Deciphering the influence of TOD on metro ridership: An integrated approach of extended node-place model and interpretable machine learning with planning implications

Many global high-density cities have embraced transit-oriented development (TOD) strategies around metro stations in a strong push toward promoting transit trips. However, the general TOD principles must be adapted to a variety of local features. Thus, conducting a baseline study that deciphers the...

Full description

Saved in:
Bibliographic Details
Published in:Journal of transport geography Vol. 104; p. 103455
Main Authors: Su, Shiliang, Wang, Zhuolun, Li, Bozhao, Kang, Mengjun
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.10.2022
Subjects:
Metro ridership
Nonlinear association
Random forest regression
Shapley additive explanations
Transit-oriented development
Nonlinear association
Shapley additive explanations
Metro ridership
Transit-oriented development
Random forest regression
ISSN:0966-6923, 1873-1236
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Many global high-density cities have embraced transit-oriented development (TOD) strategies around metro stations in a strong push toward promoting transit trips. However, the general TOD principles must be adapted to a variety of local features. Thus, conducting a baseline study that deciphers the locality-specific differences and similarities in the influence of TOD on metro ridership is of paramount importance. Using the case of Shanghai in China, this paper demonstrated a novel approach to unveiling the locality-specific influence of TOD on metro ridership that integrated the node-functionality-place model with interpretable machine learning. By exploring a wide range of explanatory variables, we discovered that the relative importance of TOD structural factors (e.g., node, place and functionality) and neighborhood sociodemographics as well as their interactions presented great spatial and temporal heterogeneity for boarding and alighting on weekdays and weekends. Among the TOD structural factors, functionality was of the highest importance, and a greater contribution was observed within the central districts during peak hours. Regarding the interactions among the TOD structural factors, the interaction between node and functionality presented the highest relative importance, followed by that between place between functionality and that between node and place. Additionally, neighborhood sociodemographics also accounted for a noticeable contribution, especially in the outskirts. Based on the locality-specific estimations, the affinity propagation clustering algorithm was further used to cluster the TODs into difference groups as nuanced representations of the TOD-metro ridership relationships. The findings refreshed the knowledge base concerning the spatiotemporal heterogeneity in the nonlinear influence of TOD on metro ridership and provided new insights into spatial planning. The proposed approach showed high computational feasibility with strong theoretical underpinnings and thus offered broad potential applications into other high-density cities worldwide.
ISSN:0966-6923
1873-1236
DOI:10.1016/j.jtrangeo.2022.103455