TY - JOUR A2 - Ma, Jingsen AU - Wang, Shuwei AU - Zhou, Ronggui AU - Zhao, Lin PY - 2015 DA - 2015/06/09 TI - Forecasting Beijing Transportation Hub Areas’s Pedestrian Flow Using Modular Neural Network SP - 749181 VL - 2015 AB - 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. SN - 1026-0226 UR - https://doi.org/10.1155/2015/749181 DO - 10.1155/2015/749181 JF - Discrete Dynamics in Nature and Society PB - Hindawi Publishing Corporation KW - ER -