Application of Bayesian Stochastic Learning Automata for Modeling Lane Choice Behavior on High-Occupancy Toll Lanes on State Road 167

2016 ◽  
Vol 2560 (1) ◽  
pp. 97-107 ◽  
Author(s):  
Ender Faruk Morgül ◽  
Kaan Ozbay ◽  
Abdullah Kurkcu
Keyword(s):  
Choice Behavior ◽  
Learning Automata ◽  
Stochastic Learning ◽  
Toll Lanes ◽  
High Occupancy Toll Lanes ◽  
High Occupancy Toll

Effect of High-Occupancy Toll Lanes on Mass Vehicle Emissions

2009 ◽  
Vol 2123 (1) ◽  
pp. 88-96 ◽  
Author(s):  
David N. Kall ◽  
Randall L. Guensler ◽  
Michael O. Rodgers ◽  
Vishal S. Pandey
Keyword(s):  
Vehicle Emissions ◽  
Toll Lanes ◽  
High Occupancy Toll Lanes ◽  
High Occupancy Toll

scholarly journals Choice of High Occupancy/Toll Lanes under Alternative Pricing Strategies

2018 ◽  
Vol 2672 (5) ◽  
pp. 12-22
Author(s):  
Michael Janson ◽  
David Levinson
Keyword(s):  
Potential Effect ◽  
Time Of Day ◽  
General Purpose ◽  
Equilibrium Analysis ◽  
Demand Elasticity ◽  
Pricing Strategies ◽  
Toll Lanes ◽  
Hot Lanes ◽  
High Occupancy Toll Lanes ◽  
High Occupancy Toll

High occupancy/toll (HOT) lanes typically vary tolls charged to single occupant vehicles, with the toll increasing during congested periods. The toll is usually tied to time of day or to the density of vehicles in the HOT lane. The purpose of raising the toll with congestion is to discourage demand sufficiently to maintain travel speeds in the HOT lane. However, it has been demonstrated that the HOT toll may act as a signal of downstream congestion (in both general purpose (GP) and HOT lanes), causing an increase in demand for the HOT lane, at least at lower prices. This paper develops a model of lane choice to evaluate alternative HOT lane pricing strategies, including the use of GP density, to more accurately reflect the value of the HOT lane. In addition, the paper explores the potential effect these strategies would have on the HOT lane vehicle share through a partial equilibrium analysis. This analysis demonstrates the change in demand elasticity with price, showing the point at which drivers switch from a positive to negative elasticity.

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