Fiscal Policy, Regional Disparity and Poverty in China: a General Equilibrium Approach

The main objective of this research is to analyze the effects of the fiscal dimension of China’s government transfer and preferential tax policy on regional income disparity and poverty reduction. Using a computable general equilibrium model with a three-region component, we find that the preferential tax policy on the eastern coastal region of China has a significant effect on household income, as well as on the FGT indicator. The simulation results suggest that tax policy is a more effective tool to counter against China’s regional disparity than government transfer.China, Regional Disparity, Fiscal Policy, Government Transfer, Preferential Policy, Poverty, CGE, FGT

Stochastic optimization model and solution algorithm for robust double-track train-timetabling problem

Journal ArticleAbstract-By considering various stochastic disturbances unfolding in a real-time dispatching environment, this paper develops a stochastic optimization formulation for incorporating segment travel-time uncertainty and dispatching policies into a medium-term train-timetabling process that aims to minimize the total trip time in a published timetable and reduce the expected schedule delay. Based on a heuristic sequential solution framework, this study decomposes the robust timetabling problem into a series of subproblems that optimize the slack-time allocation for individual trains. A number of illustrative examples are provided to demonstrate the proposed model and solution algorithms using data collected from a Beijing-Shanghai high-speed rail corridor in China

Dynamic origin-destination demand estimation using automatic vehicle identification data

Journal ArticleAbstract-This paper proposes a dynamic origin-destination (OD) estimation method to extract valuable point-to-point splitfraction information from automatic vehicle identification (AVI) counts without estimating market-penetration rates and identification rates of AVI tags. A nonlinear ordinary least-squares estimation model is presented to combine AVI counts, link counts, and historical demand information into a multiobjective optimization framework. A joint estimation formulation and a one-sided linear-penalty formulation are further developed to take into account possible identification and representativeness errors, and the resulting optimization problems are solved by using an iterative bilevel estimation procedure. Based on a synthetic data set, this study shows the effectiveness of the proposed estimation models under different market-penetration rates and identification rates