Mobile Edge Computing (MEC) is a novel platform to bring computation resources close to local users in vicinity constrained, obtaining the nickname of Cloudlet on the edge of the network. However, due to users' behaviors, computation resources demands show spatial and temporal dynamics among different Cloudlets, which is hardly to achieve on-demand computation workload balance management. While vehicles, unique for their mobility and powerful on- board equipments, could act as computation resources transporters breaking geographically restriction, which have potential to balance computation demands in the city. To address the issue above, in this paper, we design a novel computation demand response management (DRM) mechanism called Vehicle-to-Cloudlet (V2C), considering the mobility of vehicles, computation states of vehicles, and computation demands of Cloudlets. There exists two phases in V2C mechanism: cognitive phase and game phase, respectively. In cognitive phase, which Cloudlets are computation-scarce and which vehicles are potential computation resources can be cognized. Then, in game phase, to simulate computation resources trading process among Cloudlet service provider and individual vehicles, we formulate a price-based two-stage Stackelberg game, jointly maximizing the utility of the Cloudlet and the individual utility of each vehicles. We prove that unique Nash Equilibrium (NE) and Stackelberg Equilibrium (SE) exist in this game and propose a gradient iterative algorithm to obtain the optimal solution. Finally, numerical simulations show that our solution has good scalability and also encourages vehicles to trade their own computation resources to the Cloudlet.