LAUSR

Abstract This study designs a robust multi-period tradable credit scheme (TCS) to incentivize travelers to shift from internal combustion engine vehicles (ICEVs) to zero-emissions vehicles (ZEVs) over a long-term planning horizon to reduce vehicular emissions. The need for robust design arises because of uncertainty in forecasting travel demand over a planning horizon in the order of several years. The robust multi-period TCS design is formulated as a bi-level model. In the upper level, the central authority (CA) determines the TCS parameters (credit allocation and charging schemes) by vehicle type to minimize the worst-case vehicular emissions rate, i.e. the maximum vehicular emissions rate under the possible travel demand scenarios. The upper-level model is a mixed-integer nonlinear program. In the lower level, travelers minimize their generalized travel costs under the TCS parameters obtained in the upper level. These parameters are used to determine the vehicle type choice, between ICEVs and ZEVs, using a binomial logit function, and influence route selection based on the difference in credits charged on links for these two vehicle types. The lower-level model is a mathematical program with equilibrium constraints. The bi-level model is solved using a cutting plane method. Numerical experiments illustrate that the incentive to shift to ZEVs is fostered by allocating more credits and charging fewer credits to ZEV travelers compared to ICEV travelers. Further, the proposed TCS design reduces volatility in the realized vehicular emissions rates under different travel demand scenarios compared to a TCS design that does not consider demand uncertainty.