Stop purpose classification for vehicle fleets

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.

RELATED APPLICATION

This application claims priority under 35 U.S.C. § 119 to Italian Patent Application No. 102017000131516, filed on Nov. 17, 2017, the content of which is incorporated by reference herein in its entirety.

BACKGROUND

A vehicle fleet can include multiple vehicles, such as cars, vans, trucks, specialist vehicles (e.g., mobile construction machinery), and/or the like used for a work purpose. Vehicle fleet management includes management of a range of functions associated with the vehicle fleet, such as vehicle financing, vehicle maintenance, vehicle telematics (e.g., tracking and diagnostics), driver management, speed management, fuel management, health and safety management, and/or the like. Vehicle fleet management allows entities (e.g., companies), which rely on transportation in business, to remove or minimize risks associated with vehicle investment, to improve efficiency and productivity, to reduce transportation and staff costs, to provide compliance with government regulations, and/or the like.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The following detailed description of example implementations refers to the accompanying drawings. The same reference numbers in different drawings can identify the same or similar elements.

A vehicle fleet management system utilizes global positioning system (GPS) data to inform a fleet manager of locations of a fleet of vehicles. For example, a vehicle can include or be associated with a GPS device that provides a current location of the vehicle to the vehicle fleet management system.

Some implementations, described herein, can provide a classification platform that trains a machine learning model, based on known work order stops and non-work order stops, to generate a data set. The machine learning model, once trained with the data set, can automatically classify vehicle stops as work related (e.g., a work order or a business stop) or non-work related (e.g., a non-work order or a personal stop) based on location (e.g., GPS) information and without knowing work order information (e.g., unlabeled or unknown stops) associated with a fleet of vehicles. For example, the classification platform can receive the location information from user devices associated with the vehicles, and can identify stops associated with the vehicles by clustering or grouping the location information. The classification platform can extract stop-wise features, points of interest (POI) features, stop cluster features, and sequential features from information associated with the identified stops. The classification platform can utilize the stop-wise features, the POI features, the stop cluster features, and the sequential features to determine work order stops and non-work order stops associated with the vehicles.

FIGS. 1A-1Hare diagrams of an overview of an example implementation100described herein. As shown inFIG. 1A, assume that user devices are associated with corresponding vehicles (e.g., vehicle 1, vehicle 2, . . . , vehicle Z, where Z>2) of a vehicle fleet. As shown by reference number105, the user devices can provide location information and work order information, associated with each vehicle, to a classification platform. The classification platform can receive the location information and the work order information from the user devices, and can store the location information and the work order information. In some implementations, the classification platform can only receive the location information from the user devices, and the user devices (or a vehicle fleet management system associated the user devices) cannot provide the work order information.

In some implementations, the location information for each vehicle can include GPS information, information indicating a geographic location of the vehicle (e.g., in latitude and longitude at a particular point in time provided by a time stamp), information indicating a velocity of the vehicle, and/or the like. For example, the location information can indicate that the first vehicle (vehicle 1) is located at a particular latitude and longitude (e.g., 39° 44′ North latitude and 75° 32′ West longitude), a particular address (e.g., 123 ABC Street in ABC Town), and/or the like. In some implementations, the work order information for each vehicle can include information indicating a schedule of work tasks to be completed by a driver of the vehicle over a period of time, information indicating locations of where the work tasks are to be completed, information associated with past stops of the vehicle, information associated with purposes of the past stops (e.g., work order or non-work order), and/or the like. For example, the work order information can indicate that the second vehicle (vehicle 2) is scheduled to deliver packages to twenty-five different locations in eight hours. In some implementations, the work order information can be received from devices other than the user devices, such as a fleet management system, one or more devices associated with an owner or a manager of the vehicle fleet, and/or the like.

In some implementations, the user devices can provide vehicle information associated with the vehicles to the classification platform. The vehicle information can include information associated with an operating status of a vehicle (e.g., information associated with whether an engine of the vehicle is turned off, whether the engine is turned on, whether the engine is operating properly, a fuel level of the vehicle, and/or the like), information indicating a number of passengers in the vehicle, information generated by an electronic control unit (ECU) or an engine control module (ECM) of the vehicle, and/or the like.

As further shown inFIG. 1A, the location information for the first vehicle can include a sequence of GPS pings (e.g., {P11, . . . , P1n}) that characterizes routes traveled by the first vehicle, the location information for the second vehicle can include a sequence of GPS pings (e.g., {P21, . . . , P2n}) that characterizes routes traveled by the second vehicle, and the location information for the Zth vehicle can include a sequence of GPS pings (e.g., {PZ1, . . . , PZn}) that characterizes routes traveled by the Zth vehicle. In some implementations, each GPS ping (e.g., Pi) can include a vehicle identifier (e.g., vi), a latitude and longitude, or position, of the vehicle (e.g., pi), an odometer distance of the vehicle (e.g., di), a timestamp (e.g., ti), an event code (e.g., ei) that provides status information about the vehicle, and/or the like.

As further shown inFIG. 1A, the work order information for the first vehicle can include a sequence of information (e.g., {W11, . . . , W1m}) that characterizes a schedule of work tasks for a driver of the first vehicle, the work order information for the second vehicle can include a sequence of information (e.g., {W21, . . . , W2m}) that characterizes a schedule of work tasks for a driver of the second vehicle, and the work information for the Zth vehicle can include a sequence of information (e.g., {WZ1, . . . , WZm}) that characterizes a schedule of work tasks for a driver of the Zth vehicle. In some implementations, each work order data element (e.g., Wi) can include the vehicle identifier (e.g., vi), the position of the work order (e.g., pi), the timestamp (e.g., ti), a status code (e.g., ci, indicating pending, started, or completed) associated with a work task of the vehicle, and/or the like. In some implementations, there can be vehicles for which no work order information exists (e.g., vehicles for which work order information is not used to train the machine learning model). In such implementations, the machine learning model can be utilized (e.g., in a prediction phase) to infer whether vehicle stops, of such vehicles, are work stops or non-work stops.

As shown by reference number110inFIG. 1B, the classification platform can group the location information into multiple groups that describe activities of the vehicles. For example, as shown inFIG. 1B, the classification platform can group the location information into engine off information115, idling information120, and journey information125. Engine off information115can include location information associated with engine off events (e.g., when the engines of the vehicles are turned off), and such location information can be generated when the engines are turned off. No further location information is generated from the vehicles while the engines are turned off. Idling information120can include location information associated with vehicles with engines turned on, but where the vehicles are not moving or are moving slowly in a small geographic area. For example, assume that H(pi, pi-1) is a haversine distance between two points, and that a pair of consecutive GPS signals (Pi-1and Pi) are associated with a particular vehicle. In such an example, the particular vehicle is determined to be idling when the following constraints are satisfied:

H⁡(pi,pi-1)ti-ti-1≤a⁢⁢threshold⁢⁢(e.g.,1.4⁢⁢meters⁢/⁢second),(1)
to ensure that the speed is close to zero; and (2) H(pi; pi−1)≤a threshold (e.g., 150 meters), to ensure that Pi and Pi−1 are actually close and to avoid artifacts due to lost GPS signals. Journey information125can include location information that is not grouped into engine off information115and idling information120.

In some implementations, the classification platform can utilize a rule-based clustering technique to group the location information into engine off information115, idling information120, and journey information125. The rule-based clustering technique can be utilized for the location information clustering since the location information can include a large amount of information, and the rule based clustering is linearly complex with respect to the input size. Furthermore, the rule-based clustering technique can be parallelized and can efficiently utilize modern parallel computing frameworks. In some implementations, the classification platform can utilize another clustering technique (e.g., a K-Means technique, a hierarchical clustering technique, a mean shift technique, a density-based spatial clustering of applications with noise (DBSCAN) technique, and/or the like) to group the location information into engine off information115, idling information120, and journey information125. In some implementations, the classification platform can classify each GPS signal as an engine off GPS signal, an idling GPS signal, or a journey GPS signal, and can utilize the rule-base clustering technique on chronologically adjacent engine off GPS signals and idling signals, not separated by journey GPS signals, to identify vehicle stops.

As shown by reference number130inFIG. 1C, the classification platform can combine engine off information115and idling information120to form information indicating identified vehicle stops135. In some implementations, engine off information115, idling information120, and journey information125can be sorted chronologically for each vehicle, by the classification platform, before engine off information115and idling information120are combined. In some implementations, the identified vehicle stops135can include consecutive engine off information115and idling information120that are not separated by journey information125. As shown by reference number140, the classification platform can remove and/or discard journey information125(e.g., from storage) after the identified vehicle stops135are determined.

In some implementations, an identified vehicle stop can include a group of chronologically consecutive GPS signals that are associated with idling or engine off conditions and that satisfy spatiotemporal locality constraints. Each identified vehicle stop can include several characteristics, such as a number of aggregated GPS signals, a start and an end of the vehicle stop (e.g., first and last timestamps of GPS signals associated with the vehicle stop), a stop duration (e.g., a time between the end and the start of the vehicle stop), maximum and minimum latitudinal and longitudinal coordinates of the GPS signals, and/or the like. In some implementations, a vehicle stop need not be associated with an engine off GPS signal, but can include only idling GPS signals. For example, a driver of package delivery companies can make deliveries without turning off a vehicle.

In some implementations, identified vehicle stops135can identify areas where vehicles perform particular types of tasks. For example, a vehicle stop can include information associated with time spent parking the vehicle, time spent in close proximity to the stop, time spent finding the parking place, and/or the like. In another example, a vehicle can stop and move multiple times in a small area, such as when a vehicle is located at a depot and is loaded at multiple load points, when a vehicle is unloading cargo to a customer, and/or the like.

As shown by reference number145inFIG. 1D, the classification platform can store the work order information (e.g., {W11, . . . , W1m}, {W21, . . . , W2m}, and {WZ1, . . . , WZm}). As further shown inFIG. 1D, the classification platform can match identified vehicle stops135with corresponding work order information. In some implementations, if the work order information does not match a particular one of identified vehicle stops135, the classification platform can provide a designation (e.g., “None”) indicating that the work order information does not match the particular one of identified vehicle stops135. In some implementations, if work order information does not exist for a particular one of identified vehicle stops135, the classification platform can provide a designation (e.g., “?”) indicating that the work order information does not exist for the particular one of identified vehicle stops135. In some implementations, the classification platform can combine and store the matching identified vehicle stops135and the corresponding work order information as matched information150. In some implementations, the classification platform can combine a GPS signal associated with a matching identified vehicle stop (e.g., P11) with corresponding work order information (e.g., W11) to create a matched pair (e.g., {P11, W11}). In instances where the work order information does not match a particular identified vehicle stop, the classification platform can combine a GPS signal associated with the particular identified vehicle stop (e.g., P18) with a non-match indicator (e.g., None) to create a pair (e.g., {P18, None}). In instances where the work order information does not exist a particular identified vehicle stop, the classification platform can combine a GPS signal associated with the particular identified vehicle stop (e.g., P288) with a non-existing indicator (e.g., ?) to create a pair (e.g., {P288, ?}).

In some implementations, the classification platform can create the pairs associated with work order information (e.g., {P, W} and {P, None}) for customers that provide work order information, and can utilize such pairs as a training set of data to train the machine learning model. In some implementations, the machine learning model can include a mathematical function, and training the machine learning model can include finding best parameters for the machine learning model (e.g., values that minimize a certain score on the training set, where the score assesses a quality of the machine learning model). In some implementations, for customers that do not provide work order information, the classification platform can utilize the machine learning model to automatically classify vehicle stops for such customers.

In some implementations, the classification platform can determine a vehicle stop to be a work order vehicle stop if the vehicle stop matches temporally and spatially with a work order item. In some implementations, a spatial match can be considered satisfied in a different way for vehicle stops that are associated with only idling GPS signals, and vehicle stops associated with at least one engine off GPS signal. For example, the classification platform can consider vehicle stops associated with only idling GPS signals to be work order vehicle stops if a centroid of the vehicle stops is within a haversine distance of predetermined quantity (e.g., 150 meters) with respect to a location of the work order information associated a particular vehicle. The classification platform can consider a vehicle stop associated with at least one engine off GPS signal to be a work order vehicle stop if the at least one engine off GPS signal is within the haversine distance of the predetermined quantity with respect to the location of the work order information. In some implementations, a temporal match can be considered satisfied if a time period, between a start time and an end time of a vehicle stop, intersects a time period associated with when a work order was started and completed. In some implementations, the classification platform can utilize the determination of whether the vehicle stop matches temporally and spatially with a work order item to create the training set of data for training the machine learning model.

As shown inFIG. 1E, the classification platform can extract multiple (e.g., over one-hundred) features from matched information150(e.g., that includes work order information for customers that provide work order information, and does not include work order information for customers that do not provide work order information). In some implementations, the classification platform can extract the features into particular types of features, such as stop-wise features155, POI features160, stop cluster features165, and sequential features170.

In some implementations, stop-wise features155can include features associated with a stop duration (e.g., a time between a first GPS signal and a last GPS signal associated with a vehicle stop), a start time of the vehicle stop (e.g., an hour of a day, a day of a week, a day of a month, a day of a year, and/or the like), time spent with the engine off (e.g., for each engine off GPS signal, the time spent can include a time between the engine off GPS signal and a first following GPS signal that is not an engine off GPS signal), a geographic shape associated with a vehicle stop (e.g., the GPS signals associated with the vehicle stop can be used to determine a geographical area that includes a width, a height, an area, and/or the like), a vehicle stop type (e.g., an engine off vehicle stop if the vehicle stop is associated with at least one engine off GPS signal, or an idling vehicle stop otherwise), an odometer distance from a first GPS signal to a last GPS signal of the vehicle stop, a sum of haversine distances between consecutive GPS signals associated with the vehicle stop, a haversine distance between a first GPS signal and a last GPS signal of the vehicle stop (e.g., this feature, combined with the odometer distance feature and the geographic shape feature can aid in distinguishing between vehicle stops where the vehicle moves around in a specific geographic area, and vehicle stops where the vehicle slowly moves in a straight direction), a total count of GPS signals associated with the vehicle stop, an average speed of the vehicle (e.g., as determined by odometer readings associated with a first GPS signal and a last GPS signal of the vehicle stop, divided by a duration of the vehicle stop), a number of engine off GPS signals associated with the vehicle stop, and/or the like.

In some implementations, POI features160can include features associated with points of interest in a geographic area surrounding each vehicle stop (e.g., as obtained from an external data source, such as Google Maps, OpenStreetMap, and/or the like). In some implementations, POI features160can include features identifying POI types, such as a bank, a university, a hotel, a restaurant, a rest area, a grocery store, a school, a shopping center, a gas station, an open parking area, a vehicle repair facility, and/or the like. For each of these POI types, the classification platform can generate a feature that includes information associated with a shortest distance from a location of the POI type to a location corresponding to an engine off GPS signal.

In some implementations, stop cluster features165can include features that describe characteristics of vehicle stops surrounding a particular vehicle stop, so that the classification platform can characterize a geographic area associated with the particular vehicle stop. In some implementations, the classification platform, for each vehicle stop, analyzes a radius (e.g., of 250 meters) around the vehicle stop and collects statistics about surrounding vehicle stops, such as a vehicle entropy (Ev) calculated as:

Ev=∑v∈V⁢nvN⁢ln⁡(nvN),
where V is a set of vehicles in a vehicle fleet, N is a total number of vehicle stops for all of the vehicles of the vehicle fleet inside the radius, and nvis a total number of vehicle stops of a particular vehicle (v) inside the radius; an average, a sum, a maximum, and a minimum duration of the vehicle stops within the radius; a number of nearby stops, and/or the like.

In some implementations, sequential features170can include features associated with a sequence of vehicle stops performed by each vehicle for each vehicle stop. In some implementations, sequential features170can include vehicle stop sequences of a vehicle in a time period (e.g., a day), which enables the classification platform to classify personally semantic locations. For example, it is unlikely that a driver of a vehicle will have two consecutive lunch stops in a single day.

In some implementations, a sequence of vehicle stops performed by a vehicle for a particular vehicle stop can include at least two vehicle stops prior to the particular vehicle stop and at least two vehicle stops after the particular vehicle stop. From the four vehicle stops (e.g., the least two vehicle stops prior to the particular vehicle stop and the at least two vehicle stops after the particular vehicle stop), the classification platform can calculate a set of features, for the four vehicle stops, based on the stop-wise features and the POI features associated with the four vehicle stops, such as stop duration, time with the engine off, distance from a POI, and/or the like. In some implementations, the classification platform can calculate, for the four vehicle stops, time periods between the four vehicle stops, distances between the four vehicle stops, and/or the like. The time periods and distances between the four vehicle stops can enable the classification platform to identify stops that are located far apart (e.g., which can indicate that the vehicle took a long detour to reach a particular location and that the particular location is semantically relevant). In some implementations, based on a sequence of vehicle stops in a working day by a vehicle, the classification platform can calculate a ranking of each vehicle stop in the working day (e.g., indicating a fraction of vehicle stops that occurred before each vehicle stop on the same day), a temporal percentage of the day covered by each vehicle stop (e.g., indicating a fraction of time of the working day elapsed before each vehicle stop), and/or the like.

As shown inFIG. 1F, the classification platform can utilize stop-wise features155, POI features160, stop cluster features165, and sequential features170(e.g., associated with work order information provided by customers) to train a learning algorithm in order to determine a machine learning model. In some implementations, the classification platform can utilize one or more of stop-wise features155, POI features160, stop cluster features165, or sequential features170to train the learning algorithm in order to determine the machine learning model. In some implementations, the machine learning model can include a random forest classifier model, a support vector machine model, a neural network model, a gradient boosted trees model, and/or the like. In some implementations, the machine learning model can include a technique that automatizes a computer decision without directly programming the decision, and that allows learning from a set of examples (e.g., a training set). In some implementations, the machine learning model can perform binary classification in order to determine whether particular data belongs to one class or to another class. In some implementations, the machine learning model can be created based on the training set and the learning algorithm, and can describe learned behavior. In some implementations, training the machine learning model with the training set can provide optimal values for parameters of the machine learning model (e.g., the parameters can be optimal based on a cost function that estimates an error committed by the machine learning model).

As shown inFIG. 1G, the classification platform can utilize stop-wise features155, POI features160, stop cluster features165, and sequential features170(e.g., associated with work order information provided by customers) with the trained machine learning model to determine inferred information associated with work order stops175(e.g., work related vehicle stops) and non-work order stops180(e.g., non-work related vehicle stops). In some implementations, the classification platform can utilize one or more of stop-wise features155, POI features160, stop cluster features165, or sequential features170with the trained machine learning model to determine the inferred information associated with work order stops175and non-work order stops180.

In some implementations, work order stops175can include vehicle stops related to work orders, as determined by the location information and the work order information received from the vehicles and/or a vehicle fleet management system. For example, each of work order stops175can include a matched pair of information (e.g., {PZ9, WZ9}) that includes one or more GPS signals associated with an identified vehicle stop (e.g., PZ9) and inferred work order information (e.g., WZ9) for the identified vehicle stop.

In some implementations, non-work order stops180can include vehicle stops that are not related to work orders, as determined by the location information and the work order information received from the vehicles and/or a vehicle fleet management system. For example, each of non-work order stops180can include a matched pair of information (e.g., {PZ11, None}) that includes one or more GPS signals associated with an identified vehicle stop (e.g., PZ11) and an indication inferring that there is no corresponding work order information (e.g., None) for the identified vehicle stop.

In some implementations, the classification platform can store the inferred information associated with work order stops175and non-work order stops180in storage associated with the classification platform. In some implementations, the classification platform can provide the inferred information associated with work order stops175and non-work order stops180for display to a user of the classification platform, to another device for display, and/or the like.

For example, as shown inFIG. 1H, the classification platform can provide a user interface that indicates a total number of work order stops (e.g., 33,833) and a total number of non-work order stops (e.g., 144,343) for all of the vehicles of the vehicle fleet. The user interface can provide a ranked list of vehicles, in the vehicle fleet, with the most non-work order stops (e.g., vehicle 1, vehicle 8, . . . , vehicle 72), and a portion providing a ranked list of vehicles with the most work order stops (e.g., vehicle 88, vehicle 76, . . . , vehicle 11). In some implementations, the classification platform can provide other information associated with work order stops175and non-work order stops180, such as reports indicating stops per vehicle, overall stop statistics for all vehicles, a number of work stops per vehicle, a number of work stops per driver, a number of non-work stops per vehicle, a number of non-work stops per driver, and/or the like. In some implementations, the classification can provide a map or a timeline associated with a vehicle trip differently for a work stop and a non-work stop.

In some implementations, the classification of vehicle stops into work and non-work vehicle stops can enable vehicle fleet managers to log and make decisions about vehicle drivers. The classification of vehicle stops into work and non-work vehicle stops can be used to generate reports and alerts that provide enhanced insights to the vehicle fleet managers.

In this way, implementations described herein can provide a classification platform that utilizes machine learning to automatically classify vehicle stops as work related or non-work related based on location information associated with a fleet of vehicles. Classifying vehicle stops for a fleet of vehicles can help identify and reduce non-work related vehicle stops, which can reduce network resources that might otherwise be consumed during non-work related vehicle stops. Reducing non-work related vehicle stops can also conserve resources of the vehicles, reduce vehicle usage, reduce natural resource usage, and/or the like that might otherwise be consumed during non-work related vehicle stops. Furthermore, the classification platform can classify vehicle stops as work related or non-work related based on big data (e.g., hundreds, thousands, millions, hundreds of millions, and/or the like data points associated with hundreds, thousands, and/or the like vehicles) that cannot be physically processed by humans.

As indicated above,FIGS. 1A-1Hare provided merely as an example. Other examples are possible and can differ from what was described with regard toFIGS. 1A-1H.

FIG. 2is a diagram of an example environment200in which systems and/or methods, described herein, can be implemented. As shown inFIG. 2, environment200can include a user device210, a classification platform220, and a network230. Devices of environment200can interconnect via wired connections, wireless connections, or a combination of wired and wireless connections.

User device210includes a device that is capable of communicating with one or more other devices included in environment200. For example, user device210can include a mobile phone (e.g., a smart phone, a radiotelephone, etc.), a laptop computer, a tablet computer, a handheld computer, a gaming device, a wearable communication device (e.g., a smart wristwatch, a pair of smart eyeglasses, etc.), a Global Positioning Satellite (GPS) device, an electronic control unit (ECU) of a vehicle, an engine control module (ECM) of a vehicle, or a similar type of device. In some implementations, user device210can be capable of receiving and/or providing location information and work order information, associated with a vehicle, from and/or to classification platform220.

Classification platform220includes one or more devices that utilizes machine learning to determine a machine learning model and to automatically classify vehicle stops as work related or non-work related based on location information associated with a fleet of vehicles on the basis of the learned machine learning model. In some implementations, classification platform220can be designed to be modular such that certain software components can be swapped in or out depending on a particular need. As such, classification platform220can be easily and/or quickly reconfigured for different uses. In some implementations, classification platform220can receive information from and/or transmit information to one or more user devices210and/or one or more vehicles.

In some implementations, as shown, classification platform220can be hosted in a cloud computing environment222. Notably, while implementations described herein describe classification platform220as being hosted in cloud computing environment222, in some implementations, classification platform220cannot be cloud-based (i.e., can be implemented outside of a cloud computing environment) or can be partially cloud-based.

Cloud computing environment222includes an environment that hosts classification platform220. Cloud computing environment222can provide computation, software, data access, storage, etc. services that do not require end-user knowledge of a physical location and configuration of system(s) and/or device(s) that hosts classification platform220. As shown, cloud computing environment222can include a group of computing resources224(referred to collectively as “computing resources224” and individually as “computing resource224”).

Computing resource224includes one or more personal computers, workstation computers, server devices, or other types of computation and/or communication devices. In some implementations, computing resource224can host classification platform220. The cloud resources can include compute instances executing in computing resource224, storage devices provided in computing resource224, data transfer devices provided by computing resource224, etc. In some implementations, computing resource224can communicate with other computing resources224via wired connections, wireless connections, or a combination of wired and wireless connections.

As further shown inFIG. 2, computing resource224includes a group of cloud resources, such as one or more applications (“APPs”)224-1, one or more virtual machines (“VMs”)224-2, virtualized storage (“VSs”)224-3, one or more hypervisors (“HYPs”)224-4, and/or the like.

Application224-1includes one or more software applications that can be provided to or accessed by user device210. Application224-1can eliminate a need to install and execute the software applications on user device210. For example, application224-1can include software associated with classification platform220and/or any other software capable of being provided via cloud computing environment222. In some implementations, one application224-1can send/receive information to/from one or more other applications224-1, via virtual machine224-2.

Virtual machine224-2includes a software implementation of a machine (e.g., a computer) that executes programs like a physical machine. Virtual machine224-2can be either a system virtual machine or a process virtual machine, depending upon use and degree of correspondence to any real machine by virtual machine224-2. A system virtual machine can provide a complete system platform that supports execution of a complete operating system (“OS”). A process virtual machine can execute a single program, and can support a single process. In some implementations, virtual machine224-2can execute on behalf of a user (e.g., user device210or an operator of classification platform220), and can manage infrastructure of cloud computing environment222, such as data management, synchronization, or long-duration data transfers.

Virtualized storage224-3includes one or more storage systems and/or one or more devices that use virtualization techniques within the storage systems or devices of computing resource224. In some implementations, within the context of a storage system, types of virtualizations can include block virtualization and file virtualization. Block virtualization can refer to abstraction (or separation) of logical storage from physical storage so that the storage system can be accessed without regard to physical storage or heterogeneous structure. The separation can permit administrators of the storage system flexibility in how the administrators manage storage for end users. File virtualization can eliminate dependencies between data accessed at a file level and a location where files are physically stored. This can enable optimization of storage use, server consolidation, and/or performance of non-disruptive file migrations.

Hypervisor224-4can provide hardware virtualization techniques that allow multiple operating systems (e.g., “guest operating systems”) to execute concurrently on a host computer, such as computing resource224. Hypervisor224-4can present a virtual operating platform to the guest operating systems, and can manage the execution of the guest operating systems. Multiple instances of a variety of operating systems can share virtualized hardware resources.

The number and arrangement of devices and networks shown inFIG. 2are provided as an example. In practice, there can be additional devices and/or networks, fewer devices and/or networks, different devices and/or networks, or differently arranged devices and/or networks than those shown inFIG. 2. Furthermore, two or more devices shown inFIG. 2can be implemented within a single device, or a single device shown inFIG. 2can be implemented as multiple, distributed devices. Additionally, or alternatively, a set of devices (e.g., one or more devices) of environment200can perform one or more functions described as being performed by another set of devices of environment200.

FIG. 3is a diagram of example components of a device300. Device300can correspond user device210, classification platform220, and/or computing resource224. In some implementations, user device210, classification platform220, and/or computing resource224can include one or more devices300and/or one or more components of device300. As shown inFIG. 3, device300can include a bus310, a processor320, a memory330, a storage component340, an input component350, an output component360, and a communication interface370.

Software instructions can be read into memory330and/or storage component340from another computer-readable medium or from another device via communication interface370. When executed, software instructions stored in memory330and/or storage component340can cause processor320to perform one or more processes described herein. Additionally, or alternatively, hardwired circuitry can be used in place of or in combination with software instructions to perform one or more processes described herein. Thus, implementations described herein are not limited to any specific combination of hardware circuitry and software.

FIG. 4is a flow chart of an example process400for classifying vehicle stops as work related or non-work related. In some implementations, one or more process blocks ofFIG. 4can be performed by classification platform220. In some implementations, one or more process blocks ofFIG. 4can be performed by another device or a group of devices separate from or including classification platform220, such as user device210.

As shown inFIG. 4, process can include receiving location information and work order information associated with multiple vehicles (block410). For example, classification platform220can receive location information and work order information associated with multiple vehicles of a vehicle fleet. In some implementations, classification platform220can receive the location information from one or more of the multiple vehicles and/or from one or more user devices210associated with the multiple vehicles. In some implementations, classification platform220can receive the work order information from one or more of the multiple vehicles, one or more user devices210associated with the multiple vehicles, and/or from another source (e.g., from a vehicle fleet management system responsible for managing the vehicle fleet). In some implementations, user devices210can receive the work order information from one or more sensors provided in the multiple vehicles or from one or more sensors associated with user devices210, and can provide the work order information to classification platform220.

In some implementations, the location information for each vehicle can include information indicating a geographic location of the vehicle (e.g., in latitude and longitude), information indicating a velocity of the vehicle, and/or the like. For example, the location information can indicate that a vehicle is located at a particular latitude and longitude, a particular address, and/or the like. In some implementations, the work order information for each vehicle can include information indicating a schedule of work tasks to be completed by a driver of the vehicle over a period of time, information indicating locations of where the work tasks are to be completed, and/or the like. For example, the work order information can indicate that a vehicle is scheduled to deliver packages at different locations at different times of the day.

As further shown inFIG. 4, process400can include grouping the location information into engine off information, idling information, and journey information (block420). For example, classification platform220can cluster or group the location information into engine off information, idling information, and journey information. In some implementations, classification platform220can utilize a rule-based clustering technique to group the location information into engine off information, idling information, and journey information.

In some implementations, the engine off information can include location information associated with engine off events (e.g., when the engines of the vehicles are turned off), and such location information can be generated when the engines are turned off. In some implementations, no location information is generated from the vehicles while the engines are turned off. In some implementations, the idling information can include location information associated with vehicles with engines turned on, but where the vehicles are not moving or are moving slowly in a small geographic area. In some implementations, the journey information can include location information that is not grouped into the engine off information and the idling information.

As further shown inFIG. 4, process400can include combining the engine off information and the idling information to generate vehicle stop information (block430). For example, classification platform220can combine the engine off information and the idling information to generate vehicle stop information. In some implementations, classification platform220can chronologically sort the engine off information, the idling information, and the journey information, for each vehicle, before combining the engine off information and the idling information. In some implementations, the vehicle stop information can include consecutive engine off information and idling information that are not separated by journey information. In some implementations, each vehicle stop in the vehicle stop information can include a group of chronologically consecutive GPS signals that are associated with idling or engine off conditions and that satisfy spatiotemporal locality constraints. Each vehicle stop can include several characteristics, such as a number of aggregated GPS signals, a start and an end of the vehicle stop, a stop duration, maximum and minimum latitudinal and longitudinal coordinates of the GPS signals, and/or the like.

As further shown inFIG. 4, process400can include matching corresponding work order information with the vehicle stop information to generate matched information for stops having work order information to create a training set (block440). For example, classification platform220can match corresponding work order information with the vehicle stop information to generate matched information for stops having work order information to create a training set. In some implementations, if the vehicle stop does not match a particular one of the work orders in the work order information, classification platform220can provide a designation (e.g., “None”) indicating that the stop information does not match the particular one of vehicle stops. In some implementations, if work order information does not exist for a particular one of identified vehicle stops135, the classification platform can provide a designation (e.g., “?”) indicating that the work order information does not exist for the particular one of identified vehicle stops135. In some implementations, classification platform220can combine a GPS signal associated with a matching vehicle stop (e.g., P11) with corresponding work order information (e.g., W11) to create a matched pair (e.g., {P11, W11}). In instances where the work order information does not match a vehicle stop, classification platform220can combine a GPS signal associated with the vehicle stop (e.g., P18) with a non-match indicator (e.g., None) to create a pair (e.g., {P18, None}). In instances where the work order information does not exist a particular identified vehicle stop, the classification platform can combine a GPS signal associated with the particular identified vehicle stop (e.g., P288) with a non-existing indicator (e.g., ?) to create a pair (e.g., {P288, ?}).

As further shown inFIG. 4, process400can include extracting stop-wise features, POI features, stop cluster features, and sequential features from the vehicle stop information (block450). For example, classification platform220can extract stop-wise features, POI features, stop cluster features, and sequential features from the vehicle stop information. In some implementations, the stop-wise features can include features associated with a stop duration, a start time of the vehicle stop, time spent with the engine off, a geographic shape associated with a vehicle stop, a vehicle stop type, an odometer distance from a first GPS signal to a last GPS signal of the vehicle stop, a sum of haversine distances between consecutive GPS signals associated with the vehicle stop, a haversine distance between a first GPS signal and a last GPS signal of the vehicle stop, a total count of GPS signals associated with the vehicle stop, an average speed of the vehicle, a number of engine off GPS signals associated with the vehicle stop, and/or the like.

In some implementations, the POI features can include features associated with points of interest in a geographic area surrounding each vehicle stop. In some implementations, the POI features can include features identifying POI types, such as a bank, a university, a hotel, a restaurant, a rest area, a grocery store, a school, a shopping center, a gas station, an open parking area, a vehicle repair facility, and/or the like. For each of these POI types, classification platform220can generate a feature that includes information associated with a shortest distance from a location of the POI type to a location corresponding to an engine off GPS signal.

In some implementations, the stop cluster features can include features that describe characteristics of vehicle stops surrounding a particular vehicle stop, so that classification platform220can characterize a geographic area associated with the particular vehicle stop. In some implementations, classification platform220, for each vehicle stop, can analyze a radius around the vehicle stop and collect statistics about surrounding vehicle stops.

In some implementations, the sequential features can include features associated with a sequence of vehicle stops performed by each vehicle prior to and after each vehicle stop.

As further shown inFIG. 4, process400can include utilizing the stop-wise features, the POI features, the stop cluster features, and the sequential features with a model to determine work order stops and non-work order stops for the vehicles without available work order information (block460). For example, classification platform220can utilize the stop-wise features, the POI features, the stop cluster features, and the sequential features with a model to determine work order stops and non-work order stops for the vehicles without available work order information. In some implementations, classification platform220can utilize one of the stop-wise features, the POI features, the stop cluster features, or the sequential features to train a machine learning model. In some implementations, classification platform220can utilize a combination of one or more of the stop-wise features, the POI features, the stop cluster features, or the sequential features to train the machine learning model. In some implementations, classification platform220can utilize one or more of the stop-wise features, the POI features, the stop cluster features, or the sequential features with the trained machine learning model to determine information associated with work order stops (e.g., work related vehicle stops) and non-work order stops (e.g., non-work related vehicle stops).

In some implementations, work order stops175can include vehicle stops related to work orders, as determined by the location information and the work order information received from the vehicles and/or a vehicle fleet management system. For example, each of work order stops175can include a matched pair of information (e.g., {PZ9, WZ9}) that includes one or more GPS signals associated with an identified vehicle stop (e.g., PZ9) and inferred work order information (e.g., WZ9) for the identified vehicle stop.

In some implementations, non-work order stops180can include vehicle stops that are not related to work orders, as determined by the location information and the work order information received from the vehicles and/or a vehicle fleet management system. For example, each of non-work order stops180can include a matched pair of information (e.g., {PZ11, None}) that includes one or more GPS signals associated with an identified vehicle stop (e.g., PZ11) and an indication inferring that there is no corresponding work order information (e.g., None) for the identified vehicle stop.

In some implementations, classification platform220can utilize one or more of the stop-wise features, the POI features, the stop cluster features, and the sequential features with a random forest classifier model (e.g., which includes multiple decision trees) to determine work order stops and non-work order stops for the vehicles. In this case, classification platform220can utilize one or more of the stop-wise features, the POI features, the stop cluster features, and the sequential features to train the random forest classifier model.

In some implementations, classification platform220can train the random forest classifier model using a ten-fold cross validation procedure to choose an optimal number (T) and a maximum depth (D) of decision trees. In this case, classification platform220can apply an area under the receiver operating characteristic (AUROC) curve to evaluate the optimal number and the maximum depth of the decision trees. The AUROC curve represents a curve of variation of a false positive rate (FPR) with respect to a true positive rate (TPR) at different values of a classification threshold, where TPR=TP/(TP+FN), FPR=FP/(FP+TN), and TP, FP, TN and FN are the true positives, false positives, true negatives, and false negatives, respectively. In one example, classification platform220can vary T between 100 and 200, and can vary D among 12, 15, 20, 22, and 25 when training the random forest classifier model.

As further shown inFIG. 4, process400can include providing information associated with the work order stops and the non-work order stops for the vehicles (block470). For example, classification platform220can provide information associated with the work order stops and the non-work order stops for the vehicles. In some implementations, classification platform220can provide the information associated with the work order stops and the non-work order stops for the vehicles to storage associated with classification platform220. In some implementations, classification platform220can provide the information associated with the work order stops and the non-work order stops for display to a user of classification platform220, to another device for display, and/or the like. For example, classification platform220can provide the information associated with the work order stops and the non-work order stops for the vehicles via a user interface as shown inFIG. 1H.

In some implementations, classification platform220can utilize the information associated with the work order stops and the non-work order stops to automatically perform a variety of functions. For example, in the future, if the vehicles are driverless, classification platform220can provide instructions, to a vehicle performing a non-work order stop, to alter a route or return to a home office. Classification platform220can send messages (e.g., via a fleet management system, a telephone call, a text message, and/or the like) to a vehicle fleet manager alerting the manager of unauthorized non-work order stops. Classification platform220can disable a vehicle determined to be performing or having performed a non-work order stop. Classification platform220can alter a work schedule associated with a particular vehicle that performed a non-work order stop (e.g., to reduce a number of work tasks), can send additional work tasks to user device210of another vehicle (e.g., work tasks that were previously scheduled for the particular vehicle). Based on weather or traffic information, and the information associated with the work order stops and the non-work order stops, classification platform220alter work schedules associated with one or more vehicles.

In some implementations, the random forest classifier model approach, utilized with the features described above, can be improved by additions and/or alterations. For example, the dataset described above, or a similar dataset, can be utilized for multi-class classification (e.g., by enlarging the set of stop purpose types beyond work order stops and non-work order stops).

Furthermore, additional sets of features can be utilized, such as features indicating an industry type. For example, different industry segments can have different work order characteristics. Additionally, based on the observation that information on previous and subsequent stops turned out to be quite effective, but perhaps less effective than expected, classification platform220can exploit the full sequence of stops by explicitly modeling the time component (e.g., with a graphical model or a recurrent neural network).

AlthoughFIG. 4shows example blocks of process400, in some implementations, process400can include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted inFIG. 4. Additionally, or alternatively, two or more of the blocks of process400can be performed in parallel.

Some implementations, described herein, can provide a classification platform that trains a machine learning model, based on known work order stops and non-work order stops, to generate a data set. The machine learning model, once trained with the data set, can automatically classify vehicle stops as work related (e.g., a work order stop) or non-work related (e.g., a non-work order stop) based on location (e.g., GPS) information and without knowing work order information associated with a fleet of vehicles. For example, the classification platform can receive the location information from user devices associated with the vehicles, and can identify stops associated with the vehicles by clustering or grouping the location information. The classification platform can extract stop-wise features, POI features, stop cluster features, and sequential features from information associated with the identified stops. The classification platform can utilize the stop-wise features, the POI features, the stop cluster features, and the sequential features to determine work order stops and non-work order stops associated with the vehicles.