Travelers decide whether to participate in ridesharing based on the trade-off between the travel time and the expense. However, it is still unclear how travelers’ values of time affect their ridesharing behaviors on the congested network. To this end, a path-based ridesharing traffic assignment model was proposed by considering travelers’ heterogenous values of time. In the proposed model, travelers are divided into several classes according to their values of time, and travelers in each class choose their travel modes and routes simultaneously which cost the least. Moreover, travelers in different classes could share the same vehicle to complete their trips together in the proposed model. This paper further discusses how the high-occupancy toll lane affects travelers’ ridesharing behaviors. Numerical results show that: (1) travelers with different values of time show differences in their ridesharing behavior; (2) the single-class ridesharing traffic assignment model may miscalculate the ridesharing scale of users; and (3) building high-occupancy toll lanes plays a positive role in promoting ridesharing for travelers with heterogeneous values of time.

Cloud operators face massive unused excess computing capacity with a stochastic non-stationary nature due to time-varying resource utilization with peaks and troughs. Low-priority spot (pre-emptive) cloud services with real-time pricing have been launched by many cloud operators, which allow them to maximize excess capacity revenue while keeping the right to reclaim capacities when resource scarcity occurs. However, real-time spot pricing with the non-stationarity of excess capacity has two challenges: (1) it faces incomplete peak–trough and pattern shifts in excess capacity, and (2) it suffers time and space inefficiency in optimal spot pricing policy, which needs to search over the large space of history-dependent policies in a non-stationary state. Our objective was to develop a real-time pricing method with a spot pricing scheme to maximize expected cumulative revenue under a non-stationary state. We first formulated the real-time spot pricing problem as a non-stationary Markov decision process. We then developed an improved reinforcement learning algorithm to obtain the optimal solution for real-time pricing problems. Our simulation experiments demonstrate that the profitability of the proposed reinforcement learning algorithm outperforms that of existing solutions. Our study provides both efficient optimization algorithms and valuable insights into cloud operators’ excess capacity management practices.