人口的成長與財富的增加造成私人運具持有率不斷上升，過多的車輛在有限的道路容量下容易產生擁擠甚至堵塞的情況，在都市區域的尖峰時刻，壅塞的情況更為明顯，嚴重造成路網績效下降。擁擠的交通狀態對周遭環境產生許多負面效用，車輛在道路上的延滯，增加旅行時間的同時，也使得能源的消耗量上升以及空氣汙染量之增加，在近年來環境保護意識成為全民共識之時，對於節省能源與空氣汙染進行管制為必需之措施，而降低交通不必要之擁塞情形為交通政策之重要方向。
道路不敷使用和私人運具所造成的負面影響，可透過道路擴建解決道路容量不足之問題，以及建設便利之大眾運輸以降低使用私人運具之數量，然而道路擴建以及大眾運輸之建置成本相當龐大，對於政府之財政造成極大之負擔，因此私人興建道路便成為增加道路容量可行之方案，但私人興建道路為一公共建設投資，且必須徵收道路維護等基金，故在路段上收取費用無可避免，道路收費成為解決問題的選項之一。
隨著智慧型運輸系統 (Intelligent Transportation Systems, ITS) 的推動以及科技的進步，擁擠收費被視為一個可行而有效處理道路擁擠狀態的方案。此種策略在過去已有許多國家施行，最著名的例子為倫敦以及新加坡，在實施擁擠收費後有效的降低收費區域內之交通量，降低整體車流之延滯並提高旅行速度。
本研究在同時考慮降低路網總旅行時間以及收費，且駕駛者選擇最小化自身旅行成本行為下，使用依時性交通指派模式呈現駕駛人行為，將需求產生時間分割時段，比較系統最佳化以及使用者均衡狀態下各路段於時段內旅行速度，以決定收費路段，並利用基因演算法搜尋費率，獲得依時性之擁擠收費策略；求解架構於八節點小路網進行初步實驗，並在五十節點包括高速公路之市區路網印證其效果，獲得平均旅行時間25.8%以及一般化成本24.2%之改善。

The rise in population and prosperous economies lead to the increasing number of private vehicle ownerships. Congestion is a general phenomenon while an excess of vehicles with limited capacity of road especially in urban area rush hours. The queuing and delays of vehicles on network result in increasing energy consumption and air pollution as well as rising travel time. As the awareness of environment protection is being a common sense, the restriction of energy consumption and air pollution become a necessary measure. As a result, eliminating the unnecessary congestion is an important direction of traffic policy.
The negative effects owing to insufficient road capacity and excess private vehicles may solved by road and public transportation construction. However, cost of the infrastructure forming a heavy financial burden to the government. To encourage roadway build privately, tolling on the road to support the maintenance is a necessity.
By the promotion of Intelligent Transportation Systems and the development of technology, congestion pricing have been considered an effective manner to deal with the road way crowd. The most significant case is London and Singapore. After carried out the policy, the traffic volume was decreased and the travel speed was improved.
Considering decreasing total travel time and toll while the road users choose path with the minimum cost. The research will present the route choice behavior of the network user by time-dependent traffic assignment model. The time interval with demand generation will be divided into several sections. The study obtains a dynamic congestion pricing strategy utilizing a genetic algorithm process. The experiment was conducted on 8-node network initially. The results showed that average travel time and generalized cost have improved for 25.8% and 24.2% on 50-node network.

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