本研究提出一利用工程上最佳化的方式,進行交通政策的制定和測站點位置的選定,來 減少道路上空氣汙染濃度。此方法運用最佳化的方式,在觀測點汙染濃度符合法規的前提 下,提升道路的速限以降低社會成本。在計算汙染的時候,本研究發現觀測點位置的選定會 影響到汙染濃度的高低,但在以往制定最佳交通政策的文獻中,所選擇的觀測站位置,並沒 有考慮到所選擇的觀測站是否能夠代表該路網全體的汙染情況,而在選擇觀測站的文獻當 中,其所提出的大多為量化性的準則,且在設置測站後,並沒有再去進行該地區的汙染控制。故在本論文中,我們將結合制定交通法規和逐步增加觀測站的方法,找出代表路網的觀 測站並訂制交通法規來管控汙染,最後使得該地區的汙染都符合政府所要求的法規標準。一 開始我們先決定初始觀測點,然後利用交通法規的制定確保該地區的汙染符合法規標準,同 時最小化社會成本。接著再找尋汙染濃度不合法規的點,將該點設為新的觀測站後,再重新 制訂法規。經過這一系列增加觀測站和制定新的交通政策,我們就可以只用有限的觀測站代 表一個大面積的地區並使路網上各處汙染符合法規。在計算汙染濃度的部分,我們結合使用 者平衡、高斯汙染擴散模型、車輛排放係數、路網結構和定量的環境因素做計算。同時,為了更接近實際的狀況,我們也將問題延伸到加入人為和環境的不確定因素。在人為不確定因 素的部份我們應用隨機使用者平衡車流模型;在環境不確定因素的部分,則利用文獻上提供 的模型來模擬風向、風速和大氣穩定度。這些不確定因素的加入使得原本的問題成為考量機 率的可靠度最佳化問題。我們將分別使用六條路的簡單的路網結構和台南市區域的路網展示 未考量不確定因素和考量不確定因素的法規制定結果。結果顯示雖然加入不確定因素大幅增 加了模型的複雜度和計算成本,但是這樣的政策制定結果反而更能反映真實世界的汙染情況。

We propose a methodology to sequentially determine the optimal traffic regulations in compliance to air quality standards and obtain the corresponding optiaml receptor locations. Similar studies in the literature focus primarily on the optimal traffic policy-setting for specific receptors only without considering the representativeness of these receptors to the area of interest.Although critera in selecting good receptor locations do exist, they are predominately qualitative guidelines. To determine the best regulatory setting of a target area, we start with an initial receptor location and taylor traffic policy to make sure the air quality at the receptoris in compliance to the government standards with the minimal societal cost. By sequentially add/removing receptors and setting new policies,we are able to use a limited number of receptors to represent a large area. The pollution concentration at the receptor is calculated by integrating user equilibrium, the Gaussian dispersion model, and emission factors from on-road vehicles for a given road network structure and deterministic environmental conditions. We also extend the study to consider uncertainties in the environment. Stochastic user equilibrium is adopted to consider perception uncertainty in traffic simulation. Ambient uncertainties such as wind speed, wind direction, and atmosphere stability conditions for pollution concertration modeling are also considered. These uncertainty models result in probabilistic optimization problems that is later solved using the state-of-the-art techniques. Simple road structure with six roads and the road network for the city of Tainan in Taiwan are both studied using the proposed methods with and without considering uncertainty. Results show that although including uncertainty significantly increases model complexity and computation cost, they should be emphasized in real world policy decision-marking under uncertainty.

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