本研究提出一套可交易式油票系統，提供政府部門以收取油票之方式改變用路人行為以減低碳排放量。首先，在此套系統中政府部門必須先訂定欲發放給各個用路人之油票數，爾後擬定出一套類似擁擠定價的收費系統，當用路人使用到路網中的擁擠路段時政府便依規定向用路人收取油票。在此套系統下，用路人得以自行決定是否要進入該路網，或者是依政府規定之收取油票方式選擇自己的路徑；用路人甚至可決定不使用自己擁有的油票而將之轉售於他人。本研究將此問題以雙層模式方式作建構，並以基因演算法(Genetic Algorithm)及化學反應最適演算法(Chemical Reaction Optimization)求解以得出在可交易式油票系統下之整體路網表現。結果顯示，透過可交易式油票系統可減少路網中的總排碳量，此結論也可提供政府部門另一項紓解都市交通擁擠之策略。

To reduce the carbon emissions from a transportation network, we investigate a tradable credit system in which government agencies can control the amount of carbon emission by adjusting road users’ travel behavior. In this system, government agencies first issue a certain amount of tradable credits to each potential road user. A toll scheme similar to congestion pricing is then employed to charge road users if they use the links in congested areas. Road users then decide whether to make trips and route choices based on government policies, or they can trade their credits with those who need more. We construct the problem as a bi-level program and develop a Genetic Algorithm (GA) and a new metaheuristic called Chemical Reaction Optimization (CRO) to evaluate the performance of a tradable credit system. The numerical results show that a tradable credit system can reduce the total carbon emissions of a transportation network and suggest an alternative way to alleviate urban traffic congestion.

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