Forecasting Beijing Transportation Hub Areas’s Pedestrian Flow Using Modular Neural Network
Along with the increasing proportion of urban public transportation trip, pedestrian flow in transportation hub areas increased. For effectively improving the emergency handling ability of related management apartments and preventing the incident of pedestrian congestion, this paper studied the meth...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
|
| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2015/749181 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832562321446141952 |
|---|---|
| author | Shuwei Wang Ronggui Zhou Lin Zhao |
| author_facet | Shuwei Wang Ronggui Zhou Lin Zhao |
| author_sort | Shuwei Wang |
| collection | DOAJ |
| description | Along with the increasing proportion of urban public transportation trip, pedestrian flow in transportation hub areas increased. For effectively improving the emergency handling ability of related management apartments and preventing the incident of pedestrian congestion, this paper studied the method of pedestrian flow forecast in Beijing transportation hub areas. Firstly, 34 typical sidewalks in Beijing transportation hub areas were surveyed to obtain 2200 valid data. Secondly, correlation analysis was used to analyze the relationship between pedestrian flow and its influential factors. 11 significant influential factors were extracted. Thirdly, forecasting model was established with modular neural network. The surveyed pedestrian flow sample was fuzzy clustered according to the regional land use where the transportation hub existed. Then, membership function based on the distance measure was constructed. Through fuzzy discrimination, online selection for the subnetwork of the information can be achieved. Consequently, the self-adaptation of the neural network on information processing was improved. Finally, this paper tested the pedestrian flow sample of a transportation hub in Beijing. It was concluded that the accuracy of pedestrian flow forecasting model using modular neural network was higher than other neural network models. There was also improvement in the adaptability to environment. |
| format | Article |
| id | doaj-art-61064044e1a04f1089793c586ccc47bf |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-61064044e1a04f1089793c586ccc47bf2025-02-03T01:22:54ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2015-01-01201510.1155/2015/749181749181Forecasting Beijing Transportation Hub Areas’s Pedestrian Flow Using Modular Neural NetworkShuwei Wang0Ronggui Zhou1Lin Zhao2Research Institute of Highway, Ministry of Transport, No. 8 Xi Tu Cheng Road, Haidian District, Beijing 100088, ChinaResearch Institute of Highway, Ministry of Transport, No. 8 Xi Tu Cheng Road, Haidian District, Beijing 100088, ChinaKey Laboratory of Transportation Engineering, Beijing University of Technology, No. 100 Ping Le Yuan, Chaoyang District, Beijing 100124, ChinaAlong with the increasing proportion of urban public transportation trip, pedestrian flow in transportation hub areas increased. For effectively improving the emergency handling ability of related management apartments and preventing the incident of pedestrian congestion, this paper studied the method of pedestrian flow forecast in Beijing transportation hub areas. Firstly, 34 typical sidewalks in Beijing transportation hub areas were surveyed to obtain 2200 valid data. Secondly, correlation analysis was used to analyze the relationship between pedestrian flow and its influential factors. 11 significant influential factors were extracted. Thirdly, forecasting model was established with modular neural network. The surveyed pedestrian flow sample was fuzzy clustered according to the regional land use where the transportation hub existed. Then, membership function based on the distance measure was constructed. Through fuzzy discrimination, online selection for the subnetwork of the information can be achieved. Consequently, the self-adaptation of the neural network on information processing was improved. Finally, this paper tested the pedestrian flow sample of a transportation hub in Beijing. It was concluded that the accuracy of pedestrian flow forecasting model using modular neural network was higher than other neural network models. There was also improvement in the adaptability to environment.http://dx.doi.org/10.1155/2015/749181 |
| spellingShingle | Shuwei Wang Ronggui Zhou Lin Zhao Forecasting Beijing Transportation Hub Areas’s Pedestrian Flow Using Modular Neural Network Discrete Dynamics in Nature and Society |
| title | Forecasting Beijing Transportation Hub Areas’s Pedestrian Flow Using Modular Neural Network |
| title_full | Forecasting Beijing Transportation Hub Areas’s Pedestrian Flow Using Modular Neural Network |
| title_fullStr | Forecasting Beijing Transportation Hub Areas’s Pedestrian Flow Using Modular Neural Network |
| title_full_unstemmed | Forecasting Beijing Transportation Hub Areas’s Pedestrian Flow Using Modular Neural Network |
| title_short | Forecasting Beijing Transportation Hub Areas’s Pedestrian Flow Using Modular Neural Network |
| title_sort | forecasting beijing transportation hub areas s pedestrian flow using modular neural network |
| url | http://dx.doi.org/10.1155/2015/749181 |
| work_keys_str_mv | AT shuweiwang forecastingbeijingtransportationhubareasspedestrianflowusingmodularneuralnetwork AT rongguizhou forecastingbeijingtransportationhubareasspedestrianflowusingmodularneuralnetwork AT linzhao forecastingbeijingtransportationhubareasspedestrianflowusingmodularneuralnetwork |