Optimization Of Public Parking Lot Site Selection

With the rapid growth of C hinese economy and traffic demand, the phenomenon of traffic congestion and blocking has appeared in many cities, especially in large and medium-sized cities, and the difficulty of driving and parking has become the increasingly prominent contradictions. It is urgent to fully explore the parking planning because that public parking lot has far-reaching influence on urban traffic, and the key of park ing planning is location. Based on the analysis of traditio nal site selection algorithms of public parking lot, an algorithm according to GIS and genetic algorithm is established. The results obtained by this paper are as follows:(1) Analyzed and summarized the current situation of parking lot location at home and abroad, then listed the problems for site selection of parking lot, which laid the research foundation for this paper.(2) Described the basic theory of urban public parking site selection and GIS. Including the principle, rules, common methods and influences about urban public parking lot site selection; common methods and influences about parking demand forecasting; and the advantages, procedures and steps that GIS applied to public parking lot site selection.(3) The steps of parking lot site selection based on GIS and genetic algorithm were introduced. First, the site selection target and its influence factors were determined; then the quantitative influence factors of site selection were analyzed by means of GIS spatial analysis techniques, and a serial of candidate sites are obtained; at last the best location were obtained based on genetic algorithm.(4) Taking public parking lot site selection of Anning, Lanzhou for example. After the investigation of parking, this paper analyzed the features of Anning, Lanzhou parking. Then this paper compared the different models for parking demand forecasting and forecasted the parking demand of Anning, Lanzhou reasonably. Combined with GIS technology, the public parking demand was distributed according to differe nt land use. At last the public parking lot locations of Anning, Lanzhou were selected with GIS and genetic algorithm model, and the methods are justified to be feasible and reasonable.