@inproceedings{discovery10064498,
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
           month = {January},
         journal = {Transportation Research Board 97th Annual Meeting},
            year = {2018},
       booktitle = {Proceedings of the Transportation Research Board 97th Annual Meeting},
           title = {Forecasting the Peak-Period Station-to-Station Origin-Destination Matrix in Urban Rail Transit System: Case Study of Chongqing, China},
       publisher = {National Academy of Sciences},
        keywords = {Urban rail transit, Station-to-station ridership, Gravity model, Peak period coefficient, Deterrence function},
             url = {https://trid.trb.org/view/1495294},
        abstract = {The maximum one-direction section passenger flow within peak hour is an important indicator for planning and design of urban rail transit. To determine it, it is necessary to forecast passengers' departure time and route choice during peak period. As the basis of this process, the peak-period station-to-station origin-destination (OD) matrix reflects the passengers' travel needs. This paper tests traditional gravity models in forecasting the peak-period station-to-station origin and destination (OD) matrix in urban rail transit with a real-world case study of Chongqing, China. To solve its over-estimation when deterrence between two stations is too little, the gravity-model-based Peak Period Coefficient (PPC) model is introduced. Comparing results show that with the same dataset, the PPC model is superior to the gravity model. Its standard deviation is only 12.90 passengers, reduced by 56.02\%.},
          author = {Cheng, Y and Ye, X and Zhou, L}
}