Abstract
This chapter is concerned with first-best marginal cost pricing (MCP) in a stochastic network with both supply and travel demand uncertainty and perception errors within the travelers’ route choice decision processes. To account for the travelers’ perception error, moment analysis is adopted in this chapter to derive the mean and variance of total perceived travel time of the network. We then developed a Perceived Risk-Based Stochastic Network Marginal Cost Pricing (PRSN-MCP) model. Furthermore, in order to illustrate the effect of incorporating both stochastic supply and demand into the PRSN-MCP model, the calculation of the PRSN-MCP model is divided up into four scenarios under different simplifications of network uncertainties. Numerical examples are also provided to demonstrate the importance and properties of the proposed model. The main finding is that ignoring the effect of stochastic travel demand, capacity degradation, and travelers’ perception error may significantly reduce the performance of the first-best MCP tolls, especially under high traveler’s confidence and network congestion levels.
Highlights
It is well known, due to stochastic variations in both supply and demand, that travel time almost always involves a measure of uncertainty
In order to illustrate the importance of incorporating both stochastic supply and demand into the proposed PRSN-marginal cost pricing (MCP) model, the calculation of PRSN-MCP can be separated into four scenarios based on (1) network uncertainty caused by the stochasticity of travel demand; and (2) network uncertainty induced by the stochastic supply
By comparing the difference of the expected total perceived travel time achieved by the risk-based SN-MCP (RSN-MCP) tolls and the Perceived Risk-Based Stochastic Network Marginal Cost Pricing (PRSNMCP) tolls, we examine the effect of incorporating the traveler’s perception error into the RSN-MCP tolls
Summary
Due to stochastic variations in both supply and demand, that travel time almost always involves a measure of uncertainty. Boyles et al [19] proposed a first-best congestion pricing model considering network capacity uncertainty and user valuation of travel time reliability, while [18] investigated marginal cost pricing in a stochastic traffic network in which demand uncertainty is explicitly considered. To account for the travelers’ perception error, researchers usually assume the commonly adopted Gumbel variate as the random error term and use the conventional logit-based Stochastic User Equilibrium (SUE) model This approach may not reflect the travelers’ perception of the random travel time exactly. In order to explicitly consider both supply and demand aspects of a stochastic network and to reflect the travelers’ perception error of the random travel time, this investigation extends [18] by (1) considering both the stochastic travel demand and link capacity degradation, and (2) incorporating travelers’ perception error into the first-best MCP analysis.
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