A web-based car-hailing dispatching management platform

Abstract

This invention relates to the field of ride-hailing dispatch management and discloses a ride-hailing dispatch management platform, including an online vehicle monitoring module, an order module, and an analysis module. The online vehicle monitoring module acquires historical and real-time vehicle data for each online vehicle; the order module receives real-time order information within the dispatch area; the analysis module analyzes the historical data to obtain the online index, on-time index, and violation index for each online vehicle; further, by analyzing the online index, on-time index, violation index, real-time order information, and real-time vehicle data for each online vehicle, a comprehensive evaluation index is obtained. A higher comprehensive evaluation index indicates that the ride-hailing driver of that online vehicle performs excellently in all aspects; therefore, when assigning orders, priority is given to online vehicles with the highest comprehensive evaluation index, thereby improving the enthusiasm and satisfaction of excellent ride-hailing drivers.

Description

Technical Field

[0001] This invention relates to the field of ride-hailing dispatch management, specifically to a ride-hailing vehicle dispatch management platform. Background Technology

[0002] As an emerging mode of transportation, ride-hailing services have not only been welcomed by a large number of passengers, but have also provided a large number of freelance employment opportunities. More and more people are using their own or leased vehicles to become ride-hailing drivers.

[0003] With the continuous advancement of internet technology, the gradual improvement of policies, and the rapid development of the economy, more and more people are choosing ride-hailing services as their mode of transportation. When dispatching orders, ride-hailing services typically prioritize orders based on the distance between the ride-hailing vehicle and the origin, as well as the driver's positive review rate, in order to improve customer satisfaction and thus enhance the competitiveness of the ride-hailing platform.

[0004] However, since most passengers do not actively rate ride-hailing drivers, the positive review rate cannot truly reflect the service quality of ride-hailing drivers. Therefore, using the positive review rate as the main metric for dispatching orders will create a certain degree of unfairness, reduce the satisfaction and enthusiasm of ride-hailing drivers, lead to driver churn, and ultimately reduce the number of vehicles online. Summary of the Invention

[0005] The purpose of this invention is to provide a ride-hailing dispatch and management platform to solve the following technical problems:

[0006] How to improve the enthusiasm and satisfaction of ride-hailing drivers.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A ride-hailing dispatch management platform, the dispatch management platform comprising:

[0009] The online vehicle monitoring module is used to monitor all online vehicles within the dispatch area of ​​the ride-hailing platform and acquire data for each online vehicle; the online vehicle data includes historical data and real-time vehicle data.

[0010] The order module is used to receive real-time order information within the scheduling area;

[0011] The analysis module is used to analyze historical data of online vehicles within the dispatch area to obtain the online index, on-time index, and violation index of each online vehicle; then, by analyzing the online index, on-time index, violation index, real-time order information, and real-time vehicle data of each online vehicle, a comprehensive evaluation index of each online vehicle is obtained.

[0012] As a further aspect of the present invention: the historical data includes daily online time, daily offline time, daily order information, and violation information;

[0013] The daily order information includes the total number of orders completed each day, the estimated completion time for each order, and the actual completion time.

[0014] The violation information includes the number of order cancellations, red light violations, and complaints per day.

[0015] As a further aspect of the present invention: the online index of the online vehicle numbered i is obtained by the formula:

[0016]

[0017] Calculate the online index P of the online vehicle with ID i. i ;

[0018] Where N is the preset number of past days, n∈N; T oni T represents the online working hours of vehicle number i on day n. gni Let γ1 be the peak-hour online duration of vehicle i on day n; γ2 be the first online weighting coefficient; γ3 be the second online weighting coefficient; ε be the de-uniting coefficient; and μ1 be the first adjustment coefficient.

[0019] As a further aspect of the present invention: the on-time index of the online vehicle numbered i is determined by the formula:

[0020]

[0021] Calculate the on-time index H of online vehicle number i. i ;

[0022] Where H0 is the basic timeliness index; Z ni D represents the number of online vehicles with ID i that completed their orders without exceeding the timeout period on day n. ni Let ρi be the total number of orders completed by the online vehicle with ID i on day n; ρ0 be the standard on-time rate; and μ2 be the second adjustment coefficient.

[0023] As a further aspect of the present invention: the violation index of online vehicle number i is determined by the formula:

[0024]

[0025] Calculate the violation index W i ;

[0026] Among them, C ni T represents the number of complaints against vehicle number i on day n. iLet X be the number of times vehicle number i runs a red light on day n; X1 is the first preset coefficient, X1≥1; Q ni Let i be the number of order cancellations on day n for online vehicle number i.

[0027] As a further aspect of the present invention: the real-time vehicle data includes the real-time location of the vehicle; the real-time order information includes the departure location information.

[0028] As a further aspect of the present invention: the comprehensive evaluation index is expressed by the formula:

[0029]

[0030] Calculate the comprehensive evaluation index K of the online vehicle with ID i. i ;

[0031] in, The first proportionality coefficient; This is the second proportionality coefficient; It is the third proportionality coefficient; It is the fourth proportionality coefficient; This represents the real-time distance between the online vehicle with ID i and its origin when the order is dispatched.

[0032] As a further aspect of the present invention: the analysis process for the comprehensive evaluation index of online vehicle number i is as follows:

[0033] S1: Obtain the daily online working time of online vehicle number i by using the daily online time and daily offline time of online vehicle number i;

[0034] S2: Analyze the daily online time, daily offline time and preset peak period of online vehicle number i to obtain the daily peak period online duration of online vehicle number i;

[0035] S3: The online index of vehicle number i is obtained by analyzing the daily online working time and daily peak-hour online time of online vehicles numbered i in the past preset number of days;

[0036] S4; Determine whether an order has timed out by obtaining the estimated completion time and actual completion time of each order for online vehicle number i; Obtain the number of orders completed without timeout for online vehicle number i each day by analyzing the daily order information;

[0037] S5; By analyzing the number of orders completed on time and the total number of orders completed each day for online vehicles numbered i in the past preset number of days, the on-time index of online vehicle number i is obtained;

[0038] S6: By analyzing the number of red light violations, complaints, and order cancellations of online vehicles for a preset number of days i in the past, a violation index is obtained;

[0039] S7: Obtain the real-time distance between online vehicle number i and the origin at the time of dispatch by using the origin location information and the real-time location of online vehicle number i.

[0040] The beneficial effects of this invention are:

[0041] (1) This invention monitors all online vehicles within the dispatch area of ​​the ride-hailing platform through an online vehicle monitoring module, and obtains historical and real-time vehicle data for each online vehicle; receives real-time order information within the dispatch area through an order module; analyzes the historical data of online vehicles within the dispatch area through an analysis module to obtain the online index, on-time index, and violation index for each online vehicle; and then obtains the comprehensive evaluation index for each online vehicle by analyzing the online index, on-time index, violation index, real-time order information, and real-time vehicle data for each online vehicle; and evaluates each online vehicle in more dimensions through the online index, on-time index, and violation index. The higher the comprehensive evaluation index, the better the ride-hailing driver of the online vehicle performs in all aspects. Therefore, when assigning orders, priority is given to the online vehicle with the highest comprehensive evaluation index, thereby improving the enthusiasm and satisfaction of excellent ride-hailing drivers. Attached Figure Description

[0042] The invention will now be further described with reference to the accompanying drawings.

[0043] Figure 1 This is a system module framework diagram of one embodiment of the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] Please see Figure 1 As shown, in one embodiment, a ride-hailing dispatch and management platform is provided, including:

[0046] The online vehicle monitoring module is used to monitor all online vehicles within the dispatch area of ​​the ride-hailing platform and acquire data for each online vehicle; the online vehicle data includes historical data and real-time vehicle data.

[0047] The order module is used to receive real-time order information within the scheduling area;

[0048] The analysis module is used to analyze the historical data of online vehicles within the dispatch area to obtain the online index, on-time index, and violation index of each online vehicle; then, by analyzing the online index, on-time index, violation index, real-time order information, and real-time vehicle data of each online vehicle, a comprehensive evaluation index of each online vehicle is obtained.

[0049] Each of the online vehicles mentioned has a unique corresponding number i;

[0050] Through the above technical solution, this embodiment monitors all online vehicles within the dispatch area of ​​the ride-hailing platform using an online vehicle monitoring module, and obtains historical and real-time vehicle data for each online vehicle; it receives real-time order information within the dispatch area through an order module; and it analyzes the historical data of online vehicles within the dispatch area using an analysis module to obtain the online index, on-time index, and violation index for each online vehicle. Furthermore, by analyzing the online index, on-time index, violation index, real-time order information, and real-time vehicle data for each online vehicle, a comprehensive evaluation index for each online vehicle is obtained. By using the online index, on-time index, and violation index to evaluate each online vehicle from multiple dimensions, the higher the comprehensive evaluation index, the better the ride-hailing driver performs in all aspects. Therefore, when assigning orders, priority is given to the online vehicle with the highest comprehensive evaluation index, thereby improving the enthusiasm and satisfaction of excellent ride-hailing drivers.

[0051] As one embodiment of the present invention, the historical data includes daily online time, daily offline time, daily order information, and violation information;

[0052] The daily order information includes the total number of orders completed each day, the estimated completion time for each order, and the actual completion time.

[0053] The violation information includes the number of order cancellations, red light violations, and complaints per day;

[0054] The real-time vehicle data includes the vehicle's real-time location;

[0055] The real-time order information includes the departure location information;

[0056] It should be noted that the methods used to obtain information such as daily online time, daily offline time, total number of orders completed each day, estimated completion time for each order, actual completion time, number of order cancellations, number of red light violations, number of complaints, real-time vehicle location, and departure location are all existing technologies and will not be detailed here.

[0057] As one embodiment of the present invention, the analysis process of the comprehensive evaluation index of the online vehicle numbered i is as follows:

[0058] S1: Obtain the daily online working time of online vehicle number i by using the daily online time and daily offline time of online vehicle number i;

[0059] S2: Analyze the daily online time, daily offline time and preset peak period of online vehicle number i to obtain the daily peak period online duration of online vehicle number i;

[0060] S3: The online index of vehicle number i is obtained by analyzing the daily online working time and daily peak-hour online time of online vehicles numbered i in the past preset number of days;

[0061] S4; Determine whether an order has timed out by obtaining the estimated completion time and actual completion time of each order for online vehicle number i; Obtain the number of orders completed without timeout for online vehicle number i each day by analyzing the daily order information;

[0062] S5; By analyzing the number of orders completed on time and the total number of orders completed each day for online vehicles numbered i in the past preset number of days, the on-time index of online vehicle number i is obtained;

[0063] S6: By analyzing the number of red light violations, complaints, and order cancellations of online vehicles for a preset number of days i in the past, a violation index is obtained;

[0064] S7: Obtain the real-time distance between online vehicle number i and the origin at the time of dispatch by using the origin location information and the real-time location of online vehicle number i.

[0065] S8: The comprehensive evaluation index of online vehicle number i is obtained by analyzing the online index, on-time index, violation index and real-time distance of online vehicle number i;

[0066] Through the above technical solution, this embodiment first obtains the daily online working time of online vehicle number i by using the daily online and offline times of online vehicle number i; then, it analyzes the daily online and offline times of online vehicle number i during preset peak hours to obtain the daily peak-hour online time of online vehicle number i; next, it analyzes the daily online working time and daily peak-hour online time of online vehicle number i over a preset number of days to obtain the online index of online vehicle number i; then, it determines whether the order has timed out by obtaining the estimated completion time and actual completion time of each order of online vehicle number i; and finally, it obtains the online index of online vehicle number i by analyzing the daily order information. The system calculates the following: First, it analyzes the number of orders completed on time each day for vehicle number i. Then, it analyzes the daily number of orders completed on time and the total number of completed orders for vehicle number i over a preset number of days to obtain its on-time index. Next, it analyzes the number of red-light violations, complaints, and order cancellations for vehicle number i over a preset number of days to obtain its violation index. Then, it uses the origin location information and the real-time location of vehicle number i to determine the real-time distance between vehicle number i and the origin at the time of order dispatch. Finally, it analyzes the online index, on-time index, violation index, and real-time distance of vehicle number i to obtain its comprehensive evaluation index.

[0067] It should be noted that the preset peak hours are preset time periods, obtained based on experience, and will not be described in detail here.

[0068] In one embodiment of the present invention, in step S3, the online index of the online vehicle with number i is calculated using the formula:

[0069]

[0070] Calculate the online index P of the online vehicle with ID i. i ;

[0071] Where N is the preset number of past days, n∈N; T oni T represents the online working hours of vehicle number i on day n. gni γ1 is the peak-hour online duration of vehicle i on day n; γ2 is the first online weighting coefficient; γ1 is the second online weighting coefficient; ε is the de-uniting coefficient; μ1 is the first adjustment coefficient;

[0072] Through the above technical solution, this embodiment The online index P is the cumulative online working time within a preset number of days. The longer the cumulative online working time, the higher the online index P. i The higher; The online index P is calculated by accumulating the online time during peak periods within a preset number of days. The longer the accumulated online time during peak periods, the higher the online index P.i The higher; This is the standard deviation of the online duration during peak hours within a preset number of days, used to reflect the fluctuation of the online duration during peak hours for vehicle number i within that preset number of days. The smaller the value, the more stable the online time of vehicle number i during peak hours each day; therefore, the online index P is higher. i The larger the value, the better. The online index is evaluated from three dimensions: cumulative online working hours, cumulative online time during peak hours, and whether the online time during peak hours is stable. This encourages ride-hailing drivers who want to get priority orders to work hard in three directions: cumulative online working hours, cumulative online time during peak hours, and stable online time during peak hours, in order to obtain priority order qualifications, while ensuring that there are enough vehicles to meet the order demand during peak hours.

[0073] It should be noted that the preset number of days N; the first online weight coefficient γ1, the second online weight coefficient γ2, the de-unit coefficient ε, and the adjustment coefficient μ1 were preset values ​​obtained based on experience, and will not be described in detail here.

[0074] In one embodiment of the present invention, in step S5, the on-time index of the online vehicle numbered i is determined by the formula:

[0075]

[0076] Calculate the on-time index H of online vehicle number i. i ;

[0077] Where H0 is the basic timeliness index; Z ni D represents the number of online vehicles with ID i that completed their orders without exceeding the timeout period on day n. ni Let ρi be the total number of orders completed by the online vehicle with ID i on day n; ρ0 be the standard on-time rate; and μ2 be the second adjustment coefficient.

[0078] Through the above technical solution, this embodiment Let i be the on-time rate of the online vehicle on day n. The average on-time rate of the online vehicle with ID i over the past preset number of days; This is the ratio of cumulative online time to cumulative online work time during peak periods over a preset number of days. Because peak periods are prone to traffic congestion, which can lead to timeouts, this ratio is used... Calculate the preset on-time rate of online vehicle number i over the past preset number of days, and the ratio of cumulative online time during peak hours to cumulative online working time over the past preset number of days. The larger the value, the lower the preset on-time rate; The difference between the average on-time rate of online vehicle number i over the past preset number of days and the preset on-time rate; when When this occurs, it indicates that the average on-time rate of online vehicle number i over the past preset number of days is greater than the preset on-time rate, and the difference between the average on-time rate of online vehicle number i over the past preset number of days and the preset on-time rate is... The larger the value, the higher the on-time index H. i The larger; when When the on-time rate of online vehicle number i over the past preset number of days is equal to the preset on-time rate, the on-time index H is... i =H0; when When this occurs, it indicates that the average on-time rate of online vehicle number i over the past preset number of days is less than the preset on-time rate, and the absolute value of the difference between the average on-time rate of online vehicle number i over the past preset number of days and the preset on-time rate is... The larger the value, the higher the on-time index H. i The smaller;

[0079] It should be noted that the basic punctuality index H0, the preset punctuality rate ρ0, and the second adjustment coefficient μ2 are preset values ​​obtained based on experience, and will not be described in detail here.

[0080] In one embodiment of the present invention, in step S6, the violation index of the online vehicle with ID i is determined by the formula:

[0081]

[0082] Calculate the violation index W i ;

[0083] Among them, C ni T represents the number of complaints against vehicle number i on day n. i Let X be the number of times vehicle number i runs a red light on day n; X1 is the first preset coefficient, X1≥1; Q ni The number of order cancellations on day n for online vehicle number i;

[0084] Through the above technical solution, this embodiment The cumulative number of complaints over a predetermined number of days; The cumulative number of red light violations over a predetermined number of days in the past; The cumulative number of order cancellations over a preset number of days in the past; This represents the cumulative number of violations; the higher the cumulative number of violations, the higher the violation index W. i The larger;

[0085] It should be noted that the first preset coefficient X1 is a preset value obtained based on experience, and will not be described in detail here.

[0086] In one embodiment of the present invention, in step S8, the comprehensive evaluation index is expressed by the formula:

[0087]

[0088] Calculate the comprehensive evaluation index K of the online vehicle with ID i. i ;

[0089] in, The first proportionality coefficient; This is the second proportionality coefficient; It is the third proportionality coefficient; It is the fourth proportionality coefficient; This represents the real-time distance between the online vehicle with ID i and its origin when the order is dispatched.

[0090] Through the above technical solution, the online index P in this embodiment i The higher the comprehensive evaluation index K, the better. i The larger the value; the higher the on-time index H i The higher the comprehensive evaluation index K, the better. i The larger the value; the higher the violation index W. i The lower the overall evaluation index K, the better. i The larger the value, the greater the real-time distance between the online vehicle (number i) and the origin when dispatching the order. The smaller the value, the better the overall evaluation index K. i The larger the index, the more likely the platform will prioritize assigning orders to online index P when the distances are similar. i High punctuality index H i High and violation index W i Low online vehicle count;

[0091] It should be noted that the first proportionality coefficient Second proportionality coefficient Third proportionality coefficient and the fourth proportionality coefficient These are preset values, obtained based on experience, and will not be detailed here.

[0092] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A ride-hailing dispatch and management platform, characterized in that, The scheduling management platform includes: The online vehicle monitoring module is used to monitor all online vehicles within the dispatch area of ​​the ride-hailing platform and acquire data for each online vehicle; the online vehicle data includes historical data and real-time vehicle data. The order module is used to receive real-time order information within the scheduling area; The analysis module is used to analyze the historical data of online vehicles within the dispatch area to obtain the online index, on-time index, and violation index of each online vehicle; then, by analyzing the online index, on-time index, violation index, real-time order information, and real-time vehicle data of each online vehicle, a comprehensive evaluation index of each online vehicle is obtained. serial number i The online index of online vehicles is calculated using the formula: ； Calculation number i Online vehicle online index ; in, Preset the number of days in the past. ; Number i Online vehicles Daily online work hours; Number i Online vehicles Peak online time of the day; This is the first online weighting coefficient; This is the second online weighting coefficient; To remove the unit coefficient; This is the first adjustment coefficient; The on-time performance index of online vehicle number i is calculated using the formula: ； Calculation number i On-time performance index of online vehicles ; in, This is the basic on-time index; Number i Online vehicles The number of orders completed within the specified time limit; Number i Online vehicles Total number of orders completed in one day; Standard on-time rate; This is the second adjustment coefficient.

2. The ride-hailing dispatch and management platform according to claim 1, characterized in that, The historical data includes daily online time, daily offline time, daily order information, and violation information; The daily order information includes the total number of orders completed each day, the estimated completion time for each order, and the actual completion time. The violation information includes the number of order cancellations, red light violations, and complaints per day.

3. The ride-hailing dispatch and management platform according to claim 1, characterized in that, serial number i The violation index of online vehicles is calculated using the formula: ； Calculate the violation index ; in, Number i Online vehicles Number of complaints per day; Number i Online vehicles Number of times a day you run a red light; The first preset coefficient, ; Number i Online vehicles Number of order cancellations per day.

4. The ride-hailing dispatch and management platform according to claim 3, characterized in that, The real-time vehicle data includes the real-time location of the vehicle; the real-time order information includes the departure location information.

5. The ride-hailing dispatch and management platform according to claim 4, characterized in that, The comprehensive evaluation index is expressed by the following formula: ； Calculation number i Comprehensive evaluation index of online vehicles ; in, The first proportionality coefficient; This is the second proportionality coefficient; It is the third proportionality coefficient; It is the fourth proportionality coefficient; The number used when dispatching orders i The real-time distance between online vehicles and their origin.

6. A ride-hailing dispatch and management platform according to claim 5, characterized in that, The analysis process for the comprehensive evaluation index of online vehicle number i is as follows: S1: By number i Get the number of daily online and offline times for vehicles. i The daily online working hours of the online vehicles; S2: By number i The system analyzes the daily online and offline times of online vehicles, as well as preset peak hours, to obtain vehicle ID numbers. i The daily peak-hour online time of online vehicles; S3: By numbering the past preset days i The online vehicles were analyzed to obtain their serial numbers based on daily online working hours and peak-hour online hours. i The online index of online vehicles; S4; Obtain the number i The system analyzes the estimated and actual completion times of each online vehicle order to determine if an order has timed out; it also obtains order numbers by analyzing daily order information. i The number of online vehicles that complete orders within the specified time each day; S5; By numbering the preset number of past days i The number of online vehicles that complete orders within the specified time each day and the total number of completed orders each day are analyzed to obtain the number. i The on-time performance index of online vehicles; S6: By numbering the past preset days i The system analyzes the number of red light violations, complaints, and order cancellations per day for online vehicles to obtain a violation index. S7: Based on departure location information and number i The online vehicle's real-time location is obtained when the order is dispatched. i The real-time distance between online vehicles and their origin.

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