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作 者:罗霞[1] 李树超 刘硕智[1] 吴颢 LUO Xia;LI Shu-chao;LIU Shuo-zhi;WU Hao(School of Transportation and Logistics,Southwest Jiaotong University,Chengdu Sichuan 610031,China;Smart City Research Institute of China Electronics Technology Group Corporation,Shenzhen Guangdong 518000,China)
机构地区:[1]西南交通大学交通运输与物流学院,四川成都610031 [2]中电科新型智慧城市研究院有限公司,广东深圳518000
出 处:《计算机仿真》2020年第6期111-116,256,共7页Computer Simulation
基 金:四川省科技计划项目应用基础研究(重点)(2017JY0072)。
摘 要:选取成都市104路公交作为研究线路,并通过阈值法提取通勤人群样本,结果从11843名公交乘客中筛选了1384名公交通勤乘客。其次,利用基于AVL数据的到离站推导算法和公交车辆上车站点识别算法,得到乘客上车站点信息。然后使用乘车频次统计法和空间聚类法得到1384个通勤OD。最后,利用上述方法推导成都市工作日早高峰所有公交通勤乘客的OD矩阵,并对通勤出行需求进行仿真,然后以104路为例分析其刷卡次数分布特征和通勤OD频次分布特征。相比较于传统的基于AVL数据的OD推导,结合IC卡数据更加准确地推导通勤OD并可视化模拟了通勤出行需求的时空特征,研究结果可为成都市的公交线网规划提供一定的参考价值。In this paper,the bus No.104 in Chengdu was selected as the research route and the commuter sample was extracted using the threshold method.The result was that 1,384 public transportation passengers were selected from 11,843 bus passengers.Secondly,the information about the passenger boarding site was obtained by using the AVL data-based to-station derivation algorithm and the bus-on-car site identification algorithm.Then,the 1384 commute ODs were obtained by using the frequency statistics method and the spatial clustering method.Finally,we used the above method to derive the OD matrix of all public transportation passengers in the morning of the city working day,simulated the commuter travel demand,and then analyzed the distribution characteristics of the card number and the distribution of commuter OD frequency by taking 104 road as an example.Compared with the traditional OD derivation based on AVL data,this paper combines the IC card data to derive the commute OD more accurately and visualize the spatio-temporal characteristics of commuter travel demand.The research results can provide a certain reference value for the bus network planning in Chengdu.
分 类 号:U491.1[交通运输工程—交通运输规划与管理] TP391[交通运输工程—道路与铁道工程]
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