An Analysis of Factors Influencing Metro Station Ridership: Insights from Taipei Metro

TL;DR

This study investigates various factors influencing Taipei metro station ridership using regression models, revealing key determinants like nearby shopping malls, accessibility, and day type, with distinct weekday and weekend patterns.

Contribution

It introduces a comprehensive analysis combining traditional and complex network theory factors to identify influences on metro ridership at station level.

Findings

Ridership influenced by shopping malls, proximity to city center, and transportation hubs.

No significant difference between factors affecting boarding and alighting.

Weekday ridership driven by commuting, weekend ridership by commercial access.

Abstract

Travel demand analysis at the planning stage is important for metro system development. In practice, travel demand can be affected by various factors. This paper focuses on investigating the factors influencing Taipei metro ridership at station level over varying time periods. Ordinary Least Square (OLS) multiple regression models with backward stepwise feature selection are employed to identify the influencing factors, including land use, social economic, accessibility, network structure information, etc. Network structure factors are creatively quantified based on complex network theory to accurately measure the related information. To enhance goodness-of-fit, the dummy variable distinguishing transportation hub is incorporated in the modeling. The main findings in this paper are three-fold: First, there is no distinct difference between influencing factors of boarding and those of alighting; Second, ridership is significantly associated with the number of nearby shopping malls, distance to city center, days since opening, nearby bus stations and dummy variable for transportation hub; Finally, the ridership on weekdays is mainly affected by commuting activities, while the ridership on weekends is driven by commercial access.