Forecasting Method for Urban Rail Transit Ridership at Station Level Using Back Propagation Neural Network
Direct forecasting method for Urban Rail Transit (URT) ridership at the station level is not able to reflect nonlinear relationship between ridership and its predictors. Also, population is inappropriately expressed in this method since it is not uniformly distributed by area. In this paper, a new v...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2016/9527584 |
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| _version_ | 1832564309216985088 |
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| author | Junfang Li Minfeng Yao Qian Fu |
| author_facet | Junfang Li Minfeng Yao Qian Fu |
| author_sort | Junfang Li |
| collection | DOAJ |
| description | Direct forecasting method for Urban Rail Transit (URT) ridership at the station level is not able to reflect nonlinear relationship between ridership and its predictors. Also, population is inappropriately expressed in this method since it is not uniformly distributed by area. In this paper, a new variable, population per distance band, is considered and a back propagation neural network (BPNN) model which can reflect nonlinear relationship between ridership and its predictors is proposed to forecast ridership. Key predictors are obtained through partial correlation analysis. The performance of the proposed model is compared with three other benchmark models, which are linear model with population per distance band, BPNN model with total population, and linear model with total population, using four measures of effectiveness (MOEs), maximum relative error (MRE), smallest relative error (SRE), average relative error (ARE), and mean square root of relative error (MSRRE). Also, another model for contribution rate of population per distance band to ridership is formulated based on the BPNN model with nonpopulation variables fixed. Case studies with Japanese data show that BPNN model with population per distance band outperforms other three models and the contribution rate of population within special distance band to ridership calculated through the contribution rate model is 70%~92.9% close to actual statistical value. The result confirms the effectiveness of models proposed in this paper. |
| format | Article |
| id | doaj-art-085b9f5143354372a47702dfedb6bc3e |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-085b9f5143354372a47702dfedb6bc3e2025-02-03T01:11:12ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2016-01-01201610.1155/2016/95275849527584Forecasting Method for Urban Rail Transit Ridership at Station Level Using Back Propagation Neural NetworkJunfang Li0Minfeng Yao1Qian Fu2College of Transportation Engineering, Tongji University, Shanghai 201804, ChinaCollege of Architecture, Huaqiao University, Xiamen 361021, ChinaCollege of Transportation Engineering, Leeds University, Leeds LS2 9JT, UKDirect forecasting method for Urban Rail Transit (URT) ridership at the station level is not able to reflect nonlinear relationship between ridership and its predictors. Also, population is inappropriately expressed in this method since it is not uniformly distributed by area. In this paper, a new variable, population per distance band, is considered and a back propagation neural network (BPNN) model which can reflect nonlinear relationship between ridership and its predictors is proposed to forecast ridership. Key predictors are obtained through partial correlation analysis. The performance of the proposed model is compared with three other benchmark models, which are linear model with population per distance band, BPNN model with total population, and linear model with total population, using four measures of effectiveness (MOEs), maximum relative error (MRE), smallest relative error (SRE), average relative error (ARE), and mean square root of relative error (MSRRE). Also, another model for contribution rate of population per distance band to ridership is formulated based on the BPNN model with nonpopulation variables fixed. Case studies with Japanese data show that BPNN model with population per distance band outperforms other three models and the contribution rate of population within special distance band to ridership calculated through the contribution rate model is 70%~92.9% close to actual statistical value. The result confirms the effectiveness of models proposed in this paper.http://dx.doi.org/10.1155/2016/9527584 |
| spellingShingle | Junfang Li Minfeng Yao Qian Fu Forecasting Method for Urban Rail Transit Ridership at Station Level Using Back Propagation Neural Network Discrete Dynamics in Nature and Society |
| title | Forecasting Method for Urban Rail Transit Ridership at Station Level Using Back Propagation Neural Network |
| title_full | Forecasting Method for Urban Rail Transit Ridership at Station Level Using Back Propagation Neural Network |
| title_fullStr | Forecasting Method for Urban Rail Transit Ridership at Station Level Using Back Propagation Neural Network |
| title_full_unstemmed | Forecasting Method for Urban Rail Transit Ridership at Station Level Using Back Propagation Neural Network |
| title_short | Forecasting Method for Urban Rail Transit Ridership at Station Level Using Back Propagation Neural Network |
| title_sort | forecasting method for urban rail transit ridership at station level using back propagation neural network |
| url | http://dx.doi.org/10.1155/2016/9527584 |
| work_keys_str_mv | AT junfangli forecastingmethodforurbanrailtransitridershipatstationlevelusingbackpropagationneuralnetwork AT minfengyao forecastingmethodforurbanrailtransitridershipatstationlevelusingbackpropagationneuralnetwork AT qianfu forecastingmethodforurbanrailtransitridershipatstationlevelusingbackpropagationneuralnetwork |