在智慧城市中，都會區的停車問題可以透過共享車位資訊以及合理的推薦車位系統來解決。
現今多數的推薦車位方法會依車主的觀點，找最近的停車位或是收費最少的停車位，但這可能會導致停車分佈不均，且許多車主無法找到適合的停車位。
從政府的觀點來看，從停車位置走到目的地的距離可以稍作犧牲來增加停車使用率。
在這篇論文，我們提出兩個停車位推薦方案來提升停車格使用率。
我們依據台南市路邊停車收費路段格位的資訊，進行數個實驗來觀察不同推薦方法下走路距離與停車格使用率的權衡。

In smart city, urban parking problems can be solved by sharing availability statuses of parking spaces and proper suggesting schemes.
Most of the suggesting schemes recommend the nearest or the cheapest parking space from the driver's point of view.
However, such selfish decision may lead to imbalance of parking areas, so that some drivers could not find proper parking spaces.
From the government's point of view, little walking distance can be sacrificed to increase the utilization of parking spaces.
In this paper, we propose two parking space suggesting schemes to improve utilization of parking spaces.
Based on the open data of parking spaces in Tainan, several experiments are conducted to analyze the tradeoff between walking distance and utilization in proposed suggesting schemes.

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