This study estimates and manages the stochastic traffic dynamics in a bi-modal transportation system, and gives hints on how increasing data availability in transport and cities can be utilized to estimate transport supply functions and manage transport demand simultaneously. In the bi-modal system, travelers’ mode choices are based on their perceptions of the two travel modes: driving or public transit. Some travelers who have access to real-time road (car) traffic information may shift their mode based on the information received (note that real-time information about public transit departures/arrivals is not considered here). For the roadway network, the within-day traffic evolution is modeled through a Macroscopic Fundamental Diagram (MFD), where the flow dynamics exhibits a certain level of uncertainty. A non-parametric approach is proposed to estimate the MFD. To improve traffic efficiency, we develop an adaptive pricing mechanism coupled with the learned MFD. The adaptive pricing extends the study of Liu and Geroliminis (2017) to the time-dependent case, which can better accommodate temporal demand variations and achieve higher efficiency. Numerical studies are conducted on a one-region theoretical city network to illustrate the dynamic evolution of traffic, the MFD learning framework, and the efficiency of the adaptive pricing mechanism.

这项研究估计和管理双模式运输系统中的随机交通动态，并暗示如何利用运输和城市中日益增加的数据可用性来估计运输供应功能并同时管理运输需求。在双峰制中，旅行者的模式选择基于他们对两种行驶模式的看法：驾驶或公共交通。一些可以访问实时道路（汽车）交通信息的旅行者可能会根据接收到的信息改变他们的方式（请注意，此处不考虑有关公共交通工具起降的实时信息）。对于道路网络，通过宏观基本图（MFD）对日内流量演变进行建模，其中流量动态表现出一定程度的不确定性。提出了一种非参数方法来估计MFD。为了提高流量效率，我们开发了一种自适应定价机制，并结合了所学的MFD。自适应定价将Liu和Geroliminis（2017）的研究扩展到时间相关的案例，可以更好地适应时间需求变化并实现更高的效率。在一个区域的理论城市网络上进行了数值研究，以说明交通的动态演变，MFD学习框架以及自适应定价机制的效率。