  Ridership estimation at station level plays a critical role in metro
transportation planning. Among various existing ridership estimation methods,
direct demand model has been recognized as an effective approach. However,
existing direct demand models including Geographically Weighted Regression
(GWR) have rarely included local model selection in ridership estimation. In
practice, acquiring insights into metro ridership under multiple influencing
factors from a local perspective is important for passenger volume management
and transportation planning operations adapting to local conditions. In this
study, we propose an Adapted Geographically Weighted Lasso (Ada-GWL) framework
for modelling metro ridership, which performs regression-coefficient shrinkage
and local model selection. It takes metro network connection intermedia into
account and adopts network-based distance metric instead of Euclidean-based
distance metric, making it so-called adapted to the context of metro networks.
The real-world case of Shenzhen Metro is used to validate the superiority of
our proposed model. The results show that the Ada-GWL model performs the best
compared with the global model (Ordinary Least Square (OLS), GWR, GWR
calibrated with network-based distance metric and GWL in terms of estimation
error of the dependent variable and goodness-of-fit. Through understanding the
variation of each coefficient across space (elasticities) and variables
selection of each station, it provides more realistic conclusions based on
local analysis. Besides, through clustering analysis of the stations according
to the regression coefficients, clusters' functional characteristics are found
to be in compliance with the facts of the functional land use policy of
Shenzhen. These results of the proposed Ada-GWL model demonstrate a great
spatial explanatory power in transportation planning.
