01520121

Component

In Beijing, the average daily subway ridership reached about 10,000,000, which ranks third in the world after those of Tokyo and Seoul. Measuring and evaluating the large scale passenger crowd in the subway network is very important for both operators and passengers. This paper describes an attempt to develop passenger crowd index for subway network. The real time passenger traffic data was collected via video surveillance, laser detector and automatic fare collection system. The density, speed and flow data were extracted from different data sources. The crowd index was modeled with three variables: the intensity of the crowd, the scale of the crowd and the duration of the crowd. The crowd index was calculated at four hierarchies: facility, station, line and network. The crowd index allows cross-sectional and time series comparisons. The developed index was suggested as congestion intensity evaluation, early warning and quick response measures for crowd monitoring and control especially at condition of sudden outburst large scale passenger flow. The model has been applied in Beijing based on the project of internet of things in Beijing urban railway network. The crowd level and distribution of Beijing subway network have been calculating and updating every three minutes. The result information is disseminated to government decision makers, subway operators and passengers via website, micro-blogging system and variable message signs placed in some stations. It shows that the indices reflect the performance of the network from crowd and delay aspect accurately. It is believed that the proposed method has a potential in crowd management and mitigation.

This paper was sponsored by TRB committee AHB45 Traffic Flow Theory and Characteristics.

01503729

14-0467

English

20p

2014

Transportation Research Board 93rd Annual Meeting

Digital/other

Figures; References; Tables

Crowds; Mathematical models; Passenger traffic measurement; Passenger volume; Subways

Beijing (China)

Data and Information Technology; Operations and Traffic Management; Public Transportation; I72: Traffic and Transport Planning

Transportation Research Board Annual Meeting 2014 Paper #14-0467

TRIS, TRB, ATRI

Jan 27 2014 2:14PM