01559842

Component

Urban rail transit ridership at station-level is an important part of urban rail transit ridership, critical for determining the scale of station and access facilities. Present direct forecasting method of ridership is based on multivariate linear regression with population weighted by distance-decay function. These methods are not able to reflect non-linear relationship between ridership and its predictors. It is also inappropriate to use population in these linear models since it is not uniformly distributed. In this paper, a new variable, population per distance band, is considered and a back propagation neural network model (BPNN) which can reflect non-linear relationship between ridership and its predictors is proposed to forecast ridership with key factors that affect ridership obtained through partial correlation analysis. The performance of the model is compared with three other models, using four MOEs, maximum relative error, smallest relative error, average relative error and mean square root of relative error. Also, a model of contribution rate of population per distance band to ridership is formulated based on the BPNN model when non-population variables are fixed as background variables. Case studies with Japanese data show the effectiveness of our model. The result shows all MOEs of BPNN are much better than the other three models. Contribution rate of population within special distance band to ridership calculated through the model is very close to actual statistical value, and the reason for difference between the two is explained. Ridership can be calculated using results from contribution rate model directly and more quickly by multiplying population within special band and corresponding rate without operating BPNN once more.

This paper was sponsored by TRB committee AP065 Rail Transit Systems.

01550057

15-3012

English

16p

2015

Transportation Research Board 94th Annual Meeting

Digital/other

Figures; References; Tables

Distance; Forecasting; Methodology; Neural networks; Population; Rail transit; Ridership

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

Transportation Research Board Annual Meeting 2015 Paper #15-3012

TRIS, TRB, ATRI

Dec 30 2014 1:01PM