城市交通和空间结构的相互依赖关系为理论建模和实证分析提出诸多挑战，尤其是考虑到个体、空间、交通设施等的各种异质性及由此带来的选择和分选效应。本项目研究多重异质性下城市交通发展驱动城市空间重构的理论机理，重点以实证分析揭示中国城市交通发展与空间重构的互动响应过程和规律，包括交通投资及可达性的改变对城市空间结构的影响和城市空间重构对城市交通出行的影响等，并以解决交通拥堵等城市病和改善城市空间结构为政策目标，提出中国城市空间优化和交通协同发展的政策建议。本项目旨在将家庭、企业和交通的异质性纳入空间经济分析框架，拓展城市空间结构理论，探索针对差异化的家庭、企业的区位选址和交通决策中的互反馈机制和自选择效应等建立实证策略，并构建基于异质性主体建模的政策模拟平台，对不同空间发展策略下城市交通出行变化及其社会经济、环境影响等进行政策模拟，为相关决策提供科学依据和支撑。

The mutual dependency and interaction between urban transportation and urban spatial structure have posed great challenges for theoretical modeling and empirical analysis, especially considering the heterogeneity of individuals, space, and transport infrastructure, as well as the resulting spatial selection and sorting effects. This study examines the theoretical mechanism of urban spatial restructuring driven by the transport development under multiple heterogeneity, and empirically analyzes the processes and patterns of the interaction between transport development and spatial restructuring in Chinese cities, including the impacts of transport investment and accessibility improvement on urban spatial structure and the impacts of urban spatial restructuring on travel behavior. Aiming to solve such urban challenges as traffic congestion and optimize urban spatial organization, this study proposes urban strategies on the coordinated development of urban transport and land use in Chinese cities. The study tries to expand the theory of urban spatial structure by introducing the heterogeneity of households, firms and transportation into the NEG models, and explores the feasible empirical strategies that take the mutual feedback and the self-selection effect into account when studying the location choices and the travel behavior of heterogeneous households and firms. A computational simulation platform is developed using the agent-based modeling approach to examine the changes of urban travel pattern and its socio-economic and environmental impacts under different spatial development scenarios, so as to provide the scientific basis and supports for the related policy making.