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This paper considers the problem of supply-demand imbalances in Mobility-on-Demand (MoD) services. These imbalances occur due to uneven stochastic travel demand and can be mitigated by proactively rebalancing empty vehicles to areas where the demand is high. To achieve this, we propose a method that takes into account uncertainties of predicted travel demand while minimizing pick-up time and rebalance mileage for autonomous MoD ride-hailing. More precisely, first travel demand is predicted using Gaussian Process Regression (GPR) which provides uncertainty bounds on the prediction. We then formulate a stochastic model predictive control (MPC) for the autonomous ride-hailing service and integrate the demand predictions with uncertainty bounds. In order to guarantee constraint satisfaction in the optimization under estimated stochastic demand prediction, we employ a probabilistic constraining method with user-defined confidence interval, using Chance Constrained MPC (CCMPC). The benefits of the proposed method are twofold. First, travel demand uncertainty prediction from data can naturally be embedded into the MoD optimization framework, allowing us to keep the imbalance at each station below a certain threshold with a user-defined probability. Second, CCMPC can be relaxed into a Mixed-Integer-Linear-Program (MILP) and the MILP can be solved as a corresponding Linear-Program, which always admits an integral solution. Our transportation simulations show that by tuning the confidence bound on the chance constraint, close to optimal oracle performance can be achieved, with a median customer wait time reduction of 4% compared to using only the mean prediction of the GPR.

• Proposes a multi-period TCS to develop an incentive mechanism to minimize emissions. • Incentivize travelers to shift to zero-emissions vehicles (ZEVs). • Proposes a robust TCS design due to demand uncertainty during the planning horizon. • The robust design of multi-period TCS leads to higher market penetration of ZEVs. • Robust TCS design leads to the higher reliability in minimizing emissions. 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. [ABSTRACT FROM AUTHOR]