TransCAD交通分配方法介紹
交通分配方法
The following are traffic assignment methods encountered in transportation planning practice, all of which are available in TransCAD:
All-or-Nothing Assignment (AON)
全有全無分配法
Under All-or-Nothing Assignment, all traffic flows between O-D pairs are assigned to the shortest paths connecting the origins and destinations. This model is unrealistic in that only one path between every O-D pair is used, even if there is another path with the same or nearly the same travel time or cost. Also, traffic on links is assigned without consid
第一文庫網(wǎng) ering whether or not there is adequate capacity or heavy congestion; travel time is a fixed input and does not vary depending on the congestion on a link.在全有全無分配模型中，OD點之間的交通量全部分配到起訖點之間的最短路上。這個模型是不切實際的，因為每個OD對的數(shù)值只分配到一條路徑上，即使存在另外一條時間、成本相同或相近的路線。同樣，交通量分配的時候沒有考慮是否有足夠的通行能力，即使已經(jīng)出現(xiàn)嚴重的擁堵；路線的運行時間為一個輸入的固定值，它不因為路線的擁堵而變化。
STOCH Assignment
STOCH分配法
STOCH Assignment distributes trips between O-D pairs among multiple alternative paths that connect the O-D pairs. The proportion of trips that is assigned to a particular path equals the choice probability for that path, which is calculated by a logit route choice model. Generally speaking, the smaller the travel time of a path, compared with the travel times of the other paths, the higher its choice probability would be. STOCH Assignment, however, does not assign trips to all the alternative paths, but only to paths containing links that are considered "reasonable." A reasonable link is one that takes the traveler farther away from the origin and/or closer to the destination. The link travel time in STOCH Assignment is a fixed input and is not dependent on link volume. Consequently, the method is not an equilibrium method.
STOCH分配法將交通量分配到OD點對之間的多條路徑上。各條路線的分配比例根據(jù)路線的選擇概率確定，而此概率用一個logit路線選擇模型來計算。一般而言，運行時間更短的線路被選擇的概率就更高。事實上，STOCH分配模型并不是將交通量分配到所有可供選擇的路線上，而只分配到包含“可行路段”的路徑上。一個合理的路段應該讓旅行者離起點更遠，而且/或者離終點更近。在STOCH分配模型中，路段運行時間是一個輸入的固定值，與交通量無關(guān)。因此，這種方法不是一個平衡的方法。
Incremental Assignment 增量分配法
Incremental Assignment is a process in which fractions of traffic volumes are assigned in steps. In each
step, a fixed proportion of total demand is assigned, based on All-or-Nothing Assignment. After each step, link travel times are recalculated based on link volumes. When there are many increments used, the flows may resemble an equilibrium assignment; however, this method does not yield an equilibrium solution. Consequently, there will be inconsistencies between link volumes and travel times that can lead to errors in evaluation measures. Also, Incremental Assignment is influenced by the order in which volumes for O-D pairs are assigned, raising the possibility of additional bias in the results.
增量分配法中交通量是分次分步加載的。在每一步中，加載一定百分比的交通需求。單次分配是基于全有全無分配法的。每加載一次之后，運行時間要根據(jù)當前交通量重新計算。如果加載的次數(shù)很多，分配出的結(jié)果看起來就像一個平衡分配法；但事實上，這種方法事實上并未產(chǎn)生一個平衡的結(jié)果。因此，交通量和運行時間之間的矛盾就會導致評價指標的誤差。同時，每次分配的OD量的比例將影響增量分配法的結(jié)果，這增加了分配結(jié)果的誤差。
Capacity Restraint 容量限制法
Capacity Restraint attempts to approximate an equilibrium solution by iterating between all-or-nothing traffic loadings and recalculating link travel times based on a congestion function that reflects link capacity. Unfortunately, this method does not converge and can flip-flop back and forth in the loadings on some links (Sheffi, 1985, p. 113). The capacity restraint method as implemented in some software packages attempts to lessen this problem by smoothing the travel times and by averaging the flows over a set of the last iterations. This method does not converge to an equilibrium solution and has the additional problem that the results are highly dependent on the specific number of iterations run. Performing one more or one less iteration usually changes the results substantially.
容量限制法試圖產(chǎn)生一個平衡的結(jié)果，它是反復的采用全有全無分配，且根據(jù)一個反映路段容量的擁堵函數(shù)反復的計算路段運行時間。然而，不幸的`是，這種方法不收斂，它會在某些路段上反復加載。為了減小這個問題，某些軟件在應用這種方法的時候，在最后一次迭代中濾去時間因素平均分配交通量。這種方法不能收斂于一個平衡結(jié)果，而且還產(chǎn)生一個附加問題，即分配結(jié)果很大程度上依賴于迭代次數(shù)。多一次或者少一次迭代通常都會影響結(jié)果。
User Equilibrium (UE)
用戶平衡法
User Equilibrium uses an iterative process to achieve a convergent solution, in which no travelers can improve their travel times by shifting routes. In each iteration, network link flows are computed, which incorporate link capacity restraint effects and flow-dependent travel times. The formulation of the UE problem as a mathematical program, and the Frank-Wolf solution method employed in TransCAD, are described in Technical Notes on Traffic Assignment.
用戶平衡法采用一個反復的過程來得到一個平衡解，在這種方法中旅行者不能通過改變路線來改變旅行時間。在每一次迭代中，路段交通量都會重新計算，計算中同時考慮了路段通行能力和運行時間。用戶平衡法可以用精確的數(shù)學程序表達，TransCAD采用的是Frank-Wolf法，這種方法詳見“交通分配技術(shù)要點”。
Stochastic User Equilibrium (SUE)
隨機用戶平衡法
Stochastic User Equilibrium is a generalization of user equilibrium that assumes travelers do not have perfect information concerning network attributes and/or they perceive travel costs in different ways. SUE assignments produce more realistic results than the deterministic UE model, because SUE permits use of less attractive as well as the most-attractive routes. Less-attractive routes will have lower utilization, but will not have zero flow as they do under UE. SUE is computed in TransCAD using the Method of Successive Averages (MSA), the only known convergent method (Sheffi and Powell, 1982; Sheffi, 1985). Due to the nature of this method, a large number of iterations should be used.
隨機用戶平衡法是一個廣義的用戶平衡法，它假設(shè)道路使用者不能獲得精確的路網(wǎng)信息，而且/或者不會意識到不同路徑的運輸成本的差別。相比用戶平衡法，隨機用戶平衡法會產(chǎn)生一個更現(xiàn)實的結(jié)果，因為他同時允許最優(yōu)路徑和較差路徑。較差路線分配量較少，但是不會像用戶平衡性中那樣出現(xiàn)零交通量。對于隨機用戶平衡模型，TransCAD中采用的是目前所知唯一收斂的方法：連續(xù)平均法。由于這種方法本身的特性，它需要進行大量的迭代。
System Optimum Assignment (SO)
系統(tǒng)最佳分配法
System Optimum Assignment computes an assignment that minimizes total travel time on the network. Under SO Assignment, no users can change routes without increasing their total travel time on the system, although it is possible that travelers could reduce their own travel times. SO Assignment can be thought of as a model in which congestion is minimized when travelers are told which routes to use. Obviously not a behaviorally realistic model, SO assignment can be useful in analyzing Intelligent Transportation System (ITS) scenarios.
系統(tǒng)最佳分配法通過計算路線的最小運行時間進行分配。在系統(tǒng)最佳分配法中，如果不加大運行時間道路使用者就不能改變出行路線，盡管旅行者實際上有可能會減少其運行時間。假如道路使用者都被告知最優(yōu)路線，系統(tǒng)最佳分配法將會產(chǎn)生最小的擁堵。很明顯這個模型是不現(xiàn)實的，系統(tǒng)最佳分配法在智能交通系統(tǒng)假設(shè)分析中是十分有用的。
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