出行分布规律与模型的系统比较
Systematic comparison of trip distribution laws and models
摘要：
出行分布规律是交通和城市规划中出行需求特征描述的基础。在过去几年中，已经考虑了几种方法。其中之一是所谓的重力定律，其中假设旅行次数与出发地和目的地的人口有关，并随着距离的增加而减少。这个定律的数学表达式类似于牛顿的万有引力定律，这解释了它的名字。另一种流行的方法是受到干预机会理论的启发，该理论认为距离对目的地选择没有影响，只扮演了它们之间干预机会数量的替代品的角色。在本文中，我们进行了彻底的比较，这两种方法在他们的能力估计通勤流量测试他们对经验出行数据在不同的规模和来自不同的国家。不同版本的重力和干预机会定律，包括最近提出的辐射定律，被用来估计一个人必须从一个单位通勤到另一个单位的概率，称为出行分布定律。基于这些概率分布规律，采用不同的出行分布模型对通勤网络进行了模拟。我们发现，重力法比干预机会法更好地估计通勤流，保持网络的结构，并适合通勤距离分布，虽然它不能预测通勤流在大距离。最后，我们表明，不同的方法可以使用在没有详细的数据进行校准，因为他们唯一的参数只取决于地理单元的规模。
英文摘要：
Trip distribution laws are basic for the travel demand characterization needed in transport and urban planning. Several approaches have been considered in the last years. One of them is the so-called gravity law, in which the number of trips is assumed to be related to the population at origin and destination and to decrease with the distance. The mathematical expression of this law resembles Newton's law of gravity, which explains its name. Another popular approach is inspired by the theory of intervening opportunities which argues that the distance has no effect on the destination choice, playing only the role of a surrogate for the number of intervening opportunities between them. In this paper, we perform a thorough comparison between these two approaches in their ability at estimating commuting flows by testing them against empirical trip data at different scales and coming from different countries. Different versions of the gravity and the intervening opportunities laws, including the recently proposed radiation law, are used to estimate the probability that an individual has to commute from one unit to another, called trip distribution law. Based on these probability distribution laws, the commuting networks are simulated with different trip distribution models. We show that the gravity law performs better than the intervening opportunities laws to estimate the commuting flows, to preserve the structure of the network and to fit the commuting distance distribution although it fails at predicting commuting flows at large distances. Finally, we show that the different approaches can be used in the absence of detailed data for calibration since their only parameter depends only on the scale of the geographic unit.
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