ABSTRACT

Dynamic Network Loading (DNL) models are typically formulated as a system of differential equations where travel times, densities or any other variable that indicates congestion is endogenous. However, such endogeneities increase the complexity of the Dynamic Traffic Assignment (DTA) problem due to the interdependence of DNL, route choice and demand. In this paper, attempting to exploit the growing accessibility of traffic-related data, we suggest that congestion can be instead captured by exogenous variables, such as travel time observations. We propagate the traffic flow based on an exogenous travel time function, which has a piece-wise linear form. Given piece-wise stationary route flows, the piece-wise linear form of the travel time function allows us to use an efficient event-based modelling structure. Our Data-Driven Network Loading (DDNL) approach is developed in accordance with the theoretical DNL framework ensuring vehicle conservation and FIFO. The first simulation experiment-based results are encouraging, indicating that the DDNL can contribute to improving the efficiency of applications where the monitoring of historical network-wide flows is required.

Abbreviations: DDNL – Data Driven Network Loading; DNL – Dynamic Network Loading; DTA – Dynamic Traffic Assignment; ITS – Intelligent Transportation Systems; OD – Origin Destination; TTF – Travel Time Function; LTT – Linear Travel Time; DL – Demand level

摘要

动态网络负载（DNL）模型通常被公式化为微分方程组，其中旅行时间，密度或任何其他表明拥堵的变量都是内生的。但是，由于DNL，路线选择和需求之间的相互依赖性，这种内生性增加了动态交通分配（DTA）问题的复杂性。在本文中，尝试利用与交通相关数据的日益增长的可访问性，我们建议可以通过外源捕获拥塞。变量，例如旅行时间观察。我们基于外部旅行时间函数传播交通流量，该函数具有分段线性形式。给定分段的固定路径流，旅行时间函数的分段线性形式使我们可以使用有效的基于事件的建模结构。我们的数据驱动网络加载（DDNL）方法是根据可确保车辆节约和先进先出的理论DNL框架开发的。基于模拟实验的第一个结果令人鼓舞，这表明DDNL可以有助于提高需要监视历史网络范围流量的应用程序的效率。

缩写：DDNL –数据驱动的网络加载；DNL –动态网络加载；DTA –动态流量分配；ITS –智能交通系统；OD –起点目的地；TTF –旅行时间功能；LTT –线性旅行时间；DL –需求水平