Acting as a new type of mobile terminals, vehicles are able to access Internet in real-time. Consequently, a specific kind of Location-Based Services (LBS), usually named Vehicular LBS (VLBS), has received significant attention because of its bright prospects. VLBS can answer drivers' location-dependent queries to Points of Interest and provide more dedicated services for drivers by utilizing transportation information. Accompanying with convenience, however, users may suffer from some serious privacy leak problems. Previous work has proposed a series of privacy protection methods for LBS. As a well-known method for its high effectiveness in protecting privacy, computational Private Information Retrieval (cPIR) can provide provable privacy protection. Yet, it is usually considered impractical because of its prohibitive computational cost. An important research question arises: can cPIR be improved and used in VLBS to preserve privacy? We answer it by proposing a privacy preserving framework for VLBS based on it. Under the restriction of road network, the proposed framework, which applies the available transportation information as prior knowledge for cPIR, can drastically reduce the computational cost. We perform several experiments on a real dataset to validate its effectiveness.