摘要
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.
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.
出处
《浙江大学学报（理学版）》
CAS
CSCD
北大核心
2018年第1期82-91,共10页
Journal of Zhejiang University（Science Edition）
基金
国家自然科学基金资助项目(41671391
41471313
41101356
41101371
41171321)
国家科技基础性工作专项(2012FY112300)
海洋公益性行业科研专项经费资助项目(2015418003
201305012)
浙江省科技攻关项目(2014C33G20
2013C33051)
关键词
GPS轨迹数据
数理统计
地理统计
高效益寻客
GPS trajectory data
mathematical statistics
geo-statistics
high profitable passengers seeking
