Built Environment Renewal Strategies Aimed at Improving Metro Station Vitality via the Interpretable Machine Learning Method: A Case Study of Beijing

Zhenbao Wang · Shihao Li · Yushuo Zhang · Xiao Wang · Shuyue Liu · Dong Liu

9 citations0/0 files storedSustainability

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Urban Transport and Accessibility Transportation Planning and Optimization Urban Design and Spatial Analysis

Abstract

Understanding the built environment’s impact on metro ridership is essential for developing targeted strategies for built environment renewal. Taking into consideration the limitations of existing studies, such as not proposing targeted strategies, using unified pedestrian catchment areas (PCA), and not determining the model’s accuracy, Beijing was divided into three zones from inside to outside by the distribution pattern of metro stations. Three PCAs were assumed for each zone and a total of 27 PCA combinations. The study compared the accuracy of the Ordinary Least Square (OLS) and several machine learning models under each PCA combination to determine the model to be used in this study and the recommended PCA combination for the three zones. Under the recommended PCA combinations for the three zones, the model with the highest accuracy was used to explore the built environment’s impact on metro ridership. Finally, prioritized stations for renewal were identified based on ridership and the built environment’s impact on metro ridership. The results are as follows: (1) The eXtreme Gradient Boosting (XGBoost) model has a higher accuracy and was appropriate for this study. The recommended PCA combination for the three zones in Beijing was 1000 m_1200 m_1800 m. (2) During the morning peak hours, the density of office and apartment facilities greatly influenced the ridership, with a strong threshold effect and spatial heterogeneity. Our research framework also provides a new way for other cities to determine the scope of Transit-Oriented Development (TOD) and proposes a new decision-making method for improving the vibrancy of metro stations.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 9 citations exist upstream.

References

2010Travel and the Built Environment 2017Multiscale Geographically Weighted Regression (MGWR)2018Applying a random forest method approach to model travel mode choice behavior 2017A comparative study of machine learning classifiers for modeling travel mode choice 2010Transit-oriented development in a high-density city: Identifying its association with transit ridership in Seoul, Korea 2019Inference in Multiscale Geographically Weighted Regression 2020Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models 2012Application of geographically weighted regression to the direct forecasting of transit ridership at station-level

Cited by

2024Investigating the Nonlinear Effect of Land Use and Built Environment on Public Transportation Choice Using a Machine Learning Approach 2025The influences of built environment on ultra-peak for urban rail transit station passenger flows based on lasso-multiscale geographically weighted regression 2025Applying machine learning to decode built environment thresholds for public and active transport distances in the global south

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.