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基于模体和复杂网络的成都市交通旅客出行研究OA

Regional Traffic Passenger Travel Based on Motif and Complex Network

中文摘要英文摘要

设计出行模体模型,对旅客出行序列进行抽取转化,构建基于铁路和航空出行旅客的出行模体,基于四川省成都市的旅客出行数据进行实证分析.2节点至4节点构成的旅客出行模体,可以表示绝大多数的旅客出行模式.主要出行模体为2个节点间的单向单次出行和往返出行,并且随时间推移,铁路出行对用户的吸引力逐渐增加,航空出行逐渐减少.研究铁路旅客出行网络和航空旅客出行网络特征,2种出行网络均为异质性网络,网络中大度节点倾向于连接小度节点,网络聚类系数较大,网络中不同站点连接紧密.成都市的铁路人群出行网络结构稳定且大多集中于我国东南部地区,航空出行网络逐步发展且具有更高的分散性.网络效率研究结果表明,旅客出行网络连通性一般,多集中在特定地区.

A travel model(TM)was designed to extract and transform the passenger travel sequence,and a travel model based on regional railway and air travel passengers was constructed.The empirical analysis was conducted based on the passenger travel data of a large city in Sichuan Province.The passenger travel model consisting of two to four nodes can represent the vast majority of passenger travel modes.The main travel models are one-way single trip and round-trip trip between two nodes.As time goes by,the attractiveness of railway travel to users gradually increases,while air travel gradually decreases.The characteristics of regional railway travel network(RRTN)and air passenger travel network(RATN)were studied.Both travel networks are heterogeneous.Large nodes in the network tend to connect small nodes.The network clustering coefficient is large,and different stations in the network are closely connected.The structure of the railway passenger travel network in this region is stable and most of them are concentrated in the southeast of China.The air travel network is gradually developed and has a higher dispersion.The results of network efficiency research show that the connectivity of regional passenger travel networks is general,mostly concentrated in specific regions.

徐进;宋浩男

西南交通大学 经济管理学院,四川 成都 610031||西南交通大学 综合交通大数据应用技术国家工程实验室,四川 成都 610031||西南交通大学 四川省服务科学与创新重点实验室,四川 成都 610031

交通运输

出行模体复杂网络拓扑特征出行流向网络效率

Travel MotifComplex NetworkTopological CharacteristicsTravel DirectionNetwork Efficiency

《铁道运输与经济》 2024 (002)

数据驱动的跨项目知识转移方法研究:知识图谱与迁移学习视角

40-46,70 / 8

国家自然科学基金项目(72171197);教育部人文社会科学研究项目(21XJAZH003)

10.16668/j.cnki.issn.1003-1421.2024.02.05

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