Smart mobility and transport inequity: Insights from mobile phone data

Nowadays, people use mobile applications (APPs) to access transport-related information and services. This study examines the complex interrelationship of smart mobility APP use, travel patterns, and transport inequity. Nanjing, a large city in Eastern China, is chosen as a case study. Smart mobility APP use and travel patterns are derived from passively collected mobile phone data of 8.33 million residents, capturing the use of 23 APPs and physical movements at the 500-meter grid level. Transport disadvantaged (TD) places are identified by poorer public transport accessibility, while transport and socially disadvantaged (T&SD) places are further identified by demographic composition and housing price. Machine learning models are used to examine the nonlinear relationships between APP use and travel characteristics at the city level, as well as across disadvantaged and reference places. At the city level, the results show that more smart mobility APP use is associated with longer travel distance, more diverse destinations and a wider range of activity space, but not more home-based trips. Statistically, TD and T&SD population tend to have significantly fewer trips, visited less diverse destinations and travelled farther. Focusing on transport inequity, smart mobility APP use shows higher importance in supporting TD places than their corresponding reference places. Yet, its importance weakens when transport disadvantage overlaps with demographic or economic disadvantage, suggesting that socially-advantaged groups living in transport disadvantaged areas still face greater difficulties in benefiting from smart mobility APPs.