由於資訊科技技術的進步，許多感測器被部署在智慧城市的道路交通網上，實現了自動化智慧交通管理。本文提出了一種基於能量模型的交通異常區域檢測系統，以自動檢測異常區域。我們觀測到公路交通網的車流交通移動行為，類似物理中的熱傳導模型的方式將高溫區域的熱能轉移到較低的溫度區域。我們運用庫侖定律的概念來獲得道路上各類事件對車流所造成的影響。此外，我們還討論了鄰近地區的關係，以決定這些地區發生的事件的候選發生地點。模型中所使用的權重代表了現實環境中影響車流的傳導能力。隨後，我們提出了一個異常檢測模型，該模型可以及時算出交通事故的確切發生位置。最後，將我們的方法與現有的方法進行比較。實驗結果表明，我們對車流分佈的估計比現有的方法更接近實際的感測器記錄。通過大量的實驗，證明了我們的模型的實用性以及在事故檢測準確度方面優於現有方法。 Thanks to the advancements in sensor technology, many sensors are deployed on road networks in smart cities to achieve the automation development of smart solutions for traffic management. In this paper, we propose a traffic anomalous location detection system based on an energy model to automatically detect anomalous locations where incidents may occur. We utilize the heat diffusion model in physics by observing the level of traffic flow spreading along the road networks in a similar way to the heat energy transferring from a high temperature region to a lower one. The weights used in the model represent the environmental conditions which affect the ``conductibility'' of the traffic flow. Subsequently, we apply the concept of Coulomb's law to acquire the influence caused by various distinct types of incidents on the road. We construct an anomaly detection model that computes the source location and the type of a traffic incident in a timely fashion. The experimental results show that our estimation of the traffic flow distribution is much closer to the actual sensor records than that of existing approaches including an un-directed heat diffusion model and autoregressive integrated moving average model (ARIMA). On the other hand, we analyze and compare the effectiveness of our model with a support vector regression (SVR) model under different traffic flow conditions. Extensive experiments are presented to demonstrate the performance and utility of our model which outperforms the existing approaches in terms of incident location detecting accuracy. Furthermore, our model has the ability to provide various distinct incident types detection which outperforms the detecting accuracy of the support vector machine (SVM) approach.