Alpha-reliable combined mean traffic equilibrium model with stochastic travel times

TL;DR

This paper introduces a new combined mean travel time index under stochastic traffic conditions, capturing various risk attitudes, and develops a corresponding traffic equilibrium model with rigorous mathematical analysis and numerical validation.

Contribution

It proposes the CMTT index based on PTT concepts, and formulates a Wardropian equilibrium model solved by an advanced algorithm, extending traffic risk modeling.

Findings

Risk-pessimism benefits low congestion networks.

Risk-optimism benefits high congestion networks.

Mathematically proved properties of CMTT and CMTE.

Abstract

Based on the reliability budget and percentile travel time (PTT) concept, a new travel time index named combined mean travel time (CMTT) under stochastic traffic network was proposed. CMTT here was defined as the convex combination of the conditional expectations of PTT-below and PTT-excess travel times. The former was designed as a risk-optimistic travel time index, and the latter was a risk-pessimistic one. Hence, CMTT was able to describe various routing risk-attitudes. The central idea of CMTT was comprehensively illustrated and the difference among the existing travel time indices was analysed. The Wardropian combined mean traffic equilibrium (CMTE) model was formulated as a variational inequality and solved via an alternating direction algorithm nesting extra-gradient projection process. Some mathematical properties of CMTT and CMTE model were rigorously proved. In the end, a numerical example was performed to characterize the CMTE network. It is founded that that risk-pessimism is of more benefit to a modest (or low) congestion and risk network, however, it changes to be risk-optimism for a high congestion and risk network.