基于乘客需求数据的定制商务班车站点选址方法
|
摘要
针对基础的聚类算法无法适应定制商务班车站点设置的问题,在传统的基于密度的带有噪声的空间聚类算法基础上,通过衡量类簇精细化服务指标确定分组效果,并对聚类效果不理想的组别依据其数据特征自动更新以扫描半径和最小包含点数为代表的聚类参数,进行迭代聚类,直到聚类效果达标为止。同时,结合节点重要度的思想改进基于密度的带有噪声的空间聚类算法,使其能够输出备选站点。研究结果表明,改进的算法能够较好地根据数据特征给出应有分组,给出的扫描半径和最小包含点参数能够较好地适应分组情况,备选节点能够有效地匹配周围的交通资源。
Based on the traditional algorithm of density-based spatial clustering of applications with noise(DBSCAN),this paper determined the grouping effect by measuring cluster refinement service index. For groups with unsatisfactory clustering effect, the clustering parameters represented by scanning radius and minimum points were updated automatically according to their data characteristics,and then iterative clustering was carried out until the clustering effect was up to standard. At the same time,the DBSCAN algorithm was combined with the idea of node importance,which enabled it to output alternative sites. The results show that the improved DBSCAN algorithm can give the proper grouping according to the data characteristics,and the scanning radius and the minimum points parameters can be better adapted to the grouping situation,and the alternative nodes can effectively match the traffic resources around them.
Based on the traditional algorithm of density-based spatial clustering of applications with noise(DBSCAN),this paper determined the grouping effect by measuring cluster refinement service index. For groups with unsatisfactory clustering effect, the clustering parameters represented by scanning radius and minimum points were updated automatically according to their data characteristics,and then iterative clustering was carried out until the clustering effect was up to standard. At the same time,the DBSCAN algorithm was combined with the idea of node importance,which enabled it to output alternative sites. The results show that the improved DBSCAN algorithm can give the proper grouping according to the data characteristics,and the scanning radius and the minimum points parameters can be better adapted to the grouping situation,and the alternative nodes can effectively match the traffic resources around them.
引文
[1]邹彦雯.定制商务班车线路设计研究[D].北京:北京交通大学,2017.
[2]胡学文,何保红,何民,等.美国辅助公交发展政策初探[J].现代城市研究,2015(5):119-125.
[3]叶英杰.基于空间co-location模式挖掘的城市公交站点的选址分析[D].昆明:云南大学,2014.
[4]SAKA A A. Model for determining optimum bus-stop spacing in urban areas[J]. Journal of Transportation Engineering,2001,127(3):195-199.
[5]CHIEN S I,QIN Z Q. Optimization of bus stop locations for improving transit accessibility[J]. Transportation planning and Technology,2004,27(3):211-227.
[6]ZIARI H,KEYMANESH M R,KHABIRI M M. Locating stations of public transportation vehicles for improving transit accessibility[J]. Transport,2007,22(2):99-104.
[7]ALONSO B,MOURA J L,DELL’OLIO L,et al. Bus stop location under different levels of network congestion and elastic demand[J]. Transport,2011,26(2):141-148.
[8]MOURA J L,ALONSO B,IBEAS A,et al. A two-stage urban bus stop location model[J]. Networks and Spatial Economics,2012,12(3):403-420.
[9]郭戎格.基于IC卡数据的定制公交线路优化[D].北京:北京交通大学,2017
[10]KIM B,KIM S,PARK J. A school bus scheduling problem[J]. European Journal of Operational Research,2012,218(2):577-585.
[11]SHAFFIEI Z A,ABAS Z A,SHIBGHATULLAH A S. An optimized intelligent automation for university shuttle bus driver scheduling using mutual swapping and harmony search[J]. International journal of computer science and information security,2016,14(8):875-879.
[12]CIPRIANI E,GORI S,PETRELLI M. Transit network design:A procedure and an application to a large urban area[J].Transportation Research Part C Emerging Technologies,2012,20(1):3-14.
[13]NIKOLIC M,TEODOROVIC D. Transit network design by Bee Colony Optimization[J]. Expert Systems with Applications,2013,40(15):5945-5955.
[14]王忠民,韩娜,宋辉,等.基于改进DBSCAN的移动用户兴趣点提取方法[J].西安邮电大学学报,2015,20(6):102-105.
[15]韩利钊,钱雪忠,罗靖,等.基于区域划分的DBSCAN多密度聚类算法[J].计算机应用研究,2018,35(6):1668-1671.
[16]林姿琼,黄伟婷.改进的DBSCAN空间聚类算法研究[J].漳州师范学院学报(自然科学版),2009(1):22-25.
[17]蒋雄,胡德欢.公交网络中心化及节点重要度评估研究[J].市政技术,2014,32(3):28-31.
[18]王振报,韩东,陈艳艳.大城市公交枢纽布局规划实用方法[J].北京工业大学学报,2011,37(10):1524-1529.
[19]赵文,夏桂书,苟智坚,等.一种改进的DBSCAN算法[J].四川师范大学学报(自然科学版),2013,36(2):312-316.
[20]周红芳,王鹏. DBSCAN算法中参数自适应确定方法的研究[J].西安理工大学学报,2012,28(3):289-292.
[21]程申,刘铮,阴丽娜.城市新区公交线路及站点规划方法研究[J].交通标准化,2010(1):96-100.
[22]刘力铭,邓二畅,刘涛,等.城市公交站点选址评价分析[J].物流科技,2017(3):71-73.
[23]何世伟.综合交通枢纽规划——理论与方法[M].北京:人民交通出版社,2012.
[24]潘述亮,俞洁,卢小林,等.灵活型公交服务系统及其研究进展综述[J].城市交通,2014,12(2):62-68.
[25]王飞.定制公交线路和站点规划研究[D].北京:北京交通大学,2017.
[2]胡学文,何保红,何民,等.美国辅助公交发展政策初探[J].现代城市研究,2015(5):119-125.
[3]叶英杰.基于空间co-location模式挖掘的城市公交站点的选址分析[D].昆明:云南大学,2014.
[4]SAKA A A. Model for determining optimum bus-stop spacing in urban areas[J]. Journal of Transportation Engineering,2001,127(3):195-199.
[5]CHIEN S I,QIN Z Q. Optimization of bus stop locations for improving transit accessibility[J]. Transportation planning and Technology,2004,27(3):211-227.
[6]ZIARI H,KEYMANESH M R,KHABIRI M M. Locating stations of public transportation vehicles for improving transit accessibility[J]. Transport,2007,22(2):99-104.
[7]ALONSO B,MOURA J L,DELL’OLIO L,et al. Bus stop location under different levels of network congestion and elastic demand[J]. Transport,2011,26(2):141-148.
[8]MOURA J L,ALONSO B,IBEAS A,et al. A two-stage urban bus stop location model[J]. Networks and Spatial Economics,2012,12(3):403-420.
[9]郭戎格.基于IC卡数据的定制公交线路优化[D].北京:北京交通大学,2017
[10]KIM B,KIM S,PARK J. A school bus scheduling problem[J]. European Journal of Operational Research,2012,218(2):577-585.
[11]SHAFFIEI Z A,ABAS Z A,SHIBGHATULLAH A S. An optimized intelligent automation for university shuttle bus driver scheduling using mutual swapping and harmony search[J]. International journal of computer science and information security,2016,14(8):875-879.
[12]CIPRIANI E,GORI S,PETRELLI M. Transit network design:A procedure and an application to a large urban area[J].Transportation Research Part C Emerging Technologies,2012,20(1):3-14.
[13]NIKOLIC M,TEODOROVIC D. Transit network design by Bee Colony Optimization[J]. Expert Systems with Applications,2013,40(15):5945-5955.
[14]王忠民,韩娜,宋辉,等.基于改进DBSCAN的移动用户兴趣点提取方法[J].西安邮电大学学报,2015,20(6):102-105.
[15]韩利钊,钱雪忠,罗靖,等.基于区域划分的DBSCAN多密度聚类算法[J].计算机应用研究,2018,35(6):1668-1671.
[16]林姿琼,黄伟婷.改进的DBSCAN空间聚类算法研究[J].漳州师范学院学报(自然科学版),2009(1):22-25.
[17]蒋雄,胡德欢.公交网络中心化及节点重要度评估研究[J].市政技术,2014,32(3):28-31.
[18]王振报,韩东,陈艳艳.大城市公交枢纽布局规划实用方法[J].北京工业大学学报,2011,37(10):1524-1529.
[19]赵文,夏桂书,苟智坚,等.一种改进的DBSCAN算法[J].四川师范大学学报(自然科学版),2013,36(2):312-316.
[20]周红芳,王鹏. DBSCAN算法中参数自适应确定方法的研究[J].西安理工大学学报,2012,28(3):289-292.
[21]程申,刘铮,阴丽娜.城市新区公交线路及站点规划方法研究[J].交通标准化,2010(1):96-100.
[22]刘力铭,邓二畅,刘涛,等.城市公交站点选址评价分析[J].物流科技,2017(3):71-73.
[23]何世伟.综合交通枢纽规划——理论与方法[M].北京:人民交通出版社,2012.
[24]潘述亮,俞洁,卢小林,等.灵活型公交服务系统及其研究进展综述[J].城市交通,2014,12(2):62-68.
[25]王飞.定制公交线路和站点规划研究[D].北京:北京交通大学,2017.