DRACULA: DYNAMIC ROUTE ASSIGNMENT COMBINING USER LEARNING AND MICRO-SIMULATION
DRACULA: DYNAMIC ROUTE ASSIGNMENT COMBINING USER LEARNING AND MICRO-SIMULATION
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DRACULA：结合用户学习和微模拟的动态路线分配
DOI：
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发表时间：
1995
期刊：
影响因子：
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通讯作者：
D. Watling
中科院分区：
文献类型：
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作者：
R. Liu;D. Vliet;D. Watling
传统上，城市道路网络中的交通分析是基于平衡的概念，即一个固定的行程矩阵的设计期间进行评估分配到一个网络中，每个环节的旅行时间可以精确地定义由一个单调增加的成本流函数。在平衡条件下，路线的选择由Wardrop原则决定，即所有使用的路线都具有相等的最小旅行成本，这些成本是通过上述成本流量关系确定的。因此，我们不仅对每个O-D对有多少驾驶员行驶进行了非常精确的假设，而且还对他们使用的路线（尽管不一定是确切的比例）以及随之而来的行驶时间进行了非常精确的假设。此外，时间段内的时间变化通常被忽略。很明显，这幅图代表了对真实的生活的过度简化。每个O-D对的车辆行程数量在不同的日子之间变化，实际上组成这些行程的个人司机也是如此。同样，旅行条件每天都有很大的不同，部分原因是需求波动，但也有一些因素，如天气条件变化，事故等，最后不是所有的司机都能找到最佳路线，特别是对于不经常开车的司机。有明确的证据表明，供求条件的变化的净效应是显着增加交通输出的平均值，如交通时间和燃料消耗。例如，Mutale（1992）发现，由于北利兹网络的可变性，旅行时间比平衡时间增加了14%。
Traditionally the analysis of traffic in urban road networks is based on the concept of equilibrium whereby a fixed trip matrix for the design period to be evaluated is assigned to a network where travel times on each link can be defined precisely by a monotonically increasing cost-flow function. Under equilibrium conditions the choice of route or routes is governed by the Wardrop principle that all routes used have equal and minimum travel costs themselves being determined through the above mentioned cost-flow relationships. Thus we are making very precise assumptions not only as to how many drivers travel each O-D pair but also as to which routes they use (although not necessarily the exact proportions) and what the consequent travel times will be. In addition temporal variations within the time period are generally ignored. Clearly this picture represents an over-simplification of real life . The number of vehicle trips per O-D pair varies between days as indeed do the individual drivers who make up those trips. Equally travel conditions vary widely from day to day, partly due to fluctuating demand but also due to factors such as weather conditions vary incidents etc. And finally not all drivers succeed in finding the optimum route, particularly for infrequent drivers. There is clear evidence that the net effect of variability in supply and demand conditions is to significantly increase the mean values of traffic outputs such as traffic time and fuel consumption. For example Mutale (1992) found a 14% increase in travel times over equilibrium due to variability in a north Leeds network.