference on Information and Communication Technology.Bandung,Indonesia.2016.1-4.
9温雅静.基于热点载客区域的出租车应急调度方案研究[硕士学位论文].北京:北京交通大学,2014.
10张致宁.基于K-means和DBSCAN的轨迹数据挖掘研究.中国战略新兴产业,2017,(44):113-114.
11 Ye Y,Zheng Y,Chen YK,et al.Mining individual life pattern based on location history.Proceedings of the Tenth International Conference on Mobile Data Management:Systems,Services and Middleware.Taipei,China.2009.1-10.
12 Karli S,Saygin Y.Mining periodic patterns in spatiotemporal sequences at different time granularities.Intelligent Data Analysis,2009,13(2):301-335.[doi:10.3233/IDA-2009-0368]
13谭川豫.移动对象轨迹分析技术研究[硕士学位论文].长沙:国防科技大学,2010.
14 H?gerstrand T.Reflections on“What about People in Regional Science?”.Papers of the Regional Science Association,1989,66(1):1-6.[doi:10.1007/BF01954291]
15成海霞.基于Android的出租车载客热点推荐系统[硕士学位论文].湘潭:湖南科技大学,2016.
16 Yuan J,Zheng Y,Zhang LH,et al.Where to find my next passenger.Proceedings of the 13th International Conference on Ubiquitous Computing.Beijing,China.2011.109-118.
17姬波,叶阳东,肖煜.基于信息瓶颈方法的出租车空载聚集区聚类算法.小型微型计算机系统,2013,34(9):2139-2143.[doi:10.3969/j.issn.1000-1220.2013.09.035]
18张明月.基于出租车轨迹的载客点与热点区域推荐[硕士学位论文].湘潭:湖南科技大学,2013.