Multi-modal interactions at the network-level remain unexplored due to the lack of high-resolution data for all transportation modes involved. The current work investigates the effect of multi-modal interactions at space-mean network speed for each mode using the dataset from pNEUMA experiment that was carried out in a congested city centre network of Athens, Greece. Explanatory variables considered are the accumulation and the stopped fraction of vehicles of each mode. Firstly, a multi-modal mean speed MFD is considered by assuming that the mean speed of each mode can be expressed in terms of accumulations of all modes. The quality of multi-modal MFD fits is compared to the uni-modal ones, where the mean speed of a given mode is assumed to be a function of the accumulation of that mode only. Secondly, the classical two-fluid model is extended to multi-modal networks. An analysis on the ergodicity assumption in the context of stopped fraction is also presented. This work also introduces a network-level dynamic model that uses the stopped fraction of vehicles. This so-called extended trip-based model simulates the stop-and-go pattern of the vehicles thereby reproducing the evolution of network congestion. This work is the first to explore in this direction. The results from the classical trip-based and the extended trip-based models are compared and validated with the empirical data.

由于缺乏所有涉及的交通方式的高分辨率数据，网络层面的多模式交互仍未被探索。当前的工作使用来自pNEUMA的数据集研究了多模态交互在空间平均网络速度下对每种模式的影响实验是在希腊雅典拥挤的市中心网络中进行的。考虑的解释变量是每种模式车辆的累积和停止比例。首先，通过假设每个模式的平均速度可以用所有模式的累加表示来考虑多模式平均速度MFD。多模态 MFD 拟合的质量与单模态相比较，其中假定给定模式的平均速度仅是该模式累积的函数。其次，将经典的双流体模型扩展到多模态网络。还介绍了在停止分数的背景下对遍历性假设的分析。这项工作还引入了一个使用车辆停止部分的网络级动态模型。这种所谓的基于扩展行程的模型模拟了车辆的走走停停模式，从而再现了网络拥堵的演变。这项工作是第一个在这个方向上进行探索的工作。将基于经典旅行和基于扩展旅行的模型的结果与经验数据进行比较和验证。