Efficient travel allocation system driven by dynamic car rental data

Abstract

The invention relates to the technical field of car rental, and discloses a dynamic car rental data-driven efficient travel allocation system, which comprises a region division module, a car rental environment evaluation module, a car matching module and a dynamic allocation module, and is characterized in that the region division module is used for dividing a current city into a plurality of regions according to a current city layout; the vehicle rental environment evaluation module is used for calculating the regional demand degree to evaluate the vehicle delivery condition in the region, and the vehicle matching module is used for calculating the vehicle behavior index according to the vehicle behavior data to judge whether the vehicle needs to be maintained or not; the dynamic allocation module is used for matching optimal rental vehicle information according to a vehicle adaptation degree calculation result and sending the optimal rental vehicle information to the user, so that resource utilization maximization and service efficiency optimization are realized; the problems that no vehicle can be used in peak periods and vehicles are idle in valley periods are effectively solved.

Description

Technical Field

[0001] The present invention relates to the field of car rental technology, and in particular to an efficient travel allocation system driven by dynamic car rental data. Background Art

[0002] Car rental is a service that meets temporary vehicle needs through leasing. Its roots lie in modern society's pursuit of flexible and efficient travel options. With accelerating urbanization, increasing population mobility, and shifting consumer attitudes, more and more people are embracing an asset-light lifestyle, avoiding the high fixed costs associated with car ownership. Simultaneously, the booming sharing economy and digital technologies are providing technological support for the car rental industry, enabling it to gradually transform from a traditional offline model to a convenient online and offline integrated service. The development of car rental has undergone a profound transformation, from traditional offline rental to the internet-based sharing economy. Initially relying on offline storefronts, shared car rental platforms have emerged in recent years, leveraging algorithms to match idle individual vehicles with renters, reducing costs and expanding supply. In China, various travel companies are also expanding into the car rental business, integrating big data and intelligent technologies to optimize the user experience. In the future, the car rental industry will evolve towards electrification, intelligence, and globalization, becoming a more integrated part of people's daily travel ecosystem.

[0003] Currently, most car rental systems allocate vehicles based solely on geographical distance, lack effective forecasts of regional demand fluctuations, and are unable to deploy vehicles in advance, resulting in problems such as no vehicles available during peak periods and idle vehicles during off-peak periods. Summary of the Invention

[0004] (1) Technical problems solved

[0005] In response to the shortcomings of the existing technology, the present invention provides an efficient travel allocation system driven by dynamic car rental data. It has the advantages of maximizing resource utilization and optimizing service efficiency through data-driven decision-making, effectively alleviating the problems of no cars available during peak periods and idle vehicles during off-peak periods.

[0006] (2) Technical solution

[0007] To achieve the above objectives, the present invention provides the following technical solutions: an efficient travel allocation system driven by dynamic car rental data, including a regional division module, a car rental environment assessment module, a vehicle matching module and a dynamic allocation module.

[0008] The region division module is used to divide the current city into multiple regions according to the Beidou positioning system and the current city layout, and mark and number the multiple regions to form a unified data format;

[0009] The car rental environment assessment module is used to collect car rental environment data in each area, and calculate the regional demand based on the car rental environment data to evaluate the vehicle deployment situation in the area;

[0010] The vehicle matching module is used to collect vehicle behavior data and user demand data of each region, and calculate a vehicle behavior index according to the vehicle behavior data to determine whether the vehicle needs to be maintained, and calculate a vehicle adaptation degree according to the vehicle behavior data and the user demand data;

[0011] The dynamic allocation module is used to send the best car rental vehicle information to the user according to the vehicle adaptation degree calculation result.

[0012] Preferably, the numbering expression for marking and numbering the multiple regions is: Q1, Q2, Q3, ···, Qn. n Q1 represents the number of the first region after division, Q2 represents the number of the second region after division, and Qn represents the number of the nth region after division. n n represents the total number of regions in the current city.

[0013] Preferably, the car rental environment data includes historical car rental demand Hlx of the current region, available car rental amount Hky of the current region, outside population growth Hrz of the current region, private car growth Hcz of the current region, public transportation coverage Hjf of the current region, mobile payment popularization rate Hyp of the current region, road congestion index Hly of the current region, and parking index Htz of the current region.

[0014] Preferably, the calculation formula of the region demand degree is:

[0015]

[0016] In the formula, QYxq represents the calculated value of the region demand degree; 1-Hjf represents the influence of the public transportation deficiency coefficient on car rental; 1+Hcz+Hly represents the comprehensive influence of the private car and road congestion on car rental; and 1+Htz represents the influence of the parking difficulty coefficient on car rental.

[0017] Hlx*Hrz*(1-Hjf)*Hyp represents the promotion influence of historical demand, outside population, public transportation deficiency, and payment convenience on car rental demand.

[0018] Hky*(1+Hcz+Hly)*(1+Htz) represents the inhibition influence of available vehicles, private car growth, road congestion, and parking difficulty on car rental demand.

[0019] Preferably, when QYxq>1, it represents that the vehicle deployment in the current region is unreasonable, and there is a situation of insufficient vehicle deployment; when 0.8≤QYxq≤1, it represents that the vehicle deployment in the current region is reasonable; and when QYxq<0.8, it represents that the vehicle deployment in the current region is unreasonable, and there is a situation of excessive vehicle deployment.

[0020] When there is unreasonable vehicle delivery, the current area available rental amount is dynamically adjusted until the calculated value of the area demand is reasonable.

[0021] Preferably, the vehicle behavior data includes vehicle position data Cwz, vehicle basic configuration data Cpz, vehicle driving mileage Cxs, vehicle energy consumption data Cnh, vehicle failure rate Cgz, and vehicle usage frequency Ccs.

[0022] The user demand data includes user position Ywz, expected configuration Ypz, user estimated rental duration Ysc, user estimated driving mileage Ylc, and user historical rental credit Yxy.

[0023] Preferably, the calculation formula of the vehicle behavior index is as follows:

[0024]

[0025] In the calculation formula, CLxw represents the calculation result of the vehicle behavior index; Cxsp represents the average vehicle energy consumption data; Cgzp represents the average vehicle failure rate; and Ccs represents the average vehicle usage frequency.

[0026] Preferably, when the calculation result of the vehicle behavior index is greater than the vehicle behavior index threshold value, it is determined that the vehicle needs to be maintained, and the position information of the vehicle is extracted for feedback.

[0027] Preferably, the calculation formula of the vehicle adaptation degree is as follows:

[0028]

[0029] In the calculation formula, CLsp represents the calculation result of the vehicle adaptation degree. represents the coincidence index of the vehicle basic configuration data and the expected configuration; Ywz-Cwz represents the distance between the vehicle and the user; Ylcmax represents the maximum value of the user's estimated rental duration; and Yscmax represents the maximum value of the user's estimated driving mileage.

[0030] Preferably, the dynamic allocation module calculates the vehicle adaptation degree of each vehicle with the user according to the vehicle of the current area, marks the vehicle with the highest vehicle adaptation degree as the best rental vehicle, and extracts the position information of the best rental vehicle to send to the user.

[0031] Compared with the prior art, the present application provides a dynamic rental data-driven efficient travel allocation system, which has the following beneficial effects:

[0032] The application solves the problem that the current car rental system only relies on geographical distance to allocate vehicles and lacks regional demand prediction and dynamic allocation capability by multi-module cooperation. Firstly, the regional division module divides the city into numbered regions to provide a unified framework for subsequent data analysis and resource allocation. Secondly, the car rental environment evaluation module collects multi-dimensional data, quantifies regional demand through formulas, dynamically evaluates the rationality of vehicle deployment, and avoids vehicle shortage during peak periods or idling during trough periods. Thirdly, the vehicle matching module calculates vehicle behavior index and adaptation based on vehicle behavior data and user demand data to ensure that the vehicle state is good and accurately matches user demand. Finally, the dynamic allocation module calculates the best vehicle based on the adaptation and real-time feedback of location information to prioritize the allocation of vehicles that best meet user demand. Through data-driven decision-making, resource utilization maximization and service efficiency optimization are achieved, effectively solving the problems of no available cars during peak periods and idle vehicles during trough periods. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 The figure is a schematic diagram of the system. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0035] Please refer to Figure 1 , the dynamic car rental data-driven efficient travel allocation system includes a regional division module, a car rental environment evaluation module, a vehicle matching module, and a dynamic allocation module.

[0036] The regional division module is used to divide the current city into multiple regions according to the Beidou positioning system and the current city layout, and mark and number the multiple regions to form a unified data format.

[0037] The numbering expression is: Q1, Q2, Q3,..., Q n , wherein Q1 represents the number of the first region after division, Q n represents the number of the n-th region after division, and n represents the total number of regions divided in the current city.

[0038] By dividing the city into multiple numbered regions, standardized and fine-grained management of data is achieved, providing a unified framework for subsequent car rental environment evaluation, vehicle matching, and dynamic allocation. By clearly defining the regions, efficient resource allocation is supported, avoiding data confusion and resource waste, and providing a key foundation for the dynamic car rental system to achieve accurate operation and efficient service.

[0039] The car rental environment evaluation module is used to collect car rental environment data of each area and evaluate the vehicle deployment in the area according to the calculation of the area demand degree based on the car rental environment data;

[0040] The car rental environment data includes historical car rental demand Hlx of the current area, available car rental Hky of the current area, outside population growth Hrz of the current area, private car growth Hcz of the current area, public transportation coverage Hjf of the current area, mobile payment popularization rate Hyp of the current area, road congestion index Hly and parking index Htz of the current area;

[0041] The calculation formula of the area demand degree is:

[0042]

[0043] In the formula, QYxq represents the calculation value of the area demand degree; 1-Hjf represents the influence of the public transportation deficiency coefficient on car rental; 1+Hcz+Hly represents the comprehensive influence of the private car and road congestion on car rental; and 1+Htz represents the influence of the parking difficulty coefficient on car rental.

[0044] Hlx*Hrz*(1-Hjf)*Hyp represents the promotion influence of the historical demand, outside population, public transportation deficiency and payment convenience on car rental demand.

[0045] Hky*(1+Hcz+Hly)*(1+Htz) represents the inhibition influence of the available vehicle, private car growth, road congestion and parking difficulty on car rental demand.

[0046] When QYxq>1, it represents that the vehicle deployment in the current area is unreasonable, and there is a situation of insufficient vehicle deployment; when 0.8≤QYxq≤1, it represents that the vehicle deployment in the current area is reasonable; and when QYxq<0.8, it represents that the vehicle deployment in the current area is unreasonable, and there is a situation of excessive vehicle deployment.

[0047] When there is an unreasonable vehicle deployment, the available car rental of the current area is dynamically adjusted until the calculation value of the area demand degree is reasonable.

[0048] The car rental environment evaluation module realizes the dynamic evaluation of the rationality of vehicle deployment by collecting multi-dimensional data and quantifying the area demand degree based on the formula, realizes the accurate judgment of whether the area vehicle deployment is reasonable by balancing the factors of promoting demand and inhibiting demand, and thus provides data support for the dynamic deployment of vehicles, avoids the situation of no available car in peak period or idle resources in trough period, and improves the operation efficiency and user satisfaction.

[0049] The vehicle matching module is used to collect vehicle behavior data and user demand data of each region, and calculate a vehicle behavior index according to the vehicle behavior data to determine whether the vehicle needs to be maintained, and calculate a vehicle adaptation degree according to the vehicle behavior data and the user demand data;

[0050] The vehicle behavior data includes vehicle position data Cwz, vehicle basic configuration data Cpz, vehicle driving mileage Cxs, vehicle energy consumption data Cnh, vehicle failure rate Cgz, and vehicle use frequency Ccs.

[0051] The user demand data includes user position Ywz, expected configuration Ypz, user estimated rental duration Ysc, user estimated driving mileage Ylc, and user historical rental credit Yxy.

[0052] The calculation formula of the vehicle behavior index is:

[0053]

[0054] In the calculation formula, CLxw represents the calculation result of the vehicle behavior index, Cxsp represents the average vehicle energy consumption data, Cgzp represents the average vehicle failure rate, and Ccs represents the average vehicle use frequency.

[0055] When the calculation result of the vehicle behavior index is greater than a vehicle behavior index threshold value, it is determined that the vehicle needs to be maintained, and the position information of the vehicle is extracted for feedback.

[0056] The calculation formula of the vehicle adaptation degree is:

[0057]

[0058] In the calculation formula, CLsp represents the calculation result of the vehicle adaptation degree. represents a coincidence index of the vehicle basic configuration data and the expected configuration, Ywz-Cwz represents the distance between the vehicle and the user, Ylcmax represents the maximum user estimated rental duration, and Yscmax represents the maximum user estimated driving mileage.

[0059] The vehicle matching module collects vehicle behavior data and user demand data, and calculates a vehicle behavior index and a vehicle adaptation degree, thereby realizing real-time monitoring of the vehicle state and accurate matching of user demand, determining whether the vehicle needs to be maintained through the vehicle behavior index, ensuring the safety and reliability of the vehicle, calculating the vehicle adaptation degree to consider multiple factors and assign the most suitable vehicle to the user, improving user experience and operation efficiency, optimizing vehicle resource utilization, and prolonging the service life of the vehicle through a dynamic feedback mechanism, thereby providing data-driven decision support for an efficient travel allocation system.

[0060] The dynamic allocation module calculates the vehicle adaptation degree of each vehicle with the user according to the vehicle of the current area, marks the vehicle with the highest vehicle adaptation degree as the best rental vehicle, and extracts the position information of the best rental vehicle and sends it to the user;

[0061] The dynamic allocation module marks and extracts the position information of the best rental vehicle and sends it to the user, realizes accurate matching of user demand and vehicle resources, preferentially allocates vehicles that best meet the user's preferences, reduces waiting time and improves satisfaction, optimizes vehicle utilization, balances regional supply and demand, avoids resource waste, and provides real-time and intelligent allocation support for efficient travel allocation systems.

[0062] Although embodiments of the present application have been shown and described, it will be understood by those having ordinary skill in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An efficient travel coordination system driven by dynamic car rental data, featuring: Including area division module, car rental environment assessment module, vehicle matching module and dynamic allocation module; The region division module is used to divide the current city into multiple regions according to the Beidou positioning system and the current city layout, and mark and number the multiple regions to form a unified data format; The car rental environment assessment module is used to collect car rental environment data in each area, and calculate the regional demand based on the car rental environment data to evaluate the vehicle deployment situation in the area; The vehicle matching module is used to collect vehicle behavior data and user demand data in each area, and calculate the vehicle behavior index based on the vehicle behavior data to determine whether the vehicle needs maintenance, and calculate the vehicle compatibility based on the vehicle behavior data and user demand data; The dynamic allocation module is used to match the best rental vehicle information according to the vehicle suitability calculation result and send it to the user.

2. The dynamic car rental data-driven efficient travel coordination system according to claim 1, characterized in that: The numbering expression for marking and numbering the multiple regions is: Q1, Q2, Q3, . . ., Q n , where Q1 represents the number of the first area after division, Q n Represents the number of the nth area after division, and n represents the total number of areas divided into the current city.

3. The dynamic car rental data-driven efficient travel coordination system according to claim 2, characterized in that: The car rental environment data includes the historical car rental demand Hlx in the current area, the available car rental volume Hky in the current area, the growth of foreign population in the current area Hrz, the growth of private cars in the current area Hcz, the public transportation coverage rate Hjf in the current area, the mobile payment penetration rate Hyp in the current area, the road congestion index Hly and the parking index Htz.

4. The dynamic car rental data-driven efficient travel coordination system according to claim 3 is characterized by: The calculation formula for the regional demand is: In the formula, QYxq represents the calculated value of regional demand; 1-Hjf represents the impact of the public transportation shortage coefficient on car rental; 1+Hcz+Hly represents the combined impact of private cars and road congestion on car rental; 1+Htz represents the impact of the parking difficulty coefficient on car rental; Hlx*Hrz*(1-Hjf)*Hyp represents the impact of historical demand, influx of foreign population, insufficient public transportation, and convenient payment on the demand for car rentals; Hky*(1+Hcz+Hly)*(1+Htz) represents the inhibitory effect of available vehicles, private car growth, road congestion, and parking difficulty on car rental demand.

5. The dynamic car rental data-driven efficient travel coordination system according to claim 4 is characterized by: When QYxq>1, it means that the vehicle deployment in the current area is unreasonable and there is a shortage of vehicles; when 0.8≤QYxq≤1, it means that the vehicle deployment in the current area is reasonable; when QYxq<0.8, it means that the vehicle deployment in the current area is unreasonable and there is an over-deployment of vehicles; When there is unreasonable deployment of vehicles, the available rental car quantity in the current area will be dynamically adjusted until the calculated value of regional demand is reasonable.

6. The dynamic car rental data-driven efficient travel coordination system according to claim 5, characterized in that: The vehicle behavior data includes vehicle location data Cwz, vehicle basic configuration data Cpz, vehicle mileage Cxs, vehicle energy consumption data Cnh, vehicle failure rate Cgz, and vehicle usage times Ccs; The user demand data includes user location Ywz, desired configuration Ypz, user expected car rental duration Ysc, user expected mileage Ylc, and user historical car rental credibility Yxy.

7. The dynamic car rental data-driven efficient travel coordination system according to claim 6, characterized in that: The calculation formula of the vehicle behavior index is: In the calculation formula, CLxw represents the calculation result of the vehicle behavior index; Cxsp represents the average energy consumption data of the vehicle; Cgzp represents the average vehicle failure rate; Ccs represents the average number of times a vehicle is used.

8. The dynamic car rental data-driven efficient travel coordination system according to claim 7, characterized in that: When the calculated result of the vehicle behavior index is greater than the vehicle behavior index threshold, it is determined that the vehicle needs maintenance, and the location information of the vehicle is extracted for feedback.

9. The dynamic car rental data-driven efficient travel coordination system according to claim 8, characterized in that: The calculation formula of the vehicle adaptability is: In the calculation formula, CLsp represents the calculation result of vehicle fitness; Represents the compliance index between the basic configuration data of the vehicle and the expected configuration; Ywz-Cwz represents the distance between the vehicle and the user; Ylcmax represents the maximum expected rental time of the user; Yscmax represents the maximum expected mileage of the user.

10. The dynamic car rental data-driven efficient travel coordination system according to claim 9, characterized in that: The dynamic allocation module calculates the vehicle compatibility between each vehicle and the user based on the vehicles in the current area, marks the vehicle with the highest vehicle compatibility as the best rental vehicle, and extracts the location information of the best rental vehicle and sends it to the user.

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