Abstract: A device may receive vehicle operation data associated with operation of a plurality of vehicles, and may process the vehicle operation data to generate processed vehicle operation data. The device may extract multiple features from the processed vehicle operation data, and may train machine learning models, with the multiple features, to generate trained machine learning models that provide model outputs. The device may process the multiple features, with a feature selection model and based on the model outputs, to select sets of features from the plurality of features, and may process the sets of features, with the trained machine learning models, to generate indications of driving behavior and reliabilities of the indications. The device may select a set of features, from the sets of features, based on the indications and the reliabilities, where the set of features may be calculated by a device associated with a particular vehicle.

Abstract: A method and system for identifying recurrent stops of a vehicle fleet having a plurality of vehicles. The method comprises retrieving historical GPS tracks of the vehicle fleet over a period of time; detecting stops made by the vehicle fleet along travelled routes that are associated with the historical GPS tracks; constructing a coverage area that covers the travelled routes; discretizing the coverage area into a plurality of cells; determining whether a cell is a recurrent stop based on a fleet stay period associated with that cell; and classifying a determined recurrent stop into a plurality of categories.

Abstract: A method and system for classifying a vehicle based on low frequency GPS tracks. The method and system comprise retrieving a low frequency GPS track having a sampling interval of at least 20 seconds; deriving additional data from the low frequency GPS track, the additional data including interval speed and instantaneous acceleration of the vehicle; extracting a plurality of data sets from the low frequency GPS track and the additional data; generating a plurality of features from the extracted data sets; and providing the plurality of generated features to a classifier that classifies the vehicle into a predetermined class.

Abstract: A method and system for classifying a vehicle based on low frequency GPS tracks. The method and system comprise retrieving a low frequency GPS track having a sampling interval of at least 20 seconds; deriving additional data from the low frequency GPS track, the additional data including interval speed and instantaneous acceleration of the vehicle; extracting a plurality of data sets from the low frequency GPS track and the additional data; generating a plurality of features from the extracted data sets; and providing the plurality of generated features to a classifier that classifies the vehicle into a predetermined class.

Abstract: A device may receive vehicle operation data associated with operation of a plurality of vehicles, and may process the vehicle operation data to generate processed vehicle operation data. The device may extract multiple features from the processed vehicle operation data, and may train machine learning models, with the multiple features, to generate trained machine learning models that provide model outputs. The device may process the multiple features, with a feature selection model and based on the model outputs, to select sets of features from the plurality of features, and may process the sets of features, with the trained machine learning models, to generate indications of driving behavior and reliabilities of the indications. The device may select a set of features, from the sets of features, based on the indications and the reliabilities, where the set of features may be calculated by a device associated with a particular vehicle.

Abstract: A device receives location information and work order information associated with multiple vehicles, and groups the location information into engine off information, idling information, and journey information. The device combines, based on the journey information, the engine off information and the idling information to generate vehicle stop information associated with the plurality of vehicles, and matches corresponding work order information with the vehicle stop information to generate matched information. The device extracts stop-wise features, points of interest features, stop cluster features, and sequential features from the vehicle stop information, and utilizes the stop-wise features, the points of interest features, the stop cluster features, and the sequential features with a model to determine work order stops and non-work order stops for the multiple vehicles. The device provides information associated with the work order stops and the non-work order stops for the multiple vehicles.

Abstract: A method and system for identifying recurrent stops of a vehicle fleet having a plurality of vehicles. The method comprises retrieving historical GPS tracks of the vehicle fleet over a period of time; detecting stops made by the vehicle fleet along travelled routes that are associated with the historical GPS tracks; constructing a coverage area that covers the travelled routes; discretizing the coverage area into a plurality of cells; determining whether a cell is a recurrent stop based on a fleet stay period associated with that cell; and classifying a determined recurrent stop into a plurality of categories.

Abstract: A device may receive vehicle operation data associated with operation of a plurality of vehicles, and may process the vehicle operation data to generate processed vehicle operation data. The device may extract multiple features from the processed vehicle operation data, and may train machine learning models, with the multiple features, to generate trained machine learning models that provide model outputs. The device may process the multiple features, with a feature selection model and based on the model outputs, to select sets of features from the plurality of features, and may process the sets of features, with the trained machine learning models, to generate indications of driving behavior and reliabilities of the indications. The device may select a set of features, from the sets of features, based on the indications and the reliabilities, where the set of features may be calculated by a device associated with a particular vehicle.

Abstract: A device may receive vehicle operation data associated with operation of a plurality of vehicles, and may process the vehicle operation data to generate processed vehicle operation data. The device may extract multiple features from the processed vehicle operation data, and may train machine learning models, with the multiple features, to generate trained machine learning models that provide model outputs. The device may process the multiple features, with a feature selection model and based on the model outputs, to select sets of features from the plurality of features, and may process the sets of features, with the trained machine learning models, to generate indications of driving behavior and reliabilities of the indications. The device may select a set of features, from the sets of features, based on the indications and the reliabilities, where the set of features may be calculated by a device associated with a particular vehicle.

Abstract: A method and system for identifying recurrent stops of a vehicle fleet having a plurality of vehicles. The method comprises retrieving historical GPS tracks of the vehicle fleet over a period of time; detecting stops made by the vehicle fleet along travelled routes that are associated with the historical GPS tracks; constructing a coverage area that covers the travelled routes; discretizing the coverage area into a plurality of cells; determining whether a cell is a recurrent stop based on a fleet stay period associated with that cell; and classifying a determined recurrent stop into a plurality of categories.

Abstract: A device receives location information and work order information associated with multiple vehicles, and groups the location information into engine off information, idling information, and journey information. The device combines, based on the journey information, the engine off information and the idling information to generate vehicle stop information associated with the plurality of vehicles, and matches corresponding work order information with the vehicle stop information to generate matched information. The device extracts stop-wise features, points of interest features, stop cluster features, and sequential features from the vehicle stop information, and utilizes the stop-wise features, the points of interest features, the stop cluster features, and the sequential features with a model to determine work order stops and non-work order stops for the multiple vehicles. The device provides information associated with the work order stops and the non-work order stops for the multiple vehicles.

Abstract: A system and method for optimizing traveled routes of a fleet. The method comprises reconstructing routes traveled by vehicles of the fleet based on historical GPS traces of the fleet; determining stops made by the vehicles along the traveled routes; and classifying the stops into work stops and non-work stops; and optimizing routes for the work stops.

Abstract: A device receives location information and work order information associated with multiple vehicles, and groups the location information into engine off information, idling information, and journey information. The device combines, based on the journey information, the engine off information and the idling information to generate vehicle stop information associated with the plurality of vehicles, and matches corresponding work order information with the vehicle stop information to generate matched information. The device extracts stop-wise features, points of interest features, stop cluster features, and sequential features from the vehicle stop information, and utilizes the stop-wise features, the points of interest features, the stop cluster features, and the sequential features with a model to determine work order stops and non-work order stops for the multiple vehicles. The device provides information associated with the work order stops and the non-work order stops for the multiple vehicles.

Abstract: A device can obtain acceleration data and GPS location data. The GPS location data can include a plurality of GPS location data samples relating to vehicles. The acceleration data can include a plurality of acceleration data samples relating to one or more of the vehicles. The device can determine, using the plurality of acceleration data samples or the plurality of GPS location data samples, a potential wrong-way travel event associated with a vehicle, determine whether the vehicle is associated with an industry in which vehicles legitimately engage in wrong-way travel after determining the potential wrong-way travel event, and perform one or more actions to cause information regarding the potential wrong-way travel event to be provided to a user device based on determining the potential wrong-way travel event and determining whether the vehicle is associated with an industry in which vehicles legitimately engage in wrong-way travel.

Abstract: A device can receive message data associated with a vehicle tracking unit (VTU). The device can identify, based on the message data, the VTU as an inactive VTU. The device can compute a feature vector, associated with the VTU, based on the message data and on environmental data associated with the VTU. The feature vector can be computed based on identifying the VTU as an inactive VTU. The device can determine, using an inactivity classification model, an inactivity classification associated with the VTU. The inactivity classification can be determined based on providing the feature vector, associated with the VTU, as an input to the inactivity classification model. The device can cause an inactivity action to be performed based on the inactivity classification.

Abstract: A device can receive message data associated with a vehicle tracking unit (VTU). The device can identify, based on the message data, the VTU as an inactive VTU. The device can compute a feature vector, associated with the VTU, based on the message data and on environmental data associated with the VTU. The feature vector can be computed based on identifying the VTU as an inactive VTU. The device can determine, using an inactivity classification model, an inactivity classification associated with the VTU. The inactivity classification can be determined based on providing the feature vector, associated with the VTU, as an input to the inactivity classification model. The device can cause an inactivity action to be performed based on the inactivity classification.

Abstract: A video classification platform receives a first message that includes a link to video data including video frames captured by one or more cameras associated with the vehicle, and including information concerning a vehicle and a driving event. The platform obtains the video data using the link, identifies the vehicle and the driving event based on the information, and determines objects in a video frame. The platform constructs a collision cone of the vehicle for the video frame and determines, based on the collision cone, a set of objects that have a potential to collide with the vehicle. The platform assigns a category to the driving event based on the set of objects, the collision cone of the vehicle, and the information, generates a second message that includes the category and the information, and sends the second message to a client device to display the second message.

Abstract: A video classification platform receives a first message that includes a link to video data including video frames captured by one or more cameras associated with the vehicle, and including information concerning a vehicle and a driving event. The platform obtains the video data using the link, identifies the vehicle and the driving event based on the information, and determines objects in a video frame. The platform constructs a collision cone of the vehicle for the video frame and determines, based on the collision cone, a set of objects that have a potential to collide with the vehicle. The platform assigns a category to the driving event based on the set of objects, the collision cone of the vehicle, and the information, generates a second message that includes the category and the information, and sends the second message to a client device to display the second message.

Abstract: A device can obtain acceleration data and GPS location data. The GPS location data can include a plurality of GPS location data samples relating to vehicles. The acceleration data can include a plurality of acceleration data samples relating to one or more of the vehicles. The device can determine, using the plurality of acceleration data samples or the plurality of GPS location data samples, a potential wrong-way travel event associated with a vehicle, determine whether the vehicle is associated with an industry in which vehicles legitimately engage in wrong-way travel after determining the potential wrong-way travel event, and perform one or more actions to cause information regarding the potential wrong-way travel event to be provided to a user device based on determining the potential wrong-way travel event and determining whether the vehicle is associated with an industry in which vehicles legitimately engage in wrong-way travel.

Abstract: A method and system for identifying recurrent stops of a vehicle fleet having a plurality of vehicles. The method comprises retrieving historical GPS tracks of the vehicle fleet over a period of time; detecting stops made by the vehicle fleet along travelled routes that are associated with the historical GPS tracks; constructing a coverage area that covers the travelled routes; discretizing the coverage area into a plurality of cells; determining whether a cell is a recurrent stop based on a fleet stay period associated with that cell; and classifying a determined recurrent stop into a plurality of categories.