Freight vehicle similarity calculation method and device, equipment and storage medium

Abstract

The invention discloses a freight vehicle similarity calculation method and device, equipment and a storage medium. Comprising the steps that a travel set of each vehicle is generated based on a driving track of the vehicle, and a travel in the travel set comprises a starting point longitude, a starting point latitude, an end point longitude and an end point latitude; obtaining a first travel set corresponding to the first vehicle and a second travel set corresponding to the second vehicle, and calculating the similarity between travels in the first travel set and the second travel set; calculating the similarity between the first travel set and the second travel set based on the similarity between the travels in the first travel set and the second travel set; and based on the similarity between the first travel set and the second travel set, obtaining the similarity between the two vehicles. According to the method, the vehicle freight history is converted into the travel sets, the similarity of the sets is calculated, the similarity of the vehicles in the historical freight travel is obtained, and an efficient vehicle recommendation method is provided for highway freight vehicle scheduling.

Description

Technical Field

[0001] This application relates to the field of transportation technology, and more specifically, to a method, apparatus, equipment, and storage medium for calculating the similarity of freight vehicles. Background Technology

[0002] In modern logistics, efficient dispatching of freight vehicles is crucial for reducing transportation costs and improving service efficiency. With the development of information technology, more and more logistics companies are using technologies such as GPS and GIS to collect and record historical vehicle travel data. However, extracting valuable information from historical travel data, especially accurately analyzing the similarity of travel routes among different vehicles, has always been a challenging technical problem for the industry. Summary of the Invention

[0003] This application provides a method, apparatus, device, and storage medium for calculating the similarity of freight vehicles, in order to at least solve the technical problem of difficulty in accurately analyzing the similarity of freight vehicles in related technologies.

[0004] According to one aspect of the embodiments of this application, a method for calculating the similarity of freight vehicles is provided, including: Based on the vehicle's driving trajectory, a trip set is generated for each vehicle. The trip set includes the origin longitude, origin latitude, destination longitude, and destination latitude. Obtain the first trip set corresponding to the first vehicle and the second trip set corresponding to the second vehicle, and calculate the similarity between the trips in the first trip set and the second trip set; Calculate the similarity between the trips in the first trip set and the second trip set based on the similarity between the trips in the second trip set; The similarity between the two vehicles is obtained based on the similarity between the first trip set and the second trip set.

[0005] In one implementation, a trip set for each vehicle is generated based on its driving trajectory, including: Obtain the vehicle's driving trajectory; Based on the driving trajectory, the vehicle's stop point sequence is obtained; The stop point sequence is subjected to morphological feature recognition, and the stop point sequence is divided into multiple trips based on the recognized morphological features; Based on the multiple trips, a trip set is generated for each vehicle.

[0006] In one implementation, calculating the similarity between trips in the first trip set and the second trip set includes: Calculate the distance between the starting points and the distance between the ending points of two trips in the first trip set and the second trip set; Calculate the average of the distances between the starting points and the distances between the ending points; The average value and the preset attenuation parameter are substituted into the trip similarity calculation model to obtain the similarity between trips.

[0007] In one implementation, the trip similarity calculation model is as follows:

[0008] in, This represents the average distance between the starting points and the ending points of trips i and j. This is the attenuation parameter.

[0009] In one implementation, it further includes: The distances between the starting points and the distances between the ending points are corrected. The average value of the distance between the starting points and the distance between the ending points is determined based on the corrected data.

[0010] In one implementation, calculating the similarity between the first trip set and the second trip set based on the similarity between trips in the first trip set and the second trip set includes: Based on the similarity between trips in the first trip set and the second trip set, the similarity between the first trip set and the second trip set is calculated; Based on the similarity between trips in the first trip set and the second trip set, the similarity between the second trip set and the first trip set is calculated; The average similarity of the two sets of trips is taken as the final similarity between the first and second sets of trips.

[0011] In one implementation, for each trip in the first trip set, the trip with the highest similarity in the second trip set is found, and the similarity value is recorded; The similarity between the first trip set and the second trip set is obtained based on the average similarity value of all records. For each trip in the second trip set, find the trip in the first trip set with the highest similarity and record the similarity value; The similarity between the second trip set and the first trip set is obtained based on the average similarity value of all records.

[0012] According to another aspect of the embodiments of this application, a freight vehicle similarity calculation device is provided, comprising: The trip set generation module is used to generate a trip set for each vehicle based on the vehicle's driving trajectory. The trip set includes the origin longitude, origin latitude, destination longitude, and destination latitude. The trip similarity calculation module is used to obtain the first trip set corresponding to the first vehicle and the second trip set corresponding to the second vehicle, and to calculate the similarity between the trips in the first trip set and the second trip set; The trip set similarity calculation module is used to calculate the similarity between the first trip set and the second trip set based on the similarity between the trips in the first trip set and the second trip set; The vehicle similarity calculation module is used to obtain the similarity between two vehicles based on the similarity between the first trip set and the second trip set.

[0013] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the above-described freight vehicle similarity calculation method through the computer program.

[0014] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer program, which is configured to execute the above-described freight vehicle similarity calculation method when running.

[0015] The technical solutions provided in this application embodiment may include the following beneficial effects: This application provides an innovative vehicle similarity calculation method for the field of highway freight vehicle scheduling by generating trip sets based on vehicle driving trajectories and calculating the similarity between historical trip sets. This method transforms the vehicle's transportation history into a structured trip set, making similarity calculation more accurate and efficient. By comparing the trip sets of two vehicles, vehicles with similar transportation patterns can be quickly identified, thus providing a scientific basis for vehicle recommendation and scheduling and reducing operating costs. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart of an optional method for calculating the similarity of freight vehicles according to an embodiment of this application; Figure 2 This is a schematic diagram of a vehicle similarity calculation method according to an embodiment of this application; Figure 3 This is a flowchart of a one-way trip set similarity calculation according to an embodiment of this application; Figure 4 This is a schematic diagram of a freight vehicle similarity calculation device according to an embodiment of this application; Figure 5 This is a schematic diagram of the structure of an optional electronic device according to an embodiment of this application. Detailed Implementation

[0017] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0018] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0019] The following is in conjunction with the appendix Figure 1-5 The method for calculating the similarity of freight vehicles according to embodiments of this application is described in detail. For example... Figure 1 As shown, the method mainly includes the following steps: S101 generates a trip set for each vehicle based on its driving trajectory. The trip set includes the origin longitude, origin latitude, destination longitude, and destination latitude.

[0020] In one implementation, a set of trips for each vehicle is generated based on its driving trajectory. First, the driving trajectory of the vehicle is obtained, and based on the driving trajectory, a sequence of vehicle stop points is obtained.

[0021] Vehicle trajectory data is collected using technologies such as GPS. Then, the trajectory data is analyzed, and features such as speed changes and dwell time are used to identify vehicle stop points. Next, the identified stop points are arranged in chronological order to form a vehicle stop point sequence. This sequence provides the foundational data for subsequent trip set generation and similarity calculations.

[0022] Furthermore, morphological feature recognition is performed on the stop point sequence. Based on the identified morphological features, the stop point sequence is divided into multiple trips. Based on the multiple trips, a trip set for each vehicle is generated.

[0023] Morphological feature analysis is performed on the acquired vehicle stop sequence to identify key features such as stop duration, speed changes, and path inflection points. Using these features, the stop sequence is segmented using morphological methods to divide it into individual trips. Specifically, thresholds can be set; for example, a point is considered a trip boundary when a vehicle's dwell time exceeds a preset value or its speed change reaches a certain level. Subsequently, based on the segmented trip information, including the latitude and longitude of each trip's start and end points, a trip set for each vehicle is generated. This set records the vehicle's driving path and stop details in detail, providing accurate data support for subsequent trip similarity analysis and vehicle recommendation.

[0024] For each vehicle Generate its itinerary set: ; in, Indicates vehicle The i-th journey is represented by the starting longitude, starting latitude, ending longitude, and ending latitude.

[0025] S102 Obtain the first trip set corresponding to the first vehicle and the second trip set corresponding to the second vehicle, and calculate the similarity between the trips in the first trip set and the second trip set.

[0026] For any two vehicles, obtain the first set of trips corresponding to the first vehicle and the second set of trips corresponding to the second vehicle.

[0027] Furthermore, calculate the distance between the starting points and the distance between the ending points of the two trips in the first trip set and the second trip set.

[0028] For any vehicle , Each can obtain its historical itinerary set. , For any two sets... , The geographical distance between the starting point and the ending point is calculated using the Haversine formula:

[0029] in, , For vehicles The latitude and longitude of the starting or ending point of the i-th trip. , For vehicles The latitude and longitude of the starting or ending point of the j-th trip.

[0030] Furthermore, calculate the average of the distances between the starting points and the ending points.

[0031] Furthermore, the average value and the preset attenuation parameter are substituted into the trip similarity calculation model to obtain the similarity between trips.

[0032] In one implementation, the trip similarity calculation model is as follows:

[0033] in, This represents the average distance between the starting points and the ending points of trips i and j. This is an attenuation parameter. If set to 200 kilometers, it affects the half-life of similarity and can be adjusted according to the scenario.

[0034] In an optional implementation, the method further includes correcting the distance between the starting points and the distance between the ending points; and determining the average value of the distance between the starting points and the distance between the ending points based on the corrected data.

[0035] Because the vehicle dispatching range for road freight is relatively large—for example, the distance between the starting points of trips i and j is 30 kilometers, and the distance between their ending points is 40 kilometers—freight scenarios generally assume that the two trips are completely similar. Therefore, to achieve the desired effect, the distance needs to be adjusted using the following formula:

[0036] in, This represents the distance without attenuation. The meaning of the correction result is that when the actual distance between the start and end points is less than... When the distance is zero, the two points coincide.

[0037] S103 calculates the similarity between the first set of trips and the second set of trips based on the similarity between the trips in the first set of trips and the second set of trips.

[0038] In this embodiment of the application, the two-way averaging method is used to calculate the similarity between two trip sets.

[0039] Specifically, based on the similarity between the trips in the first trip set and the second trip set, the similarity between the first trip set and the second trip set is calculated; based on the similarity between the trips in the first trip set and the second trip set, the similarity between the second trip set and the first trip set is calculated; the average of the similarities of the two trip sets is taken as the final similarity between the first trip set and the second trip set.

[0040] Specifically, for each trip in the first trip set, the trip with the highest similarity in the second trip set is found, and the similarity value is recorded; based on the average of the similarity values ​​of all records, the similarity between the first trip set and the second trip set is obtained.

[0041] For each trip in the second trip set, find the trip in the first trip set with the highest similarity and record the similarity value; based on the average of the similarity values ​​of all records, obtain the similarity between the second trip set and the first trip set.

[0042] In one embodiment, → Direction: For sets For each route in the sequence, find the set. The route with the highest similarity is selected and its similarity is recorded. Then, the average of all recorded similarities is calculated and denoted as . right The similarity.

[0043] → Direction: Similarly, for sets For each route in the sequence, find the set. The route with the highest similarity is selected and its similarity is recorded. Then, the average of all routes is taken and denoted as _____. right The similarity.

[0044] Furthermore, the average of the similarities in the two directions is taken as the final result, and the specific formula is as follows:

[0045] in, That is, vehicles and vehicles Similarity of itinerary sets. Indicates vehicle Itinerary collection Any journey With vehicles Itinerary collection The set of trip similarity scores obtained from all trips is of size n. Indicates vehicle Itinerary collection Any journey With vehicles Itinerary collection The set of trip similarity scores obtained from all trips is of size m; m and n represent the trip similarity scores respectively. and Size.

[0046] S104 calculates the similarity between two vehicles based on the similarity between the first trip set and the second trip set.

[0047] The similarity between two vehicles is determined by the similarity between their historical travel routes.

[0048] Furthermore, based on the similarity between vehicles, a vehicle recommendation service is provided to cargo owners or dispatchers to help them quickly select suitable vehicles.

[0049] For example, if vehicle A and vehicle B have similar trip sets, and vehicle A is unable to fulfill the transportation task, vehicle B can be recommended to join the transportation task as well.

[0050] To facilitate understanding of the methods in the embodiments of this application, the following description is provided in conjunction with the appendix. Figure 2 Further description. For example... Figure 2 The diagram illustrates a method for calculating the similarity of a set of vehicle trips.

[0051] First, generate a trip set for each vehicle. For each vehicle, generate a trip set T containing all its trip information. Iterate through set T to obtain the trip set Ti for the current vehicle. Then, iterate through set T again to obtain the trip set Tj for the current vehicle.

[0052] To determine if j is greater than i, check the indices i and j of the two trip sets to ensure that the same trip pair is not counted repeatedly. If j is not greater than i, return to the step of obtaining Tj and continue to select the next trip set for comparison.

[0053] If j is greater than i, then calculate the similarity between trip set Ti and trip set Tj. And calculate the similarity between trip set Tj and trip set Ti.

[0054] Calculate the overall similarity Sij between vehicle i and vehicle j: Combine the similarity between Ti and Tj and Tj and Ti to calculate the overall similarity Sij between vehicle i and vehicle j.

[0055] Output: Output the calculated overall similarity Sij.

[0056] Check if the similarity of all possible vehicle pairs in set T has been calculated. If it is not equal to the size of set T, return to the step of obtaining Tj and continue processing the next vehicle pair. If the similarity of all possible vehicle pairs in set T has been calculated, the process ends.

[0057] like Figure 3 As shown, a method for calculating the similarity between sets of one-way trips is presented.

[0058] For each trip in set Ta, initialize its maximum similarity with all trips in set Tb as sim_ai_max.

[0059] Traverse set Ta: Get the current itinerary t_ai in set Ta.

[0060] Traverse set Tb: Get the current trip t_bj in set Tb.

[0061] Calculate similarity: Calculate the similarity sim_ai_bj between trips t_ai and t_bj.

[0062] Update the maximum similarity: If sim_ai_bj is greater than sim_ai_max, then update sim_ai_max to sim_ai_bj.

[0063] Check set size: Check if all trips in set Tb have been traversed. If not, continue traversing; if all trips have been traversed, check if all trips in set Ta have been traversed.

[0064] Check the size of set Ta: If all journeys in set Ta have been traversed, then calculate the average of all sim_ai_max to obtain the set similarity between set Ta and set Tb.

[0065] This application provides an innovative vehicle similarity calculation method for the field of highway freight vehicle scheduling by generating trip sets based on vehicle travel trajectories and calculating the similarity between trip sets. This method transforms a vehicle's transportation history into a structured trip set, making similarity calculation more accurate and efficient. By comparing the trip sets of two vehicles, vehicles with similar transportation patterns can be quickly identified, thus providing a scientific basis for vehicle recommendation and scheduling. This not only improves the accuracy and efficiency of vehicle scheduling but also helps optimize resource allocation, reduce empty runs and waiting time, and lower operating costs.

[0066] According to another aspect of the embodiments of this application, a freight vehicle similarity calculation apparatus for implementing the above-described freight vehicle similarity calculation method is also provided. For example... Figure 4 As shown, the device includes: The trip set generation module 601 is used to generate a trip set for each vehicle based on the vehicle's driving trajectory. The trip set includes the origin longitude, origin latitude, destination longitude, and destination latitude. The trip similarity calculation module 602 is used to obtain the first trip set corresponding to the first vehicle and the second trip set corresponding to the second vehicle, and to calculate the similarity between the trips in the first trip set and the second trip set; The trip set similarity calculation module 603 is used to calculate the similarity between the first trip set and the second trip set based on the similarity between the trips in the first trip set and the second trip set; The vehicle similarity calculation module 604 is used to obtain the similarity between two vehicles based on the similarity between the first trip set and the second trip set.

[0067] It should be noted that the freight vehicle similarity calculation device provided in the above embodiments is only illustrated by the division of the above functional modules when executing the freight vehicle similarity calculation method. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the freight vehicle similarity calculation device and the freight vehicle similarity calculation method embodiments provided in the above embodiments belong to the same concept, and the implementation process is detailed in the method embodiments, which will not be repeated here.

[0068] According to another aspect of the embodiments of this application, an electronic device corresponding to the freight vehicle similarity calculation method provided in the foregoing embodiments is also provided to execute the freight vehicle similarity calculation method described above.

[0069] Please refer to Figure 5 This illustrates a schematic diagram of an electronic device provided by some embodiments of this application. For example... Figure 5 As shown, the electronic device includes: a processor 700, a memory 701, a bus 702, and a communication interface 703. The processor 700, the communication interface 703, and the memory 701 are connected via the bus 702. The memory 701 stores a computer program that can run on the processor 700. When the processor 700 runs the computer program, it executes the freight vehicle similarity calculation method provided in any of the foregoing embodiments of this application.

[0070] The memory 701 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 703 (which can be wired or wireless), such as the Internet, wide area network, local area network, or metropolitan area network.

[0071] Bus 702 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. Memory 701 is used to store programs. After receiving execution instructions, processor 700 executes the program. The freight vehicle similarity calculation method disclosed in any of the aforementioned embodiments of this application can be applied to processor 700, or implemented by processor 700.

[0072] The processor 700 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 700 or by instructions in software form. The processor 700 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 701. Processor 700 reads the information in memory 701 and, in conjunction with its hardware, completes the steps of the above method.

[0073] The electronic device provided in this application embodiment and the freight vehicle similarity calculation method provided in this application embodiment are based on the same inventive concept and have the same beneficial effects as the methods they adopt, operate or implement.

[0074] According to another aspect of the embodiments of this application, a computer-readable storage medium corresponding to the freight vehicle similarity calculation method provided in the foregoing embodiments is also provided, wherein a computer program (i.e., a program product) is stored thereon, and when the computer program is run by a processor, it executes the freight vehicle similarity calculation method provided in any of the foregoing embodiments.

[0075] It should be noted that examples of computer-readable storage media may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0076] The computer-readable storage medium provided in the above embodiments of this application and the freight vehicle similarity calculation method provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application stored therein.

[0077] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0078] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

Claims

1. A freight vehicle similarity calculation method characterized by, The method comprises the following steps: Based on the driving track of the vehicle, a trip set of each vehicle is generated, and the trips in the trip set include the starting longitude, the starting latitude, the ending longitude and the ending latitude; Obtain the first trip set corresponding to the first vehicle and the second trip set corresponding to the second vehicle, and calculate the similarity between the trips in the first trip set and the second trip set; Based on the similarity between the trips in the first trip set and the second trip set, the similarity of the first trip set and the second trip set is calculated; Based on the similarity of the first trip set and the second trip set, the similarity between the two vehicles is obtained.

2. The method of claim 1, wherein, Based on the driving track of the vehicle, a trip set of each vehicle is generated, comprising: Obtaining the driving track of the vehicle; Based on the driving track, the stop point sequence of the vehicle is obtained; The shape feature of the stop point sequence is recognized, and the stop point sequence is divided into multiple trips according to the recognized shape feature; Based on the multiple trips, a trip set of each vehicle is generated.

3. The method of claim 1, wherein, The similarity between the trips in the first trip set and the second trip set is calculated, comprising: The distance between the starting points and the distance between the ending points of two trips in the first trip set and the second trip set are calculated; The average value of the distance between the starting points and the distance between the ending points is calculated; The average value and the preset attenuation parameter are brought into the trip similarity calculation model to obtain the similarity between the trips.

4. The method of claim 3, wherein, The trip similarity calculation model is as follows: wherein, wherein, d represents the average of the start-to-start and end-to-end distances for trip i, j, and σ is a decay parameter.

5. The method according to claim 3 or 4, characterized in that, Further comprising: The distance between the starting points and the distance between the ending points are corrected; The average value of the distance between the starting points and the distance between the ending points is determined based on the corrected data.

6. The method of claim 1, wherein, Based on the similarity between the trips in the first trip set and the second trip set, the similarity of the first trip set and the second trip set is calculated, comprising: Based on the similarity between the trips in the first trip set and the second trip set, the similarity of the first trip set relative to the second trip set is calculated; Based on the similarity between the trips in the first trip set and the second trip set, the similarity of the second trip set relative to the first trip set is calculated; The average value of the similarity of the two direction trip sets is taken as the final similarity of the first trip set and the second trip set.

7. The method of claim 6, wherein: For each trip in the first trip set, find the trip in the second trip set with the highest similarity, and record the similarity value; Based on the average value of all recorded similarity values, the similarity of the first trip set relative to the second trip set is obtained; For each trip in the second trip set, find the trip in the first trip set with the highest similarity, and record the similarity value; Based on the average value of all recorded similarity values, the similarity of the second trip set relative to the first trip set is obtained.

8. A freight vehicle similarity calculation device characterized by comprising: The method comprises the following steps: A trip set generation module is used to generate a trip set of each vehicle based on the driving track of the vehicle, and the trips in the trip set include the starting longitude, the starting latitude, the ending longitude and the ending latitude; The journey similarity calculation module is configured to obtain a first journey set corresponding to the first vehicle and a second journey set corresponding to the second vehicle, and calculate the similarity between the journeys in the first journey set and the second journey set. The journey set similarity calculation module is configured to calculate the similarity between the first journey set and the second journey set based on the similarity between the journeys in the first journey set and the second journey set. The vehicle similarity calculation module is configured to obtain the similarity between the two vehicles based on the similarity between the first journey set and the second journey set.

9. An electronic device, comprising: The vehicle similarity calculation module is configured to obtain the similarity between the two vehicles based on the similarity between the first journey set and the second journey set.

10. A computer readable medium characterized by The vehicle similarity calculation module is configured to obtain the similarity between the two vehicles based on the similarity between the first journey set and the second journey set. The vehicle similarity calculation module is configured to obtain the similarity between the two vehicles based on the similarity between the first journey set and the second journey set.

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