Map matching algorithms try to map a series of location observations to an underlying network, to generate a single hypothesized true location or route. For some applications, including route choice modeling, such map matching is not required, and may introduce unnecessary biases into the model. This paper proposes a methodology for mapping location data to a underlying network in a probabilistic fashion, to try to avoid these potential biases. A framework for generating probabilistic matchings is presented, and the results of the algorithm are presented for a demonstration trip, to demonstrate the viability of the algorithm.