Demand management with limited cooperation among travellers: A doubly dynamic approach. (February 2020)

• Record Type: Journal Article
• Authors: Yildirimoglu, Mehmet; Ramezani, Mohsen
• Is Part Of: Transportation research. Volume 132(2020)
• Page Start: 267
• Page End: 284
• Publication Date: 2020-02
• Subjects: Morning commute -- Equilibrium -- Single reservoir -- Urban traffic -- Network fundamental diagram -- Macroscopic fundamental diagram -- Demand management -- Day-to-day assignment; Transportation -- Research -- Periodicals; Transportation -- Mathematical models -- Periodicals
• DOI: 10.1016/j.trb.2019.02.012
• Languages: English
• ISSNs: 0191-2615

Abstract

Highlights: We develop a demand management model that considers limited cooperation settings among travellers. We incorporate the proposed management model into a day-to-day evolution model where people adapt to changing traffic conditions. The results confirm that minor changes in travellers schedule can significantly improve the overall network performance. Abstract: 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.