Work

AI-enabled autonomous driving for urban sustainability: a Nanjing central area case study

Year: 2025

Type: article

Abstract: With the rapid advancement of artificial intelligence technology, autonomous driving is profoundly reshaping urban transportation patterns and spatial structures.This research explores how AI-enabled autonomous driving can enhance urban sustainability by intensifying land use and optimizing urban density. Taking the central urban area of Nanjing as a case study, this research constructs a traffic accessibility–floor area ratio (FAR) correlation model. By integrating multi-scenario simulations and machine learning approaches, it quantitatively analyzes the impact mechanisms of autonomous driving technology on urban spatial structure. The findings reveal that: (1) Autonomous driving significantly enhances overall traffic accessibility, driving a shift in FAR distribution from a monocentric concentration toward peripheral expansion; (2) The Xinjiekou core area maintains an ultra-high FAR (3.8–4.8), demonstrating the functional agglomeration resilience of traditional central business districts (CBDs) under technological transformation; (3) In peripheral areas such as the southern part of Hexi New Town, FAR increases by up to 61.1%, forming linear high-density development zones along rail transit corridors; (4) A reduction in low-FAR districts indicates a transition toward a more intensive urban spatial structure. Studies have shown that autonomous driving technologies promote the more intensive use of urban space and facilitate adjustments to building density. These effects are manifested in the maintenance of high density in core areas, increased density in peripheral zones, and the contraction of low-density regions. Empirical evidence suggests that artificial intelligence (AI) plays a significant role in enhancing land-use efficiency and optimizing sustainable urban density.

Source: Ecological Indicators

DOI: 10.1016/j.ecolind.2025.114053

Authors: Xinyu Hu, Chunyu Zhou, Zhen Xu, Dian Shao, Jiayu Li, Weike Zheng

Institutions: Nanjing Forestry University, Southeast University

Language: English

Topic: Urban Transport and Accessibility

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

SDGs: Sustainable cities and communities, Climate action, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,650