Abstract:

To investigate the nonlinear relationship between the built environment and pedestrian volume， based on street view imagery from Nanjing's main urban area， pedestrian volume and streetscape features were extracted using deep learning. Multi-source data were integrated to construct built environment indicators at both the neighborhood and street levels. An Extreme Gradient Boosting （XGBoost） model identified key factors in the built environment and characterized their nonlinear associations with pedestrian volume. The results reveal that among the neighborhood-level built environment factors， distance from the city center， the number of service facilities， and the number of restaurants significantly affect pedestrian volume； whereas among the street-level built environment factors， terrain， sky index， and green view index contribute more substantially. Additionally， all variables exhibit nonlinear associations with distinct interaction patterns. The impact of distance from the city center and terrain on pedestrian volume tends to be negative； in contrast， the interaction patterns. The impact of distance from the city center and terrain on pedestrian volume tends to be negative. In contrast， the number of service facilities， the number of restaurants， and the green view index are positively associated with pedestrian volume. Moreover， the nonlinear relationship between sky index and pedestrian volume presents an inverted “U”-shaped pattern. The research findings presented can serve as a theoretical reference for optimizing urban spatial layout and improving pedestrian-friendly transportation policies.

Key words: engineering of communications and transportation system, street view image, built environment, nonlinear relationship, extreme gradient boosting model