Showing 6,481 - 6,500 results of 7,701 for search '"Beijing"', query time: 0.05s Refine Results
  1. 6481

    A Cost Function Approach to the Prediction of Passenger Distribution at the Subway Platform by Xiaoxia Yang, Xiaoli Yang, Zhenling Wang, Yuanlei Kang

    Published 2018-01-01
    “…According to the limited observation and field data collection of Beijing Xuanwumen subway station, passenger behaviors and basic attributes at the platform are analyzed. …”
    Get full text
    Article
  2. 6482
  3. 6483
  4. 6484
  5. 6485

    Evolution Game Model of Travel Mode Choice in Metropolitan by Chaoqun Wu, Yulong Pei, Jingpeng Gao

    Published 2015-01-01
    “…Finally, the model is applied to Beijing inhabitants’ travel mode choices during morning peak hours and draws the conclusion that the proportion of inhabitants travelling by rail would increase when traffic congestion is more severe. …”
    Get full text
    Article
  6. 6486
  7. 6487
  8. 6488
  9. 6489
  10. 6490
  11. 6491
  12. 6492

    Research on Coordinated Passenger Inflow Control for the Urban Rail Transit Network Based on the Station-to-Line Spatial-Temporal Relationship by Ruixia Yang, Weiteng Zhou, Baoming Han, Dewei Li, Bin Zheng, Fangling Wang

    Published 2022-01-01
    “…The proposed model and solution strategy are evaluated on a well-known Beijing network with 10 operating lines. The refined inflow control scheme is displayed with the accurate inbound volume at each station during each time period. …”
    Get full text
    Article
  13. 6493
  14. 6494
  15. 6495
  16. 6496
  17. 6497

    Forecast of China’s Carbon Emissions Based on ARIMA Method by Longqi Ning, Lijun Pei, Feng Li

    Published 2021-01-01
    “…In this paper, we first use the software Eviews to make an analysis of randomness on data of carbon emissions in the four representative provinces and city, Beijing, Henan, Guangdong, and Zhejiang, in terms of their carbon emissions data from 1997 to 2017. …”
    Get full text
    Article
  18. 6498
  19. 6499
  20. 6500

    Performance analysis of K-nearest neighbor, support vector machine, and artificial neural network classifiers for driver drowsiness detection with different road geometries by Zhenlong Li, Qingzhou Zhang, Xiaohua Zhao

    Published 2017-09-01
    “…The analysis was based on data obtained from a study that involved 22 subjects in the driving simulator located in the Traffic Research Center, Beijing University of Technology. Second, six classifiers were constructed for six curve segments, respectively, while only one classifier was constructed for all straight segments because the waveforms by subtracting the road curvature from the steering angle in the curve segments were different from the waveforms of the straight segments. …”
    Get full text
    Article