| At present, most big cities in China are going through the suburbanization and motorization process, and the imbalance between transport supply and demand is leading to a series of traffic congestion problems. The coordination between land-use and transport system is considered to be the essential way of solve urban traffic problems. Therefore, exploring the optimization of the spatial distribution of shopping center (SC) to coordinate the relationship between business land-use and transport system is an effectual way of solving traffic problems.This paper firstly discuses the characteristics of SC distribution, and points out that the key factors influencing such distribution include population, transport conditions, and consumers' shopping behavior. Based on shopping travel questionnaires, the characteristics of consumer choice of SC are analyzed, and it is found that accessibility, size of SC, and price are the primary factors influencing consumers' SC choice. An SC choice model is thereupon established to forecast its patron flow and a shopping mode choice model is also established to calculate the mode split.The paper then embeds the SC choice model in the p-median model and obtains a new model with the objective of minimizing the average shopping travel distance of urban residents. Considering the difference between transport modes, a multi-objective model is also constructed. Genetic Algorithm (GA) with dynamic parameter control strategy is adopted to solve them. A numeric experiment is conducted to demonstrate these models effects with relevant data of Dalian city.To validate whether the optimization SC distribution can reduce travel demand, the shopping traffic impact assessment method is proposed. The analytic hierarchy process (AHP) is employed to construct a model assessing the level of service (LOS) of road network. Finaly, numerical experimentation is carried out. The result shows that the transport demand can be reduced by optimizing the SC spatial distribution.
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