由於車輛行駛於高速公路上必須在收費站做短暫的停留以支付通行費，這也造成了高速公路 最嚴重的交通壅塞常發生在靠近收費站附近，所以一般常藉由建置電子收費系統來降低交通壅塞 情況。然而，電子收費執法系統的自動車牌影像辨識(ALPR)技術其辨識率並不總是可靠，所以還 是需要藉由人工方式來再檢查與更正辨識結果以增加準確率，如此也增加了額外的人事成本。本 篇論文中，我們考慮一多車道自由流的電子收費系統，並將尋找未支付通行費車輛問題制定成一 個雙分圖匹配問題，基於此雙分圖下提出一照片-交易配對演算法(PT演算法）。我們在NS-2模擬器 和三個不同的交通場景：擁擠交通量，正常交通量和稀疏交通量中對PT演算法的性能進行評估。模擬結果顯示，PT演算法大大降低了影像辨識次數，並且更適合與可靠應用於電子收費執法系統中。

Since the heaviest traffic congestion on highways occurs near toll gates where vehicles ma ke a short stop to pay the toll, an electronic toll collection (ETC) system is usually built to eliminate t he traffic jams. However, the identification precision of automatic license plate recognition (ALPR) is not always reliable.Human review and correction will be needed to improve the accuracy and therefore will result in extra manual operation cost. In this paper, we consider multilane-free-flow ETC systems and formulate the non-payment vehicle searching problem into a matching problem and propose a Photograph-to-Transaction matching algorithm (PT algorithm) based on bipartite graph. The performance of the PT algorithm was evaluated in ns-2 simulator and three different traffic scenarios: congested traffic, normal traffic and sparse traffic. The simulation results show that our algorithm greatly reduce the number of plate recognitions, and is more feasible and reliable for ETC enforcement.