  It is essential for the cellular network operators to provide cellular
location services to meet the needs of their users and mobile applications.
However, cellular locations, estimated by network-based methods at the
server-side, bear with {\it high spatial errors} and {\it arbitrary missing
locations}. Moreover, auxiliary sensor data at the client-side are not
available to the operators. In this paper, we study the {\em cellular
trajectory cleansing problem} and propose an innovative data cleansing
framework, namely \underline{D}ynamic \underline{T}ransportation
\underline{N}etwork based \underline{C}leansing (DTNC) to improve the quality
of cellular locations delivered in online cellular trajectory services. We
maintain a dynamic transportation network (DTN), which associates a network
edge with a probabilistic distribution of travel times updated continuously. In
addition, we devise an object motion model, namely, {\em travel-time-aware
hidden semi-Markov model} ({\em TT-HsMM}), which is used to infer the most
probable traveled edge sequences on DTN. To validate our ideas, we conduct a
comprehensive evaluation using real-world cellular data provided by a major
cellular network operator and a GPS dataset collected by smartphones as the
ground truth. In the experiments, DTNC displays significant advantages over six
state-of-the-art techniques.
