TransCAD交通分配方法介绍
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)
全有全无分配法
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
第一文库网 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.在全有全无分配模型中，OD点之间的交通量全部分配到起讫点之间的最短路上。这个模型是不切实际的，因为每个OD对的数值只分配到一条路径上，即使存在另外一条时间、成本相同或相近的路线。同样，交通量分配的时候没有考虑是否有足够的通行能力，即使已经出现严重的拥堵；路线的运行时间为一个输入的固定值，它不因为路线的拥堵而变化。
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分配法将交通量分配到OD点对之间的多条路径上。各条路线的分配比例根据路线的选择概率确定，而此概率用一个logit路线选择模型来计算。一般而言，运行时间更短的线路被选择的概率就更高。事实上，STOCH分配模型并不是将交通量分配到所有可供选择的路线上，而只分配到包含“可行路段”的路径上。一个合理的路段应该让旅行者离起点更远，而且/或者离终点更近。在STOCH分配模型中，路段运行时间是一个输入的固定值，与交通量无关。因此，这种方法不是一个平衡的方法。
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载的。在每一步中，加载一定百分比的交通需求。单次分配是基于全有全无分配法的。每加载一次之后，运行时间要根据当前交通量重新计算。如果加载的次数很多，分配出的结果看起来就像一个平衡分配法；但事实上，这种方法事实上并未产生一个平衡的结果。因此，交通量和运行时间之间的矛盾就会导致评价指标的误差。同时，每次分配的OD量的比例将影响增量分配法的结果，这增加了分配结果的误差。
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.
容量限制法试图产生一个平衡的结果，它是反复的采用全有全无分配，且根据一个反映路段容量的拥堵函数反复的计算路段运行时间。然而，不幸的`是，这种方法不收敛，它会在某些路段上反复加载。为了减小这个问题，某些软件在应用这种方法的时候，在最后一次迭代中滤去时间因素平均分配交通量。这种方法不能收敛于一个平衡结果，而且还产生一个附加问题，即分配结果很大程度上依赖于迭代次数。多一次或者少一次迭代通常都会影响结果。
User Equilibrium (UE)
用户平衡法
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.
用户平衡法采用一个反复的过程来得到一个平衡解，在这种方法中旅行者不能通过改变路线来改变旅行时间。在每一次迭代中，路段交通量都会重新计算，计算中同时考虑了路段通行能力和运行时间。用户平衡法可以用精确的数学程序表达，TransCAD采用的是Frank-Wolf法，这种方法详见“交通分配技术要点”。
Stochastic User Equilibrium (SUE)
随机用户平衡法
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.
随机用户平衡法是一个广义的用户平衡法，它假设道路使用者不能获得精确的路网信息，而且/或者不会意识到不同路径的运输成本的差别。相比用户平衡法，随机用户平衡法会产生一个更现实的结果，因为他同时允许最优路径和较差路径。较差路线分配量较少，但是不会像用户平衡性中那样出现零交通量。对于随机用户平衡模型，TransCAD中采用的是目前所知唯一收敛的方法：连续平均法。由于这种方法本身的特性，它需要进行大量的迭代。
System Optimum Assignment (SO)
系统最佳分配法
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.
系统最佳分配法通过计算路线的最小运行时间进行分配。在系统最佳分配法中，如果不加大运行时间道路使用者就不能改变出行路线，尽管旅行者实际上有可能会减少其运行时间。假如道路使用者都被告知最优路线，系统最佳分配法将会产生最小的拥堵。很明显这个模型是不现实的，系统最佳分配法在智能交通系统假设分析中是十分有用的。
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