优化人口分布以减少城市道路交通需求

应用遗传算法和GIS优化城市人口空间分布，减少小汽车交通的出行距离．研究中考虑了业务、购物／休闲和通勤交通．为了得到目标函数，建立了BL模型(business location model)，用它来分析企业问的关系和企业与家庭的关系．模型把企业划分成9类，并认为小区内的企业数量和居住人口是影响小区间交通需求的主要因素．然后将小区内的人口当做外生变量，用遗传算法计算使小汽车出行的总距离最小的人口分布格局，从而得知人口集中在中央商务区(CBD)和挟乘车站的步行圈内时可以极大地减少城市道路交通需求。

Control of urban road traffic demand by optimizing population distribution

This study aims to optimize urban population distribution with GAs (genetic algorithms) and GIS(geographical information system), which minimizes car-based traffic distance. Business, shopping/leisure and commuting trips are considered. To get the objective function, business location model, which analyzes the relationship between enterprises and the relationship between enterprises and households, is developed. In the model industries are divided into nine categories, and located quantities of them and population in zones are main factors affecting traffic demand between zones. Zonal located populations are set as exogenous variables, and then GAs is used to determine the optimum population distribution that may minimize total trip lengths of car traffic: concentrating the population in the central business district (CBD) and walk distance of transfer stations.