近年來隨著物聯網技術的興起，目前已廣泛應用在製造、運輸、醫療等行業上，但應用在軌道上的案例並不多見。因此本研究欲結合物聯網技術初步建置一套軌道監測系統，並對接收的大量數據進行處理與分析，希望能提高軌道的檢查與管理效率。
經過簡單的比較後，本研究選擇Arduino 及樹莓派(Raspberry Pi)，並搭配相關的感測模
組，建置了一套軌道監測系統模組，主要的監測項目包含了軌道的振動加速度及道碴阻力，以4G LTE 的無線傳輸方式將數據回傳，並以新市車站北側的彎道作為監測的地點。
而為了處理每日監測系統模組回傳的大量數據，本研究透過 Python 先將異常值剃除
後，再以時間為依據，將數據依班次進行分類，並儲存到了資料庫當中，已進行有效的管理。
本研究的監測成果發現相同車種在道碴阻力與振動加速度的變化趨勢上具有一定的相似程度，且動力集中式的自強號及莒光號兩種列車於機車頭通過時，產生的道碴阻力特別大，應是與列車重量有關。列車的通過速度亦會對道碴阻力與振動加速度造成影響，列車通過速度越快於外軌側的道碴阻力及振動加速度越大，內軌側反之。而根據監測約兩個月的數據趨勢可以看出，在外軌側的道碴阻力呈些微下降的趨勢，內軌側則反之，推測應是內軌側受到擠壓的機會較多所致，振動加速度則是沒有太大的變化，惟莒光號造成的平均振動加速度即變異數較大，推測應也與莒光號的機車頭明顯較大有關。
本研究提出了一個應用物聯網技術的軌道監測系統，且初步證明能用於長期的監測，並可以有效處理每日接收回來的大量數據，期待後續可以增加監測項目，並廣泛應用，以提升軌道監測之效率。

In recent years, IoT technology has been widely used, but less in railway .Therefore, this research intends to build a track monitoring system by using IoT technology, and analyze data received, hoping to improve the efficiency of track management.
This research chooses Arduino and Raspberry Pi, and cooperates with sensor to build a track monitoring system. The monitoring items include the vibration of the track and ballast resistance, and is transmitted by 4G LTE.
In order to deal with the data returned by the monitoring system, this study first shaves outliers through Python, and then classifies the data according to time, and stores them in the database, which has been effectively management.
This research found that the ballast resistance and vibration of the same type of trains have a certain degree of similarity, and the resistance of Tze-Chiang and Chu-Kuang especially large, it related to the weight of the train. The speed of the train will also affect the resistance and vibration. The faster speed, the greater the resistance on the outer rail side and vibration, and vice versa for the inner rail. According to the data of two months, the resistance on the outer rail side shows a slight downward trend, and the opposite is true on the inner rail side. The acceleration did not change much, but the average vibration caused by the Chu-Kuang had a large variation, which related to the significantly larger locomotive head.
This research proposes a track monitoring system using IoT technology, and it has been initially proved that it can be used for long-term monitoring and can effectively process data received every day. It is expected that monitoring items can be added in the future and widely used to improve the track monitoring efficiency.

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