上海市早高峰轨道交通缓挤的时空行为规划策略 研究——以轨道交通9号线为例
Study on Spatio-Temporal Behavior Planning Strategies for Alleviating Morning Peak Congestion in Shanghai's Urban Rail Transit: A Case Study of Metro Line 9
陈子浩
同济大学建筑与城市规划学院 博士研究生
罗歆兰
同济大学建筑与城市规划学院 硕士研究生
王 德（通信作者）
同济大学建筑与城市规划学院 教授，博士生导师
游智敏
同济大学建筑与城市规划学院 硕士研究生
周新刚
同济大学建筑与城市规划学院 副教授，博士生导师
摘要: 针对上海市严峻的轨道交通高峰客流拥挤问题，在时空行为规划框架下进行缓挤策略研究。以轨道交通9号线为例，利
用智能卡数据模拟乘客出行时空轨迹，基于拥挤形成的时空过程视角识别轨道交通拥挤情况，划分拥挤类型，并追溯拥
挤客流的来源与去向。同时，深入挖掘乘客的出行目的、出行规律和出行约束等出行特征。在此基础上，针对不同出行特
征人群提出信息推荐、预约进站、调整上班时间以及转移就业岗位等时间、空间和行为策略，并定量评估策略实施的缓
挤潜力，为治理城市轨道交通拥挤问题提供可借鉴的视角、方法和策略。
Abstract: In response to the severe congestion problem during peak passenger flow in Shanghai's urban rail transit, this paper conducts a study on decongestion strategies under the framework of spatio-temporal behavior planning. Taking Metro Line 9 as an example,
we use smart card data to simulate passengers' spatio-temporal travel trajectories, identify the congestion situation in urban rail
transit, divide the types of congestion, and trace the sources and destinations of congested passenger flow from the perspective of
the spatio-temporal process of congestion formation. At the same time, we deeply mine the travel characteristics of passengers, such as travel purposes, travel patterns, and travel constraints. On this basis, we propose temporal, spatial, and behavior strategies
such as information recommendation, reservation for entry, adjustment of work time, and job relocation for different travel characteristic groups, and quantitatively evaluate the congestion mitigation potential of strategy implementation, providing perspectives, methods, and strategies for reference of managing urban rail transit congestion.
关键词：时空行为规划；时空过程视角；城市轨道交通拥挤；缓挤策略；轨道交通9号线
Keyword: spatio-temporal behavior planning; spatio-temporal process perspective; urban rail transit congestion; decongestion strategy; Metro Line 9
中图分类号：TU984
文献标识码: A
资金资助
国家 国家自然科学基金课题 52378069
邹庆茹. 城市轨道交通网络高峰客流拥挤管控
研究[D]. 北京：北京交通大学，2020.
ZOU Qingru. Research on peak-congestion control
and management for urban rail transit network[D].
Beijing: Beijing Jiaotong University, 2020.
王波. 上海轨道交通早高峰客流拥挤与居民通
勤关系分析[J]. 城市轨道交通研究，2016，19
（7）：75-78.
WANG Bo. Relationship between the rush hour passenger flow congestion and residents commuting
in Shanghai rail transit system[J]. Urban Mass Transit, 2016, 19(7): 75-78.
王德，胡杨. 城市时空行为规划：概念、框架与展
望[J]. 城市规划学刊，2022（1）：44-50.
WANG De, HU Yang. Urban spatial-temporal activity planning: concept, framework and
prospect[J]. Urban Planning Forum, 2022(1): 44-50.
王亚洁. 国外城市轨道交通与站域土地利用互
动研究进展[J]. 国际城市规划，2018，33（1）：
111-118.
WANG Yajie. International studies on the inter-
action between urban rail transit and land use of station areas[J]. Urban Planning International,
2018, 33(1): 111-118.
杨陶源，吴海燕，杨静，等. 基于微观仿真的城市
轨道交通车站设施服务水平研究[J]. 交通信息
与安全，2016，34（1）：123-129.
YANG Taoyuan, WU Haiyan, YANG Jing, et al. Development of the level of service for the facilities
of urban rail transit stations based on microscopic simulation[J]. Journal of Transport Information and
Safety, 2016, 34(1): 123-129.
赵鹏军，曹毓书. 基于多源地理大数据与机器学
习的地铁乘客出行目的识别方法[J]. 地球信息
科学学报，2020，22（9）：1753-1765.
ZHAO Pengjun, CAO Yushu. Identifying metro trip purpose using multi-source geographic big
data and machine learning approach[J]. Journal of
Geo-information Science, 2020, 22(9): 1753-1765.
CHU K K A, CHAPLEAU R. Augmenting transit
trip characterization and travel behavior comprehension:
multiday location-stamped smart card transactions[J]. Transportation Research Record, 2010, 2183(1): 29-40.
LONG Y, LIU X, ZHOU J, et al. Early birds, night owls, and tireless/recurring itinerants: an exploratory analysis of extreme transit behaviors in Beijing, China[J]. Habitat International, 2016, 57: 223-232.
WU Y, LU J, CHEN Y. Study of the influencing factors of urban rail transit travel behavior: a case study of Xi'an[J]. Applied Mechanics and Materials, 2015, 744-746: 2049-2052.
DIJST M, VIDAKOVIC V. Travel time ratio: the key factor of spatial reach[J]. Transportation, 2000, 27(2): 179-199.
史小俊. 地铁车站应对大客流的组织措施[J]. 城
市轨道交通研究，2009，12（10）：70-72.
SHI Xiaojun. Counter measures in passenger flow
organization for metro station[J]. Urban Mass Transit, 2009, 12(10): 70-72.
陈子浩，胡杨，王德，等. 欧洲城市时间政策的主
要议题及相应实践[J]. 城市发展研究，2022，
29（11）：89-96.
CHEN Zihao, HU Yang, WANG De, et al. Main topics and corresponding practices of European
urban time polices[J]. Urban Development
Studies, 2022, 29(11): 89-96.
胡杨，陈子浩，王德，等. 城市时间政策的西欧经
验与启示：实践评述、发展脉络与行动框架[J/
OL]. 国 际 城 市 规 划：1-18[2024-02-18]. https://
doi.org/10.19830/j.upi.2022.449.
HU Yang, CHEN Zihao, WANG De, et al. Urban time policies in western Europe: practice review, development context and action framework[J/OL].Urban Planning International: 1-18[2024-02-18]. https://doi.org/10.19830/j.upi.2022.449.
王倩，郭继孚，葛昱，等. 北京市轨道交通车站预
约出行实践与思考[J]. 城市交通，2021，19（1）：
89-94.
WANG Qian, GUO Jifu, GE Yu, et al. Practice and thoughts on reservation travel in Beijing metro stations[J]. Urban Transport of China, 2021, 19(1):
89-94.
田贵超，张建同，胡一竑. 基于乘客行为的城市
轨道交通分时定价策略研究[J]. 上海管理科学，
2013，35（5）：77-83.
TIAN Guichao, ZHANG Jiantong, HU Yihong. Research on urban rail transit's time differential pricing strategy based on passenger behavior[J].
Shanghai Management Science, 2013, 35(5): 77-83.
INGVARDSON J B, NIELSEN O A, RAVEAU S, et al. Passenger arrival and waiting time distributions dependent on train service frequency
and station characteristics: a smart card data analysis[J]. Transportation Research Part C: Emerging Technologies, 2018, 90: 292-306.
GUO Z, ZHAO J, WHONG C, et al. Redesigning
subway map to mitigate bottleneck congestion: an
experiment in Washington DC using Mechanical Turk[J]. Transportation Research Part A: Policy and Practice, 2017, 106: 158-169.
中国城市轨道交通协会. 城市轨道交通2016年
度统计和分析报告[J]. 城市轨道交通，2017（1）：
20-36.
China Association of Metros. China metros annual
statistical and analysis report, 2016[J]. China Metros, 2017(1): 20-36.
