Substitution or Complement? Uncovering the Interplay between Ride-hailing Services and Public Transit

TL;DR

This study investigates how ride-hailing services interact with public transit in Shanghai, revealing a shift towards more complementary relationships and identifying key factors influencing these dynamics using advanced machine learning techniques.

Contribution

It introduces a data-driven framework to classify TNC-PT relationships and applies machine learning to analyze nonlinear effects of influential factors.

Findings

Increase in complementary TNC-PT relationships by 9.22%

Decrease in substitutive relationships by 9.06%

Significant nonlinear effects of distance to metro and bus stop density

Abstract

The literature on transportation network companies (TNCs), also known as ride-hailing services, has often characterized these service providers as predominantly substitutive to public transit (PT). However, as TNC markets expand and mature, the complementary and substitutive relationships with PT may shift. To explore whether such a transformation is occurring, this study collected travel data from 96,716 ride-hailing vehicles during September 2022 in Shanghai, a city characterized by an increasingly saturated TNC market. An enhanced data-driven framework is proposed to classify TNC-PT relationships into four types: first-mile complementary, last-mile complementary, substitutive, and independent. Our findings reveal a substantial increase in the complementary ratio (9.22%) and a relative decline in the substitutive ratio (9.06%) compared to previous studies. Furthermore, to examine the nonlinear impact of various influential factors on these ratios, a machine learning method integrating categorical boosting (CatBoost) and Shapley additive explanations (SHAP) is proposed. The results show significant nonlinear effects in some variables, including the distance to the nearest metro station and the density of bus stops.