Fleet management analysis and improvement

Abstract

To provide fleet analytics and identifying a remediation action for fleet management.SOLUTION: A method and system for fleet management is described. Fleet management data for a target fleet of vehicles is received. A target vehicle vector that represents a target vehicle of the target fleet is generated based on the fleet management data. One or more similar vehicles are identified, from a plurality of reference vehicles, that are similar to the target vehicle using a distance metric between the target vehicle vector and a plurality of reference vehicle vectors. The plurality of reference vehicle vectors represent the plurality of reference vehicles. Vehicle characteristics of the target vehicle and the one or more similar vehicles are identified, the vehicle characteristics affecting vehicle efficiency of the target vehicle. The vehicle characteristics affecting vehicle efficiency of the target vehicle and corresponding vehicle characteristics of the one or more similar vehicles are displayed.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] Field of the Invention The present application relates to fleet management, and more particularly to methods and systems for analyzing fleet characteristics and developing amendment proposals therefor.

[0002] Related Application The present application claims priority to and relates to U.S. Provisional Application No. 63 / 546,420 filed on October 30, 2023, the disclosure of which is hereby incorporated herein by reference in its entirety. [Background Art]

[0003] Background Logistics, distribution and delivery companies sometimes use truck fleets (owned vehicle groups) to transport goods. A fleet may comprise several to tens or hundreds of various types of trucks, which are managed by a fleet manager. The goals of a fleet manager generally include reducing costs associated with the operation and access to assets (trucks, trailers, loading and unloading equipment, maintenance equipment, etc.), avoiding unforeseen events and risks, and maximizing the uptime of assets. Fleet managers also seek to minimize or eliminate inefficiencies in data acquisition to achieve these goals.

[0004] Even when data on individual trucks within a manager's own fleet is available, insights can potentially be improved by using statistical data regarding other trucks similar to the individual trucks. For example, a fleet manager of a 2019 Freightliner Class 8 truck may know the mileage, when to perform preventive maintenance, the typical fuel efficiency over the service life of the truck (e.g., miles per gallon), etc., yet maintenance and fleet management decisions may be improved by considering data from other similar trucks.

[0005] Embodiments have been described with respect to these and other general considerations. While relatively specific issues have been discussed, it should be understood that embodiments should not be limited to solving the specific problems identified in the background art. [Overview of the project] [Problems that the invention aims to solve]

[0006] overview This disclosure relates to providing fleet analysis and identifying corrective actions for fleet management. [Means for solving the problem]

[0007] In one embodiment, a method for fleet management is provided. Fleet management data for a target fleet of vehicles is received. Based on the fleet management data, a target vehicle vector is generated representing the target vehicle in the target fleet. From a plurality of reference vehicles, one or more similar vehicles similar to the target vehicle are identified using a distance metric between the target vehicle vector and the plurality of reference vehicle vectors. The plurality of reference vehicle vectors represent a plurality of reference vehicles. Vehicle characteristics of the target vehicle and one or more similar vehicles are identified, and the vehicle characteristics affect the vehicle efficiency of the target vehicle. The vehicle characteristics that affect the vehicle efficiency of the target vehicle and the corresponding vehicle characteristics of one or more similar vehicles are displayed.

[0008] In one embodiment, receiving fleet management data includes receiving the maintenance history of the target fleet of vehicles.

[0009] In one embodiment, the method includes determining improvement measures to control at least one of the target vehicles based on vehicle characteristics that affect the vehicle efficiency of the target vehicles.

[0010] The method may include generating a target fleet vector representing the target fleet based on fleet management data. It may also include generating a simulated fleet vector based on the target fleet vector and a plurality of reference fleet vectors using a distance metric, where the plurality of reference fleet vectors represent a plurality of reference fleets of vehicles, and the simulated fleet vector represents a simulated fleet of vehicles. Either method may include determining corrective actions for target fleet management based on the simulated fleet vector, and generating the target fleet vector may include generating a target vehicle vector for each vehicle in the target fleet that represents the vehicle.

[0011] The process for generating simulated fleet vectors may include generating a simulated vehicle vector for each vehicle in the target fleet, using a distance metric, based on the corresponding target vehicle vector and multiple reference vehicle vectors.

[0012] In one embodiment, the distance metric is the Manhattan distance metric.

[0013] In one embodiment, the process of identifying one or more similar vehicles may include using a similarity algorithm to sort a target fleet of multiple reference vehicles into multiple groups; and calculating a distance metric between vehicles within each of the multiple groups.

[0014] In one embodiment, fleet management data can represent the workload of a target fleet of vehicles, and a simulated fleet of vehicles can be generated to perform the workload of the target fleet of vehicles.

[0015] In one embodiment, the simulated fleet of vehicles includes at least one target vehicle from the target fleet of vehicles having the modifications indicated by the corrective action.

[0016] In one embodiment, the simulated vehicle fleet has a different number of vehicles than the target vehicle fleet, and the improvement measures indicate the addition of vehicles to the target vehicle fleet or the removal of vehicles from the target vehicle fleet.

[0017] In another embodiment, a system for fleet management is provided. The system comprises a processor and a non-transient computer-readable memory having computer-readable instructions that, when executed by the processor, cause the processor to receive fleet management data about a target fleet of vehicles; generate target vehicle vectors representing target vehicles in the target fleet based on the fleet management data; identify one or more similar vehicles from a plurality of reference vehicles that are similar to the target vehicle, using a distance metric between the target vehicle vectors and a plurality of reference vehicle vectors representing a plurality of reference vehicles; identify vehicle characteristics of the target vehicle and one or more similar vehicles that affect the vehicle efficiency of the target vehicle; and display the vehicle characteristics affecting the vehicle efficiency of the target vehicle and the corresponding vehicle characteristics of one or more similar vehicles.

[0018] In some embodiments, computer-readable instructions further cause the processor to receive the maintenance history of the target fleet of vehicles.

[0019] In one embodiment, the computer-readable instructions further cause the processor to determine improvement measures for managing the vehicle based on vehicle characteristics that affect the vehicle's efficiency.

[0020] The computer-readable instructions cause a processor to: generate a target fleet vector representing a target fleet based on fleet management data; generate a simulated fleet vector representing a simulated vehicle fleet based on the target fleet vector and a plurality of reference fleet vectors representing a plurality of reference vehicle fleets using a distance metric; and determine an improvement measure for management of the target fleet based on the simulated fleet vector, wherein generating the target fleet vector is contemplated to include generating, for each vehicle in the target fleet, a respective target vehicle vector representing the vehicle.

[0021] In some embodiments, the computer-readable instructions cause a processor to generate, for each vehicle in the target fleet, a simulated vehicle vector from the plurality of reference fleet vectors using a distance metric based on a corresponding target vehicle vector and a plurality of reference vehicle vectors.

[0022] In some embodiments, the distance metric is a Manhattan distance metric.

[0023] In some embodiments, it is contemplated that the computer-readable instructions further cause a processor to: sort a plurality of reference vehicles and a target fleet of vehicles into a plurality of groups using a similarity algorithm; and calculate an inter-vehicle distance metric within groups of the plurality of groups.

[0024] In some embodiments, it is contemplated that the fleet management data represents a workload of a target fleet of vehicles, and the simulated vehicle fleet is generated to execute the workload of the target fleet of vehicles.

[0025] In certain embodiments, the simulated vehicle fleet includes at least one target vehicle from the target fleet of vehicles having a modification indicated by the improvement measure.

[0026] In some embodiments, the simulated fleet of vehicles has a different number of vehicles than the target fleet of vehicles, and addition of vehicles to the target fleet of vehicles or removal of vehicles from the target fleet of vehicles is indicated by the improvement measure.

[0027] This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

[0028] Non-limiting and non-exhaustive examples are described with reference to the following figures. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] [Figure 1] FIG. 1 is a block diagram of an example system for improving fleet management according to an example embodiment. [Figure 2] FIG. 2 is a block diagram showing an example of vectorization for individual trucks according to an example embodiment. [Figure 3] FIG. 3 is a diagram of a reference vehicle identified based on a target vehicle according to an example embodiment. [Figure 4] FIG. 4 is a diagram of a simulated fleet identified using a set of reference trucks identified by the fleet comparison engine of FIG. 1 according to an example embodiment. [Figure 5] FIG. 5 is a diagram of a user interface for comparison between a target fleet and a plurality of simulated fleets according to an example embodiment. [Figure 6] FIG. 6 is a diagram of a user interface for comparison between a target fleet and a plurality of simulated fleets according to an example embodiment. [Figure 7] FIG. 7 is a diagram of a user interface for comparison between a target vehicle and a plurality of reference vehicles according to an example embodiment. [Figure 8]Figure 8 is a flowchart of an exemplary method for identifying improvement measures for fleet management, according to an exemplary embodiment. [Figure 9] Figure 9 is a block diagram showing an example of a physical component of a computer device that can implement an aspect of this disclosure. [Modes for carrying out the invention]

[0030] Detailed explanation In the following detailed description, reference will be made to the accompanying drawings, which constitute part of this specification, illustrating specific embodiments or examples. These embodiments may be combined, other embodiments may be used, or structural modifications may be made without departing from the disclosure. Embodiments can be implemented as methods, systems, or devices. Accordingly, embodiments can take the form of hardware implementations, full software implementations, or implementations combining software and hardware embodiments. Accordingly, the following detailed description should not be taken as limiting, and the scope of this disclosure is defined by the claims and their equivalents.

[0031] As explained in the background, maintenance and fleet management decisions can be improved by considering data from similar trucks. However, identifying similar trucks can be difficult because each truck has a wide range of characteristics, options, and usage criteria. For example, even a single truck can have significantly different fuel economy depending on whether it is driven primarily in urban areas with many steep inclines (e.g., where frequent stops are necessary) or on long-distance routes on flat terrain where fuel economy is relatively better. For example, it can be difficult to consider different usage criteria and other characteristics depending on whether a statistically significant number of trucks are available for analysis. However, even if imperfect, trucks with sufficiently similar criteria and characteristics can be identified for useful analysis, which may help identify corrective actions to improve fleet operations.

[0032] In various embodiments, the fleet comparison engine is configured to receive fleet management data relating to a target fleet of vehicles. The target fleet of vehicles may be a fleet of trucks for a logistics company, distribution company, manufacturer, or other suitable fleet. To perform comparisons with other reference vehicle fleets more quickly and efficiently, the fleet comparison engine generates a target vehicle vector representing the target vehicle in the target fleet based on the fleet management data. In some examples, the target vehicle vector is a vector of numerical values ​​(e.g., integer values ​​and / or floating-point values). The fleet comparison engine can then use a distance metric between the target vehicle vector and multiple reference vehicle vectors to identify one or more similar vehicles from multiple reference vehicles that are similar to the target vehicle. Multiple reference vehicle vectors represent multiple reference vehicles. The fleet comparison engine identifies the target vehicle and the vehicle characteristics of one or more similar vehicles that affect the vehicle efficiency of the target vehicle, and displays the vehicle characteristics affecting the vehicle efficiency of the target vehicle and the corresponding vehicle characteristics of one or more similar vehicles. For example, the vehicle characteristics of the target vehicle may include any physical and / or driving characteristics of the target vehicle that can be measured and classified. For example, vehicle characteristics may include the vehicle's class, brand, model, year of manufacture, height, width, length, weight, number of axles, load capacity, tire rated load, tire pressure, odometer reading, average fuel consumption (MPG), fuel efficiency components (e.g., wind deflector), mean time between failures (MTBF), center of gravity, vehicle motion response time, and / or driving system.

[0033] In some cases, the fleet comparison engine generates a target fleet vector representing the target fleet based on fleet management data, and also generates a simulated fleet vector based on the target fleet vector and multiple baseline fleet vectors using a distance metric. Multiple baseline fleet vectors represent multiple baseline vehicle fleets, and the simulated fleet vector represents a simulated fleet of vehicles. The baseline fleet vector represents multiple baseline fleets of vehicles different from the vehicle target fleet, and the simulated fleet vector represents a simulated fleet of vehicles. A simulated fleet of vehicles may be considered a "fantasy" fleet of vehicles in that at least some individual vehicles, and possibly all vehicles within the simulated fleet, may not be operated by the fleet manager of the vehicle target fleet. However, the analysis can be improved by expanding the data pool on which the analysis may be performed and processing the data pool for efficient comparison. Thus, the fleet comparison engine can provide comparative data to understand fleet operations and / or determine improvement measures for managing the vehicle target fleet based on the simulated fleet vector. For example, a simulated fleet vector could indicate that one or more specific trucks within a given fleet are contributing to a larger-than-expected share of inefficiency (e.g., poor performance in miles per gallon), potentially leading to benefits from maintenance, replacements, driver training, etc.

[0034] Many other embodiments relating to computer devices are described herein. For example, Figure 1 is a block diagram of an example of a system 100 for comparing and improving fleet management according to an exemplary embodiment. System 100 includes a fleet comparison engine 110 configured to display vehicle characteristics affecting vehicle efficiency and to determine improvement measures for managing a target fleet, such as a target fleet 150, based on vehicle data from different vehicles. The target fleet 150 includes several vehicles, indicated as trucks 152 and 154. Although only two trucks are shown, the target fleet 150 can have 10, 50, several hundred, or any preferred number of trucks. In some examples, all trucks in the target fleet 150 are of the same type, such as medium-duty trucks for local delivery, long-haul trucks, or other appropriate types. In other examples, the target fleet 150 has two, three, or more trucks of different types. For ease of explanation, an example relating to truck 152 is described, but these examples are also applicable to other vehicles, such as truck 154, truck 162, truck 164, or other vehicles. Although only one target fleet is shown, a company or organization can operate vehicles in two, three, or more target fleets. Furthermore, while the example shown in Figure 1 uses trucks as vehicles within target fleet 150, in other examples, target fleet 150 may include other vehicles such as vans, cars, boats, airplanes, and helicopters. In one example, a company's first target fleet has vans, its second target fleet has trucks, and its third target fleet has airplanes.

[0035] Truck 152 can communicate with a target fleet data store 120, for example, to provide vehicle data for storage, analysis, and fleet management. The vehicle data of Truck 152 may include truck class, truck brand, truck model, year of manufacture, diagnostic data, odometer readings, fuel consumption, average fuel consumption (MPG), mean time between failures (MTBF), vehicle telematics data, location data (e.g., GPS coordinates), driver management (e.g., attention and driving characteristics), or other suitable data for fleet management applicable to a specific vehicle. That is, the vehicle data of Truck 152 includes physical and / or driving characteristics associated with Truck 152. In some examples, vehicle data may be captured by one or more sensors and processors (not shown) within Truck 152 used in the normal operation of Truck 152, such as fuel sensors, engine sensors (e.g., fuel pressure, oil temperature, RPM, etc.), engine controller, GPS receiver, dashboard or infotainment display controller. In another example, track 152 includes a standalone processor or device (e.g., a GPS receiver) that captures some of the vehicle data. Track 152 may include a communication device 153 or other suitable telematics device, which may include a mobile network interface (e.g., a 3G / 4G / 5G transceiver), a satellite interface, a Wi-Fi interface, a Bluetooth® interface, a data port, or other suitable devices for transmitting vehicle data. The communication device 153 may acquire vehicle data from sensors and / or processors (e.g., via a CAN bus, an OBD-III port, or other suitable port). Transmission of vehicle data may utilize a network 180, which may include one or more networks such as a local area network (LAN), a wide area network (WAN), a corporate network, or the Internet, and may include one or more wired, wireless, and / or optical portions.In some examples, the communication device 153 can communicate with smartphones, tablets, laptops, etc., acting as an intermediary to the network 180 or the target fleet data store 120.

[0036] The target fleet data store 120 may be a computer device, network server, cloud storage service, database, or other suitable data store. Generally, the target fleet data store 120 stores fleet information relating to the target fleet 150. In some examples, the target fleet data store 120 also provides a user interface for software that processes the fleet information and has fleet management functions (e.g., route planning, maintenance planning, fleet status). The fleet information may preferably include vehicle data from each vehicle in the target fleet 150, along with additional data related to fleet management (e.g., owner information, service contracts and maintenance contracts relating to the fleet, trailer information, cargo details (including weight and capacity utilization), driver profiles, spare parts inventory information, route planning information, etc.). The fleet information may also include location data received from GPS services, fuel consumption data received from gas stations, tractor-trailer information received from weighing stations, weather, road conditions, etc.

[0037] The system 100 may also include one or more maintenance facilities, such as maintenance facility 140. Maintenance facility 140 may be a truck service station, fuel station, repair station, or other suitable facility for the maintenance of truck 152. In some examples, maintenance facility 140 includes a diagnostic device (not shown) that receives vehicle data relating to truck 152. For example, the diagnostic device is a computer device or tablet with a user interface for inputting data. For example, a technician performing an oil change on truck 152 can input information related to the oil change into the user interface, such as the current mileage, the viscosity of the new oil, and the oil analysis of the used oil. In another example, the diagnostic device is an engine diagnostic device that communicates with truck 152 via, for example, an OBD-III port, a 9-pin Type 2 port, a Controller Area Network (CAN) bus port, or other suitable interface.

[0038] As described above, the fleet comparison engine 110 is configured to determine corrective actions for the management of the target fleet 150. To this end, the fleet comparison engine 110 utilizes vehicle data from the target fleet 150 along with vehicle data from multiple reference fleets 160. Each reference fleet 160 can correspond to a reference fleet datastore 170, and generally corresponds to the target fleet 150 and the target fleet datastore 120, respectively. For the sake of clarity, a single example of a reference fleet 160 and a reference fleet datastore 170 is described in various examples, but additional examples (e.g., 50, 100, 300 or more) may be used in other examples. The reference fleet 160 may include additional tracks (not shown) that have different levels of similarity to the tracks of the target fleet 150.

[0039] The reference fleet 160 includes trucks 162 and 164, which may be similar to trucks 152 and 154 of the target fleet 150. That is, trucks 162 and 164 may each have a communication device (not shown) suitable for providing vehicle data to the reference fleet data store 170. Furthermore, trucks 162 and 164 may also visit the maintenance facility 140 for maintenance. The vehicle data of trucks 162 and 164 may be provided to the fleet comparison engine 110 via the reference fleet data store 170, either directly from trucks 162 and 164 or in any appropriate combination.

[0040] Figure 2 shows a block diagram illustrating an example of vectorization for individual tracks according to an exemplary embodiment. As described above, the fleet comparison engine 110 is configured to identify vehicle characteristics of the target vehicle that affect the vehicle efficiency of the target vehicle, and can determine corrective actions for managing the target fleet 150 based on vehicle data from different vehicles. Different vehicles may include vehicles in the target fleet 150 and vehicles in the reference fleet 160. In some situations, the fleet comparison engine 110 can access vehicle data for thousands or tens of thousands of vehicles from a number of different fleets. To improve processing speed and efficiency for determining suitable corrective actions, the fleet comparison engine 110 generates a target fleet vector 200 representing the target fleet 150 and a similar reference fleet vector (not shown) for the reference fleet 160.

[0041] The target fleet vector 200 is based on vehicle data 252 for truck 152, vehicle data 254 for truck 154, and so on. In some examples, the fleet comparison engine 110 generates target vehicle vectors for each truck, such as target vehicle vector 253 for truck 152 and target vehicle vector 255 for truck 154. In the example shown in Figure 2, vehicle data 252 includes truck class, truck brand, truck model, year, odometer reading, average fuel consumption (MPG), service area, and mean time between failures (MTBF). In other examples, additional criteria may be included in or omitted from vehicle data 252. Similar information for truck 154 is shown as vehicle data 254. In the illustrated example, the target fleet vector 200 is generated by concatenating the target vehicle vectors (e.g., 253, 355) of the target fleet 150.

[0042] In the example shown in Figure 2, the target vehicle vector 253 is based on values ​​from vehicle data 252, with some values ​​copied as integers (e.g., class and year) and others converted from text to integer indices (e.g., converting Freightliner to 0, Volvo to 1). While only integer values ​​are shown in the example in Figure 2, other examples may use floating-point values ​​in addition to or instead of integer values. In some examples, one or more values ​​from vehicle data 252 may be processed to obtain a single value for the target vehicle vector 253. For example, the fleet comparison engine 110 can run a hash function on class, odometer, and service area to obtain a hybrid value for the target vehicle vector 253. The hybrid value may be generated in place of or in addition to the base value of the hybrid value and inserted into the target vehicle vector 253. In some examples, two, three, or more hybrid values ​​are used to improve vehicle-to-vehicle matchmaking. In yet another example, some values ​​may be weighted to have a greater impact on the identified corrective actions.

[0043] Hybrid values ​​can also represent parameters based on other values ​​in the vehicle data. For example, the utilization rate of a particular truck may not be directly available as a value from the vehicle data, but may be determined based on a location map of where the truck is located. In one example, utilization rate is the percentage of time a truck is performing useful tasks such as loading, unloading, or driving, as opposed to idling, maintenance, or stopping at a rest stop. Utilization rate may also be determined based on geolocation data to map where the truck has moved. In one example, a truck may be considered utilized when the Manhattan distance or Euclidean distance of its GPS location is greater than 2 miles, 5 miles, or other appropriate thresholds. In another example, a truck may be considered utilized when it is located outside a given geofence area.

[0044] In some examples, the fleet comparison engine 110 is configured to group vehicles based on specific characteristics of interest or using a similarity algorithm. For example, the fleet comparison engine 110 may perform locality-sensitive hashing or other suitable similarity algorithms to sort vehicles into multiple groups. Generally, the similarity algorithm is configured to identify groups, nearest neighbors, or subsets of vehicles from the target fleet 150 and / or reference fleet 160 where the members of the group are similar on one or more criteria or features. For example, the first group may include trucks with load capacities between 20,000 and 30,000 pounds, and the second group may include trucks with load capacities between 30,000 and 60,000 pounds. Other suitable similarity algorithms include the MinHash algorithm and tree-based methods such as regression trees and classification trees. In the case of LSH (locality-sensitive hashing) as the similarity algorithm, LSH can be used to indicate that similar vehicles belong to the same hash group or "bucket," while dissimilar vehicles belong to different groups. In various examples, different hashes can be used to represent groups of vehicles separated by type, class, region, or a combination thereof (e.g., Class 8 sleeper trucks located in the Midwest). Conveniently, the fleet comparison engine 110 can perform an approximate nearest neighbor search using only the vehicle in the hash group, which in some examples takes O(n) time. 2 The processing complexity can be reduced from O(n). For example, the fleet comparison engine 110 can calculate the distance metric between vehicles within a group, instead of between all available reference vehicles.

[0045] In some examples, the target fleet vector includes aggregated information based on vehicle data (for example, by aggregating or concatenating the target vehicle vectors of the target fleet). For example, the target fleet vector could represent the number of vehicles in the Midwest region, the number of vehicles in the Southeast region, the number of long-haul vehicles, the number of Class 8 vehicles, etc.

[0046] In addition to generating the target fleet vector, the fleet comparison engine 110 generates a reference fleet vector corresponding to at least a portion of the reference fleet 160. The reference fleet vector is generated similarly to the target fleet vector, but is based on the vehicle data of the reference fleet. The vehicle fleet comparison engine 110 can then use the vectors (i.e., the target fleet vector and the reference fleet vector) to compare the target fleet of vehicles with the reference fleet of vehicles, instead of comparing individual trucks. Advantageously, the target fleet vector and the reference fleet vector can include hybrid values ​​that emphasize desirable features, such as fuel economy, rather than less desirable features (e.g., truck brand). For example, a fleet manager of a target vehicle with a specific vehicle configuration (e.g., 60% Class 8 trucks, 40% Class 7 trucks) can compare the target vehicle with other similar reference vehicles with the same or similar configurations within the same industry.

[0047] Subsequently, the comparison between the target fleet and the reference fleet can be performed using only individual values ​​or subsets of values ​​within the vector, in various examples. In some situations, a comparison based on one or two hybrid values ​​is processed more quickly than a comparison based on all values ​​in the vector. In other situations, a comparison of additional values ​​in the vector may be used to identify a more similar reference fleet.

[0048] The target fleet vector 200 is shown as a one-dimensional vector or array, but in other examples, the target fleet vector 200 can be implemented as a multi-dimensional vector, a multi-dimensional matrix, or other suitable data structure. Furthermore, in some examples, the target fleet vector 200 can be maintained as separate vectors, such as separate vectors for each vehicle in the target fleet, or separate vectors for each class of vehicle, each region, etc.

[0049] The fleet comparison engine 110 enables fleet managers to evaluate the relative performance of similar tracks, identify outliers, and pinpoint the potential causes of low-performing outliers. By using a target fleet vector and a reference fleet vector along with one or more distance metrics, the fleet comparison engine 110 efficiently identifies which reference fleets are suitable for comparison, and furthermore, which tracks are suitable for comparison. In some examples, the distance metric is a cosine similarity metric performed using the values ​​of the target fleet vector and the respective values ​​of the reference fleet vector to obtain a similarity score of the target fleet vector to the corresponding reference fleet vector. In this way, similarity values ​​of the target fleet to the reference fleet are obtained, and reference fleets with similarity values ​​exceeding a threshold (e.g., greater than 0.9), or reference fleets within the "top 5" in similarity, can be selected for further analysis.

[0050] Figure 3 shows a diagram of a reference vehicle identified based on a target vehicle such as truck 152. As described above, the fleet comparison engine 110 can be configured to identify a simulated fleet of vehicles based on the target fleet and the reference fleet. In the example shown in Figure 3, to assemble the simulated fleet, the fleet comparison engine 110 uses a suitable distance metric (described above) to compare the target vehicle vector for truck 152 with the reference vehicle vectors of other trucks in the reference fleet data store 170. The fleet comparison engine 110 generates a similarity score for each pairing of trucks (i.e., one truck from the reference fleet paired with truck 152) and can select a set of trucks to constitute the simulated fleet, as described below. In the example shown in Figure 3, the set of trucks includes the five reference trucks with the highest similarity scores to target truck 152 (shown as trucks 310, 312, 314, 316, and 318). The set of trucks can be ranked according to one or more appropriate criteria. In the example shown in Figure 3, trucks 310, 312, 314, 316, and 318 are ranked according to fuel consumption (MPG). In other examples, trucks are ranked according to utilization rate, mean time between failures, carbon dioxide emissions, cost of ownership, fleet availability, or other appropriate parameters. The fleet comparison engine 110 can continue the above process to identify different sets of reference trucks for each truck in the target fleet. Thus, for a target fleet with five trucks, the fleet comparison engine 110 can generate five sets of five reference trucks.

[0051] As described above, the reference fleet data store 170 may include vehicle data from trucks other than the target fleet, and may even include data from competing fleets. In some cases, parts of the vector may be anonymized to prevent personal or proprietary information from being disclosed to the fleet administrator of the target fleet.

[0052] Figure 4 shows a diagram of a simulated fleet identified using a set of reference tracks identified by the fleet comparison engine 110. A simulated fleet of vehicles may be considered a "fantasy" fleet of vehicles in that at least some of the individual vehicles may not be operated by the fleet manager of the target fleet of vehicles. Using the example in Figure 3, five simulated fleets are identified, and each simulated fleet has five trucks. Each truck is identified by a fleet number and a truck number, so the first simulated fleet 410 includes trucks 1-1, 2-1, 3-1, 4-1 and 5-1, the third simulated fleet 430 includes trucks 1-3, 2-3, 3-3, 4-3 and 5-3, and the fifth simulated fleet 450 includes trucks 1-5, 2-5, 3-5, 4-5 and 5-5. For simplicity, the second and fourth simulated fleets are not shown. Each simulated fleet contains one truck corresponding to one truck in the target fleet, and the trucks are arranged across the simulated fleets so that similar vehicles of the same rank are grouped together in the same simulated fleet. That is, simulated fleet 410 is the "best" simulated fleet, containing base truck 310 and a base truck similarly ranked (i.e., 1st place) from another set of base trucks. Simulated fleet 450 is the "worst" simulated fleet, containing base truck 318 and a base truck similarly ranked (i.e., 5th place) from another set of base trucks.

[0053] Figures 5, 6, 7, 8, and 9 show examples of user interfaces that can be generated by the fleet comparison engine 110, or suitable data for the user interface 500 can be provided for rendering by a client computer device.

[0054] Figure 5 shows a diagram of the user interface 500 for comparing the target fleet with several simulated fleets. In Figure 5, the average fuel consumption of the entire truck fleet is compared between the target fleet and 10 simulated fleets (for example, similar to simulated fleets 410, 430, and 450). Thus, the fleet comparison engine 110 can be used to determine the relative efficiency of the target fleet with respect to the fleet manager and to identify improvement measures for the target fleet.

[0055] Figure 6 shows a diagram of the user interface 600 for comparing the target fleet with several simulated fleets. In Figure 6, the standard deviation of fuel consumption (MPG) is plotted against the fleet's average fuel consumption, indicating that the target fleet's operations can be improved by increasing the consistency of fuel consumption (e.g., improving budgeting capabilities) and improving average fuel consumption (reducing operating costs).

[0056] Figure 7 shows a diagram of a user interface 700 for comparing a target vehicle (target truck 702) with several reference vehicles (reference truck 1, reference truck 10). In Figure 7, the user interface 700 provides a visual comparison of the target vehicle from the target fleet with similar reference trucks from a simulated fleet. In this example, reference truck 10 (column 710) corresponds to the truck with the worst performance (in terms of fuel efficiency) among trucks similar to target truck 702, while reference truck 1 (column 718) corresponds to the truck with the best performance (in terms of fuel efficiency) among similar trucks. As shown in Figure 7, the fleet comparison engine 110 can highlight values ​​from corresponding vectors that are farther from target truck 702, according to a preferred distance metric, as described above. In the example shown in Figure 7, the fleet comparison engine 110 highlights the Idle Hours Pct, which indicates that the worst-performing truck (710) is idle for 28% of the time, while the best-performing truck (718) is idle for only 11% of the time. In this example, the fleet comparison engine 110 can determine improvement measures for managing the target fleet, including the target truck 702, where improvement measures may include driver training to reduce idling, rerouting delivery routes to avoid inefficient truck stoppages, adding an auxiliary power unit (APU) to provide trailer refrigeration without operating the truck's motor, and other suitable improvement measures. The fleet comparison engine 110 may also highlight different transmission types and determine improvement measures including changing the transmission from a manual transmission to an automatic transmission, or changing to trucks equipped with automatic transmissions.

[0057] Figure 8 shows a flowchart of an exemplary method 800 for identifying improvement measures for fleet management, according to an exemplary embodiment. The technical processes shown in these figures are performed automatically unless otherwise noted. In any embodiment, some steps of the process can be repeated, possibly with different parameters or data. The steps in one embodiment may be performed in a different order than the top-to-bottom order shown in Figure 8. The steps are performed sequentially, partially overlapping, or entirely in parallel. Thus, the order in which the steps of method 800 are performed may vary from one execution of the process to another. Also, steps can be omitted, combined, renamed, grouped, executed on one or more machines, or otherwise deviated from the illustrated flow, provided that the processes to be performed are operational and conform to at least one claim. The steps in Figure 8 can be performed by a fleet comparison engine 110 or other suitable computer device.

[0058] Method 800 begins with step 802. In step 802, fleet management data (e.g., vehicle data, fleet information) about the target fleet of vehicles is received. For example, the fleet comparison engine 110 may receive vehicle data from the target fleet 150 (e.g., trucks 152 or 154), fleet information from the target fleet data store 120, fleet information from the reference fleet data store 170, and / or vehicle data from the reference fleet 160. In some examples, step 802 includes receiving the maintenance history of the target fleet of vehicles from, for example, maintenance equipment 140. In some examples, the vehicle management data represents the workload of the target fleet of vehicles, and a simulated fleet of vehicles is generated to perform the workload of the target fleet of vehicles.

[0059] In step 804, a target vehicle vector is generated based on the fleet management data, representing the target vehicles of the target fleet.

[0060] In step 806, one or more similar vehicles are identified from multiple reference vehicles using a distance metric between the target vehicle vector and the multiple reference vehicle vectors. The multiple reference vehicle vectors represent multiple reference vehicles. In some examples, the distance metric is the Euclidean distance metric, the Manhattan distance metric, or the Jackard similarity metric. For example, the multiple reference vehicles and the target fleet of vehicles are sorted into multiple groups using locality-sensitive hashing (or other preferred similarity algorithms as described above). The distance between vehicles within a specific group is then calculated using the preferred distance metric. Processing time and resources are reduced because the distance metric is calculated only within a group, and not for all reference vehicles and target vehicles.

[0061] Step 808 identifies the vehicle characteristics of the target vehicle and one or more similar vehicles. Specifically, it identifies the vehicle characteristics of the target vehicle that affect its vehicle efficiency. As mentioned above, vehicle characteristics include physical characteristics and / or driving characteristics related to the target vehicle. For example, once one or more similar vehicles have been identified, the vehicle characteristics of the target vehicle and one or more similar vehicles are compared based on vehicle management data, and their relative performance is evaluated. Based on the comparison, one or more vehicle characteristics are identified as affecting the vehicle efficiency of the target vehicle. For example, the vehicle efficiency of the target vehicle is improved or altered by changing certain physical or driving characteristics that may be helpful.

[0062] In some embodiments, users should understand that they may be able to provide or select one or more vehicle characteristics of the subject vehicle in order to evaluate its relative performance compared to one or more similar vehicles. For example, if a user is particularly interested in the fuel efficiency of the subject truck, they may compare the fuel consumption rates of the subject vehicle with those of one or more similar vehicles.

[0063] Step 810 displays the vehicle characteristics that affect the vehicle efficiency of the target vehicle, along with the corresponding vehicle characteristics of one or more similar vehicles.

[0064] In some cases, Method 800 further includes determining corrective actions for managing the subject vehicle based on vehicle characteristics that affect the vehicle efficiency of the subject vehicle. Different corrective actions may be determined by the fleet comparison engine 110. Examples of corrective actions include notifying of deficiencies or delays in preventive maintenance routines to reduce mean interval between failures and / or mean recovery time; recommending aerodynamic packages (e.g., body kits) to improve fuel efficiency; installing auxiliary power units to reduce idling time; changing driver routes to avoid prolonged stops at inefficient truck stops; recommending driver training to reduce idling time; recommending further analysis of the vehicle for consideration of replacement; recommending switching to a vehicle with an automatic transmission to improve fuel efficiency; recommending switching to a vehicle with a different engine to improve fuel efficiency; and recommending tire pressure adjustments to improve tire wear.

[0065] Method 800 may further include the steps of: generating a target fleet vector representing a target fleet based on fleet management data; generating a simulated fleet vector based on the target fleet vector and a plurality of reference fleet vectors using a distance metric; and determining corrective actions for the management of the target fleet based on the simulated fleet vector. The plurality of reference fleet vectors represent a plurality of reference fleets of vehicles, and the simulated fleet vector represents a simulated fleet of vehicles. The step of generating the target fleet vector may include generating a target vehicle vector for each vehicle in the target fleet that represents that vehicle.

[0066] A target fleet vector can correspond to a target fleet vector 200. Therefore, a target fleet vector can include one or more target vehicle vectors 253, 255, etc. In some examples, a target fleet vector may include additional fleet information related to, for example, route planning, maintenance planning, fleet status, etc. Furthermore, while a simulated fleet vector can generally correspond to a target fleet vector, vehicle data from similar vehicles may be used, as described above with respect to Figures 3-5. For example, a simulated fleet vector can correspond to a simulated fleet 410, a simulated fleet 430, or other appropriate simulated fleets.

[0067] In some embodiments, vehicle management data represents the workload of the target vehicle fleet, and a simulated vehicle fleet is generated to perform the workload of the target vehicle fleet. The simulated vehicle fleet may also include at least one target vehicle from the target vehicle fleet that has the modifications indicated by the corrective action. In some embodiments, the simulated vehicle fleet has a different number of vehicles than the target vehicle fleet, and the addition of vehicles to or removal of vehicles from the target vehicle fleet is indicated by the corrective action.

[0068] In some examples, the step of generating a simulated fleet vector includes, for each vehicle in the target fleet, generating a simulated vehicle vector based on the corresponding target vehicle vector and multiple reference vehicle vectors using a distance metric.

[0069] In some examples, using a simulated fleet, the fleet comparison engine 110 can generate estimated or predicted driving parameters based on changes in the simulated fleet relative to the target fleet. In one example, the fleet comparison engine 110 can replace target vehicles in the target fleet with newer vehicles and estimate driving parameters such as fuel efficiency (fleet miles / gallon), utilization rate, and mean time between failures. Other changes include changes in vehicle engine horsepower, addition of aerodynamic packages to vehicles, adjustments to driver behavior (reducing idling), adjustments to route planning, and changes in maintenance frequency.

[0070] Figure 9 and the related description provide consideration of various operating environments in which embodiments of this disclosure can be implemented. However, the devices and systems illustrated and discussed in relation to Figure 9 are for illustrative and illustrative purposes only and do not limit the vast number of computer device configurations that can be used to implement embodiments of this disclosure as described herein.

[0071] Figure 9 is a block diagram showing the physical components (e.g., hardware) of a computer device 900 capable of carrying out aspects of this disclosure. Although one computer device is shown, it should be understood that the present invention can be carried out with multiple computer devices. The computer device components described below may have computer-executable instructions for carrying out a fleet management application 920 on a computer device (e.g., a fleet comparison engine 110), including computer-executable instructions for the fleet management application 920 that can be executed to carry out the methods disclosed herein. In a basic configuration, the computer device 900 may comprise at least one processing unit 902 and a system memory 904. Depending on the configuration and type of the computer device, the system memory 904 may include, but is not limited to, volatile memory (e.g., random access memory), non-volatile memory (e.g., read-only memory), flash memory, or any combination of such memories. The system memory 904 may include an operating system 905 and one or more program modules 906 suitable for running a fleet management application 920, such as one or more components relating to Figures 1 and 3, particularly a fleet comparison engine 921 (for example, corresponding to the fleet comparison engine 110).

[0072] The operating system 905 may be suitable, for example, for controlling the operation of the computer device 900. Furthermore, embodiments of the present disclosure can be implemented in combination with a graphics library, another operating system, or any other application program, and are not limited to a particular application or system. This basic configuration is shown in Figure 9 by the components within the dashed line 908. The computer device 900 may have additional features or functions. For example, the computer device 900 may include additional data storage devices (removable and / or non-removable), such as magnetic disks, optical disks, or tapes. Such additional storage devices are shown in Figure 9 by a removable storage device 909 and a non-removable storage device 910.

[0073] As described above, numerous program modules and data files can be stored in system memory 904. While running on processing unit 902, program module 906 (e.g., fleet management application 920) can perform processing including, but not limited to, the embodiments described herein. Other program modules that can be used, in particular to identify improvements for fleet management, according to embodiments of this disclosure may include a fleet comparison engine 921.

[0074] Furthermore, embodiments of the present disclosure can be implemented on an electrical circuit including discrete electronic elements, a packaged or integrated electronic chip including logic gates, a circuit utilizing a microprocessor, or on a single chip including electronic elements or a microprocessor. For example, embodiments of the present disclosure can be implemented via a system-on-a-chip (SOC) in which each or many of the components shown in Figure 9 can be integrated onto a single integrated circuit. Such an SOC device may include one or more processing units, graphics units, communication units, system virtualization units, and various application functions, all of which are integrated (or "burned") onto a chip substrate as a single integrated circuit. The functionality described herein regarding the ability of a client to switch protocols when operating via an SOC can be operated via application-specific logic integrated with other components of the computer device 900 on a single integrated circuit (chip). Embodiments of the present disclosure can also be implemented using other technologies capable of performing logical operations, such as AND, OR, and NOT, including but not limited to mechanical, optical, fluid, and quantum technologies. Furthermore, embodiments of the present disclosure can be implemented within a general-purpose computer or on other circuits or systems.

[0075] Furthermore, the computer device 900 may have one or more input devices 912, such as a keyboard, mouse, pen, sound or voice input device, touch or swipe input device, etc. It may also include output devices 914, such as a display, speaker, printer, etc. The above devices are examples and other devices may be used. The computer device 900 may have one or more communication connections 916 that enable communication with other computer devices 950. Examples of preferred communication connections 916 include, but are not limited to, radio frequency (RF) transmitters, receivers, and / or transceiver circuits, universal serial buses (USB), parallel ports, and / or serial ports.

[0076] As used herein, the term "computer-readable medium" may encompass computer storage mediums. Computer storage mediums may include volatile and non-volatile, removable and non-removable media implemented in any manner or technique for storing information, such as computer-readable instructions, data structures, and program modules. System memory 904, removable storage device 909, and non-removable storage device 910 are all examples of computer storage mediums (e.g., memory storage). Examples of computer storage mediums include RAM, ROM, electrically erasable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROMs, digital multi-purpose disks (DVDs) or other optical storage devices, magnetic cassettes, magnetic tapes, magnetic disk storage devices or other magnetic storage devices, or any other manufactured articles that can be used to store information and can be accessed by computer device 900. Such computer storage mediums may be part of computer device 900. Computer storage mediums do not include carrier waves or other propagating or modulated data signals.

[0077] Communication media can be embodied by computer-readable instructions, data structures, program modules, or other data within modulated data signals such as carriers or other transport mechanisms, and include any information distribution medium. The term “modulated data signal” can mean a signal having one or more characteristics that are set or modified in a manner that encodes information into the signal. Examples of communication media, though not limited to them, include wired media such as wired networks and direct wired connections, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.

[0078] The descriptions and illustrations of one or more embodiments provided herein are not intended in any way to limit or restrict the scope of the disclosure as described in the claims. The embodiments, examples, and detailed descriptions provided herein are considered sufficient to transfer ownership and enable others to manufacture and use the best embodiments of the claimed disclosure. The claimed disclosure should not be construed as being limited to any embodiments, examples, or detailed descriptions provided herein. Various features (both structural and method) whether shown and described in combination or separately are intended to be selectively included or omitted in order to produce embodiments having a particular set of features. While the descriptions and illustrations provided herein have been made, those skilled in the art can envision variations, modifications, and alternative embodiments that do not deviate from the broader scope of the claimed disclosure and belong to the spirit of broader embodiments of the general inventive concept embodied herein.

[0079] The use of any and all examples or illustrative language (e.g., "etc.") provided herein is intended solely to better illuminate the disclosed embodiments and, unless otherwise claimed, does not limit the scope of the disclosed embodiments. Numerous modifications and adaptations will be readily apparent to those skilled in the art. [Explanation of Symbols]

[0080] 100 Systems 110 Fleet Comparison Engines 120 Target fleet data stores 140 Maintenance Equipment 150 target fleets 152 trucks 153 Communication devices 154 trucks 160 Standard Fleet 162 Tracks 164 trucks 170 Standard Fleet Datastore 180 Networks

Claims

1. A method for fleet management, which is performed by a computer device. Steps include receiving fleet management data for the target fleet of vehicles; A step of generating a target fleet vector representing the target fleet of the vehicle based on the aforementioned fleet management data; A step of identifying one or more reference fleets, each containing multiple reference vehicles similar to the target vehicle of the target fleet, using a distance metric between the target fleet vector and multiple reference fleet vectors representing multiple reference vehicles of one or more reference fleets; A step of identifying vehicle characteristics of the target fleet and the one or more reference fleets that affect the vehicle characteristics selected as the vehicle efficiency of one or more target vehicles of the target fleet; and A step of displaying vehicle characteristics that affect the vehicle characteristics selected as the vehicle efficiency of one or more target vehicles in the target fleet, and the corresponding vehicle characteristics of one or more similar vehicles selected from the one or more reference fleets. A method that includes this.

2. The method according to claim 1, wherein the fleet management data includes the maintenance history of the vehicles relating to the target fleet.

3. Further comprising the step of determining improvement measures for managing the target fleet based on a comparison of vehicle characteristics that affect the vehicle characteristics selected as the vehicle efficiency of one or more target vehicles of the target fleet and the corresponding vehicle characteristics of one or more reference fleets, The method according to claim 1, wherein the improvement measures are selected to adjust the vehicle efficiency of one or more of the subject vehicles of the subject fleet to be closer to the vehicle efficiency of one or more of the reference fleet, based on future use.

4. A step of calculating a distance metric between a target fleet vector and a plurality of reference fleet vectors representing a plurality of reference fleets of vehicles to generate a simulated fleet vector representing a simulated fleet of vehicles; and The step of determining improvement measures for managing the target fleet based on the simulated fleet vector. It further includes, The aforementioned improvement measures are selected to adjust the vehicle efficiency of one or more of the subject vehicles in the subject fleet to be closer to the vehicle efficiency of one or more of the reference fleet, based on future use. The method according to claim 3, wherein the step of generating the target fleet vector includes generating a target vehicle vector for each vehicle in the target fleet that represents the vehicle.

5. The method according to claim 4, wherein the step of generating the simulated fleet vectors includes generating a simulated vehicle vector for each vehicle in the target fleet, using the distance metric, based on the corresponding target vehicle vector and the plurality of reference vehicle vectors.

6. The method according to claim 1, wherein the distance metric is the Manhattan distance metric.

7. The step of identifying one or more similar vehicles is, Using a similarity algorithm, sort the multiple reference vehicles and the target fleet of vehicles into multiple groups; and To calculate the distance metric between vehicles within the group of the aforementioned multiple groups. The method according to claim 1, including the method described in claim 1.

8. The aforementioned fleet management data represents the workload of the vehicles in the aforementioned target fleet; and The simulated fleet of vehicles generates the workload of the target fleet of vehicles in an executable manner. The method according to claim 4.

9. The method according to claim 8, wherein the simulated fleet of vehicles includes at least one target vehicle from the target fleet of vehicles having the modifications indicated by the corrective measures.

10. The simulated fleet of vehicles has a different number of vehicles than the target fleet of vehicles; and The improvement measures instruct the addition of a vehicle to the aforementioned target fleet or the removal of a vehicle from the aforementioned target fleet. The method according to claim 8.

11. A system for fleet management, Processor and A non-transient computer-readable memory having computer-readable instructions, wherein when executed by the processor, it provides to the processor, To receive fleet management data for the target fleet of vehicles; Based on the aforementioned fleet management data, a target fleet vector representing the target fleet of the vehicle is generated; Using the distance metric between the target fleet vector and multiple reference fleet vectors representing multiple reference vehicles of one or more reference fleets, one or more reference fleets containing multiple reference vehicles similar to the target vehicle of the vehicle's target fleet are identified; The vehicle characteristics of the target fleet and the one or more reference fleets, which include vehicle characteristics that affect the vehicle characteristics selected as the vehicle efficiency of one or more target vehicles of the target fleet; and A system comprising a non-transient, computer-readable memory that displays the vehicle characteristics that influence the vehicle characteristics selected as the vehicle efficiency of one or more target vehicles in the target fleet, and the corresponding vehicle characteristics of one or more similar vehicles selected from the one or more reference fleets.

12. The computer-readable instruction further provides the processor with: To receive the maintenance history of the vehicle for the aforementioned target fleet, The system according to claim 11.

13. The computer-readable instruction further provides the processor with: Based on the vehicle characteristics that affect the vehicle efficiency of one or more of the target vehicles in the target fleet and the corresponding vehicle characteristics of the one or more reference fleets, improvement measures for managing the target fleet are determined. The system according to claim 11, wherein the improvement measures are selected to adjust the vehicle efficiency of one or more of the subject vehicles of the subject fleet to be closer to the vehicle efficiency of one or more of the reference fleet, based on future use.

14. The computer-readable instruction further provides the processor with: The distance metric between the target fleet vector and multiple reference fleet vectors representing multiple reference fleets of vehicles is calculated to generate a simulated fleet vector representing a simulated fleet of vehicles; and Based on the simulated fleet vector, the system determines improvement measures for managing the target fleet. The aforementioned improvement measures are selected to adjust the vehicle efficiency of one or more of the subject vehicles in the subject fleet to be closer to the vehicle efficiency of one or more of the reference fleet, based on future use. Generating the aforementioned target fleet vector includes generating a respective target vehicle vector for each vehicle within the aforementioned target fleet, representing the vehicle. The system according to claim 13.

15. The computer-readable instruction further provides the processor with: The system according to claim 14, wherein for each vehicle in the target fleet, a simulated vehicle vector is generated from the plurality of reference fleet vectors using a distance metric based on the corresponding target vehicle vector and the plurality of reference vehicle vectors.

16. The system according to claim 11, wherein the distance metric is the Manhattan distance metric.

17. The computer-readable instruction further provides the processor with: Using a similarity algorithm, sort the multiple reference vehicles and the target fleet of vehicles into multiple groups; and To calculate the distance metric between vehicles within the group of the aforementioned multiple groups, The system according to claim 11.

18. The aforementioned fleet management data represents the workload of the vehicles in the aforementioned target fleet; and The simulated fleet of vehicles generates the workload of the target fleet of vehicles in an executable manner. The system according to claim 14.

19. The system according to claim 18, wherein the simulated fleet of vehicles includes at least one target vehicle from the target fleet of vehicles having the modifications shown by the corrective measures.

20. The simulated fleet of vehicles has a different number of vehicles than the target fleet of vehicles; and The improvement measures instruct the addition of a vehicle to the aforementioned target fleet or the removal of a vehicle from the aforementioned target fleet. The system according to claim 18.

21. A method according to claim 7, wherein the similarity algorithm includes hash functions assigned to the target fleet vector and the plurality of reference fleet vectors, the one or more reference fleets being determined based on one or more criteria of the hash function.

22. A method according to claim 21, wherein the one or more criteria include a vehicle class of one or more target vehicles of the target fleet.

23. A method according to claim 3, wherein the improvement measure includes proposing the replacement of at least one of the target vehicles in the target fleet based on the utilization rate of one or more target vehicles in the target fleet relative to the plurality of reference vehicles.

24. A method according to claim 23, wherein the utilization rate of one or more target vehicles is determined as the proportion of time that one or more target vehicles are performing useful work.

25. The system according to claim 17, wherein the similarity algorithm includes hash functions associated with the target fleet vector and the plurality of reference fleet vectors, A system in which the one or more criteria fleet is determined based on one or more criteria of the hash function.

26. The system according to claim 25, wherein one or more criteria include a vehicle class of one or more target vehicles of the target fleet.

27. ​​The system according to claim 13, wherein the improvement measure includes proposing the replacement of one or more target vehicles in the target fleet based on the utilization rate of one or more target vehicles in the target fleet relative to the plurality of reference vehicles.

28. The system according to claim 27, wherein the utilization rate of one or more target vehicles is determined as the proportion of time that one or more target vehicles are performing useful work.

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