Abstract
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.
Highlights
Along with the pushing of economic development and urbanization in China, traffic congestion tended with the trend from site to line and from part to expansiveness
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
The related researches suggested that developing public transportation is one of the best means to solve urban traffic congestion
Summary
Along with the pushing of economic development and urbanization in China, traffic congestion tended with the trend from site to line and from part to expansiveness. Research on forecasting pedestrian flow in transportation hub area is beneficial on improving the information awareness ability and emergency handling ability of related management apartments. It has an important significance on safe running of transportation hub and alleviating urban traffic congestion. Li [4] established forecast models of passenger distribution and daily traffic using the passenger flow data of World Expo held in Osaka, Japan, in 1970 as main reference indicator. Cetiner [5] proposed an optimism dynamical neural network and on this basis built traffic flow forecast model. The effectiveness of the model was proved through the test on pedestrian flow survey sample in Beijing transportation hub areas
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