Establishing A Regional Parking Index Model Based On Baidu Map And Web Mining

With rapid development of economy, car ownership and travel demand have been increasing steadily worldwide. Urban problems such as traffic congestions, accidents and pollutions have worsened in many cities. To find out its cause, the main reason comes from three points. Firstly, the construction speed of parking facilities is behind the growth of car, the parking lots are not enough. Secondly, the utilization rate of existing parking facilities is relatively low, and it is isolated, the car parking can't be controlled entirely. Thirdly, parking demand forecasting will not be accurate without the support of precise land use information.The POI(Point of Interest) of Baidu Map is updated every day, and it will be more detail and perfect. Based on the POI(point of interest) data of online maps and the IOI(item of interest) data of each POI, more precise land use and development information are obtained. This study aims to establish a parking index model that measures the degree of difficulty for parking in a certain urban area. Employing online map API, HTML5 and data mining technology, parking demand are forecasted with high level of accuracy. The parking index is then computed according to the parking demand and supply of parking spaces for a given area. At last, the parking index is visualized on a map to provide travel reference for the public.The result of this research will provide significant benefits for planning, construction and operations of parking facilities. Knowing how difficult to park their vehicles in destination areas, travelers can make smart travels by choosing different travel modes or travel time. Developers and city planners can improve parking facility planning and operations according to the results from the parking index model.