Traffic congestion: an experimental study of the Downs-Thomson paradox

Abstract

This study considers a model of road congestion with average cost pricing. Subjects must choose between two routes -Road and Metro. Travel cost on the road is increasing in the number of commuters who choose this route, while travel cost on the metro is decreasing in the number of its users. We examine how changes to road capacity, number of commuters, and metro pricing scheme influence the commuters' route-choice behavior. According to the Downs-Thomson paradox, improved road capacity increases travel times along both routes because it attracts more users to the road and away from the metro, thereby worsening both services. A change in route design generates two Nash equilibria; and the resulting coordination problem is amplified even further when the number of commuters is large. We find that, similar to other binary choice experiments with congestion effects, aggregate traffic flows are close to the equilibrium levels, but systematic individual differences persist over time.

Exploration of day-to-day route choice models by a virtual experiment

“…Most of these studies were concerned about how travelers' route choices are affected by various factors such as information, experience, risk, uncertainty, personality factors, as well as various transportation system components mentioned above Prashker, 2005, 2006;Ben-Elia et al, 2008Hu and Mahmassani, 1997;Lotan, 1997;Lu et al, 2011;Mahmassani and Herman, 1990;Mahmassani and Stephan, 1988;Rapoport et al, 2014;Srinivasan and Mahmassani, 2003;Yang et al, 1993). The laboratory experiments were also used to test the static UE theories such as Braess Paradox and Downs-Thomson Paradox (Dechenaux et al, 2014;Morgan et al, 2009;Rapoport et al, 2009). …”

Paradoxical Survival: Examining the Parrondo Effect across Biology

“…This is termed the anti‐Parrondo effect, and has been studied in certain social systems. Prominent examples include the Braess paradox in transportation, where adding routes to improve traffic flow can counterintuitively worsen traffic congestion, and the Downs–Thomson paradox, where, because of the decision‐making of commuters between taking public and private transport, improvements to reduce expected road travel times can result in worse traffic conditions. In a hypothetical scenario where three gunmen face one another in a shoot‐out, known as a truel, and its generalizations to larger ensembles, the weakest gunman may face better odds of survival if he does nothing; and in real‐world social settings, greater perceived success of individuals has been found to reflect diminishing actual skill, potentially rendering inadequately comprehensive assessments and interviews acutely inaccurate.…”

Towards a new intelligent traffic system based on deep learning and data integration

“…This is an essential step in traffic management to address the dilemma of rising transportation demand and limited land availability [1]. According to the study conducted by Duchenaux et al [2], increasing road capacity does not systematically lead to optimal congestion reduction, and may even causes serious traffic conditions. To achieve better traffic flow prediction performance, many prediction methods have been proposed covering a wide spectrum, such as parametric methods, non-parametric methods and hybrid methods [3]- [8].…”