A traffic load auxiliary dispatching method and system based on CUBE data

Abstract

The present invention relates to a CUBE data-based traffic load assisted dispatching method and system, comprising the following steps: surveying passenger flow in different regions, dividing a city map according to different passenger flow rates, and dividing the map into different grid areas based on differences in passenger flow rates; establishing a taxi cost and profit model algorithm, and establishing a taxi cruising cost model; dispatching taxis based on differences in passenger flow in different grid areas; and calculating the optimal taxi cruising path based on the taxi cruising cost model and passenger loading probability to complete assisted dispatching. The present invention can predict and screen taxi cruising paths, increase taxi passenger loading probability, better distribute passenger carrying capacity to different areas, reduce passenger waiting time, and facilitate passenger travel.

Description

Technical Field

[0001] The present invention relates to the technical field, and in particular to a traffic load auxiliary scheduling method and system based on CUBE data. Background Art

[0002] With the continuous development of vehicle-mounted GPS data technology, a large amount of location trajectory data has been generated. Effective mining and analysis of this location trajectory data has greatly promoted the improvement of people's living standards, especially the application and popularization of taxi GPS, whose GPS data largely reflects the travel conditions of residents.

[0003] As one of the means of transportation, taxis are an important part of urban traffic diversion. However, due to the serious information asymmetry between passengers and taxi drivers, passengers look for taxis everywhere, and taxi drivers blindly look for passengers to increase the passenger load rate, resulting in serious waste of public resources, a high taxi idling rate, and low public travel efficiency. Summary of the Invention

[0004] In order to address the above-mentioned technical defects in the prior art, the present invention provides a traffic load auxiliary scheduling method and system based on CUBE data, which can effectively solve the problems in the background technology.

[0005] In order to solve the above technical problems, the technical solutions provided by the present invention are as follows:

[0006] The embodiment of the present invention discloses a traffic load auxiliary scheduling method based on CUBE data, comprising the following steps:

[0007] Step 1: Survey the passenger flow in different areas and divide the urban map into different grid areas according to the passenger flow;

[0008] Step 2: Establish a taxi cost and profit model algorithm, and establish a taxi cruising cost model;

[0009] Step 3: Dispatch taxis based on passenger flow differences in different grid areas;

[0010] Step 4: Calculate the optimal taxi cruising route based on the taxi cruising cost model and passenger carrying probability.

[0011] In any of the above schemes, it is preferred that when investigating the passenger flow in different areas, the content of the investigation includes: the passenger's boarding point, the passenger's boarding time, the length of the passenger's boarding route and the passenger's alighting point, wherein the passenger's boarding point and the passenger's alighting point both obtain coordinates through the GPS positioning system.

[0012] In any of the above schemes, it is preferred that when investigating the length of the passenger's ride route, the GPS positioning system is first used to obtain several groups of coordinate points 1, 2, 3 / 4...n of the taxi when the passenger is riding, and then the obtained several groups of coordinate points of the taxi are calculated by the formula Calculate the length of the passenger's ride path, where s i is the distance from the i-th coordinate point to the i+1-th coordinate point, and S is the total distance traveled by the passenger.

[0013] In any of the above schemes, it is preferred that when investigating the passengers' boarding and alighting points, if the change in the taxi vehicle position collected by the GPS positioning system within a certain time interval T is less than the threshold, and the taxi stops at a certain position and remains above the time threshold, and the location is not an intersection, it is determined to be a traffic jam or a passenger getting on or off the vehicle, and then a judgment is made based on the number of nearby vehicles. If the number of vehicles within 50m is less than the threshold, the location is determined to be the passenger's boarding or alighting point, and the specific status of the location is determined based on the previous status of the vehicle.

[0014] In any of the above solutions, preferably, when investigating passenger flow in different regions using the GPS positioning system, the obtained taxi GPS data is first pre-processed.

[0015] In any of the above solutions, preferably, when pre-processing the acquired taxi GPS data, the following steps are included:

[0016] Step 1: Eliminate unreasonable data from taxi GPS data based on spatiotemporal characteristics. When the taxi GPS data shows that the taxi is on a non-traffic road, the GPS data is eliminated. When the taxi GPS data shows that the distance from the taxi to the previous location is greater than a threshold, the GPS data is eliminated.

[0017] Step 2: When the GPS data of the taxi shows that the taxi is in an empty cruising state for a day, the data is reviewed;

[0018] Step 3: When a taxi has been parked at a certain location for a period greater than a time threshold, the data is eliminated.

[0019] In any of the above solutions, it is preferred that when calculating the distance between coordinate points, the formula Calculate the distance between the i-th coordinate point and the i+1-th coordinate point; where lati is the GPS coordinate of the i-th coordinate point, a=lat i ×π / 180-lat i+1 ×π / 180,b=lngt i×π / 180-lngt i+1 ×π / 180, where lat i Indicates the latitude of point i, lat i+1 Indicates the latitude of point i+1, lngt i Indicates the longitude of point i, lngt i+1 Indicates the longitude of point i+1.

[0020] In any of the above solutions, preferably, when calculating the profit of a taxi driver, the following steps are included:

[0021] Step 1: Calculate the total income of taxi drivers;

[0022] Step 2: Calculate the cost incurred by taxi drivers;

[0023] Step 3: Calculate the taxi's profit.

[0024] In any of the above schemes, it is preferred that the formula The income of a taxi carrying a passenger, where c is the starting price of the taxi, d is the length of the passenger's driving trajectory, e is the starting kilometers, f is the secondary charging price, g is the secondary charging kilometers, and h is the tertiary charging price.

[0025] In any of the above solutions, it is preferred that, when calculating the cost incurred by the taxi driver, the cost incurred by the taxi driver is divided into vehicle fixed cost, vehicle transportation cost and vehicle cruising cost.

[0026] In any of the above schemes, it is preferred that, when calculating the costs incurred by the taxi driver, the vehicle fixed cost is calculated by the formula X = vehicle depreciation + vehicle insurance + vehicle management expenses + vehicle maintenance expenses + taxi driver's wages and benefits; wherein X is the fixed cost of the vehicle.

[0027] In any of the above solutions, it is preferred that when calculating the cost spent by the taxi driver, the formula Calculate the transportation cost of the vehicle; where Z is the total number of taxis, β is the total number of passengers carried by any vehicle, S αz is the length of the path traveled by the z-th taxi when it picks up the α-th passenger, and C is the transportation cost of the taxi per unit distance.

[0028] In any of the above solutions, it is preferred that when calculating the cost spent by the taxi driver, the formula Calculate the cruising cost of the vehicle; where Y' is the total cruising cost of the vehicle, Z is the total number of taxis, S' z is the total cruising distance of the zth taxi.

[0029] In any of the above solutions, preferably, when taxis are dispatched according to the difference in passenger flow in different grid areas, the following steps are included:

[0030] Step 1: Calculate the empty and loaded probabilities of all grids, and calculate the passenger capacity of all grids;

[0031] Step 2: Use ant colony algorithm to count and predict taxi dispatch methods;

[0032] Step 3: Screen the scheduling methods after statistics and prediction by the ant colony algorithm.

[0033] In a second aspect, a transportation load auxiliary dispatching system based on CUBE data includes:

[0034] The collection module is used to investigate the passenger flow in different areas, divide the urban map into different grid areas according to different passenger flow rates, and divide the map into different grid areas according to the difference in passenger flow rates;

[0035] Generation module, used to establish taxi cost and profit model algorithms, and to establish taxi cruising cost model;

[0036] The dispatch module is used to dispatch taxis based on the passenger flow differences in different grid areas;

[0037] The auxiliary module is used to calculate the optimal taxi cruising path based on the taxi cruising cost model and passenger carrying probability, and complete auxiliary scheduling.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] 1. The present invention predicts and screens the cruising paths of taxis by using an ant colony algorithm, thereby increasing the probability of taxis carrying passengers and increasing the net profit of taxis.

[0040] 2. The present invention predicts and screens the cruising paths of taxis by using the ant colony algorithm, which can better distribute passenger carrying capacity to different areas, reduce passengers' waiting time, and facilitate passengers' travel. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings are used to further understand the present invention and are used to explain the present invention together with the embodiments of the present invention, but do not constitute a limitation of the present invention.

[0042] Figure 1 This is a flow chart of a method for auxiliary traffic dispatching based on CUBE data provided by an embodiment of the present invention;

[0043] Figure 2This is a flow chart of a transportation cargo auxiliary dispatching system based on CUBE data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0045] It should be noted that when an element is referred to as being “fixed on” or “disposed on” another element, it may be directly on the other element or indirectly on the other element. When an element is referred to as being “connected to” another element, it may be directly connected to the other element or indirectly connected to the other element.

[0046] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention.

[0047] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature identified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0048] In order to better understand the above technical solution, the technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0049] like Figure 1 As shown, a traffic load auxiliary scheduling method based on CUBE data includes the following steps:

[0050] Step 1: Survey the passenger flow in different areas and divide the urban map into different grid areas according to the passenger flow;

[0051] Step 2: Establish a taxi cost and profit model algorithm, and establish a taxi cruising cost model;

[0052] Step 3: Dispatch taxis based on passenger flow differences in different grid areas;

[0053] Step 4: Calculate the optimal taxi cruising route based on the taxi cruising cost model and passenger carrying probability.

[0054] Specifically, when investigating passenger flow in different regions, the contents of the investigation include: the passengers' boarding points, the passengers' boarding time, the length of the passengers' boarding routes and the passengers' alighting points, among which the coordinates of the passengers' boarding points and the passengers' alighting points are obtained through the GPS positioning system.

[0055] Furthermore, when investigating the length of the passenger's ride, the GPS positioning system is first used to obtain several sets of coordinate points 1, 2, 3 / 4...n of the taxi when the passenger is riding. Then, based on the obtained several sets of coordinate points of the taxi, the formula Calculate the length of the passenger's ride path, where s i is the distance from the i-th coordinate point to the i+1-th coordinate point, and S is the total distance traveled by the passenger.

[0056] First, based on the time series of taxi trajectories, the first point with a status of "3" is identified as the pickup point. The last point with a consecutive "3" status is then extracted as the drop-off point. If the number of consecutive "3" points is greater than or equal to 3, the trajectory from the first "3" point to the last "3" point is considered a taxi pickup trajectory. Furthermore, the time of day is divided into 24 equal parts, labeled 0-23.

[0057] Furthermore, when investigating the passengers' boarding and alighting points, if the change in the taxi vehicle position collected by the GPS positioning system within a certain time interval T is less than the threshold, and the taxi stops at a certain position and remains above the time threshold, and the location is not an intersection, it is determined to be a traffic jam or a passenger boarding or alighting. Then, based on the number of nearby vehicles, if the number of vehicles within 50m is less than the threshold, the location is determined to be the passenger's boarding or alighting point, and the specific status of the location is determined based on the previous status of the vehicle.

[0058] Furthermore, when using the GPS positioning system to investigate passenger flow in different regions, the obtained taxi GPS data is first pre-processed.

[0059] Furthermore, when pre-processing the acquired taxi GPS data, the following steps are included:

[0060] Step 1: Eliminate unreasonable data from taxi GPS data based on spatiotemporal characteristics. When the taxi GPS data shows that the taxi is on a non-traffic road, the GPS data is eliminated. When the taxi GPS data shows that the distance from the taxi to the previous location is greater than a threshold, the GPS data is eliminated.

[0061] Step 2: When the GPS data of the taxi shows that the taxi is in an empty cruising state for a day, the data is reviewed;

[0062] Step 3: When a taxi has been parked at a certain location for a period greater than a time threshold, the data is eliminated.

[0063] Furthermore, when calculating the distance between coordinate points, the formula Calculate the distance between the i-th coordinate point and the i+1-th coordinate point; where lati is the GPS coordinate of the i-th coordinate point, a=lat i ×π / 180-lat i+1 ×π / 180,b=lngt i ×π / 180-lngt i+1 ×π / 180, where lat i Indicates the latitude of point i, lat i+1 Indicates the latitude of point i+1, lngt i Indicates the longitude of point i, lngt i+1 Indicates the longitude of point i+1.

[0064] Specifically, when calculating the profit of a taxi driver, the following steps are included:

[0065] Step 1: Calculate the total income of taxi drivers;

[0066] Step 2: Calculate the cost incurred by taxi drivers;

[0067] Step 3: Calculate the taxi's profit.

[0068] Furthermore, through the formula The income of a taxi carrying a passenger, where c is the starting price of the taxi, d is the length of the passenger's driving trajectory, e is the starting kilometers, f is the secondary charging price, g is the secondary charging kilometers, and h is the tertiary charging price.

[0069] Furthermore, when calculating the cost incurred by the taxi driver, the cost incurred by the taxi driver is divided into vehicle fixed cost, vehicle transportation cost and vehicle cruising cost.

[0070] Furthermore, when calculating the costs incurred by taxi drivers, the vehicle fixed costs are calculated using the formula X = vehicle depreciation + vehicle insurance + vehicle management fees + vehicle maintenance fees + taxi driver's wages and benefits; where X is the fixed cost of the vehicle.

[0071] C represents the taxi's transportation cost per unit distance. Typically, this portion of taxi transportation costs primarily includes fuel costs and vehicle maintenance and upkeep. Although these costs are related to passenger volume, the impact is relatively small, so the impact of changes in passenger volume on costs is typically not considered in research.

[0072] Furthermore, when calculating the cost of taxi drivers, the formula Calculate the transportation cost of the vehicle; where Z is the total number of taxis, β is the total number of passengers carried by any vehicle, S αz is the length of the path traveled by the z-th taxi when it picks up the α-th passenger, and C is the transportation cost of the taxi per unit distance.

[0073] Vehicle transportation costs mainly include vehicle maintenance costs and fuel costs; among them, fuel prices can be calculated based on oil prices and taxi fuel consumption per 100 kilometers, and maintenance costs need to be calculated based on actual conditions.

[0074] Furthermore, when calculating the cost of taxi drivers, the formula Calculate the cruising cost of the vehicle; where Y' is the total cruising cost of the vehicle, Z is the total number of taxis, S' z is the total cruising distance of the zth taxi.

[0075] Specifically, when taxis are dispatched based on the difference in passenger flow in different grid areas, the following steps are included:

[0076] Step 1: Calculate the empty and loaded probabilities of all grids, and calculate the passenger capacity of all grids;

[0077] Step 2: Use ant colony algorithm to count and predict taxi dispatch methods;

[0078] Step 3: Screen the scheduling methods after statistics and prediction by the ant colony algorithm.

[0079] When the number of passengers is low, the grid passenger capacity and grid passenger probability are high. This may be because when the number of passengers is high, traffic jams are more likely to occur, thus reducing the passenger probability in areas with high passenger numbers.

[0080] Furthermore, when calculating the grid's passenger capacity, the formula The passenger capacity of the grid is calculated; where θ is the number of empty taxis in the grid, θ1 is the number of empty taxis passing through the grid, θ2 is the number of empty taxis outside the grid, and d θi is the Euclidean distance between an empty taxi outside the grid and the grid.

[0081] Furthermore, when screening the dispatching methods after statistics and prediction by the ant colony algorithm, the cruising distance of the taxi after completing a passenger trip is less than or equal to five kilometers.

[0082] If the distance between the starting point of an empty taxi and the potential profit grid increases indefinitely, the cruising cost of the taxi will also increase accordingly. Therefore, the distance between the starting point of an empty taxi and the potential profit grid needs to be limited. According to actual experience and survey data, after a taxi completes a service behavior, the time from the next passenger event is about 15 minutes. Calculated according to the average cruising speed of the taxi, the average cruising speed is 20 kilometers per hour, and the approximate cruising length is 5 kilometers.

[0083] Furthermore, when screening the scheduling methods after statistics and prediction by the ant colony algorithm, the number of taxis existing in the grid during the same time period is less than the predicted total number of passengers in the grid.

[0084] The grid's passenger carrying capacity is limited, so the number of taxis passing through each grid is also limited. The grid occupancy rate constraint represents the limit on the number of empty taxis passing through the grid.

[0085] Furthermore, when screening the scheduling methods after statistics and prediction by the ant colony algorithm, the passenger carrying capacity of the grid is greater than or equal to eighty percent of the predicted total number of passengers in the grid.

[0086] When the grid's passenger carrying capacity is greater than or equal to eighty percent of the predicted total number of passengers in the grid, passengers can get a taxi within ten minutes of waiting, which can increase the taxi's net profit without affecting passengers' travel.

[0087] Furthermore, the ant colony algorithm includes the following steps:

[0088] Step 1: Initialize the parameters of the ant colony, and set the number of initial iterations to 0 and the initial time to 0;

[0089] Step 2: Use the nearest neighbor method to search for empty taxis around each potential profit grid and assign a certain number of empty taxis to each potential profit grid;

[0090] Step 3: Use the nearest neighbor method to generate an initial solution of the grid where the empty taxis are located. Place m ants in the generated initial solution grid and assign a set pheromone concentration on each grid based on the predicted total number of passengers in different grids.

[0091] Step 4: Create a taboo table for each ant;

[0092] Step 5: Calculate the path length of each ant and the pheromone increase on each grid;

[0093] Step 6: Perform statistics and enter the next cycle.

[0094] like Figure 2 As shown, the present invention also provides a traffic load auxiliary dispatching system based on CUBE data, the system comprising:

[0095] The collection module is used to investigate the passenger flow in different areas, divide the urban map into different grid areas according to different passenger flow rates, and divide the map into different grid areas according to the difference in passenger flow rates;

[0096] Generation module, used to establish taxi cost and profit model algorithms, and to establish taxi cruising cost model;

[0097] The dispatch module is used to dispatch taxis based on the passenger flow differences in different grid areas;

[0098] The auxiliary module is used to calculate the optimal taxi cruising path based on the taxi cruising cost model and passenger carrying probability, and complete auxiliary scheduling.

[0099] Compared with the prior art, the present invention provides the following beneficial effects:

[0100] 1. The present invention predicts and screens the cruising paths of taxis by using an ant colony algorithm, thereby increasing the probability of taxis carrying passengers and increasing the net profit of taxis.

[0101] 2. The present invention predicts and screens the cruising paths of taxis by using the ant colony algorithm, which can better distribute passenger carrying capacity to different areas, reduce passengers' waiting time, and facilitate passengers' travel.

[0102] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art may still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein with equivalents. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A traffic load auxiliary dispatching method based on CUBE data, characterized by: The following steps are involved: The passenger flow in different areas is investigated, and the urban map is divided according to different passenger flows, and the map is divided into different grid areas according to the difference in passenger flows; wherein, the passenger flow investigation in different areas includes investigating the passenger pick-up and drop-off points. If the change in the taxi vehicle position collected by the GPS positioning system within a certain time interval T is less than the threshold, and the taxi stops at a certain position and remains above the time threshold, and the location is not an intersection, it is determined to be a traffic jam or passengers are getting on and off. Then, it is judged based on the number of nearby vehicles. If the number of vehicles within 50m is less than the threshold, it is determined that the The location is the passenger's pick-up or drop-off point, and the specific status of the location is determined based on the previous status of the vehicle; a taxi cost and profit model algorithm is established, and a taxi cruising cost model is established; taxis are dispatched according to the difference in passenger flow in different grid areas; wherein, when dispatching taxis according to the difference in passenger flow in different grid areas, the following steps are included: calculating the empty and passenger probabilities of all grids, and calculating the passenger capacity of all grids; using the ant colony algorithm to count and predict the taxi dispatching method; screening the dispatching method after the statistics and prediction of the ant colony algorithm; when calculating the passenger capacity of the grid, the formula The passenger capacity of the computing grid is calculated; is the number of idle taxis in the grid, is the number of empty taxis passing through the grid, is the number of idle taxis outside the grid, is the Euclidean distance between an empty taxi outside the grid and the grid; the optimal taxi cruising path is calculated based on the taxi cruising cost model and the passenger probability to complete the auxiliary scheduling.

2. The CUBE data-based traffic load auxiliary dispatching method according to claim 1, characterized in that: The passenger flow survey in different areas includes: a survey on passengers' boarding points, a survey on passengers' boarding time, a survey on the length of passengers' riding routes and a survey on passengers' alighting points, wherein the coordinates of the passenger boarding point survey and the passenger alighting point survey are both obtained through the GPS positioning system.

3. The CUBE data-based traffic load auxiliary dispatching method according to claim 2, characterized in that: The passenger's travel route length survey includes: Using the GPS positioning system to obtain several groups of coordinate points 1, 2, 3 / 4...n of the taxi vehicle when the passenger is boarding; According to the obtained coordinate points of several taxi vehicles and the formula Calculate the length of the passenger's ride path, where: is the distance from the i-th coordinate point to the i+1-th coordinate point, is the total distance traveled by the passenger.

4. The CUBE data-based traffic load auxiliary dispatching method according to claim 3, characterized in that: The passenger flow survey in different regions further includes: pre-processing the acquired taxi GPS data, wherein the pre-processing of the acquired taxi GPS data includes the following steps: Eliminate unreasonable data from taxi GPS data based on spatiotemporal characteristics. When the taxi GPS data shows that the taxi is on a non-traffic road, the GPS data is eliminated. When the taxi GPS data shows that the distance from the taxi to the previous location is greater than a threshold, the GPS data is eliminated. When the GPS data of a taxi shows that the taxi is in an empty cruising state for a day, the data will be reviewed; When a taxi has been parked at a certain location for a period of time greater than a time threshold, the data will be eliminated.

5. The CUBE data-based traffic load auxiliary dispatching method according to claim 4 is characterized in that: The calculation of a taxi driver's profit involves the following steps: Step 1: Calculate the total income of taxi drivers; Step 2: Calculate the cost incurred by taxi drivers; Step 3: Calculate the taxi's profit.

6. The CUBE data-based traffic load auxiliary dispatching method according to claim 5, characterized in that: In the calculation of the cost of taxi drivers, the formula Calculate the income of a taxi carrying a passenger, where c is the starting price of the taxi, d is the length of the passenger's driving trajectory, e is the starting kilometers, f is the secondary charging price, g is the secondary charging kilometers, and h is the tertiary charging price.

7. The CUBE data-based traffic load auxiliary dispatching method according to claim 6, characterized in that: In the calculation of the cost incurred by the taxi driver, the cost incurred by the taxi driver is divided into vehicle fixed cost, vehicle transportation cost and vehicle cruising cost.

8. The CUBE data-based traffic load auxiliary dispatching system according to any one of claims 1 to 7, comprising: The collection module is used to investigate the passenger flow in different areas, divide the urban map according to different passenger flows, and divide the map into different grid areas according to the difference in passenger flows.

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