Mean-Risk Traffic Assignment Under the Continuously Distributed Risk-Aversion Factor
提出一个交通均衡框架，直接处理连续分布的风险规避偏好，比传统离散分类更精确地模拟实际路径选择行为，且计算效率更高。
From Discrete to Continuum: Risk Preference Heterogeneity in Traffic Equilibrium How do travelers with heterogeneous risk preferences choose routes in congested networks with uncertain travel times? Conventional models characterize risk preference heterogeneity using a small number of discrete user classes. In “Mean-Risk Traffic Assignment Under the Continuously Distributed Risk-Aversion Factor,” Xu, Li, Xie, Chen, and Liu develop a new traffic equilibrium framework that directly accommodates a continuous distribution of risk aversion. The resulting model generalizes the classical Wardrop equilibrium to settings with both stochastic travel times and continuous preference heterogeneity, providing a more refined representation of real-world routing behavior. This study demonstrates that the continuous approach outperforms the discrete approach by not only eliminating the inherent discretization bias—that can mislead infrastructure investment decisions—but also delivers superior computational performance, requiring less run time and memory. More broadly, the framework could be potentially applied to a wide range of biobjective congestion routing games where user preferences are continuously distributed.