Comparison of Methods for Path Flow Reassignment for Dynamic User Equilibrium

• Published in: Networks and spatial economics Vol. 12; no. 3; pp. 337 - 376
• Main Authors: Carey, Malachy, Ge, Y. E.
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.09.2012; Springer Nature B.V
• Subjects: Algorithms; Civil Engineering; Convergence; Dynamics; Economics; Economics and Finance; Equilibrium; Inflow; Mathematical models; Measurement techniques; Methods; Networks; Operations Research/Decision Theory; Regional/Spatial Science; Route choice; Studies; Traffic assignment; Traffic congestion; Traffic flow; Dynamic user equilibrium; Dynamic traffic assignment; Path inflow reassignment; Route choice; Traffic
• ISSN: 1566-113X; 1572-9427
• DOI: 10.1007/s11067-011-9159-6

Models to describe or predict of time-varying traffic flows and travel times on road networks are usually referred to as dynamic traffic assignment (DTA) models or dynamic user equilibrium (DUE) models. The most common form of algorithms for DUE consists of iterating between two components namely dynamic network loading (DNL) and path inflow reassignment or route choice. The DNL components in these algorithms have been investigated in many papers but in comparison the path inflow reassignment component has been relatively neglected. In view of that, we investigate various methods for path inflow reassignment that have been used in the literature. We compare them numerically by embedding them in a DUE algorithm and applying the algorithm to solve DUE problems for various simple network scenarios. We find that the choice of inflow reassignment method makes a huge difference to the speed of convergence of the algorithms and, in particular, find that ‘travel time responsive’ reassignment methods converge much faster than the other methods. We also investigate how speed of convergence is affected by the extent of congestion on the network, by higher demand or lower capacity. There appears to be much scope for further improving path inflow reassignment methods.