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Massive raw traffic data such as loop data, vehicle GPS data stay undeveloped for administrative or technical purpose in transportation industries in China. Information-Service-Oriented Experimental Transportation System (IETS) aims at exploring and deducing useful traffic information from real-time collecting traffic data and publishing the information as a value-added service. Knowledge discovery techniques have played an important role in IETS. The system was implemented followed a classical steps of KDD. The procedure of system data modeling was split into five phases, topic determining, data preparing, data transforming, analysis modeling and data exploring. An example was given by an observation-hypothesis-verification approach to show how OLAP methods were used to discovery unknown information beneath the traffic data in IETS. Using knowledge discovery techniques in IETS, we can find useful information that might be useful to provide decision support for the planers and managers of transportation systems.