CN112150017A - Urban passenger transport resource allocation method based on internet big data - Google Patents
Urban passenger transport resource allocation method based on internet big data Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000013468 resource allocation Methods 0.000 title claims abstract description 13
- 238000004088 simulation Methods 0.000 claims abstract description 21
- 230000029305 taxis Effects 0.000 claims abstract description 18
- 238000012216 screening Methods 0.000 claims abstract description 7
- 230000011664 signaling Effects 0.000 claims description 26
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/067—Enterprise or organisation modelling
Abstract
The invention discloses an urban passenger transport resource allocation method based on internet big data, which comprises the following steps: acquiring personnel position coordinates of each personnel within a carrying range of a taxi company; the method comprises the steps of obtaining the running state of each taxi of a taxi company, screening taxis with no-load running states and obtaining the vehicle position coordinates of the taxis with no-load running states; and creating a simulation instruction, wherein the simulation instruction enables the position coordinates of the vehicles to be changed and updates the vehicle distribution images until the characteristic values of the vehicle distribution images and the characteristic values of the personnel distribution images are in a set range, and the simulation instruction at the moment is generated to each taxi with an empty running state. According to the method and the device, the distribution state of the people in the carrying range is obtained, the distribution image of the people is obtained by combining the coordinates of the people, and the taxi in the no-load state at present is reasonably scheduled according to the distribution image of the people, so that the resource allocation of the taxi is relatively balanced.
Description
Technical Field
The invention relates to the field of passenger transport resource allocation, in particular to an urban passenger transport resource allocation method based on internet big data.
Background
The operation of the taxi is completed by carrying passengers, and during the operation, the taxi comprises two running states, wherein one running state is a carrying state, the other running state is an idle state, the carrying state is a state when the passengers take the taxi, and the idle state is a state when the passengers do not take the taxi. The taxi needs to obtain more income, the carrying state time needs to occupy most proportion, namely the carrying rate is large, the carrying rate is the ratio of the carrying state time of the taxi to the sum of the carrying state time and the no-load state time, the carrying rate enables taxi drivers to have more income, at present, a taxi company does not have an effective dispatching for the operation of the taxi, so that the resources of the taxi cannot be reasonably configured, the waiting time of people is long, meanwhile, some taxi drivers cannot timely pull passengers, and the integral carrying rate of the taxi is low.
Disclosure of Invention
The invention aims to overcome the problems in the prior art and provide an urban passenger transport resource allocation method based on internet big data.
Therefore, the invention provides an urban passenger transport resource allocation method based on internet big data, which comprises the following steps:
(1) acquiring personnel position coordinates of each personnel within a carrying range of a taxi company;
(2) establishing a blank first picture, enabling each personnel position coordinate to correspond to one pixel point on the first picture, and when the number of personnel corresponding to the personnel position coordinate is increased by one, increasing a set numerical value for the pixel point value corresponding to the personnel position coordinate, traversing all the pixel points of the first picture, and obtaining a personnel distribution image;
(3) the method comprises the steps of obtaining the running state of each taxi of a taxi company, screening taxis with no-load running states and obtaining the vehicle position coordinates of the taxis with no-load running states;
(4) establishing a blank second picture, enabling each vehicle position coordinate to correspond to one pixel point on the second picture, when the number of vehicles corresponding to the vehicle position coordinate is increased by one, increasing a set numerical value by the pixel value of the pixel point corresponding to the vehicle position coordinate, traversing all the pixel points of the second picture, and obtaining a vehicle distribution image;
(5) and creating a simulation instruction, wherein the simulation instruction enables the position coordinates of the vehicles to change and updates the vehicle distribution images until the characteristic values of the vehicle distribution images and the characteristic values of the personnel distribution images are in a set range, and the simulation instruction at the moment is generated to each taxi with an empty running state.
Further, when acquiring the personnel position coordinates of each personnel in the carrying range of the taxi company, the method comprises the following steps:
(1) acquiring signaling information within a carrying range of a taxi company from a communication company;
(2) screening user identity information corresponding to each piece of signaling information to enable each piece of user identity information to uniquely correspond to one piece of signaling information;
(3) and positioning each piece of screened signaling information through a base station, and using the position information corresponding to each piece of signaling obtained by positioning as the position coordinate of the personnel.
Further, the personnel position coordinates are represented by longitude and latitude coordinates.
Further, when acquiring the personnel position coordinates of each personnel in the carrying range of the taxi company, the method comprises the following steps:
(1) acquiring user position information and user equipment IDs (identities) collected by all APPs, and summarizing to enable each user equipment ID to be located corresponding to one user position information;
(2) and inquiring user identity information in a communication company according to the user equipment ID, and carrying out one-to-one correspondence and repeated combination on the user identity information and the user position information, so that each piece of user identity information uniquely corresponds to one piece of user position information, and the user position information is used as the personnel position coordinate.
Further, the simulation instruction is a vehicle position coordinate to which the vehicle is to arrive, and the vehicle position coordinate to which the vehicle is to arrive falls within a range in which the original vehicle position coordinate is used as a circle center and the set distance is used as a radius.
Further, the set distance of the radius is sequentially increased at each time of updating the vehicle distribution image.
The urban passenger transport resource allocation method based on the internet big data has the following beneficial effects:
1. according to the method, the state of personnel distribution in the carrying range is obtained, personnel distribution images are obtained by combining the coordinates of the personnel, and the taxi in the no-load state at present is reasonably scheduled according to the personnel distribution images, so that the resource allocation of the taxi is relatively balanced;
2. according to the invention, through acquiring the user signaling information of a communication company, acquiring the identity information of personnel from the signaling information, simultaneously corresponding each identity information to one position information, and finally acquiring the personnel distribution image according to the coordinate data in the position information, each identity information corresponds to one set gray value, and a plurality of gray values in the same position information are sequentially superposed, and finally, the gray value of each position in the personnel distribution image corresponds to the number of personnel;
3. when the taxi dispatching method is used for dispatching taxis in an idle state according to the distance, each taxi can arrive at the highest speed, and meanwhile, the taxi distribution image of the taxi is obtained according to the position of the taxi, so that the difference value of the characteristic values of the personnel distribution image and the taxi distribution image is in a certain range.
Drawings
FIG. 1 is a schematic block diagram of the overall process of the present invention;
FIG. 2 is a block diagram illustrating a first process for obtaining the coordinates of the location of each person within the range of the taxi company;
fig. 3 is a schematic block diagram of a second process of the present invention for obtaining the coordinates of the person's position for each person within the range of the taxi company.
Detailed Description
An embodiment of the present invention will be described in detail below with reference to the accompanying drawings, but it should be understood that the scope of the present invention is not limited to the embodiment.
In the present application, the type and structure of components that are not specified are all the prior art known to those skilled in the art, and those skilled in the art can set the components according to the needs of the actual situation, and the embodiments of the present application are not specifically limited.
Specifically, as shown in fig. 1, an embodiment of the present invention provides an internet big data-based urban passenger transportation resource allocation method, including the following steps:
(1) acquiring personnel position coordinates of each personnel within a carrying range of a taxi company;
(2) establishing a blank first picture, enabling each personnel position coordinate to correspond to one pixel point on the first picture, and when the number of personnel corresponding to the personnel position coordinate is increased by one, increasing a set numerical value for the pixel point value corresponding to the personnel position coordinate, traversing all the pixel points of the first picture, and obtaining a personnel distribution image;
(3) the method comprises the steps of obtaining the running state of each taxi of a taxi company, screening taxis with no-load running states and obtaining the vehicle position coordinates of the taxis with no-load running states;
(4) establishing a blank second picture, enabling each vehicle position coordinate to correspond to one pixel point on the second picture, when the number of vehicles corresponding to the vehicle position coordinate is increased by one, increasing a set numerical value by the pixel value of the pixel point corresponding to the vehicle position coordinate, traversing all the pixel points of the second picture, and obtaining a vehicle distribution image;
(5) and creating a simulation instruction, wherein the simulation instruction enables the position coordinates of the vehicles to change and updates the vehicle distribution images until the characteristic values of the vehicle distribution images and the characteristic values of the personnel distribution images are in a set range, and the simulation instruction at the moment is generated to each taxi with an empty running state.
In the present invention, the order of steps (1) and (2) cannot be reversed, the order of steps (3) and (4) cannot be reversed, step (5) is performed as the last step, and step (1), step (2), and step (3), step (4) may be reversed.
In the present invention, the taxi company carrying range refers to the range of the service carried by the taxi, for example, the carrying range of the west ampere taxi company is in the west ampere city.
In the invention, the principle of adjusting the taxis is that in places with a large number of people, the number of the taxis needing to be configured is large, namely the number of the taxis in the no-load running state needs to be configured, so that the people and the vehicles can reach a balanced state.
In the invention, the step (1) and the step (2) are used for drawing all the personnel in the carrying range of the taxi company according to the position information of the personnel to obtain a personnel distribution image, and the personnel distribution image reflects the number of the personnel at each position according to the pixel value of each pixel point on the personnel distribution image. In an image, the larger the pixel value, the brighter the color of the dot. Therefore, the brighter area in the person distribution image indicates the larger number of persons.
In the step (1), the number of people in each location area within the carrying range of the taxi company is obtained, and in the step (2), the data obtained in the step (1) is applied, wherein a blank first picture is a blank first picture under the condition that the pixel values of all the pixel points are all 0, the pixel values of the pixel points where the coordinates of the positions of the people are located are sequentially increased according to the information of the people on the coordinates of the positions of the people, the pixel values which can be increased by each person are consistent, for example, one person can increase the pixel value 30, 3 persons corresponding to the pixel points with the coordinates (1, 2) are provided, 1 person corresponding to the pixel points with the coordinates (5, 6) is provided, the pixel value of the pixel point with the coordinates (1, 2) is 30 × 3 — 90, the pixel value of the pixel point with the coordinates (5, 6) is 30 × 1 — 30, in such a way, the personnel distribution image is obtained by traversing all the pixel points of the first picture, and therefore, the personnel distribution image reflects the number of personnel at each position according to the pixel value of each pixel point on the personnel distribution image.
In the invention, the step (3) and the step (4) are used for drawing all taxis in the empty-load state within the carrying range of the taxi company according to the position information of the taxis to obtain a vehicle distribution image, and the vehicle distribution image reflects the number of the taxis in the empty-load state at each position according to the pixel value of each pixel point on the vehicle distribution image. In an image, the larger the pixel value, the brighter the color of the dot. Therefore, the brighter the area in the vehicle distribution image, the greater the number of taxis indicating an empty state.
In the invention, the carrying state and the no-load state of the taxi are determined by the taxi according to the running state of the taxi, whether a passenger exists or not can be detected by adding a sensor on a passenger seat, and the carrying state and the no-load state can also be manually set by the taxi. The position information of the taxi can be acquired and acquired through a GPS chip arranged on the taxi.
In the step (3), the number of taxis in the empty-load state in each position area within the carrying range of the taxi company is obtained, and in the step (4), the data obtained in the step (4) is applied, wherein the blank second picture is a blank second picture under the condition that the pixel value of each pixel point is 0, the pixel values of the pixel points where the taxi position coordinates in the empty-load state are located are sequentially increased according to the taxi information in the empty-load state on the taxi position coordinates in each empty-load state, and the pixel values which can be increased by each taxi in the empty-load state are consistent, for example, a taxi in the empty-load state can increase the pixel value 30, a taxi in the empty-load state corresponds to 5 pixels in coordinates (2, 9), and a taxi in the empty-load state corresponds to 0 pixel point in coordinates (7, 8), then the coordinates are (2, 9) the pixel value of the pixel point of (7, 8) is 30 × 5 ═ 150, and the pixel value of the pixel point of (7, 8) is 30 × 0 ═ 0, in this way, all the pixel points of the second picture are traversed, and the vehicle distribution image is obtained, so that the vehicle distribution image reflects the number of taxis in the no-load state at each position according to the pixel value of each pixel point on the vehicle distribution image.
In the invention, the size of the increased pixel value corresponding to each person in the step (2) is consistent with the size of the increased pixel value corresponding to each vehicle in the step (4), so that the characteristics of the images are consistent when the characteristic values of the images are extracted subsequently.
In the step (5), the distribution of the persons and the vehicles is balanced by comparing the characteristic values of the two images, when the characteristic value of the vehicle distribution image and the characteristic value of the person distribution image are in a set range, the distribution of the persons and the vehicles is considered to be balanced, for the set range, a technician reasonably sets according to the actual situation, and when the characteristic value of the vehicle distribution image is completely consistent with the characteristic value of the person distribution image, the absolute balance of the distribution of the persons and the vehicles at the moment is explained. In the step (5), the simulation instruction is an instruction sent to each taxi in the no-load state, so that the taxi runs to a set place, and the allocation balance between the people and the taxi is achieved, the simulation instruction is random, the position, reached by the taxi in the no-load state under the simulation instruction, of the taxi in the no-load state is obtained according to the simulation instruction, the vehicle distribution image is updated, until the characteristic value of the vehicle distribution image and the characteristic value of the people distribution image are within a set range, the simulation instruction is sent to the taxi in the no-load state, otherwise, the simulation instruction is continuously updated randomly, and the operation is repeated.
In an embodiment of the present invention, the personnel position coordinates are expressed by longitude and latitude coordinates. Therefore, the position of each person can be uniquely determined, meanwhile, the longitude and latitude coordinates can be distributed throughout all regions of a taxi company carrying range, and meanwhile, the longitude and latitude coordinates can be more conveniently in one-to-one correspondence with the coordinates of the pixel points of the image.
According to the invention, two modes of acquiring the personnel position coordinates of each personnel in the carrying range of the taxi company are provided, and the personnel position coordinates are acquired according to the communication signals, so that the acquired position is more accurate, the coverage area is large, and the number of users is large. Specifically, we divide it into a first mode and a second mode, and teach them separately.
The first method is as follows:
in this embodiment, as shown in fig. 2, when obtaining the personnel position coordinates of each person within the carrying range of the taxi company, the method includes the following steps:
(A) acquiring signaling information within a carrying range of a taxi company from a communication company;
(B) screening user identity information corresponding to each piece of signaling information to enable each piece of user identity information to uniquely correspond to one piece of signaling information;
(C) and positioning each piece of screened signaling information through a base station, and using the position information corresponding to each piece of signaling obtained by positioning as the position coordinate of the personnel.
In this manner, the carrier refers to a carrier, which is a common carrier, and the signaling information collects connection data with the base station in a communicable state in time. Therefore, the signaling information of all mobile phones within the carrying range of a taxi company can be obtained, the information of the mobile phone numbers corresponding to the signaling information can be obtained from the database of an operator necessarily according to the signaling information, so that each signaling information corresponds to each user identity information, meanwhile, in order to screen out a plurality of signaling information corresponding to a plurality of mobile phone numbers corresponding to one user identity information, the signaling information is repeatedly combined, so that each user identity information uniquely corresponds to one signaling information, finally, the position information corresponding to each signaling is obtained through the positioning of a base station, and the personnel position coordinates of the position information are obtained. This allows a very accurate determination of the number of people.
The second method comprises the following steps:
in this embodiment, as shown in fig. 3, when obtaining the personnel position coordinates of each person within the carrying range of the taxi company, the method includes the following steps:
(a) acquiring user position information and user equipment IDs (identities) collected by all APPs, and summarizing to enable each user equipment ID to be located corresponding to one user position information;
(b) and inquiring user identity information in a communication company according to the user equipment ID, and carrying out one-to-one correspondence and repeated combination on the user identity information and the user position information, so that each piece of user identity information uniquely corresponds to one piece of user position information, and the user position information is used as the personnel position coordinate.
In the method, the position information of the user collected by the APP at the mobile phone end is used, the position information of the user and the ID of the user, which are collected by all the APPs, are gathered, and repeated information is certainly provided, so that the user identity information is inquired in a communication company according to the ID of the user and is repeatedly combined, each piece of user identity information can uniquely correspond to one piece of user position information, and the collected user position information is used as the position coordinate of the personnel.
In this embodiment, the simulation instruction is a vehicle position coordinate to which the vehicle is to arrive, and the vehicle position coordinate to which the vehicle is to arrive falls within a range in which the original vehicle position coordinate is a center of a circle and the set distance is a radius. In brief, the original vehicle position coordinate is taken as the center of a circle, the set distance is taken as the radius, a circle is drawn, and the vehicle position coordinate to which the vehicle arrives falls in the drawn circle. Therefore, the vehicle can be scheduled according to the principle of being nearby, the position required by the personnel can be rapidly reached, the balance between the personnel and the vehicle is achieved, meanwhile, in the embodiment, the vehicle position coordinate where the vehicle needs to reach is generated through a random algorithm, and if the generated vehicle position coordinate where the vehicle needs to reach is outside the drawn circle, the vehicle position coordinate where the vehicle needs to reach is regenerated until the generated vehicle position coordinate falls inside the drawn circle.
Meanwhile, in the present embodiment, the set distance of the radius is sequentially increased at each time of updating the vehicle distribution image. Therefore, the vehicle can be dispatched more quickly, and the resource allocation balance of the personnel and the vehicle can be quickly achieved.
The above disclosure is only for a few specific embodiments of the present invention, however, the present invention is not limited to the above embodiments, and any variations that can be made by those skilled in the art are intended to fall within the scope of the present invention.
Claims (6)
1. A city passenger transport resource allocation method based on internet big data is characterized by comprising the following steps:
acquiring personnel position coordinates of each personnel within a carrying range of a taxi company;
establishing a blank first picture, enabling each personnel position coordinate to correspond to one pixel point on the first picture, and when the number of personnel corresponding to the personnel position coordinate is increased by one, increasing a set numerical value for the pixel point value corresponding to the personnel position coordinate, traversing all the pixel points of the first picture, and obtaining a personnel distribution image;
the method comprises the steps of obtaining the running state of each taxi of a taxi company, screening taxis with no-load running states and obtaining the vehicle position coordinates of the taxis with no-load running states;
establishing a blank second picture, enabling each vehicle position coordinate to correspond to one pixel point on the second picture, when the number of vehicles corresponding to the vehicle position coordinate is increased by one, increasing a set numerical value by the pixel value of the pixel point corresponding to the vehicle position coordinate, traversing all the pixel points of the second picture, and obtaining a vehicle distribution image;
and creating a simulation instruction, wherein the simulation instruction enables the position coordinates of the vehicles to change and updates the vehicle distribution images until the characteristic values of the vehicle distribution images and the characteristic values of the personnel distribution images are in a set range, and the simulation instruction at the moment is generated to each taxi with an empty running state.
2. The method for allocating urban passenger transportation resources based on internet big data as claimed in claim 1, wherein when acquiring the personnel position coordinates of each personnel within the carrying range of the taxi company, the method comprises the following steps:
acquiring signaling information within a carrying range of a taxi company from a communication company;
screening user identity information corresponding to each piece of signaling information to enable each piece of user identity information to uniquely correspond to one piece of signaling information;
and positioning each piece of screened signaling information through a base station, and using the position information corresponding to each piece of signaling obtained by positioning as the position coordinate of the personnel.
3. The method for configuring urban passenger transportation resources based on internet big data as claimed in claim 1, wherein the personnel position coordinates are expressed by longitude and latitude coordinates.
4. The method for allocating urban passenger transportation resources based on internet big data as claimed in claim 1, wherein when acquiring the personnel position coordinates of each personnel within the carrying range of the taxi company, the method comprises the following steps:
acquiring user position information and user equipment IDs (identities) collected by all APPs, and summarizing to enable each user equipment ID to be located corresponding to one user position information;
and inquiring user identity information in a communication company according to the user equipment ID, and carrying out one-to-one correspondence and repeated combination on the user identity information and the user position information, so that each piece of user identity information uniquely corresponds to one piece of user position information, and the user position information is used as the personnel position coordinate. .
5. The method as claimed in claim 1, wherein the simulation command is a vehicle position coordinate to which the vehicle is to arrive, and the vehicle position coordinate to which the vehicle is to arrive falls within a range of a set distance as a radius from the original vehicle position coordinate as a center of a circle.
6. The method as claimed in claim 5, wherein the set distance of the radius is sequentially increased at each time of updating the vehicle distribution image.
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| CN202011040790.8A CN112150017A (en) | 2020-09-28 | 2020-09-28 | Urban passenger transport resource allocation method based on internet big data |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113628302A (en) * | 2021-08-12 | 2021-11-09 | 中国民用航空飞行学院 | Flight income control prediction system |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060217885A1 (en) * | 2005-03-24 | 2006-09-28 | Mark Crady | User location driven identification of service vehicles |
| CN1932921A (en) * | 2006-09-28 | 2007-03-21 | 江苏天泽信息产业有限公司 | Method for fast positioning nearby emptying vehicle in taxi dispatching |
| CN102073970A (en) * | 2010-12-22 | 2011-05-25 | 宁波诺丁汉大学 | Method for scheduling individual taxis operated in decentralized way |
| CN102819950A (en) * | 2012-08-14 | 2012-12-12 | 王文静 | Carrying aid decision making system and method for traffic tool |
| CN103000042A (en) * | 2011-09-13 | 2013-03-27 | 柳松 | Intelligent automatic taxi scheduling system |
| CN103164954A (en) * | 2011-12-11 | 2013-06-19 | 方文淋 | System and method for remote inquiry and call of idle taxi |
| CN103985247A (en) * | 2014-04-24 | 2014-08-13 | 北京嘀嘀无限科技发展有限公司 | Taxi transport capacity scheduling system based on city taxi calling demand distribution density |
| CN105095481A (en) * | 2015-08-13 | 2015-11-25 | 浙江工业大学 | Large-scale taxi OD data visual analysis method |
| CN107577725A (en) * | 2017-08-22 | 2018-01-12 | 长安大学 | A kind of urban taxi passenger trip characteristicses visual analysis method |
| CN110033177A (en) * | 2019-03-27 | 2019-07-19 | 温州大学 | Taxi high benefit intelligence collective dispatching algorithm based on GPS big data |
-
2020
- 2020-09-28 CN CN202011040790.8A patent/CN112150017A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060217885A1 (en) * | 2005-03-24 | 2006-09-28 | Mark Crady | User location driven identification of service vehicles |
| CN1932921A (en) * | 2006-09-28 | 2007-03-21 | 江苏天泽信息产业有限公司 | Method for fast positioning nearby emptying vehicle in taxi dispatching |
| CN102073970A (en) * | 2010-12-22 | 2011-05-25 | 宁波诺丁汉大学 | Method for scheduling individual taxis operated in decentralized way |
| CN103000042A (en) * | 2011-09-13 | 2013-03-27 | 柳松 | Intelligent automatic taxi scheduling system |
| CN103164954A (en) * | 2011-12-11 | 2013-06-19 | 方文淋 | System and method for remote inquiry and call of idle taxi |
| CN102819950A (en) * | 2012-08-14 | 2012-12-12 | 王文静 | Carrying aid decision making system and method for traffic tool |
| CN103985247A (en) * | 2014-04-24 | 2014-08-13 | 北京嘀嘀无限科技发展有限公司 | Taxi transport capacity scheduling system based on city taxi calling demand distribution density |
| CN105095481A (en) * | 2015-08-13 | 2015-11-25 | 浙江工业大学 | Large-scale taxi OD data visual analysis method |
| CN107577725A (en) * | 2017-08-22 | 2018-01-12 | 长安大学 | A kind of urban taxi passenger trip characteristicses visual analysis method |
| CN110033177A (en) * | 2019-03-27 | 2019-07-19 | 温州大学 | Taxi high benefit intelligence collective dispatching algorithm based on GPS big data |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113628302A (en) * | 2021-08-12 | 2021-11-09 | 中国民用航空飞行学院 | Flight income control prediction system |
| CN113628302B (en) * | 2021-08-12 | 2023-04-07 | 中国民用航空飞行学院 | Flight income control prediction system |
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