A computer-implemented method performed by a client device having one or more processors, the computer-implemented method comprising: monitoring, by the one or more processors, data provided by an accelerometer associated with the client device; determining, by the one or more processors, whether the data indicates a particular event associated with a vehicle; and in response to determining that the data indicates the particular event associated with the vehicle: determining, by the one or more processors, one or more micro-activities associated with the vehicle based on data from at least one of the accelerometer and at least one other sensor associated with the client device, wherein the at least one other sensor comprises one or more of: a system clock, a proximity sensor, a gyroscope, a magnetic sensor, a Bluetooth indicator and a WiFi indicator; providing, by the one or more processors, results from each of the one or more micro-activities to a trained model associated with the client device; and in response to providing the results to the trained model, identifying, by the one or more processors, one or more vehicular events in a time sequence based on the one or more micro-activities associated with the vehicle; characterized by eliminating false alarm based on sleeping time exclusion by: collecting data from a real-time controller, RTC, and application history, wherein the data from RTC includes a current real world time, and the data from the application history includes usage history of any available transportation applications; performing a time-domain classification on the data collected from the RTC and application history; identifying a range of sleeping time based on the usage history, and, if the current real world time is falling into the range of detected sleeping time, suppressing the particular event. monitoring, by the one or more processors, data provided by an accelerometer associated with the client device; determining, by the one or more processors, whether the data indicates a particular event associated with a vehicle; and in response to determining that the data indicates the particular event associated with the vehicle: determining, by the one or more processors, one or more micro-activities associated with the vehicle based on data from at least one of the accelerometer and at least one other sensor associated with the client device, wherein the at least one other sensor comprises one or more of: a system clock, a proximity sensor, a gyroscope, a magnetic sensor, a Bluetooth indicator and a WiFi indicator; providing, by the one or more processors, results from each of the one or more micro-activities to a trained model associated with the client device; and in response to providing the results to the trained model, identifying, by the one or more processors, one or more vehicular events in a time sequence based on the one or more micro-activities associated with the vehicle; characterized by eliminating false alarm based on sleeping time exclusion by: collecting data from a real-time controller, RTC, and application history, wherein the data from RTC includes a current real world time, and the data from the application history includes usage history of any available transportation applications; performing a time-domain classification on the data collected from the RTC and application history; identifying a range of sleeping time based on the usage history, and, if the current real world time is falling into the range of detected sleeping time, suppressing the particular event. determining, by the one or more processors, one or more micro-activities associated with the vehicle based on data from at least one of the accelerometer and at least one other sensor associated with the client device, wherein the at least one other sensor comprises one or more of: a system clock, a proximity sensor, a gyroscope, a magnetic sensor, a Bluetooth indicator and a WiFi indicator; providing, by the one or more processors, results from each of the one or more micro-activities to a trained model associated with the client device; and in response to providing the results to the trained model, identifying, by the one or more processors, one or more vehicular events in a time sequence based on the one or more micro-activities associated with the vehicle; characterized by eliminating false alarm based on sleeping time exclusion by: collecting data from a real-time controller, RTC, and application history, wherein the data from RTC includes a current real world time, and the data from the application history includes usage history of any available transportation applications; performing a time-domain classification on the data collected from the RTC and application history; identifying a range of sleeping time based on the usage history, and, if the current real world time is falling into the range of detected sleeping time, suppressing the particular event. collecting data from a real-time controller, RTC, and application history, wherein the data from RTC includes a current real world time, and the data from the application history includes usage history of any available transportation applications; performing a time-domain classification on the data collected from the RTC and application history; identifying a range of sleeping time based on the usage history, and, if the current real world time is falling into the range of detected sleeping time, suppressing the particular event.