Mobile Device And System For Automated Transport Mode Recognition And Corresponding Method Thereof

Abstract

A method and system for automated transportation mode recognition based on sensory data measured by a plurality of sensors of a cellular mobile device of a user, the plurality of sensors at least comprising an accelerometer and a gyroscope, the plurality of sensors being connected to a monitoring mobile node application of the mobile device, wherein the mobile device measures time series of sensory parameter values based on measuring parameters obtained from the sensors, the measuring parameters comprise time series of sensory parameter values of a 3-axis accelerometer as sensor and time series of sensory parameter values of GPS-based speed measurements of a GPS receiver as sensor, and wherein the measured time series of sensory parameter values trigger the automated transportation mode recognition as input feature values to a gradient boosting machine-learning classifier, the transportation modes at least comprising the modes public transportation and/or motorcycle and/or cycling and/or train and/or tram and/or plane and/or car and/or skiing and/or boat, and the transportation mode recognition generating a transport mode label for a transport mode movement pattern of a trip.

1 . A method for automated transportation mode recognition based on sensory data measured by a plurality of sensors of a mobile device of a user, the plurality of sensors at least comprising an accelerometer and a GPS sensor, the mobile device comprising one or more wireless connections, the mobile device acting as a wireless node within a cellular data transmission network by means of antenna connections of the mobile device to the cellular data transmission network, the plurality of sensors being connected to a monitoring mobile node application of the mobile device, and the monitoring mobile node application capturing usage-based and/or user-based sensory data of the mobile device and/or the user of the mobile device, the method comprising:
 measuring time series of sensory parameter values based on measuring parameters obtained from the plurality of sensor of the mobile device, the measuring parameters comprising a time series of sensory parameter values of the accelerometer measurements and a time series of sensory parameter values of GPS-based speed measurements of the GPS sensor, the GPS sensor measuring longitude, latitude, and altitude positions of the mobile device by measuring different speeds of light delays in signals coming from two or more satellites, and   triggering the automated transportation mode recognition using the measured time series of the sensory parameter values as input feature values to a gradient boosting machine-learning classifier, wherein   the transportation mode includes at least one of public transportation, motorcycle, cycling, train, tram, plane, car, skiing, and boat, and   the transportation mode recognition generates a transport mode label for a transport mode movement pattern of a trip.   
     
     
         2 . The method for automated transportation mode recognition according to  claim 1 , wherein
 a supervised learning structure is applied to the gradient boosting machine-learning classifier during a supervised learning phase,   transport mode movement patterns of measured trips are stored in a trips database,   the sensory parameter values include sensory movement parameter values,   transport mode movement patterns of the trip are identified from the sensory movement parameter values,   each of the trips comprises the sensory movement parameter values of GPS positions by the GPSsensor and acceleration forces being applied to the mobile device on all three physical axes by the accelerometer, operating system activities parameter values of an operating system of the mobile device, and a transport mode label value, and   trips with transport mode labels detected by the gradient boosting machine-learning classifier are fed into a user back-loop for dynamic correction by a user associated with a respective trip and saved to the trips database by updating learning transport mode movement patterns of the measured trips in the trips database.   
     
     
         3 . The method for automated transportation mode recognition according to  claim 2 , further comprising monitoring the trips database to automatically detect changes in the trips database, wherein upon detecting a change in the trips database, the supervised learning phase is reinitiated. 
     
     
         4 . The method for automated transportation mode recognition according to  claim 2 , wherein the trips database is updated continuously and/or dynamically based on the user back-loop. 
     
     
         5 . The method for automated transportation mode recognition according to  claim 2 , wherein, for the supervised learning phase, the trips of the trips database are preprocessed by filtering out transport mode movement patterns, which have a time duration shorter than 1 minute and/or comprise less than 30 GPS positions and/or do not have a proper transport mode labelling. 
     
     
         6 . The method for automated transportation mode recognition according to  claim 2 , wherein, for the supervised learning phase, the trips of the trips database are preprocessed by filtering out transport mode movement patterns having duplicated GPS locations by timestamp and/or transport mode movement patterns with GPS locations having negative speed and/or transport mode movement patterns having GPS locations with an accuracy>50 m. 
     
     
         7 . The method for automated transportation mode recognition according to  claim 1 , wherein, upon detection by the gradient boosting machine-learning classifier and the generation of the transport mode label for the transport mode movement pattern of the trip, the trips are postprocessed by a set of hard coded rules, reinforcing avoidance of incorrect and/or insufficiently confident recognition. 
     
     
         8 . The method for automated transportation mode recognition according to  claim 1 , wherein users routines are automatically detected by a trip familiarity-based recognition structure increasing the accuracy of the automated transportation mode recognition. 
     
     
         9 . The method for automated transportation mode recognition according to  claim 2 , wherein the transport mode movement patterns of measured trips stored to the trips database are processed for data enrichment, where the data enrichment process comprises route-matching of the trips with the transport mode movement patterns based on roadmaps and/or GIS-geometry mapping of the trips with the transport mode movement patterns based on spatial and geographic GIS data, and/or public transport mapping based on public transport road maps and timetable data. 
     
     
         10 . The method for automated transportation mode recognition according to  claim 1 , wherein the measuring parameters of the time series of sensory parameter values of the GPS-based speed measurements further comprise extracted average GPS-based speed measurements and/or standard deviation of the GPS-based speed measurements and/or percentiles values from 0 to 100 of the GPS-based speed measurements in predefined percentile steps. 
     
     
         11 . The method for automated transportation mode recognition according to  claim 10 , wherein the percentile steps comprise a granularity of a factor 10. 
     
     
         12 . The method for automated transportation mode recognition according to  claim 1 , wherein the altitude position further comprises a standard deviation extracted from the altitude position measurements. 
     
     
         13 . The method for automated transportation mode recognition according to  claim 1 , wherein the measured time series of the sensory parameter values comprise GPS-based acceleration measurements derived based on a measured ratio between (a) a measured speed difference between a measured set of GPS parameter values and a measured subsequent set of GPS parameter values, and (b) a measured time difference between the measured set of GPS parameter values and the measured subsequent set of GPS parameter values. 
     
     
         14 . The method for automated transportation mode recognition according to  claim 13 , wherein the GPS-based acceleration measurements comprise a standard deviation extracted from the GPS-based acceleration measurements and/or a variance value of the GPS-based acceleration measurements derived based on an angle between triplets of consecutive GPS points. 
     
     
         15 . The method for automated transportation mode recognition according to  claim 1 , wherein the measuring parameters of the time series of sensory parameter values of the accelerometer measurements further comprise percentile values from 0 to 100 of the accelerometer measurements in predefined percentile steps and/or interquartile range values measured by a difference between the 75 th  and the 25 th  percentile. 
     
     
         16 . The method for automated transportation mode recognition according to  claim 15 , wherein said percentile steps comprise a granularity of a factor 10. 
     
     
         17 . The method for automated transportation mode recognition according to  claim 2 , wherein
 in a case of two or more accelerometer measurements having the same timestamps, a last one with respect to an accelerometer measurement order is selected, and an accelerometer measurement norm value is generated over the accelerometer measurements of a measured trip, and   an average of the accelerometer measurements of the measured trip is removed form said measured trip.   
     
     
         18 . The method for automated transportation mode recognition according to  claim 2 , wherein
 the operating system activities parameter values of the operating system of the mobile device comprise a unique timestamp and a map of labels with probability measures, and   in a case of an absence of a label, the probability measure is set to 0.   
     
     
         19 . The method for automated transportation mode recognition according to  claim 18 , wherein said labels of the map are normalized to ‘Automotive’, ‘Cycling’, ‘OnFoot’, ‘Running’, ‘Stationary’, ‘Unknown’, ‘Walking’, and ‘Tilting’ denoting a feature vector for naming compliance between two operating systems. 
     
     
         20 . The method for automated transportation mode recognition according to  claim 18 , further comprising assuming a label probability is valid until a next event is measured, wherein
 each label probability is multiplied by measured milliseconds elapsed until the next event, or until an end of the trip for a last received activity event,   the multiplication is performed for each label of a label list,   the multiplied label probabilities for each label are summed up, and   each sum is divided by a difference between a trip end time and a first activity event time, both in milliseconds.   
     
     
         21 . The method for automated transportation mode recognition according to  claim 20 , wherein, in a case of a label being never returned, a corresponding feature is set to 0, so that if there are no activities at all for a measured trip, all the operating system activities parameter values are set to 0. 
     
     
         22 . The method for automated transportation mode recognition according to  claim 9 , wherein
 the public transport mapping based on public transport road maps and timetable data comprises identifying for a set of measured GPS location parameters candidate stops as sequences of points that fulfill conditions of: (i) measured speed<=3 m/s; and (ii) candidate sequences are longer than 5 seconds,   the identification is performed after applying a moving average with window length 9 over an array of measured speeds, replacing each sample an average of the sample itself and the 4 samples before and after, and   for each of the candidate sequences, an average latitude and an average longitude is generated, obtaining a candidate stop position for each sequence/stop.   
     
     
         23 . The method for automated transportation mode recognition according to  claim 1 , wherein a hyperparameter configuration is applied to the gradient boosting machine-learning classifier comprising the values 225 for the n-estimators, 0.03 for the learning-rate, 30 for the max-depth, 50 for the num-leaves, 0.8 for the subsample, 0.7 for the colsample-bytree, and 5 for the min-sum-hessian-in-leaf.