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A systematic approach to the calibration of traffic assignment models.

机译：一种交通分配模型校准的系统方法。

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摘要

The urban transportation model predicts flows on a transportation network as a function of the urban system containing the network and the characteristics of the transportation network. It comprises four steps: (1) trip generation, (2) trip distribution, (3) modal split, and (4) traffic assignment. The fourth step, traffic assignment, predicts routes (paths) used between each origin-destination pair on the transportation network.; Calibration of traffic assignment models is acknowledged as a perquisite to their application. Such calibration efforts have traditionally been ad hoc. They have fallen into one of five categories: (1) modification of the network representation, (2) adjustment of travel demand, (3) selecting the traffic assignment method and assumptions, (4) adjustment of the traffic dispersion parameter for stochastic assignments, and (5) estimating congestion function parameters.; Some efforts have recently been made towards systematizing the calibration of traffic assignment. These efforts can be categorized as mathematical programming and heuristic procedures. Mathematical programming has been traditionally applied in the estimation of congestion function parameters. If observed link flows and travel times are available, least squares are applied.; In practice, however, it is hard to measure link travel times. Proposed is a generic method I term network loading (NL). This method simultaneously estimates the link flows and congestion function parameters. If equilibrium link flows are used in parameter estimation and prediction, we have what I call the user-equilibrium (UE) model. A particular implementation of the UE model is the Entropy-Maximizing (EM) model. The bilevel formulation by Suh et al. (1990) is a similar approach that I cover in this research. It has an upper-level objective function that minimizes the error in estimated link flows. The lower-level problem is the formulation of the UE assignment.; Despite their elaborate formulations, the mathematical programming methods are difficult to implement. The difficulty increases with the size of the network. Human-based expertise and heuristics provide a viable alternative approach. Fricker (1989) proposes the parameter adjustment (PA) method for the calibration of congestion function parameters. He also proposes the direct impedance adjustment (DIA) and simultaneous link speed adjustment (SLSA) methods for modifying link free-flow travel times and speeds. The three proposed procedures require only observed link flows.; The three heuristic procedures are tested on two networks, the fictitious Archerville and the real Eindhoven networks. The experiments on the Archerville network are controlled for extraneous sources of error while those on the Eindhoven network are not. The Eindhoven network is supplemented with synthetic data and more controlled tests are conducted.; The conclusions of the experiments are as follows: (1) PA is very sensitive to parameter starting values and level of congestion on the network. It performs well only on highly detailed networks. (2) DIA and SLSA improve traffic assignment performance in terms of replicating actual link flows. DIA, however, does not always perform very well. (3) DIA and SLSA perform better at higher levels of network detail. (4) DIA and SLSA can be applied to all or parts of a network. (5) SLSA is clearly superior to DIA. Accordingly, it recommended for use by transportation planners.
机译：城市交通模型根据包含该网络的城市系统和交通网络的特征来预测交通网络上的流量。它包括四个步骤：（1）行程生成，（2）行程分配，（3）模态划分和（4）交通分配。第四步，交通分配，预测交通网络上每个起点-终点对之间使用的路线（路径）。交通分配模型的校准被认为是其应用的必要条件。传统上，这种校准工作是临时的。它们已分为五类之一：（1）修改网络表示，（2）调整旅行需求，（3）选择流量分配方法和假设，（4）调整随机分配的流量分散参数， （5）估计拥塞功能参数。最近已经做出一些努力来使交通分配的校准系统化。这些工作可以归类为数学编程和启发式过程。传统上已经将数学编程应用于拥塞函数参数的估计。如果有观察到的链路流量和行进时间可用，则应用最小二乘。但是，实际上，很难测量链接的传播时间。提出了一种我称之为网络加载（NL）的通用方法。该方法同时估计链路流量和拥塞功能参数。如果在参数估计和预测中使用平衡链接流，那么我们就有所谓的用户平衡（UE）模型。 UE模型的特定实现是熵最大化（EM）模型。 Suh等人的双层配方。 （1990）是我在这项研究中介绍的类似方法。它具有上层目标功能，可最大程度地减少估计的链路流中的错误。较低层的问题是UE分配的制定。尽管有详尽的表述，但是数学编程方法却难以实现。难度随着网络规模的增加而增加。基于人的专业知识和启发式方法提供了一种可行的替代方法。 Fricker（1989）提出了参数调整（PA）方法来校准拥塞功能参数。他还提出了直接阻抗调整（DIA）和同时链路速度调整（SLSA）方法来修改链路自由流动的时间和速度。提出的三个程序仅需要观察到的链路流。这三个启发式过程在两个网络上进行了测试，这两个网络是虚拟的Archerville网络和实际的Eindhoven网络。对Archerville网络上的实验进行了控制，以检查是否有外部错误源，而对Eindhoven网络上的实验则进行了控制。艾恩德霍芬网络补充了综合数据，并进行了更多受控的测试。实验的结论如下：（1）PA对网络的参数起始值和拥塞程度非常敏感。它仅在高度详细的网络上表现良好。 （2）DIA和SLSA在复制实际链路流方面提高了流量分配性能。但是，DIA并不总是表现很好。 （3）DIA和SLSA在更高级别的网络详细信息上表现更好。 （4）DIA和SLSA可以应用于整个或部分网络。 （5）SLSA明显优于DIA。因此，建议运输计划人员使用。

著录项

  • 作者

    El-Mously, Ziad.;

  • 作者单位

    University of Pennsylvania.;

  • 授予单位 University of Pennsylvania.;
  • 学科 Transportation.
  • 学位 Ph.D.
  • 年度 1994
  • 页码 201 p.
  • 总页数 201
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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