01515244

Component

Along with the increasing of the proportion of urban public transportation trip, pedestrian flow in transportation hub areas increased. For effectively improving the information awareness ability and emergency handling ability of related management apartments, and preventing the incident of pedestrian congestion, this paper studied on the method of pedestrian flow forecast in Beijing transportation hub areas. Firstly, 34 typical sidewalks in Beijing transportation hub areas were selected in this survey to obtain 2200 effective data. The survey sites contained different kinds of road facilities, traffic conditions and environmental conditions. Secondly, correlation analysis was used to analyze the relationship between pedestrian flow and its influential factors. 11 significant influential factors were extracted such as land use of the region where the transportation existed, effective width of sidewalks, proportion of reverse pedestrians and so on. Thirdly, forecasting model was established with modular neural network (MNN). At the beginning of developing the model, 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 sub network of the information can be achieved. As a result, 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 used MNN was higher than other neural network models. There was also improvement in the adaptability to environment.

This paper was sponsored by TRB committee ABE90 Transportation in the Developing Countries. Alternate title: Study of Pedestrian Flow Forecasting Method in Beijing Transportation Hub Areas

01503729

14-2483

English

17p

2014

Transportation Research Board 93rd Annual Meeting

Digital/other

Figures; References; Tables

Correlation analysis; Forecasting; Hubs; Neural networks; Pedestrian flow

Modular neural network

Beijing (China)

Pedestrians and Bicyclists; Planning and Forecasting; I72: Traffic and Transport Planning

Transportation Research Board Annual Meeting 2014 Paper #14-2483

TRIS, TRB, ATRI

Jan 27 2014 2:52PM