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Since reform and opening up began in 1978, China has developed and urbanized rapidly, and a dual urban-rural economy has evolved in its megacity regions. Unlike the United States and other countries with neoliberal regimes that rely on the market to adjust economic relations and where there is little explicit national-level planning, China is a regionally decentralized developmental state where government plays a major role in development. Coordinated urban-rural development has been national policy since 2003, and China began to experiment with what is termed integrated urban-rural development in 2006. This article describes coordinated urban-rural development in China's 2 developing megacity regions with the most advanced coordination programs (Chengdu and Chongqing) and 2 highly urbanized megacity regions with well-developed strategies to integrate city and countryside (Shanghai and Suzhou). It describes 4 models of the coordination process: the municipal government-led top-down model (post-1990 Shanghai), empowering the entrepreneurial township government model (post-2008 Suzhou), the negotiation model (post-2003 Chengdu), and the labor transfer model (post-2003 Chongqing). It discusses the forms that integration of city and countryside have taken in the most advanced Chinese megacity regions and their implications for other cities in China and other developing countries with different characteristics and at different development stages.

Hub location problems are network design problems on the level of strategic decision-making processes. During strategic planning, input data, such as flows and setup costs, are not always known in advance. Hence, decisions have to be made in an uncertain environment. In this paper, two sources of uncertainty are considered: the flows from origins to destinations and the setup costs of hubs. A robust optimization formulation is proposed for both single and multiple allocation cases, in which the flow between each pair of nodes is assumed to be uncertain and correlated. In addition, the setup cost of a hub is related to the total flow through the hub. Nonlinear integer program models are presented for both single and multiple allocation cases, and they are solved using CPLEX. Computational tests using the Civil Aeronautics Board and Australian Post datasets are provided. The numerical results suggest that the robust optimization strategy locates more hubs than in the deterministic case with a relatively small cost increase, and the total cost of the robust solution calculated for the multiple allocation case is marginally lower than that for the single allocation case. The robust optimization strategy is proven to be effective for protecting the solution against the worst case for different uncertain parameters. INDEX TERMS Hub location problem, nonlinear integer program, robust optimization.

Motivated by the previous traffic flow model considering the real-time traffic state, a modified macroscopic traffic flow model is established. The periodic boundary condition is applied to the car-following model. Besides, the traffic state factor R is defined in order to correct the real traffic conditions in a more reasonable way. It is a key step that we introduce the relaxation time as a density-dependent function and provide corresponding evolvement of traffic flow. Three different typical initial densities, namely the high density, the medium one and the low one, are intensively investigated. It can be found that the hysteresis loop exists in the proposed periodic-boundary system. Furthermore, the linear and nonlinear stability analyses are performed in order to test the robustness of the system.