Economic implications of phantom traffic jams: evidence from traffic experiments

ABSTRACT Traffic jams occur even without bottlenecks, simply because of interaction of vehicles on the road. From a driver’s point of view, the instability of the traffic flow arises stochastically. Because the probability of a traffic jam increases with the number of cars on the road, there is a traffic flow breakdown externality. This paper offers a method to calculate this externality for traffic on a circuit. Ignoring the stochastic nature of traffic flow breakdowns results in congestion charges that are too small.

[1]  Hesham Rakha,et al.  Modeling the capacity drop phenomenon at freeway bottlenecks using the INTEGRATION software , 2012 .

[2]  Hesham Rakha,et al.  Macroscopic analysis of moving bottlenecks , 2019 .

[3]  K. Button.  1 - The Rationale for Road Pricing: Standard Theory and Latest Advances , 2004 .

[4]  Hai Yang,et al.  Analysis of the time-varying pricing of a bottleneck with elastic demand using optimal control theory , 1997 .

[5]  Kenneth A. Small,et al.  The bottleneck model: An assessment and interpretation , 2015 .

[6]  Benjamin Coifman,et al.  An Overview of Empirical Flow-Density and Speed-Spacing Relationships: Evidence of Vehicle Length Dependency , 2015 .

[7]  Alexandra Kondyli,et al.  Comparison of travel time measurement methods along freeway and arterial facilities , 2018 .

[8]  Robin Lindsey,et al.  Optimal Departure Scheduling for the Morning Rush Hour When Capacity is Uncertain. Volume 2: Modelling Transport Systems , 1996 .

[9]  Richard Arnott,et al.  Equilibrium traffic dynamics in a bathtub model: A special case , 2016 .

[10]  Dirk Helbing,et al.  Empirical Features of Congested Traffic States and Their Implications for Traffic Modeling , 2007, Transp. Sci..

[11]  George F. List,et al.  Modeling vehicle car-following behavior in congested traffic conditions based on different vehicle combinations , 2018 .

[12]  Erik T. Verhoef,et al.  Time, speeds, flows and densities in static models of road traffic congestion and congestion pricing , 1997 .

[13]  M. Fosgerau.  Congestion in the bathtub , 2015 .

[14]  Akihiro Nakayama,et al.  Metastability in the formation of an experimental traffic jam , 2009 .

[15]  Richard Arnott,et al.  A Bathtub Model of Downtown Traffic Congestion , 2013 .

[16]  Mogens Fosgerau.  On the relation between the mean and variance of delay in dynamic queues with random capacity and demand , 2008 .

[17]  Akihiro Nakayama,et al.  Phase transition in traffic jam experiment on a circuit , 2013 .

[18]  Werner Brilon,et al.  Reliability of Freeway Traffic Flow: A Stochastic Concept of Capacity , 2005 .

[19]  Hai Yang,et al.  Principle of marginal-cost pricing : How does it work in a general road network ? , 1998 .

[20]  A. Kondyli,et al.  Quantifying weather impacts on traffic operations for implementation into a travel time reliability model , 2016 .

[21]  C. Daganzo,et al.  Macroscopic relations of urban traffic variables: Bifurcations, multivaluedness and instability , 2011 .

[22]  André de Palma,et al.  Information and Time-of-Usage Decisions in the Bottleneck Model with Stochastic Capacity and Demand , 1999 .

[23]  Lily Elefteriadou,et al.  Probabilistic nature of breakdown at freeway merge junctions , 1995 .

[24]  R. Arnott.  Signalized intersection queuing theory and central business district auto congestion , 1990 .

[25]  W. Vickrey.  Congestion Theory and Transport Investment , 1969 .

[26]  Y. Sugiyama,et al.  Traffic jams without bottlenecks—experimental evidence for the physical mechanism of the formation of a jam , 2008 .

[27]  E. Verhoef,et al.  Road Congestion Pricing , 2011 .

[28]  K. Small,et al.  Hypercongestion in downtown metropolis , 2013 .