Hyperdemand: A static traffic model with backward-bending demand curves
Lewis J. Lehe and
Ayush Pandey
Economics of Transportation, 2020, vol. 24, issue C
Abstract:
Static traffic models, in the tradition of Walters (1961), typically feature a ‘‘demand curve’’ giving the vehicle flow demanded for each unit travel time (inverse speed). Traditionally, the demand curve declines because people want to drive more as travel times fall. This paper proposes that the vehicle flow demanded can, instead, plausibly rise with unit travel time (a phenomenon we call ‘‘hyperdemand’’), if congestion somehow induces some people to switch from high-to low-occupancy modes. To illustrate, we present a model of travel in an isotropic downtown where people choose among not traveling, a low-occupancy mode called ‘‘Alone’’ and a high-occupancy mode called ‘‘Pool.’’ Pool trips detour to pick up and drop off passengers en route, so congestion delays them more than Alone trips. Consequently, multiple equilibria can arise even in ‘‘light congestion,’’ and small toll increases can have dramatic impacts by eliminating equilibria.
Keywords: Carpool; Toll; Macroscopic fundamental diagram; Isotropic; Congestion; Ridesharing (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S2212012220301180
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:ecotra:v:24:y:2020:i:c:s2212012220301180
DOI: 10.1016/j.ecotra.2020.100182
Access Statistics for this article
Economics of Transportation is currently edited by Mogens Fosgerau and Erik Verhoef
More articles in Economics of Transportation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().