摘要
为减少城市交通模型的建模成本、缩短建模周期,有效提高交通模型体系的建模粒度和精度,创新研究基于手机信令大数据的宏观交通模型建设方法。首先,研究手机信令数据处理的算法流程及数据结构定义,提取人口岗位、职住关系和出行OD等指标;其次,研究如何应用手机信令特征结果改进、优化交通模型的算法流程设计和标定方法,包括人口岗位模型、出行分布选择模型、高峰小时模型和对外及过境模型等;最后,以深圳宏观交通模型体系更新与升级为例,探讨了手机信令数据指标的结果及其应用,验证了研究内容的可操作性。
In order to reduce the modeling cost and cycle time of urban transportation model,also effectively improve the modeling granularity and accuracy of transportation model,the study innovatively explored the construction method of macro transportation model based on cellphone big data.This paper presents the algorithm flow and data structure definition of cellphone data to extract the characteristic index set,including population and employment,work residence relationship and travel demand OD.Also,it explores how to apply the results of cellphone data characteristic index set to improving the algorithm flow design and parameter calibration method of transportation model,including population and employment model,trip distribution selection model,peak hour model and external model.Finally,the results and practical application of cellphone data are discussed,taking the updating and upgrading of Shenzhen macro transportation model as an example,to verify the feasibility of the research content.
作者
张凯
汤俊青
段仲渊
丘建栋
ZHANG Kai;TANG Junqing;DUAN Zhongyuan;QIU Jiandong(Shenzhen Urban Transport Planning Center Co.,Ltd,Shenzhen 518000 China;Huawei Technologies Co.,Ltd,Shenzhen 518000,China)
出处
《交通与运输》
2023年第S01期226-231,241,共7页
Traffic & Transportation
关键词
手机信令
交通模型
交通大数据
人口岗位
通勤需求
Cellphone data
Transportation model
Traffic big data
Population and employment
Commuting travel demand
