An efficient and exact event-based algorithm for solving simplified first order dynamic network loading problems in continuous time. (October 2016)

• Record Type: Journal Article
• Authors: Raadsen, Mark P.H.; Bliemer, Michiel C.J.; Bell, Michael G.H.
• Is Part Of: Transportation research. Volume 92:Part B(2016)
• Page Start: 191
• Page End: 210
• Publication Date: 2016-10
• Subjects: Simplified first order network model -- Link transmission model -- Macroscopic dynamic network loading -- Exact continuous time grid free solution; Transportation -- Research -- Periodicals; Transportation -- Mathematical models -- Periodicals
• DOI: 10.1016/j.trb.2015.08.004
• Languages: English
• ISSNs: 0191-2615

Abstract

Highlights: Simplified first order macroscopic dynamic network loading model. Event-based, grid free approach, yielding exact solutions to the LWR model. Computationally efficient on large scale generalised networks. Approximate solutions via optional and configurable flow rate change acceptance threshold. Abstract: In this paper a novel solution algorithm is proposed for exactly solving simplified first order dynamic network loading (DNL) problems for any generalised network. This DNL solution algorithm, termed eLTM (event-based Link Transmission Model), is based on the seminal Lighthill–Witham–Richards (LWR) model, adopts a triangular fundamental diagram and includes a generalised first order node model formulation. Unlike virtually all DNL solution algorithms, eLTM does not rely on time discretisation, but instead adopts an event based approach. The main advantage of this approach is the possibility of yielding exact results. Furthermore, an approximate version of the same algorithm is introduced. The user can configure an a-priori threshold that dictates the approximation error (measurable a-posteriori). Using this approximation the computational effort required decreases significantly, making it especially suitable for large scale applications. The computational complexity is investigated and results are demonstrated via theoretical and real world case studies. Fixed periods of stationary demands are included adopting a matrix demand profile to mimic basic departure time demand fluctuations. Finally, the information loss of the approximate solution is assessed under different configurations.