Showing 1 - 12 results of 12 for search '"Shanghai Metro"', query time: 0.05s Refine Results
  1. 1

    Improving equity through barrier-free transportation: an evaluation of Shanghai metro stations by Zhan Zhang, Xiongjie Yang, Chenming Jiang, Linjun Lu, Xiaoyue Li, Xiaoyu Rong, Ziqi Huang

    Published 2025-01-01
    “…A case study of two Shanghai metro stations, Xinzhuang and Xujiahui, was conducted using quantitative metrics, surveys, and interviews.ResultsA strong correlation between AHP scores and SUS ratings validated the framework’s reliability. …”
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    Article
  2. 2

    Gauge Deterioration Prediction of Urban Rail Transit Lines Based on CEEMD and SVR by JIA Qingtian, LIN Haijian, JIN Zhong

    Published 2025-01-01
    “…The prediction model is tested with 1,128 sets of track inspection sample data within the upward track section from K12+134 to K15+743 on Shanghai Metro Line 16. [Result & Conclusion] Compared with the PSO-SVR model and the ARIMA (autoregressive integrated moving average) model, the CEEMD-PSO-SVR prediction model has advantages in three performance evaluation indicators, namely root mean square error, mean absolute error, and absolute value of mean relative error.…”
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  3. 3

    A Queuing Network Based Optimization Model for Calculating Capacity of Subway Station by Hanchuan Pan, Zhigang Liu

    Published 2017-01-01
    “…Finally, this paper takes a subway station of Shanghai Metro as a case study and calculates the optimal selection probability. …”
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    Article
  4. 4

    A Case Study on Stratified Settlement and Rebound Characteristics due to Dewatering in Shanghai Subway Station by Jianxiu Wang, Tianrong Huang, Dongchang Sui

    Published 2013-01-01
    “…Based on the Yishan Metro Station Project of Shanghai Metro Line number 9, a centrifugal model test was conducted to investigate the behavior of stratified settlement and rebound (SSR) of Shanghai soft clay caused by dewatering in deep subway station pit. …”
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    Article
  5. 5

    Optimization of Bus Bridging Service under Unexpected Metro Disruptions with Dynamic Passenger Flows by Jiadong Wang, Zhenzhou Yuan, Yonghao Yin

    Published 2019-01-01
    “…Finally, we apply the proposed model to Shanghai Metro to access the effectiveness of our approaches in comparison with the current bridging strategy. …”
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    Article
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    Assigning Passenger Flows on a Metro Network Based on Automatic Fare Collection Data and Timetable by Ling Hong, Wei Li, Wei Zhu

    Published 2017-01-01
    “…An initial application to categorical O-D pairs on the Shanghai metro system, which is one of the largest systems in the world, shows that the proposed methodology works well. …”
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    Article
  8. 8

    A Real-Time Train Timetable Rescheduling Method Based on Deep Learning for Metro Systems Energy Optimization under Random Disturbances by Jinlin Liao, Feng Zhang, Shiwen Zhang, Cheng Gong

    Published 2020-01-01
    “…A well-trained decision network can provide effective solutions in real time after random disturbances occur, in order to optimize the net traction energy consumption of trains in metro systems. Based on the Shanghai Metro Line One (SML1) pilot network, this paper establishes a comprehensive model of the metro system as a training and testing environment to verify the energy-saving effect and real-time performance of the proposed method in solving the TTR problem. …”
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    Article
  9. 9

    A Real-Time Timetable Rescheduling Method for Metro System Energy Optimization under Dwell-Time Disturbances by Guang Yang, Junjie Wang, Feng Zhang, Shiwen Zhang, Cheng Gong

    Published 2019-01-01
    “…Several numerical examples tested on Shanghai Metro Line 1 (SML1) validate the energy saving effects and real-time features of the proposed method.…”
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    Article
  10. 10

    Analysis of the Development Characteristics and Influencing Factors of Freezing Temperature Field in the Cross Passage by Yan Zhuang, Junhao Chen, Jian Zhang, Jianlin Wang, Han Li

    Published 2021-01-01
    “…Based on the analysis of the temperature measurement data of the Shanghai Metro Line 15 cross passage freezing project, it was found that the gray silt layer of cross passage No. 2 outperforms that of cross passage No. 1 on the freezing effect, which is mainly attributed to the large loss of cooling capacity in the latter passage. …”
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    Article
  11. 11

    Urban Rail Transit System Network Reliability Analysis Based on a Coupled Map Lattice Model by Shaojie Wu, Yan Zhu, Ning Li, Yizeng Wang, Xingju Wang, Daniel Jian Sun

    Published 2021-01-01
    “…The actual passenger flow weight of urban transit network nodes was obtained from the Shanghai Metro public transportation card data, which were used to assess the reliability of the passenger-flow-weighted network. …”
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    Article
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