Motorists' trip Origin-Destination (O-D) demand in a vehicular network is one of the critical components for transportation applications. Specially, for transportation planning, trip O-D demand information depicts the travel pattern of travelers in a given time period, whereas in traffic engineering practice, similar information is beneficial to effectively determine the optimal traffic control strategies. Because of the rapid development of intelligent transportation systems (ITS), trip O-D demand matrices can be directly or indirectly estimated by the specific traffic information obtained from advanced sensor technologies without confronting the problems associated with traditional O-D demand survey approaches. These advanced sensor technologies include passive-type vehicle detectors (VDs) and active-type sensors, such as automatic vehicle identification (AVI). However, due to a budgetary constraint of highway agencies, it is very difficult to deploy various types of sensors in a full-scale manner. Thereby, determination of a desirable sensor deployment plan in terms of the number of sensors installed and the optimal locations for network O-D demand estimation purpose becomes a crucial issue. In the present research, the sensor location problem is formulated as a nonlinear program by incorporating traffic flow information provided by both active- and passive-type sensors, and the problem is solved by a quadratic programming approach. Numerical analysis based on a simplified real network is conducted to demonstrate the performance of the proposed model framework.