This paper proposes a demand management method that optimizes the network performance by manipulating departure times within limited cooperation settings. The framework builds on the restricted flexibility of travellers’ departure times and exposes the impact of minimal changes in travellers’ schedule on the overall network performance. The test-bed (the plant) is based on the trip-based Macroscopic Fundamental Diagram (MFD) that relates the network space-mean speed to the vehicle accumulation. Travellers in the trip-based MFD model are characterized by their specific desired arrival times, heterogeneous trip lengths, and earliness and lateness scheduling penalties. The optimization problem, on the other hand, is formulated based on the accumulation-based MFD model that relates the network production (i.e. length-weighted aggregated link flows) to the vehicle accumulation. The accumulation-based MFD is an aggregated parsimonious model and does not consider individual traveller attributes, which enables it to remain analytically tractable and makes it suitable for optimization purposes. The proposed demand management method is also incorporated into a day-to-day evolution model where travellers adapt based on their historical departure times, experienced travel costs, and individual characteristics. The results highlight that the accumulation-based MFD can be efficiently used to develop travel demand management methods with realistic representation of traffic congestion within and across days.

本文提出了一种需求管理方法，该方法可以通过在有限的协作设置内操纵出发时间来优化网络性能。该框架建立在旅行者出行时间灵活性有限的基础上，并揭示了旅行者行程表的最小变化对整体网络性能的影响。试验台（工厂）基于基于行程的宏观基本原理图（MFD），该原理图将网络空间平均速度与车辆累积量相关联。基于旅行的MFD模型中的旅行者的特征在于其特定的所需到达时间，异构的旅行长度以及提前和迟到的调度惩罚。另一方面，基于与网络生产相关的基于累积的MFD模型来表达优化问题。长度加权的汇总链接流）到车辆累计量。基于累积的MFD是一个汇总的简约模型，并且不考虑单个旅行者的属性，这使其能够在分析上易于处理，并且适合进行优化。提出的需求管理方法也被纳入到日常发展模型中，旅客可以根据其历史出发时间，经历的旅行费用和个人特征进行调整。结果表明，基于累积的MFD可以有效地用于开发出旅行需求管理方法，并可以真实地表示几天之内和几天之内的交通拥堵。这使得它在分析上仍然易于处理，并使其适合于优化目的。提出的需求管理方法也被纳入到日常发展模型中，旅客可以根据其历史出发时间，经历的旅行费用和个人特征进行调整。结果表明，基于累积的MFD可以有效地用于开发出旅行需求管理方法，并能真实地表示几天之内和几天之内的交通拥堵情况。这使得它在分析上仍然易于处理，并使其适合于优化目的。提出的需求管理方法也被纳入到日常发展模型中，旅客可以根据其历史出发时间，经历的旅行费用和个人特征进行调整。结果表明，基于累积的MFD可以有效地用于开发出旅行需求管理方法，并可以真实地表示几天之内和几天之内的交通拥堵。