An inference engine for smartphones to preprocess data and detect stationary and transportation modes

• Detecting movement and stationary statuses in transportation modes by smartphones.
• A new inference engine based on the features and human knowledge for mode detection.
• Saving 40% energy in comparison with the previous GPS-based studies.
• Recognizing the motorized mode with 95.2% accuracy.
• Recognizing the stationary states in motorized mode with 97.1% accuracy.

A smartphone can be utilized as a cost-effective device for the purposes of intelligent transportation system. To detect the movement and the stationary statuses in the motorized and non-motorized modes, this study develops a new inference engine, including two sets of rules. The first sets of rules are defined by the related thresholds on the features of smartphone sensors while the second sets are extracted from the human knowledge to improve the results of the first rules. The experimental results reveal that by utilizing Inertial Measurement Unit (IMU) sensors in the proposed inference engine, it is possible to save 40% energy in comparison with the previous research. Moreover, this engine increases the accuracy of the motorized mode detection to 95.2% and determines the stationary states in motorized mode with 97.1% accuracy.