Robust Network Pricing and System Optimization Under Combined Long-Term Stochasticity and Elasticity of Travel Demand

摘要: Network pricing serves as an instrument for congestion management, however, agencies and planners often encounter problems of estimating appropriate toll prices. Tolls are commonly estimated a single-point deterministic travel demand, which can lead to imperfect policy decisions due inherent uncertainties in future demand. Previous research has addressed this issue demand 'uncertainty‘ the context, but elastic nature along with its uncertainty not been explicitly considered. Similarly, interactions between elasticity have characterized. This study addresses these gaps proposes framework estimate nearest optimal first-best tolls under long term stochasticity The proposed method is implemented on networks varying proportions subsequent assessed. A diverse set possible scenarios covering uniform normal distributions linear exponential relationships modeled. It consistently found across numerical experiments that wide range plausible planner objectives, combined stochastic-elastic scenario coincide corresponding cases. Study results from applications suggest inclusion offsets need considering frameworks.