Please wait a minute...
Journal of ZheJIang University(Science Edition)  2018, Vol. 45 Issue (1): 82-91    DOI: 10.3785/j.issn.1008-9497.2018.01.013
    
The analysis of high profitable strategy for seeking passengers based on taxi GPS trajectory data of Shenzhen city
LIU Li1,2, ZHANG Feng1,2, DU Zhenhong1,2, LIU Renyi1,2, JIA Yujie1,2
1. Zhejiang Provincial Key Lab of GIS, Zhejiang University, Hangzhou 310028, China;
2. Department of Geographic Information Science, Zhejiang University, Hangzhou 310027, China
Download: HTML (   PDF(7079KB)
Export: BibTeX | EndNote (RIS)      

Abstract  Taxi, as an important supplement of urban public transport, plays an important role in resident daily traffic. However, because of the unbalanced spatial and temporal distribution of the potential passengers, high no-load rate and unhealthy competition keep releasing negative impacts on taxi business. With the development of ITS (intelligent transport system) technology, more and more attempts have been made to study approaches of seeking taxi passengers, while neglecting the analysis of high profitable seeking strategy. Based on mathematical statistics and geo-statistics, we propose a novel spatial and temporal analysis method based on an evaluation index, which takes the combination of income per unit time in the load state and the time for seeking passengers in the adjacent no-load state. We investigated the Shenzhen's taxi data set, which contained 13 798 taxis GPS trajectories during a week from April 18 to April 24, 2011 to conduct a comprehensive analysis on the characteristics of taxi operation and the spatial and temporal distribution of people's daily traffic. Our study indicates that the designed method yields a better reflection on the distribution of high profitable passengers, providing a new perspective for the efficient operation of the taxi.

Key wordsGPS trajectory data      mathematical statistics      geo-statistics      high profitable passengers seeking     
Received: 09 November 2016      Published: 15 December 2017
CLC:  P208  
Cite this article:

LIU Li, ZHANG Feng, DU Zhenhong, LIU Renyi, JIA Yujie. The analysis of high profitable strategy for seeking passengers based on taxi GPS trajectory data of Shenzhen city. Journal of ZheJIang University(Science Edition), 2018, 45(1): 82-91.

URL:

https://www.zjujournals.com/sci/10.3785/j.issn.1008-9497.2018.01.013     OR     https://www.zjujournals.com/sci/Y2018/V45/I1/82


基于深圳市出租车轨迹数据的高效益寻客策略研究

出租车作为城市公共交通的重要补充形式,在居民日常出行中发挥着重要作用.然而,客源时空分布不均衡导致了出租车空载率高、寻客难度大等问题,降低了出租车的运营效率.随着ITS(intelligent transport system)技术的发展,越来越多的学者将出租车寻客模式作为其研究点,目前尚缺乏对基于寻客效益指标的寻客策略方法的研究.为了提高出租车寻客效益,提出了基于寻客效益指标模型的时空分析方法,将出租车载客态单位时间收入E及相邻空载态寻客时间T相结合作为寻客效益评价指标,并采用数理统计与地理统计相结合的方法,对高效益客源的时空分布特征进行研究.采用深圳市2011年4月18-24日一周内13 798辆出租车的GPS轨迹数据,分析了深圳市出租车的运营特点及人们出行需求的时空分布特征.结果表明:该方法能够较好地反映高效益客源的时空分布特征,为出租车高效益寻客提供辅助决策支持,也为出租车高效运营提供了新的视角.

关键词: GPS轨迹数据,  数理统计,  地理统计,  高效益寻客 
[1] 唐炉亮,郑文斌,王志强,等.城市出租车上下客的GPS轨迹时空分布探测方法[J].地球信息科学学报,2015(10):1179-1186. TANG L L,ZHENG W B,WANG Z Q,et al.Space time analysis on the pick-up and drop-off of taxi passengers based on GPS big data[J].Journal of Geo-Information Science,2015(10):1179-1186.
[2] 李衢伶.基于GPS轨迹的出租车载客路径智能推荐[D].长沙:湖南科技大学,2014. LI Q L.Intelligent GPS-based Taxi Passenger Path Trajectory Recommendation[D].Changsha:Hunan University of Science and Technology,2014.
[3] LIU L,ANDRIS C,RATTI C.Uncovering cab drivers' behavior patterns from their digital traces[J].Computers,Environment and Urban Systems, 2010,34(6):541-548.
[4] LI B,ZHANG D,SUN L,et al.Hunting or waiting? Discovering passenger-finding strategies from a large-scale real-world taxi dataset[C]//Pervasive Computing and Communications Workshops(PERCOM Workshops),2011 IEEE International Conference. Seattle:IEEE,2011:63-68.
[5] ZHENG X,LIANG X,XU K.Where to wait for a taxi?[C]//Proceedings of the ACM SIGKDD International Workshop on Urban Computing.Beijing:ACM,2012:149-156.
[6] 关金平,朱竑.基于FCD的出租车空驶时空特性及成因研究——以深圳国贸CBD为例[J].中山大学学报(自然科学版),2010(S1):29-36. GUAN J P,ZHU H.Research on space-time characteristics and reasons of idle taxi based on FCD-Case in Shenzhen Guomao CBD[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2010(S1):29-36.
[7] 孙飞,张霞,唐炉亮,等.基于GPS轨迹大数据的优质客源时空分布研究[J].地球信息科学学报,2015(3):329-335. SUN F,ZHANG X,TANG L L,et al.Temporal and spatial distribution of high efficiency passengers based on GPS trajectory big data[J].Journal of Geo-Information Science,2015(3):329-335.
[8] 王郑委.基于大数据Hadoop平台的出租车载客热点区域挖掘研究[D].北京:北京交通大学,2016. WANG Z W.Research on Mining Taxi Pick-up Hotspots Area Based on Big Data Hadoop Platform[D].Beijing:Beijing Jiao Tong University,2016.
[9] 罗端高,史峰.考虑需求分布影响的城市出租车运营平衡模型[J].铁道科学与工程学报,2009(1):87-91. LUO D G,SHI F.A taxi service network equilibrium model with the influenced of demand distribution[J].Journal of Railway Science and Engineering,2009(1):87-91.
[10] PHITHAKKITNUKOON S,VELOSO M,BENTO C,et al.Taxi-aware map:Identifying and predicting vacant taxis in the city[C]//International Joint Conference on Ambient Intelligence.Berlin/Heidelberg:Springer,2010:86-95.
[11] YUANG N J,ZHENG Y,ZHANG L,et al.T-finder:A recommender system for finding passengers and vacant taxis[J]. IEEE Transactions on Knowledge and Data Engineering,2013,25(10):2390-2403.
[12] XU X,ZHOU J Y,LIU Y,et al.Taxi-RS:Taxi-hunting recommendation system based on taxi GPS data[J].IEEE Transactions on Intelligent Transportation Systems,2015,16(4):1-12.
[13] 宋炜.晒晒出租车的收入账[N].榆林日报,2012-03-27(3). SONG W.The income for bask in the taxi[N].YU LIN RI BAO,2012-03-27(3).
[14] 祁文田.基于GPS数据的出租车载客点空间特征分析[D].长春:吉林大学,2013. QI W T. Analyzing Spatial Characteristics of Taxi Pick-up with GPS Data[D].Changchun:Jilin University,2013.
[15] 张红,王晓明,过秀成,等.出租车GPS轨迹大数据在智能交通中的应用[J].兰州理工大学学报,2016(1):109-114. ZHANG H,WANG X M,GUO X C,et al.Application of taxi GPS big trajectory data in intelligent traffic system[J].Journal of Lanzhou University of Technology,2016(1):109-114.
[16] 辛飞飞,陈小鸿,林航飞.浮动车数据路网时空分布特征研究[J].中国公路学报,2008(4):105-110. XIN F F,CHEN X H,LIN H F.Research on time space distribution characteristics of floating car data in road network[J].China Journal of Highway and Transport, 2008(4):105-110.
[17] 郑运鹏,赵刚,刘健.基于出租车GPS数据的交通热区识别方法[J].北京信息科技大学学报(自然科学版),2016(1):43-47. ZHENG Y P,ZHAO G,LIU J.A novel method for traffic hotspots recognition based on taxi GPS data[J].Journal of Beijing Information Science & Technology University(Natural Science Edition),2016(1):43-47.
[18] 韩忠民.知经纬度计算两点精确距离[J].科技传播,2011(11):196,174. HAN Z M.Knowing the latitude and longitude to calculate two precise points[J].Public Communication of Science & Technology,2011(11):196,174.
No related articles found!