A method for dispatching and managing online car-hailing

Abstract

The application discloses a kind of online car-hailing dispatching management methods, belong to vehicle management technical field;By target monitoring screening to target area and target monitoring time period, obtain effective target total number, by monitoring analysis, obtain the effective total transport capacity of target point corresponding target area in target monitoring time period, and according to effective total transport capacity and effective target total number, whether the transport capacity demand of target total number is satisfied in target area is determined at present;By different transport capacity abnormality lack label in transport capacity supply analysis data, corresponding lack sustained influence analysis is implemented, the stability and effectiveness of different transport capacity abnormality lack monitoring analysis are effectively improved, according to influence analysis result, the target area of target monitoring time period is dynamically regulated online car-hailing vehicle;The application can solve the technical problems that online car-hailing supervision and analysis effect is poor and dynamic scheduling management effect is poor in specific area in specific period in prior art.

Description

Technical Field

[0001] This invention relates to the field of vehicle management technology, and more specifically to a method for managing ride-hailing dispatch. Background Technology

[0002] Ride-hailing dispatch refers to the rational allocation and scheduling of ride-hailing vehicles through a dispatch system to meet passenger demand and improve operational efficiency.

[0003] The existing ride-hailing dispatch management schemes, when implemented, do not conduct targeted and effective user identification and supervision, or effective capacity supervision and analysis for specific areas and time periods. They also fail to dynamically dispatch ride-hailing vehicles in specific areas and time periods based on effective user identification and supervision and effective capacity. This results in technical problems such as poor ride-hailing supervision and analysis and poor dynamic dispatch management. Summary of the Invention

[0004] The purpose of this invention is to provide a ride-hailing dispatch management method to solve the technical problems of poor ride-hailing supervision and analysis effects and poor dynamic dispatch management effects in existing solutions for specific areas and time periods.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A ride-hailing dispatch management method includes:

[0007] Real-time monitoring and statistics are performed on the target time points when the target is about to arrive at the station. The target monitoring period is determined based on the target time points, and the total number of valid targets within the target monitoring period is counted.

[0008] Obtain the total effective transport capacity of the target area corresponding to the target point during the target monitoring period. Analyze whether the corresponding target area meets the transport capacity demand of the target total number of people during the target monitoring period based on the total effective transport capacity. Obtain transport capacity supply analysis data consisting of transport capacity shortage labels or transport capacity shortage labels.

[0009] For different abnormal shortage labels appearing in the capacity supply analysis data, corresponding shortage persistence impact analysis is carried out, and ride-hailing vehicles are dynamically adjusted in the target area during the target monitoring period based on the impact analysis results;

[0010] Specifically, when a label indicating a slight shortage of capacity or a label indicating a severe shortage of capacity appears in the capacity supply analysis data, a first dispatch verification instruction or a second dispatch verification instruction is generated respectively. Based on the first dispatch verification instruction or the second dispatch verification instruction, the persistent impact of the slight shortage of capacity or the persistent impact of the severe shortage of capacity is analyzed during the first dispatch verification period or the second dispatch verification period respectively.

[0011] The first total duration of the mild capacity shortage label and the severe capacity shortage label appearing during the first scheduling verification period is calculated based on the first scheduling verification instruction, and the second total duration of the label appearing during the second scheduling verification period is calculated based on the second scheduling verification instruction; the verification impact degree corresponding to the scheduling verification instruction is calculated using the first total duration and the second total duration.

[0012] The impact of verification is analyzed to obtain the first and second data on the impact of capacity shortage. Based on the first and second data on the impact of capacity shortage, ride-hailing vehicles are dynamically adjusted in the target area during the target monitoring period.

[0013] In one optional implementation, the target time point is obtained, and the earliest booking time point before the target enters the station and the latest departure time point after the target leaves the station are determined based on the historical ride-hailing booking data corresponding to the historical target time point. The target monitoring period corresponding to the target time point is obtained based on the earliest booking time point and the latest departure time point.

[0014] In one optional implementation, the target area is a circular area enclosed by the target point as the center and a preset radius.

[0015] In one optional implementation, when monitoring and analyzing valid targets within a target monitoring period, targets in the target area who use map software and browse for ride-hailing services within the target monitoring period are marked as valid targets; the total number of valid targets is obtained by counting the total number of valid targets.

[0016] In one optional implementation, ride-hailing vehicles that do not pick up passengers in the target area during the target monitoring period are counted and marked as first effective capacity, and ride-hailing vehicles whose destination is the target area during the target monitoring period are counted and marked as second effective capacity. The total effective capacity in real time is obtained by counting the total number of first and second effective capacity.

[0017] In one alternative implementation, the formula is used. Calculate the capacity support Yc of the target area during the target monitoring period; where Yz is the real-time effective total capacity, Xz is the effective total number of people, and α is the capacity support influencing factor.

[0018] In one optional implementation, when analyzing whether the target area meets the capacity demand of the target total number of people during the target monitoring period based on the capacity support, if the capacity support is greater than zero and not greater than the capacity support threshold, a mild capacity shortage label is generated; if the capacity support is greater than the capacity support threshold, a severe capacity shortage label is generated.

[0019] In one optional implementation, the first total duration Tc1 of the occurrence of the mild capacity shortage tag and the severe capacity shortage tag within the first scheduling verification period is calculated according to the first scheduling verification instruction, and the second total duration Tc2 of the occurrence within the second scheduling verification period is calculated according to the second scheduling verification instruction; using the formula Calculate the verification impact degree Hy corresponding to the obtained scheduling verification instruction; where k = 1, 2; T01 is the total duration of the standard mild anomaly, T02 is the total duration of the standard severe anomaly, and T02 < T02.

[0020] In one optional implementation, if the verification impact is not greater than zero, then a "mild capacity shortage continuous impact fluctuation" label and a "severe capacity shortage continuous impact fluctuation" label are generated.

[0021] Conversely, labels indicating a mild shortage of transport capacity that continues to affect stability and a severe shortage of transport capacity that continues to affect stability are generated.

[0022] The data for analyzing the impact of mild capacity shortage on fluctuations or stable conditions constitutes the first data for analyzing the impact of capacity shortage. The data for analyzing the impact of severe capacity shortage on fluctuations or stable conditions constitutes the second data for analyzing the impact of capacity shortage.

[0023] In one optional implementation, when dynamically adjusting ride-hailing vehicles in the target area during the target monitoring period, based on the "severe shortage of transportation capacity and continuous impact stability label" in the second transportation capacity shortage impact analysis data, the system platform actively dispatches several effective transportation capacities from the nearest to the farthest distance outside the target area to the target point.

[0024] In addition, based on the "Mild Capacity Shortage Continuous Impact Fluctuation" or "Mild Capacity Shortage Continuous Impact Stable" labels in the first capacity shortage impact analysis data, and the "Severe Capacity Shortage Continuous Impact Fluctuation" labels in the second capacity shortage impact analysis data, voice prompts will guide several effective transport capacities from the target area, in order of increasing distance, to the target point.

[0025] Compared to existing solutions, the beneficial effects achieved by this invention are:

[0026] This invention obtains the total number of valid targets by monitoring and filtering target areas and target monitoring periods, which can realize the target supervision of ride-hailing in specific areas and at specific times. At the same time, it can also provide reliable data support for the subsequent ride-hailing dispatch management in specific areas and at specific times.

[0027] This invention obtains the effective total capacity of the target area corresponding to the target point during the target monitoring period through monitoring and analysis. It then integrates and calculates the real-time capacity support based on the effective total capacity and the total number of effective targets. Based on the real-time capacity support, it determines whether the target area currently meets the capacity demand of the target total number of people. At the same time, it can also provide reliable data support for the subsequent ride-hailing dispatch management of the target area, improving the diversity of monitoring and analysis of ride-hailing capacity.

[0028] This invention improves the stability and effectiveness of monitoring and analyzing different abnormal capacity shortages by performing corresponding persistent impact analysis on different capacity shortage labels appearing in the capacity supply analysis data. Based on the impact analysis results, it dynamically adjusts ride-hailing vehicles in the target area during the target monitoring period, realizing dynamic scheduling of ride-hailing vehicles in different areas at different times. This can effectively improve the regulatory analysis and dynamic scheduling management of ride-hailing vehicles in specific areas at specific times. Attached Figure Description

[0029] The present invention will now be further described with reference to the accompanying drawings.

[0030] Figure 1 This is a flowchart of a ride-hailing dispatch management method according to the present invention.

[0031] Figure 2 This is a flowchart illustrating the process of obtaining the total number of effective target individuals in this invention.

[0032] Figure 3 This is a flowchart illustrating the process of performing a persistent impact analysis of different capacity shortage labels in this invention. Detailed Implementation

[0033] 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 ordinary operators and maintenance personnel in the art without creative effort are within the scope of protection of the present invention.

[0034] like Figure 1 As shown, the present invention is a ride-hailing dispatch management method, comprising:

[0035] Real-time monitoring and statistics are performed on the target time points when the target is about to arrive at the station. Based on the target time points, the target monitoring period is determined, and the total number of valid targets within the target monitoring period is calculated; including:

[0036] like Figure 2As shown, the target time point is obtained, and the earliest booking time before the target enters the station and the latest departure time after the target leaves the station are determined based on the historical ride-hailing booking data corresponding to the historical target time point. The target monitoring period corresponding to the target time point is obtained based on the earliest booking time point and the latest departure time point. The latest departure time after the target leaves the station is identified and obtained based on the monitoring data of the exit camera device of the corresponding target point, or it can be obtained based on the latest ride-hailing order data corresponding to the historical target time point; the target here is the passenger.

[0037] It is worth noting that if there are multiple target time points within the target monitoring period, the target monitoring periods corresponding to the multiple target time points are added together to obtain the largest target monitoring period.

[0038] When monitoring and analyzing valid targets within a target monitoring period, targets that use map software and browse ride-hailing services within the target area during the target monitoring period are marked as valid targets; this can be obtained based on data from third-party map software monitoring and statistics, such as Gaode Maps.

[0039] The target area is a circular area enclosed by the target point as the center and a preset radius. The preset radius is determined based on the existing limit radius of the ride-hailing order-taking distance range.

[0040] The total number of valid targets is obtained by counting the total number of valid targets.

[0041] It should be noted that the total number of valid targets can also be determined by combining the median of the total number of valid targets with the same time attribute in the past. For example, if the time attribute corresponding to the target time point is Monday, then the total number of valid targets in the past two months for different Mondays is counted, the maximum and minimum total number of valid targets in the past two months for different Mondays are removed, and the median of the remaining total number of valid targets is obtained and set as the current total number of valid targets.

[0042] It should be explained that the target point is a train station or high-speed rail station in the city, and the target time point is the arrival time of the train or high-speed rail, with the specific unit being precise to the minute. Since some users book ride-hailing services in advance when they are about to arrive at the station so that they can get on the ride more quickly, and some users call for a ride after leaving the target point, it is necessary to determine a target monitoring period for targeted ride-hailing dispatch to meet the sudden ride-hailing demand during the period.

[0043] In this embodiment of the invention, by performing target monitoring and screening on the target area and the target monitoring period to obtain the total number of valid targets, it is possible to achieve target supervision of ride-hailing in a specific area and at a specific time. At the same time, it can also provide reliable data support for the subsequent dispatch management of ride-hailing in a specific area and at a specific time.

[0044] Obtain the total effective transport capacity of the target area corresponding to the target point during the target monitoring period. Analyze whether the total effective transport capacity meets the transport capacity demand of the target area for the target number of people during the target monitoring period, and obtain transport capacity supply analysis data; including:

[0045] The system counts ride-hailing vehicles that do not pick up passengers in the target area during the target monitoring period and marks them as the first effective capacity. It also counts ride-hailing vehicles whose destination is the target area during the target monitoring period and marks them as the second effective capacity. The total effective capacity in real time is obtained by counting the total number of the first and second effective capacities.

[0046] Through formula Calculate the capacity support degree Yc of the target area during the target monitoring period; where Yz is the real-time effective total capacity, Xz is the effective total number of target people, and α is the capacity support influence factor. The capacity support influence factor is an integer and can be negative. It is determined based on the number of online or offline ride-hailing vehicles in the target area during the target monitoring period and is used to reduce the error of the monitoring data on the real-time effective total capacity.

[0047] When analyzing whether the target area meets the capacity demand of the target total number of people during the target monitoring period based on the capacity support, if the capacity support is not greater than zero, a "sufficient capacity" label is generated; if the capacity support is greater than zero but not greater than the capacity support threshold, a "mildly scarce capacity" label is generated; if the capacity support is greater than the capacity support threshold, a "severely scarce capacity" label is generated. The capacity support threshold is determined based on the median of all historical capacity support values ​​for the same target monitoring period.

[0048] The data for capacity supply analysis consists of tags such as "ample capacity", "mildly scarce capacity", or "severely scarce capacity".

[0049] In this embodiment of the invention, the effective total capacity of the target area corresponding to the target point during the target monitoring period is obtained through monitoring and analysis. The real-time capacity support is obtained by integrating the effective total capacity and the total number of effective targets. Based on the real-time capacity support, it is determined whether the target area currently meets the capacity demand of the target total number of people. At the same time, it can also provide reliable data support for the subsequent online car-hailing dispatch management of the target area, and improve the diversity of monitoring and analysis of online car-hailing capacity.

[0050] For different abnormal capacity shortage labels appearing in the capacity supply analysis data, a corresponding scarcity persistence impact analysis is conducted, and ride-hailing vehicles are dynamically adjusted in the target area during the target monitoring period based on the impact analysis results; including:

[0051] like Figure 3As shown, when the capacity supply analysis data shows a label of mild capacity shortage or severe capacity shortage, a first dispatch verification instruction or a second dispatch verification instruction is generated respectively. Based on the first dispatch verification instruction or the second dispatch verification instruction, the continuous impact of mild capacity shortage or severe capacity shortage is analyzed during the first dispatch verification period or the second dispatch verification period respectively.

[0052] The units for both the first and second scheduling verification periods are minutes, and the duration of the first scheduling verification period is longer than that of the second scheduling verification period. The specific values ​​of the first and second scheduling verification periods can be determined based on the specific range of the target monitoring period.

[0053] It should be noted that, due to the suddenness and randomness of abnormal capacity tags appearing at a single moment, in order to improve the effectiveness and reliability of ride-hailing dispatch, it is necessary to further conduct a scarcity persistence analysis on the different abnormal tags that appear.

[0054] Based on the first scheduling verification instruction, the total duration Tc1 of the occurrence of the mild capacity shortage tag and the severe capacity shortage tag during the first scheduling verification period is calculated, and based on the second scheduling verification instruction, the total duration Tc2 of the occurrence during the second scheduling verification period is calculated; using the formula Calculate the verification impact degree Hy corresponding to the obtained scheduling verification instruction; where k = 1, 2; T01 is the total duration of the standard mild anomaly, T02 is the total duration of the standard severe anomaly, and T02 < T02.

[0055] If the verification impact is not greater than zero, then a label for "mild capacity shortage with continuous impact" and a label for "severe capacity shortage with continuous impact" will be generated.

[0056] Conversely, labels indicating a mild shortage of transport capacity that continues to affect stability and a severe shortage of transport capacity that continues to affect stability are generated.

[0057] The data for analyzing the impact of mild capacity shortage on fluctuations or stable conditions constitutes the first data for analyzing the impact of capacity shortage. The data for analyzing the impact of severe capacity shortage on fluctuations or stable conditions constitutes the second data for analyzing the impact of capacity shortage.

[0058] When dynamically adjusting ride-hailing vehicles in the target area during the target monitoring period, based on the "severe shortage of transportation capacity and continuous impact stability label" in the second analysis of the impact of transportation capacity shortage, the system platform actively dispatches several effective transportation vehicles from the nearest to the farthest distance outside the target area to the target point; here, the active dispatching through the system platform can be incentivized by increasing subsidies.

[0059] In addition, based on the "mild shortage of capacity" continuous impact fluctuation label or "mild shortage of capacity" continuous impact stability label in the first capacity shortage impact analysis data, and the "severe shortage of capacity" continuous impact fluctuation label in the second capacity shortage impact analysis data, voice prompts will guide drivers to several effective transport vehicles outside the target area, from near to far, to the target point. Here, the ride-hailing platform will provide a single voice prompt so that ride-hailing drivers can choose whether to go to the target area to accept orders.

[0060] Unlike existing technologies where ride-hailing services cannot dynamically accept orders based on platform dispatch prompts and rely solely on personal experience, leading to inconsistent order-taking performance in different areas and a poor user experience, this invention improves the stability and effectiveness of monitoring and analyzing different capacity shortages by performing corresponding persistent impact analysis on different capacity shortage tags in the capacity supply analysis data. Based on the impact analysis results, ride-hailing vehicles are dynamically adjusted in the target area during the target monitoring period, enabling dynamic dispatching of ride-hailing services in different areas at different times. This effectively improves the regulatory analysis and dynamic dispatching management of ride-hailing services in specific areas during specific times.

[0061] Furthermore, the formulas mentioned above are all numerical calculations obtained by removing dimensions and using simulation software to obtain a formula that is closest to the real situation, based on the collection of a large amount of data.

[0062] In the several embodiments provided by this invention, it should be understood that the disclosed methods can be implemented in other ways. For example, the embodiments of the invention described above are merely illustrative; for instance, the division of modules is only a logical location division, and other division methods may be used in actual implementation.

[0063] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0064] Furthermore, in the various embodiments of the present invention, the position modules can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software position modules.

[0065] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for managing ride-hailing dispatch, characterized in that, include: Real-time monitoring and statistics are performed on the target time points when the target is about to arrive at the station. The target monitoring period is determined based on the target time points, and the total number of valid targets within the target monitoring period is counted. Obtain the total effective transport capacity of the target area corresponding to the target point during the target monitoring period. Analyze whether the corresponding target area meets the transport capacity demand of the target total number of people during the target monitoring period based on the total effective transport capacity. Obtain transport capacity supply analysis data consisting of transport capacity shortage labels or transport capacity shortage labels. For different abnormal shortage labels appearing in the capacity supply analysis data, corresponding shortage persistence impact analysis is carried out, and ride-hailing vehicles are dynamically adjusted in the target area during the target monitoring period based on the impact analysis results; Specifically, when a label indicating a slight shortage of capacity or a label indicating a severe shortage of capacity appears in the capacity supply analysis data, a first dispatch verification instruction or a second dispatch verification instruction is generated respectively. Based on the first dispatch verification instruction or the second dispatch verification instruction, the persistent impact of the slight shortage of capacity or the persistent impact of the severe shortage of capacity is analyzed during the first dispatch verification period or the second dispatch verification period respectively. Based on the first scheduling verification instruction, the total duration Tc1 of the mild capacity shortage label appearing during the first scheduling verification period is calculated, and based on the second scheduling verification instruction, the total duration Tc2 of the severe capacity shortage label appearing during the second scheduling verification period is calculated. The verification impact Hy corresponding to the scheduling verification instruction is calculated using the first and second total durations. The formula for calculating the verification impact Hy is as follows: In the formula, k=1, 2; T01 is the total duration of the standard mild abnormality, T02 is the total duration of the standard severe abnormality, and T02<T01. If the verification impact is not greater than zero, then a label for "mild capacity shortage with continuous impact" and a label for "severe capacity shortage with continuous impact" will be generated. Conversely, labels indicating a mild shortage of transport capacity that continues to affect stability and a severe shortage of transport capacity that continues to affect stability are generated. The first data for analyzing the impact of a slight shortage of transport capacity is either a fluctuating label or a stable label. The second data for analyzing the impact of a severe shortage of transport capacity is either a fluctuating label or a stable label. Based on the first and second data for analyzing the impact of a severe shortage of transport capacity, ride-hailing vehicles are dynamically adjusted in the target area during the target monitoring period.

2. The ride-hailing dispatch management method according to claim 1, characterized in that, Obtain the target time point, and determine the earliest booking time point before the target enters the station and the latest departure time point after the target leaves the station based on the historical ride-hailing booking data corresponding to the historical target time point. Obtain the target monitoring period corresponding to the target time point based on the earliest booking time point and the latest departure time point.

3. The ride-hailing dispatch management method according to claim 1, characterized in that, The target area is a circular area enclosed by the target point as the center and a preset radius.

4. The ride-hailing dispatch management method according to claim 1, characterized in that, When monitoring and analyzing valid targets within the target monitoring period, targets in the target area who use map software and browse ride-hailing services within the target monitoring period are marked as valid targets; the total number of valid targets is obtained by counting the total number of valid targets.

5. The ride-hailing dispatch management method according to claim 4, characterized in that, The system counts ride-hailing vehicles that do not pick up passengers in the target area during the target monitoring period and marks them as the first effective capacity. It also counts ride-hailing vehicles whose destination is the target area during the target monitoring period and marks them as the second effective capacity. The total effective capacity is obtained by counting the total number of the first and second effective capacities in real time.

6. The ride-hailing dispatch management method according to claim 5, characterized in that, Through formula Calculate the capacity support Yc of the target area during the target monitoring period; where Yz is the real-time effective total capacity, Xz is the effective total number of people, and α is the capacity support influencing factor.

7. The ride-hailing dispatch management method according to claim 6, characterized in that, When analyzing whether the target area meets the transportation capacity demand of the target total number of people during the target monitoring period based on the transportation capacity support, if the transportation capacity support is greater than zero and not greater than the transportation capacity support threshold, a label of mild transportation capacity shortage is generated; if the transportation capacity support is greater than the transportation capacity support threshold, a label of severe transportation capacity shortage is generated.

8. The ride-hailing dispatch management method according to claim 1, characterized in that, When dynamically adjusting ride-hailing vehicles in the target area during the target monitoring period, based on the "severe shortage of transportation capacity and continuous impact stability label" in the second analysis data on the impact of transportation capacity shortage, the system platform actively dispatches several effective transportation vehicles from the nearest to the farthest distance outside the target area to the target point. In addition, based on the "Mild Capacity Shortage Continuous Impact Fluctuation" or "Mild Capacity Shortage Continuous Impact Stable" labels in the first capacity shortage impact analysis data, and the "Severe Capacity Shortage Continuous Impact Fluctuation" labels in the second capacity shortage impact analysis data, voice prompts will guide several effective transport capacities from the target area, in order of increasing distance, to the target point.

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