CN115049105A - Taxi track big data driven passenger carrying route recommendation method, device and medium - Google Patents

Taxi track big data driven passenger carrying route recommendation method, device and medium Download PDF

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CN115049105A
CN115049105A CN202210531460.1A CN202210531460A CN115049105A CN 115049105 A CN115049105 A CN 115049105A CN 202210531460 A CN202210531460 A CN 202210531460A CN 115049105 A CN115049105 A CN 115049105A
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CN115049105B (en
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夏大文
耿建
李华青
张文勇
徐海龙
白德伟
申冰琪
霍雨佳
冯夫健
魏嘉银
金良琼
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Guizhou Minzu University
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Abstract

The embodiment of the application discloses a taxi track big data driven passenger carrying route recommendation method, device and medium, which are used for rapidly planning the shortest route from a starting point to a terminal point for a vehicle. The method in the embodiment of the application comprises the following steps: acquiring a vehicle motion track data set of a target area; generating road network node data according to the vehicle motion track data set; generating a route recommendation model according to the road network node data, wherein the route recommendation model is fused with an heuristic algorithm and an optimization algorithm; acquiring a starting point and an end point of a user, wherein the starting point and the end point are positioned in the target area; and generating a recommended route from the starting point to the end point according to the route recommendation model.

Description

Taxi track big data driven passenger carrying route recommendation method, device and medium
Technical Field
The embodiment of the application relates to the technical field of data processing, in particular to a taxi track big data driven passenger carrying route recommendation method, device and medium.
Background
In the big data era, with the rapid development of digital technology, the pace of life becomes faster and faster, and with the pace, the travel demand of people becomes higher and higher. In order to meet the requirement of efficient travel of people, a passenger carrying route recommendation method driven by taxi track big data is developed.
In the prior art, a traditional passenger carrying route recommendation method driven by big data of a taxi track is usually based on a greedy algorithm or a genetic algorithm, and a large number of nodes are required to be visited to calculate the shortest route. Because a large number of nodes need to be accessed, the problems of large I/O overhead, high memory consumption and the like are easily caused. Meanwhile, under the conditions of huge calculation amount and limited calculation force, the shortest route can be obtained only by needing a large amount of calculation time, so that the problems of long operation time, low calculation efficiency and the like are caused.
In addition, the blind cruising in the urban traffic easily causes the problems of high fuel consumption, serious traffic jam and the like. The application provides an integration angle and A * The Gurobi optimization algorithm of the algorithm is applied to recommending an optimal passenger carrying route for taxi track big data in a complex city road network. Firstly, a road network node extraction method based on taxi GPS direction is provided, and the problem that the road network node is difficult to extract from taxi GPS track data is solved.Secondly, an angle-based sharp point elimination method (ASPE) is constructed, and the searching capability of the Gurobi algorithm is optimized to search for the shortest route. Thirdly, design a base based on A * Gurobi optimization algorithm of algorithm (A-Gurobi), using A * And a heuristic function of the algorithm enhances the rapid guiding capability from the departure place to the destination, and improves the execution efficiency of the Gurobi algorithm.
Disclosure of Invention
The embodiment of the application provides a taxi track big data driven passenger carrying route recommendation method, device and medium, and the shortest route from a starting point to a terminal point can be rapidly planned for a vehicle.
The first aspect of the embodiments of the present application provides a taxi track big data driven passenger carrying route recommendation method, including:
acquiring a vehicle motion track data set of a target area;
generating road network node data according to the vehicle motion track data set;
generating a route recommendation model according to the road network node data, wherein the route recommendation model is fused with an heuristic algorithm and an optimization algorithm;
acquiring a starting point and an end point of a user, wherein the starting point and the end point are positioned in the target area;
and generating a recommended route from the starting point to the end point according to the route recommendation model.
Optionally, the generating a route recommendation model according to the road network node data includes:
a is prepared from * Fusing a heuristic algorithm into a Gurobi algorithm to generate an A-Gurobi algorithm;
and generating a route recommendation model according to the road network node data and the A-Gurobi algorithm, wherein the road network node data is used as a parameter of the route recommendation model.
Optionally, the generating the recommended route from the starting point to the end point according to the route recommendation model includes:
respectively connecting the starting point with each road network node in the road network node data of the route recommendation model and the end point to obtain a plurality of line segments, wherein each road network node corresponds to one line segment, and the line segment formed by the starting point and the end point is a target line segment;
calculating the degree of included angles formed by the target line segment and other line segments respectively;
deleting road network nodes corresponding to the line segments of which the included angle degrees with the target line segments are greater than or equal to a first preset angle to obtain recommended road network nodes;
and generating a recommended route according to the recommended road network node, the starting point and the end point.
Optionally, the generating road network node data according to the vehicle motion trajectory data set includes:
extracting a target motion trail data set from the vehicle motion trail data set, wherein the operation state of the target motion trail data set is empty vehicle-passenger continuously, and the operation state comprises empty vehicle and passenger;
and generating road network node data according to the target motion trail data set.
Optionally, the generating road network node data according to the target motion trajectory data set includes:
extracting time, coordinates and directions of each point from the target motion trajectory data set;
determining road network nodes according to the coordinates and the directions of all the points;
and generating road network node data according to road network nodes, wherein the road network node data comprises a weighted undirected graph and an adjacency matrix.
Optionally, the determining the road network node according to the coordinate and the direction of each point includes:
for any target motion trajectory data, determining the adjacency relation between points according to the time of each point;
calculating direction variation according to the adjacency relation between the points and the coordinates of each point, wherein the direction variation is the angle of two line segments formed by one point and the two adjacent points respectively;
and when the direction variation is larger than or equal to a second preset angle, determining the point adjacent to the other two points in the three corresponding points as the road network node.
Optionally, the generating road network node data according to road network nodes includes:
determining the adjacency relation between the road network nodes;
calculating the distance between adjacent road network nodes according to the coordinates of each road network node;
generating an adjacency matrix and a weighted undirected graph according to the adjacency relation between the road network nodes and the distance between the adjacent road network nodes;
and generating road network node data according to the weighted undirected graph and the adjacency matrix.
Optionally, before extracting the target motion trajectory data set from the vehicle motion trajectory data set, the method further includes:
and deleting the isolated points in the vehicle motion trail data set.
A second aspect of the embodiments of the present application provides a taxi track big data driven passenger carrying route recommendation device, including:
the first acquisition unit is used for acquiring a vehicle motion trail data set of a target area;
the first generation unit is used for generating road network node data according to the vehicle motion trail data set;
a second generation unit, configured to generate a route recommendation model according to the road network node data, where the route recommendation model is fused with an heuristic algorithm and an optimization algorithm;
a second obtaining unit, configured to obtain a starting point and an ending point of a user, where the starting point and the ending point are located in the target area;
a third generating unit, configured to generate a recommended route from the starting point to the ending point according to the route recommendation model.
A third aspect of the embodiments of the present application provides a taxi track big data driven passenger carrying route recommendation device, including:
the device comprises a processor, a memory, an input and output unit and a bus;
the processor is connected with the memory, the input and output unit and the bus;
the memory stores a program, and the processor calls the program to execute the taxi track big data driven passenger carrying route recommendation method in any one of the first aspect and the first possible implementation manner.
A fourth aspect of the embodiments of the present application provides a computer-readable storage medium, where a program is stored in the computer-readable storage medium, and when the program is executed on a computer, the computer executes a taxi track big data driven passenger carrying route recommendation method in any one of the first aspect and the first possible implementation manner.
According to the technical scheme, the embodiment of the application has the following advantages:
according to the taxi track big data driven passenger carrying route recommendation method provided by the embodiment of the application, firstly, a vehicle motion track data set of a target area is obtained, then road network node data are generated according to the vehicle track data set, then a route recommendation model fused with an heuristic algorithm and an optimization algorithm is generated according to the road network node data, and finally a recommended route from a starting point to an end point is generated for a user according to the route recommendation model. Because the route recommendation model in the embodiment of the application is fused with the heuristic algorithm and the optimization algorithm, fewer nodes can be visited in the process of generating the recommended route, the calculation amount is reduced compared with a passenger carrying route recommendation method driven by taxi track big data based on a greedy algorithm, the recommended route can be generated more quickly under the condition of the same calculation power, and the calculation efficiency is improved. Meanwhile, the time spent by the user in traveling is reduced, and the user experience is favorably improved.
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Fig. 1 is a schematic flow chart of an embodiment of a passenger carrying route recommendation method driven by taxi track big data in the embodiment of the application;
fig. 2 is a schematic flow chart of another embodiment of a passenger carrying route recommendation method driven by taxi track big data in the embodiment of the application;
fig. 3 is a schematic structural diagram of an embodiment of a passenger carrying route recommendation device driven by taxi track big data in the embodiment of the application;
fig. 4 is a schematic structural diagram of another embodiment of a passenger carrying route recommending device driven by taxi track big data in the embodiment of the application;
fig. 5 is a schematic structural diagram of another embodiment of a passenger carrying route recommending device driven by taxi track big data in the embodiment of the application.
Detailed Description
The embodiment of the application provides a passenger carrying route recommending method, device and medium driven by taxi track big data, and the method, device and medium are used for rapidly planning the shortest route from a starting point to a terminal point for a vehicle.
The method of the present application may be applied to a server, a terminal, or other devices with logic processing capability, and the present application is not limited thereto. For convenience of description, the following description will be made taking the execution subject as a server as an example.
Embodiments in the present application will be described below with reference to the accompanying drawings.
Referring to fig. 1, an embodiment of a method for recommending a passenger carrying route driven by taxi track big data in the embodiment of the present application includes:
101. the method comprises the steps that a server obtains a vehicle motion track data set of a target area;
in practical applications, the generation of the recommended route is based on the big data of the movement track, and the relevant information needs to be extracted from a large amount of data, so the server needs to acquire the vehicle movement track data set of the target area firstly. It should be noted that the vehicle motion trajectory data set includes large-scale vehicle motion trajectory data, each piece of vehicle motion trajectory data is composed of a plurality of points, each point has corresponding attribute information, each piece of vehicle motion trajectory data corresponds to a vehicle, and a vehicle may include one or more pieces of vehicle motion trajectory data, which is not limited herein.
102. The server generates road network node data according to the vehicle motion track data set;
after obtaining the vehicle motion trajectory data set, the server may process the vehicle trajectory data set to generate road network node data, where the road network node data includes a plurality of road network nodes and distances between adjacent road network nodes.
103. The server generates a route recommendation model according to the road network node data;
after the road network node data is generated, the server can generate a route recommendation model according to the road network node data, and provide a route recommendation service for the vehicle through the route recommendation model. It should be noted that the route recommendation model is fused with heuristic algorithm and optimization algorithm, and has attributes of heuristic algorithm and optimization algorithm. That is, the route recommendation model can generate the shortest route from the road network node data of itself in a short time by providing the start point and the end point.
104. The server acquires a starting point and an end point of a user;
if a recommended route is generated for the user, the starting point and the ending point of the user need to be obtained firstly, and the starting point and the ending point are located in the target area, so that the server obtains the starting point and the ending point of the user. It should be noted that the starting point and the ending point are both located in the target area, and both the starting point and the ending point are represented in a coordinate form.
105. The server generates a recommended route from the starting point to the end point according to the route recommendation model.
After the server obtains the starting point and the end point of the user, the starting point and the end point can be input into a route recommendation model, and a recommended route from the starting point to the end point is generated through the route recommendation model. After the recommended route is generated, the recommended route is fed back to the user, so that the user can start from the starting point and drive to the end point according to the recommended route, and the travel plan is completed.
It should be noted that, in this embodiment, the taxi track big data driven passenger carrying route recommendation method may be executed in a stand-alone server or a distributed server, and is not limited herein. When the task is executed on the distributed server, the task is split to a plurality of servers, and the task can be executed on the plurality of servers in parallel, so that the execution efficiency can be improved.
In this embodiment, the server first obtains a vehicle motion trajectory data set of a target area, then generates road network node data according to the vehicle trajectory data set, then generates a route recommendation model that is integrated with an heuristic algorithm and an optimization algorithm according to the road network node data, and finally generates a recommended route from a starting point to an end point for a user according to the route recommendation model. Because the route recommendation model in the embodiment of the application is fused with the heuristic algorithm and the optimization algorithm, fewer nodes can be visited in the process of generating the recommended route, and compared with a taxi track big data driven passenger-carrying route recommendation method based on the greedy algorithm, the calculation amount is reduced, the recommended route can be generated more quickly under the condition of the same calculation power, and the calculation efficiency is improved. Meanwhile, the time spent by the user in traveling is reduced, and the user experience is favorably improved.
Referring to fig. 2, in an embodiment of the present application, another embodiment of a method for recommending a passenger carrying route driven by taxi track big data includes:
201. the method comprises the steps that a server obtains a vehicle motion track data set of a target area;
in this embodiment, step 201 is similar to step 101 in the previous embodiment, and is not described herein again.
202. The server deletes isolated points in the vehicle motion trail data set;
after the server obtains the vehicle motion track data set, any piece of unprocessed vehicle motion track data can be obtained, whether points in the vehicle motion track data are continuous or not is judged, if points which are discontinuous with other points are found, isolated points, namely junk data, can be judged, then deletion processing is executed, and after the execution is finished, the vehicle motion track data is marked to be the isolated points and deleted. And the server repeatedly executes the steps of acquiring the vehicle motion trail data and judging whether isolated points exist or not until all the vehicle motion trail data are processed.
203. The server extracts a target motion trail data set from the vehicle motion trail data set;
the server may perform extraction of the target motion trajectory data set from the vehicle motion trajectory data set after deleting the isolated point of the isolated points in the vehicle motion trajectory data set. Specifically, any piece of vehicle motion trajectory data for data extraction is sequentially acquired, then data with operation states of empty vehicle, passenger carrying and passenger carrying continuously is extracted and serves as target motion trajectory data, the operation states include two states of empty vehicle and passenger carrying, it needs to be noted that after one section of empty vehicle, passenger carrying and passenger carrying is extracted, data extraction is performed again from the next continuous point, and therefore each section of extracted empty vehicle, passenger carrying and passenger carrying is not repeated. After data extraction is carried out on one piece of vehicle motion track data, the vehicle motion track data is marked as the extracted target motion track data, then data extraction operation is carried out on the next piece of vehicle motion track data which is not marked as the extracted target motion track data until all pieces of vehicle motion track data are marked as the extracted target motion track data, and therefore a target motion track data set can be obtained. It should be noted that the vehicle motion trajectory data is composed of a plurality of operation state segments, and each operation state segment is composed of a plurality of continuous points.
204. The server extracts the time and the coordinates of each point from the target motion trajectory data set;
after extracting the target motion trajectory data set, the server may extract information of time, coordinates and direction of each point, thereby generating road network node data using the information.
205. The server determines the adjacency relation between the points according to the time of each point for any target motion trajectory data;
for any one target motion trajectory data, the server can determine the adjacency relation of points between the target motion trajectory data according to the time of each point in the target motion trajectory data. Wherein, for any one point, among all points before the time of the point, the point corresponding to the time closest to the time of the point is adjacent to the point; among all points subsequent to the point in time, a point corresponding to a time closest to the point in time is adjacent to the point.
206. The server calculates the direction variation according to the adjacency relation between the points and the coordinates of each point;
after obtaining the adjacency relation between the points of the target motion trajectory data, the server may calculate the direction change amount based on the adjacency relation between the points and the coordinates of the points. Specifically, the server first obtains a target point, where the target point is a point having two adjacent points, then calculates linear functions of the target point and the two adjacent points according to the coordinates of the target point and the coordinates of the two adjacent points of the target point, and then calculates angles of the two linear functions, where the angle is a direction variation corresponding to the target point, the direction variation represents an angle at which the vehicle turns at the target point, and the direction variation corresponds to 3 points, and is the target point and the two adjacent points of the target point. After calculating the direction variation of a target point, the server marks the target point as the calculated direction variation, and then calculates the direction variation of the next target point which is not marked as the calculated direction variation until the direction variations of all the target points are completely calculated.
207. When the direction variation is larger than or equal to a second preset angle, the server determines that the point adjacent to the other two points in the three corresponding points is a road network node;
in the process of calculating the direction variation in the target motion trajectory data, the server may determine, every time one direction variation is calculated, the direction variation, and if the direction variation is greater than or equal to a second preset angle, determine that a point adjacent to both of the other two points in the three points corresponding to the direction variation is a road network node. It should be noted that the second preset angle is generally set to 90 °, but may be set to other degrees, and is not limited herein.
The server may determine the road network node not only during the process of calculating the direction variation in the target motion trajectory data, but also after calculating all the direction variations in the target motion trajectory data, and the determination of the road network node is not limited herein.
The server repeatedly executes steps 205 to 207, thereby determining the road network nodes of all the target motion trajectory data.
208. The server determines the adjacency relation between the road network nodes;
due to the fact that the data volume of the acquired vehicle motion track data set of the target area is huge, under the condition of large-scale data, road network nodes obtained by the server are necessarily connected directly or indirectly. Since the adjacency relation between the road network nodes in each piece of target motion trajectory data is determined, the server can determine the adjacency relation between all the road network nodes after obtaining all the road network nodes of the target motion trajectory data.
209. The server calculates the distance between the adjacent road network nodes according to the coordinates of each road network node;
after determining the adjacency relation between all the road network nodes, the server can calculate the distance between every two adjacent road network nodes according to the coordinates of the road network nodes. It should be noted that the distance calculated here is an euclidean distance, that is, a straight-line distance between two adjacent road network nodes.
210. The server generates an adjacency matrix and a weighted undirected graph according to the adjacency relation between the road network nodes and the distance between the adjacent road network nodes;
the server may generate an undirected graph with rights for all road network nodes based on adjacency relationships between all road network nodes and distances between adjacent road network nodes. Meanwhile, the server may generate an adjacency matrix for all the road network nodes according to the adjacency relationship among all the road network nodes, where the adjacency matrix is used to represent the adjacency matrix of all the road network nodes. The graph is a data structure composed of a vertex set V and an edge set E between vertices, and is represented by a dyad G (V, E), and the weighted undirected graph vertex set is V ═ V { (V) 1 ，V 2 ，V 3 ，V 4 And E, edge set is E ═ E 12 ，e 13 ，e 21 ，e 23 ，e 24 ，e 31 ，e 32 ，e 34 ，e 42 ，e 43 The weights may be from one vertex to anotherThe distance of one vertex is represented. In this embodiment, all the road network nodes are taken as a vertex set, a straight line segment between two adjacent road network nodes is taken as an edge set, and a distance between two adjacent road network nodes is taken as a weight.
211. The server generates road network node data according to the weighted undirected graph and the adjacent matrix;
after the weighted undirected graph and the adjacency matrix are generated, the server also generates road network node data, and the road network node data is the combination of the weighted undirected graph and the adjacency matrix.
212. The server sends A * Fusing a heuristic algorithm into a Gurobi algorithm to generate an A-Gurobi algorithm;
the server can compare A with * And the heuristic algorithm is fused into the Gurobi algorithm to generate the A-Gurobi algorithm. Specifically, A is * A heuristic function of the algorithm is fused into the Gurobi algorithm, and the heuristic function is shown as a formula (1). Since the Gurobi algorithm belongs to the optimization algorithm, A * A heuristic function of the algorithm is fused into an A-Gurobi algorithm generated by the Gurobi algorithm, and the A-Gurobi algorithm has the characteristics of the optimization algorithm and the heuristic algorithm.
f (n) ═ g (n) + h (n) (formula 1)
Where f (n) represents the cost to the destination point, g (n) represents the cost from the original node to an arbitrary node n, and h (n) represents the heuristically evaluated cost from node n to the destination point.
213. The server generates a route recommendation model according to the road network node data and an A-Gurobi algorithm;
after the road network node data and the A-Gurobi algorithm are generated, the server can generate a route recommendation model according to the road network node data and the A-Gurobi algorithm, and provide a route recommendation service for the user through the route recommendation model. It should be noted that the route recommendation model is fused with heuristic algorithm and optimization algorithm, and has attributes of heuristic algorithm and optimization algorithm. That is, the route recommendation model can generate the shortest route from the road network node data of itself in a short time by providing the start point and the end point.
214. The server acquires a starting point and an end point of a user;
in this embodiment, step 214 is similar to step 104 in the previous embodiment, and is not described herein again.
215. The server respectively connects the starting point with each road network node and the end point in the road network node data of the route recommendation model to obtain a plurality of line segments, wherein each road network node corresponds to one line segment, and the line segment formed by the starting point and the end point is a target line segment;
after obtaining the start point and the end point of the user, the server may connect the start point with the end point and each road network node in the road network node data of the route recommendation model by using a straight line segment, so as to obtain a plurality of line segments. Each line segment corresponds to a road network node or end point, and each road network node or end point also corresponds to a line segment. Among all the line segments, the server may determine the line segment corresponding to the end point as a target line segment, which represents a displacement from the start point to the end point.
216. The server calculates the degree of included angles formed by the target line segment and other line segments respectively;
because the end point of each line segment comprises the starting point, all the line segments are directly connected, so that an included angle can be formed between the line segments, and the server can respectively calculate the included angle between the target line segment and each of the other line segments. For convenience of calculation, the server may establish a rectangular coordinate system with the origin of coordinates as the origin of coordinates, the east-west direction as the X axis, and the north-south direction as the Y axis, and calculate the included angle between the target line segment and each of the other line segments respectively.
217. The server deletes the road network nodes corresponding to the line segments of which the included angle degree formed by the server and the target line segment is greater than or equal to a first preset angle to obtain recommended road network nodes;
in practical applications, when an included angle between a line segment corresponding to a road network node and a target line segment exceeds a certain degree, the line segment does not usually pass through the road network node. Therefore, the server may delete the road network node corresponding to the line segment whose included angle with the target line segment is greater than or equal to the first preset angle, and leave the road network node, which is the recommended road network node.
In this embodiment, the server deletes the road network nodes other than the recommended road network node, which can reduce the calculation amount of the route recommendation model, and is beneficial to improving the operation efficiency of generating the recommended route, and meanwhile, can save the resource overhead and reduce unnecessary resource waste.
218. And the server generates a recommended route according to the recommended road network node, the starting point and the end point.
After obtaining the recommended road network nodes, the server may use the recommended road network nodes, the starting point and the end point as parameters of a route recommendation model, and generate a recommended route by using an a-Gurobi algorithm in the route recommendation model.
Referring to fig. 3, an embodiment of the passenger carrying route recommending device driven by big data of taxi track in the embodiment of the present application includes:
a first obtaining unit 301, configured to obtain a vehicle motion trajectory data set of a target area;
a first generating unit 302, configured to generate road network node data according to the vehicle motion trajectory data set;
a second generating unit 303, configured to generate a route recommendation model according to the road network node data, where the route recommendation model is fused with an heuristic algorithm and an optimization algorithm;
a second obtaining unit 304, configured to obtain a start point and an end point of the user, where the start point and the end point are located in the target area;
a third generating unit 305 for generating a recommended route from the start point to the end point according to the route recommendation model.
In the embodiment, the first obtaining unit 301 first obtains a vehicle motion trajectory data set of a target area, then the first generating unit 302 generates road network node data according to the vehicle trajectory data set, then the second generating unit 303 generates a route recommendation model fusing a heuristic algorithm and an optimization algorithm according to the road network node data, and finally the third generating unit 305 generates a recommended route from a starting point to an end point for a user according to the route recommendation model. Because the route recommendation model in the embodiment of the application is fused with the heuristic algorithm and the optimization algorithm, the server can visit fewer nodes in the process of generating the recommended route, the calculation amount is reduced compared with a passenger carrying route recommendation method driven by taxi track big data based on a greedy algorithm, the recommended route can be generated more quickly under the condition of the same calculation power, and the operation efficiency is improved. Meanwhile, the travel time of the user is reduced, and the user experience is favorably improved.
Referring to fig. 4, another embodiment of the passenger carrying route recommending device driven by taxi track big data in the embodiment of the present application includes:
a first obtaining unit 401, configured to obtain a vehicle motion trajectory data set of a target area;
a first generating unit 402, configured to generate road network node data according to the vehicle motion trajectory data set;
a second generating unit 403, configured to generate a route recommendation model according to the road network node data, where the route recommendation model is fused with an heuristic algorithm and an optimization algorithm;
a second obtaining unit 404, configured to obtain a start point and an end point of the user, where the start point and the end point are located in the target area;
a third generating unit 405, configured to generate a recommended route from the starting point to the ending point according to the route recommendation model.
In this embodiment, the second generating unit 403 is specifically configured to:
a is to be * Fusing a heuristic algorithm into a Gurobi algorithm to generate an A-Gurobi algorithm;
and generating a route recommendation model according to the road network node data and an A-Gurobi algorithm, wherein the road network node data is used as a parameter of the route recommendation model.
The third generating unit 405 is specifically configured to:
respectively connecting a starting point with each road network node and an end point in road network node data of a route recommendation model to obtain a plurality of line segments, wherein each road network node corresponds to one line segment, and the line segment formed by the starting point and the end point is used as a target line segment;
calculating the degree of included angles formed by the target line segment and other line segments respectively;
deleting road network nodes corresponding to the line segments of which the included angle degrees with the target line segments are greater than or equal to a first preset angle to obtain recommended road network nodes;
and generating a recommended route according to the recommended road network node, the starting point and the end point.
In this embodiment, the first generating unit 402 may include an extracting module 4021, a generating module 4022, and a deleting module 4023.
The extracting module 4021 is configured to extract a target motion trajectory data set from the vehicle motion trajectory data set, where the operation state of the target motion trajectory data set is continuously empty, loaded with passengers, and the operation state includes empty and loaded with passengers.
The generating module 4022 may include an extracting sub-module 40221, a determining sub-module 40222, and a generating sub-module 40223.
The extracting sub-module 40221 is configured to extract the time, coordinates, and direction of each point from the target motion trajectory data set.
A determination sub-module 40222 for:
for any target motion trajectory data, determining the adjacency relation between points according to the time of each point;
calculating direction variation according to the adjacency relation between the points and the coordinates of each point, wherein the direction variation is an angle between two line segments respectively formed by one point and the two adjacent points;
and when the direction variation is larger than or equal to a second preset angle, determining the point adjacent to the other two points in the three corresponding points as the road network node.
Generate submodule 40223 for:
determining the adjacency relation between the road network nodes;
calculating the distance between adjacent road network nodes according to the coordinates of each road network node;
generating an adjacency matrix and a weighted undirected graph according to the adjacency relation between the road network nodes and the distance between the adjacent road network nodes;
and generating road network node data according to the weighted undirected graph and the adjacency matrix.
In this implementation, the functions of each unit and each module correspond to the steps in the embodiment shown in fig. 2, and are not described herein again.
Referring to fig. 5, another embodiment of the passenger carrying route recommending device driven by taxi track big data in the embodiment of the present application includes:
a processor 501, a memory 502, an input-output unit 503, and a bus 504;
the processor 501 is connected with the memory 502, the input/output unit 503 and the bus 504;
the memory 502 holds a program that the processor 501 calls to perform in the embodiment shown in fig. 1-2.
In this embodiment, the functions of the processor 501 correspond to the steps in the embodiments shown in fig. 1 to fig. 2, which are not described herein again.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus, and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and in actual implementation, there may be other divisions, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. Furthermore, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed to a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a hardware form, and can also be realized in a software functional unit form.
The integrated unit, if implemented as a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be essentially or partially understood to be a contribution to the prior art, or all or part of the technical solution may be embodied in a software product stored in a storage medium, and include instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like.

Claims (11)

1. A taxi track big data driven passenger carrying route recommendation method is characterized by comprising the following steps:
acquiring a vehicle motion track data set of a target area;
generating road network node data according to the vehicle motion track data set;
generating a route recommendation model according to the road network node data, wherein the route recommendation model is fused with an heuristic algorithm and an optimization algorithm;
acquiring a starting point and an end point of a user, wherein the starting point and the end point are positioned in the target area;
and generating a recommended route from the starting point to the end point according to the route recommendation model.
2. The taxi track big data driven passenger carrying route recommendation method according to claim 1, wherein the generating a route recommendation model according to the road network node data comprises:
a is to be * Fusing a heuristic algorithm into a Gurobi algorithm to generate an A-Gurobi algorithm;
and generating a route recommendation model according to the road network node data and the A-Gurobi algorithm, wherein the road network node data is used as a parameter of the route recommendation model.
3. The taxi track big data driven passenger carrying route recommendation method according to claim 1, wherein the generating of the recommended route from the starting point to the end point according to the route recommendation model comprises:
respectively connecting the starting point with each road network node in the road network node data of the route recommendation model and the end point to obtain a plurality of line segments, wherein each road network node corresponds to one line segment, and the line segment formed by the starting point and the end point is a target line segment;
calculating the degree of an included angle formed by the target line segment and other line segments;
deleting the road network nodes corresponding to the line segments of which the included angles formed with the target line segments are more than or equal to a first preset angle to obtain recommended road network nodes;
and generating a recommended route according to the recommended road network node, the starting point and the end point.
4. The taxi track big data driven passenger carrying route recommendation method according to any one of claims 1 to 3, wherein the generating road network node data according to the vehicle motion track data set comprises:
extracting a target motion trail data set from the vehicle motion trail data set, wherein the operation state of the target motion trail data set is empty vehicle-passenger continuously, and the operation state comprises empty vehicle and passenger;
and generating road network node data according to the target motion trail data set.
5. The taxi track big data driven passenger carrying route recommendation method according to claim 4, wherein the generating road network node data according to the target motion track data set comprises:
extracting the time and the coordinates of each point from the target motion trajectory data set;
determining road network nodes according to the time and the coordinates of each point;
and generating road network node data according to road network nodes, wherein the road network node data comprises a weighted undirected graph and an adjacency matrix.
6. The taxi track big data driven passenger carrying route recommendation method according to claim 5, wherein the determining road network nodes according to the time and the coordinates of each point comprises the following steps:
for any target motion trajectory data, determining the adjacency relation between points according to the time of each point;
calculating direction variation according to the adjacency relation between the points and the coordinates of each point, wherein the direction variation is the angle of two line segments formed by one point and the two adjacent points respectively;
and when the direction variation is larger than or equal to a second preset angle, determining the point adjacent to the other two points in the three corresponding points as the road network node.
7. The taxi track big data driven passenger carrying route recommendation method according to claim 5, wherein the generating of road network node data according to road network nodes comprises:
determining the adjacency relation between the road network nodes;
calculating the distance between adjacent road network nodes according to the coordinates of each road network node;
generating an adjacency matrix and a weighted undirected graph according to the adjacency relation between the road network nodes and the distance between the adjacent road network nodes;
and generating road network node data according to the weighted undirected graph and the adjacency matrix.
8. The taxi track big data driven passenger carrying route recommendation method according to claim 4, wherein before extracting the target motion track data set from the vehicle motion track data set, the method further comprises:
and deleting the isolated points in the vehicle motion trail data set.
9. A taxi track big data driven passenger carrying route recommendation device is characterized by comprising:
the first acquisition unit is used for acquiring a vehicle motion trail data set of a target area;
the first generation unit is used for generating road network node data according to the vehicle motion trail data set;
a second generation unit, configured to generate a route recommendation model according to the road network node data, where the route recommendation model is fused with an heuristic algorithm and an optimization algorithm;
a second obtaining unit, configured to obtain a start point and an end point of a user, where the start point and the end point are located in the target area;
a third generating unit, configured to generate a recommended route from the starting point to the ending point according to the route recommendation model.
10. A taxi track big data driven passenger carrying route recommendation device is characterized by comprising:
the device comprises a processor, a memory, an input and output unit and a bus;
the processor is connected with the memory, the input and output unit and the bus;
the memory stores a program, and the processor calls the program to execute the taxi track big data driven passenger carrying route recommendation method according to any one of claims 1 to 8.
11. A computer-readable storage medium having stored therein a program which, when executed on a computer, causes the computer to execute the taxi track big data driven passenger route recommendation method according to any one of claims 1 to 8.
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