CN113159436A - Path recommendation method, device and equipment and readable storage medium - Google Patents
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Abstract
The invention discloses a path recommendation method, a path recommendation device, a path recommendation equipment and a readable storage medium. The method comprises the following steps: receiving navigation request information, wherein the navigation request information comprises an origin and a destination; generating a traffic schedule of a target road network in a first time period according to the navigation request information and a preset road network traffic speed prediction model, wherein the target road network comprises intersection information and road section information from an origin to a destination, and the first time period is the traffic time from the origin to the destination; and searching for a recommended path from the target road network according to the traffic schedule and a preset space-time path search algorithm. According to the embodiment of the invention, the reliability of the recommended path can be improved, and the risk of secondary congestion is reduced.
Description
Technical Field
The invention belongs to the technical field of intelligent traffic, and particularly relates to a path recommendation method, a path recommendation device, a path recommendation equipment and a readable storage medium.
Background
With the rapid development of cities, the automobile holding amount is continuously increasing. In order to alleviate the problem of traffic congestion, at present, a Dynamic Route Guidance System (DRGS) can collect vehicle information on a road in real time and evaluate road conditions, calculate an optimal driving Route by combining road network information, guide the driving of vehicles, and improve the transportation efficiency of the road network. The path recommendation is based on the existing real-time road conditions and road networks. Due to complexity and variability of a road network, effective accuracy improvement cannot be brought by simple fusion of road network vehicle speed estimated values of different data sources, and more errors are possibly introduced.
The existing path recommendation method only utilizes real-time road conditions reflecting the current road network traffic condition, the congestion identification coverage rate is low, the possible congestion in the road network cannot be predicted, and even the problem that secondary congestion is generated on congested road sections of users is possible.
Disclosure of Invention
The embodiment of the invention provides a path recommendation method, a path recommendation device and a readable storage medium, which can improve the reliability of recommended paths and reduce the risk of secondary congestion.
In a first aspect, an embodiment of the present invention provides a path recommendation method, where the method includes:
receiving navigation request information, wherein the navigation request information comprises an origin and a destination;
generating a traffic schedule of a target road network in a first time period according to the navigation request information and a preset road network traffic speed prediction model, wherein the target road network comprises intersection information and road section information from an origin to a destination, and the first time period is the traffic time from the origin to the destination;
and searching for a recommended path from the target road network according to the traffic schedule and a preset space-time path search algorithm.
In some implementations of the first aspect, the road segment information includes road segment position information and road segment length information of a plurality of road segments, and the generating of the passing schedule of the target road network in the first time period according to the navigation request information and the preset road network traffic speed prediction model includes:
determining a first vehicle flow speed of each road section in a target road network in a first time period according to the navigation request information and a preset road network vehicle flow speed prediction model;
determining the passing time of each road section according to the first vehicle flow speed of each road section and the road section length information corresponding to the road section;
and obtaining a passing time table according to the passing time of each road section.
In some implementations of the first aspect, determining a first vehicle flow speed of each road segment in the target road network in the first time period according to the navigation request information and the preset road network vehicle flow speed prediction model includes:
determining a target road network according to an origin and a destination;
acquiring a second vehicle flow speed of each road section in the target road network at different sampling moments in a second time period; and the number of the first and second groups,
acquiring a third traffic flow speed of each road section in the target road network at the current moment based on the positioning information of the preset floating car;
and inputting a preset road network traffic speed prediction model into a second traffic speed and a third traffic speed in a second time period to obtain a plurality of first traffic speeds of each road section in the target road network in the first time period.
In some implementations of the first aspect, inputting the second traffic flow speed and the third traffic flow speed in the second time period into a preset road network traffic flow speed prediction model to obtain a plurality of first traffic flow speeds of each road segment in the target road network in the first time period includes:
determining the minimum speed corresponding to each road section from the second traffic flow speed corresponding to each road section at different sampling moments and the third traffic flow speed of each road section at the current moment;
determining a fourth vehicle flow speed according to the minimum speed corresponding to each road section;
and inputting the second vehicle flow speed and the fourth vehicle flow speed in the second time period into a preset road network vehicle flow speed prediction model to obtain a plurality of first vehicle flow speeds of the target road network in the first time period.
In some implementation manners of the first aspect, each sampling time corresponds to a preset time interval, and obtaining a second vehicle flow speed of each road segment in the target road network at different sampling times in a second time period includes:
acquiring signaling data of a plurality of mobile terminals of a target road network in each preset time interval;
inputting signaling data of a plurality of mobile terminals into a preset semi-supervised classification model to obtain at least one signaling sequence corresponding to each road section;
acquiring time length information of each signaling sequence;
determining the moving speed of each mobile terminal passing through the road section according to the time length information and the length information of each road section;
acquiring the average value of the moving speeds of a plurality of mobile terminals passing through a road section to obtain the traffic flow speed of the road section in each preset time interval;
and taking the traffic flow speed of each road section in each preset time interval as a second traffic flow speed of each road section at different sampling moments.
In some implementations of the first aspect, the passing time table includes intersection passing time and road section passing time, and searching for the recommended path from the target road network according to the passing time table and a preset spatiotemporal path search algorithm includes:
determining an objective function of a preset space-time path search algorithm according to the crossing passing time and the road section passing time;
and searching a recommended path from the target road network according to the target function.
In some implementations of the first aspect, the target road network includes a plurality of road segments, and the objective function is a sum of a first distance estimate from an origin to one of the road segments and a second distance estimate from one of the road segments to a destination;
the first distance estimation value is determined according to the initial distance estimation value, the crossing passing time, the first weight of the crossing passing time, the road section passing time and the second weight of the road section passing time.
In some implementations of the first aspect, the first weight of the intersection transit time and the second weight of the road segment transit time are determined according to a ratio of all intersection transit times to all road segment transit times of the target road network.
In a second aspect, an embodiment of the present invention provides a path recommendation apparatus, where the apparatus includes:
the system comprises a receiving module, a sending module and a receiving module, wherein the receiving module is used for receiving navigation request information which comprises an origin and a destination;
the data processing module is used for generating a passing time table of a target road network in a first time period according to the navigation request information and a preset road network traffic speed prediction model, wherein the target road network comprises intersection information and road section information from an origin to a destination, and the first time period is the passing time from the origin to the destination;
and the data processing module is also used for searching the recommended path from the target road network according to the traffic schedule and a preset space-time path search algorithm.
In a third aspect, the present invention provides a path recommendation apparatus, including: a processor and a memory storing computer program instructions; the processor, when executing the computer program instructions, implements the path recommendation method described in the first aspect or any of the realizable forms of the first aspect.
In a fourth aspect, the present invention provides a computer-readable storage medium, on which computer program instructions are stored, which, when executed by a processor, implement the path recommendation method according to the first aspect or any of the realizable manners of the first aspect.
The embodiment of the invention provides a path recommendation method, which is characterized in that after navigation request information is received, a traffic schedule of a target road network in a first time period is generated according to the navigation request information and a preset road network traffic speed prediction model, wherein the target road network comprises intersection information and road section information from an origin to a destination, and the first time period is the traffic time from the origin to the destination, so that the accuracy of identification of periodic congestion and sudden congestion can be effectively improved. And then, searching for a recommended path from a target road network according to the traffic schedule and a preset spatio-temporal path search algorithm, realizing path search according to a multi-time point network traffic flow speed sequence, realizing spatio-temporal path search in time and space dimensions, improving the reliability of the recommended path and reducing the risk of secondary congestion.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments of the present invention will be briefly described below, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of a path recommendation method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of cell coverage areas of different base stations according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a predictive model of traffic flow speed of a predetermined road network according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a path recommendation device according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a path recommendation device according to an embodiment of the present invention.
Detailed Description
Features and exemplary embodiments of various aspects of the present invention will be described in detail below, and in order to make objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting. It will be apparent to one skilled in the art that the present invention may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present invention by illustrating examples of the present invention.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone.
With the rapid development of cities, the automobile holding amount is continuously increasing. In order to alleviate the problem of traffic congestion, at present, a Dynamic Route Guidance System (DRGS) can collect vehicle information on a road in real time and evaluate road conditions, calculate an optimal driving Route by combining road network information, guide the driving of vehicles, and improve the transportation efficiency of the road network. The path recommendation is based on the existing real-time road conditions and road networks. Due to complexity and variability of a road network, effective accuracy improvement cannot be brought by simple fusion of road network vehicle speed estimation of different data sources, and more errors are possibly introduced.
The existing path recommendation method only utilizes real-time road conditions reflecting the current road network traffic condition, the congestion identification coverage rate is low, the possible congestion in the road network cannot be predicted, and even the problem that secondary congestion is generated on congested road sections of users is possible.
In view of the above, embodiments of the present invention provide a path recommendation method, apparatus, device, and computer-readable storage medium, which, after receiving navigation request information, generate a transit schedule of a target road network in a first time period according to the navigation request information and a preset road network traffic speed prediction model, where the target road network includes intersection information and road segment information from an origin to a destination, and the first time period is a transit time from the origin to the destination, so that accuracy of identification of periodic congestion and sudden congestion can be effectively improved. And then, searching for a recommended path from a target road network according to the traffic schedule and a preset spatio-temporal path search algorithm, realizing path search according to a multi-time point network traffic flow speed sequence, realizing spatio-temporal path search in time and space dimensions, improving the reliability of the recommended path and reducing the risk of secondary congestion.
The following describes a path recommendation method provided by an embodiment of the present invention with reference to the drawings.
Fig. 1 is a flowchart illustrating a path recommendation method according to an embodiment of the present invention. As shown in fig. 1, the method may include S110 to S130.
And S110, receiving navigation request information.
In an embodiment of the present invention, the navigation request information includes an origin and a destination.
And S120, generating a passing time table of the target road network in a first time period according to the navigation request information and a preset road network traffic speed prediction model.
The target road network comprises intersection information and road section information from an origin to a destination, and the first time period is the passing time from the origin to the destination.
In some embodiments, the link information includes link position information and link length information of the plurality of links.
In the embodiment S120 of the present invention, the obtaining of the transit schedule of the target road network in the first time period may specifically include S121 to S123.
S121, obtaining a first vehicle flow speed of each road section in the target road network in a first time period according to the navigation request information and a preset road network vehicle flow speed prediction model.
Specifically, the obtaining of the first vehicle flow speed of each road segment in the target road network in the first time period may specifically include S210 to S230.
And S210, determining a target road network according to the starting place and the destination.
In some embodiments, because the origin and the destination are carried in the navigation request information, the range of the target road network is determined by combining the intersection information and the road section information from the origin to the destination, and the irrelevant road sections are reduced, so that the calculation amount can be reduced, and the calculation speed can be increased.
S220, obtaining a second vehicle flow speed of each road section in the target road network at different sampling moments in a second time period; and acquiring a third traffic flow speed of each road section in the target road network at the current moment based on the positioning information of the preset floating car.
After determining the target road network, a second vehicle flow speed of each road segment in the target road network at different sampling time points in a second time period may be obtained, wherein the second time period is a time period before the current time point.
In some embodiments, each sampling instant corresponds to a preset time interval. It will be appreciated that the second time period comprises a plurality of preset time intervals. The specific step of acquiring the second traffic speed of each road segment in the target road network may include: firstly, signaling data of a plurality of mobile terminals of a target road network in each preset time interval can be acquired; next, inputting signaling data of a plurality of mobile terminals into a preset semi-supervised classification model to obtain at least one signaling sequence corresponding to each road section; then, acquiring the time length information of each signaling sequence; determining the moving speed of each mobile terminal passing through the road section according to the time length information and the length information of each road section; then, obtaining the average value of the moving speeds of the plurality of mobile terminals passing through the road section to obtain the traffic flow speed of the road section in each preset time interval; and finally, taking the traffic flow speed of each road section in each preset time interval as a second traffic flow speed of each road section at different sampling moments.
In the embodiment of the present invention, the floating car refers to an automobile which is installed with a vehicle-mounted positioning device and runs on a road, wherein a mobile terminal of a user may also be associated with the floating car as the positioning device of the floating car, and the positioning device may include a bus, a taxi, and the like. The positioning device can obtain the running information of the floating vehicle, such as vehicle position, direction, speed information and the like during running. The real-time road condition of the road network can be obtained by correlating and analyzing the driving information, the road information and the time information of the floating car. Furthermore, in the embodiment of the invention, the positioning device and the mobile terminal of the passenger on the floating car can be in communication interaction with the cell of the base station. In the running process of the floating car, the running information and the base station cell position can be synchronously acquired through the positioning device, and the positioning information of the car and the base station cell attribution can be synchronously acquired through the vehicle-mounted positioning device when the floating car passes through the cell point. The location information of the vehicle may include an International Mobile Subscriber Identity (IMSI), longitude, latitude, direction, speed, time, and the like. When the mobile terminal switches the base station CELL, the signaling data of the switched base station includes IMSI, Location Area Code (LAC), CELL of the base station (CELL _ ID), etc., the time of occurrence of the switching, etc., and in addition, the positioning device on the floating car can be set to report the position information of the car at regular time when the mobile terminal is not at the switching point.
Based on the positioning device of the floating car, the signaling data of the mobile terminal, and the position information and the cell information of the base station, a semi-supervised classification model capable of identifying the signaling data of the mobile terminal can be established. In order to improve the accuracy of the semi-supervised classification model, when the mobile terminal switches the base station cell, abnormal signaling data of the floating car at the switching point can be removed firstly. Wherein the switching point refers to P shown in FIG. 21、P2……、Pn. And then, corresponding to each switching point, averaging the geographic coordinates of the plurality of floating cars at the switching point to be used as the geographic coordinates of the corresponding switching point. Next, each switching point may be matched with a preset map according to the geographic coordinates, and the road attribution of each switching point coordinate is determined, that is, the location of each switching point may be obtainedAnd storing the geographic coordinates of the switching points and the road sections and intersections corresponding to the coordinates on the roads to obtain a coordinate-road network database.
The map matching data of the base station cell can be obtained by utilizing the corresponding relation between the geographic coordinate of the floating car and the base station cell and the road attribution of the geographic coordinate, wherein the map matching data can comprise intersection-base station and road section-base station sequences, the data can be stored in a road network-base station cell database, and exemplarily, the data such as the base station cell sequence of each road section, the time interval of base station switching, the road number, the road grade, the road flow direction, the signaling generation time, the number of covered users under different cells and the like can be obtained through the road network-base station cell database.
And then, by combining a coordinate-road network database, a road network-base station cell database and indexes such as weather factors, marking the base station cell sequence of the floating car on the road section as a positive sample, and learning traffic flow characteristics by using a preset semi-supervised classification mode to obtain a preset semi-supervised classification model capable of identifying the traffic flow signaling sequence from the unlabelled mobile phone signaling data.
For example, as shown in fig. 2, communication signals may be generated in cell coverage areas of different base stations, a mobile terminal may interact with each base station based on the communication signals, and when the mobile terminal switches a cell, signaling data of each mobile terminal at the time of switching may be obtained, where the signaling data may include, for example: international Mobile Subscriber Identity (IMSI), Location Area Code (LAC), base station CELL (CELL _ ID), and time of occurrence of handover. And inputting the signaling data of different mobile terminals into a preset semi-supervised classification model to obtain at least one signaling sequence corresponding to each road section and the time length information of each signaling sequence.
As a specific example, the time length information of each signaling sequence corresponding to each road segment may be used as the travel time used by the vehicle to pass through the road. By combining each link length information, and the corresponding time length information, the moving speed of each mobile terminal through the link at each link can be determined. In order to accurately determine the traffic flow speed of the road section in each preset time interval, the average value of the moving speeds of the plurality of mobile terminals passing through the road section can be obtained, and the traffic flow speed of the road section in each preset time interval can be obtained, so that the reliability of the traffic flow speed of the road section is improved.
By combining the traffic flow speed of each road section in each preset time interval, the second traffic flow speed of each road section at different sampling moments can be accurately obtained.
Additionally, the second vehicle flow speed further comprises determining a second road junction vehicle flow speed for each road junction at a different sampling time. For example, the vehicle flow speed of the second road junction in different vehicle flow directions at different sampling moments of each road junction can be obtained by determining the average value of the vehicle flow speeds of the road section where the road junction is located, corresponding to each vehicle flow direction of the road junction.
In the embodiment of the invention, based on the positioning information of the preset floating car, the third traffic flow speed of each road section in the target road network at the current moment can be acquired.
And S230, inputting the second traffic flow speed and the third traffic flow speed into a preset road network traffic flow speed prediction model to obtain a first traffic flow speed of each road section in the target road network in a first time period.
In the embodiment of the present invention, the number of the second vehicle flow speeds is plural, for example, the preset road network vehicle flow speed prediction model may be as shown in fig. 3, and the preset road network vehicle flow speed prediction model uses a gated round robin (GRU) unit and a full connect layer (full connect layer) to obtain the first vehicle flow speed of each road segment in the target road network at the next time of the target road network, wherein a loss function of the preset road network vehicle flow speed prediction model may use a Root Mean Square Error (RMSE) function, and parameters of the model may be obtained based on back propagation of the loss function. Further, combining the first vehicle flow speed of each road section in the target road network at the next moment, and performing iterative computation through a preset road network vehicle flow speed prediction model to obtain the first vehicle flow speed of each road section in the target road network at multiple moments in the first time period.
Specifically, for example, the plurality of second flow rates are tNThe traffic flow speed at the first N moments of the moment, and the third traffic flow speed is the current moment, namely tgReal-time traffic speed at the moment. The N +1 traffic flow speeds are input into a preset road network traffic flow speed prediction model for iterative computation, namely M times of computation is executed, and t can be obtainedN+1To tN+MI.e., the first vehicle flow speed at M times within the first time period.
According to the embodiments of the present invention, after obtaining the plurality of first vehicle flow speeds according to S210 to S230, the transit time of each road segment can be obtained according to S122.
S122, determining the passing time of each road section according to the first vehicle flow speed of each road section and the road section length information corresponding to the road section.
Specifically, by dividing the link length information of the link by the plurality of first vehicle flow velocities of each link, the predicted transit time of the vehicle through each link, i.e., the transit time of each link, can be obtained.
And S123, obtaining a passing time table according to the passing time of each road section.
The transit schedule may be derived in conjunction with the transit time for each road segment, and it will be appreciated that the transit schedule may also include the transit times for different intersections, as the second vehicle flow rate may include the second intersection vehicle flow rate for each intersection at different sampling times.
In the embodiment of the invention, the accuracy of traffic signaling identification can be effectively improved by the method for identifying the traffic signaling of the road section through the semi-supervision method, the speed estimation deviation caused by insufficient sampling of the floating car method is reduced, and the accuracy of medium-short term prediction of the road network can be obviously improved based on the preset road network traffic speed prediction model.
After the passing schedule is obtained, S130 may be performed next.
S130, searching a recommended path from the target road network according to the traffic schedule and a preset space-time path searching algorithm.
The passing time table comprises crossing passing time and road section passing time, and specifically, an objective function of a preset space-time path search algorithm can be determined according to the crossing passing time and the road section passing time; and searching a recommended path from the target road network according to the target function.
As a specific example, the predicted time interval of each first vehicle flow speed in the first time period is Δ t, and the predicted transit time for driving the vehicle may be greater than Δ t or less than or equal to Δ t, taking the link length information of one link as an example. To increase the calculated speed, the time information that the vehicle passes through the section may be:
wherein T' is a preset time length,
m is the number of time intervals in the first time period.
Based on the time information of the vehicle passing through the road section, an objective function of a preset space-time path search algorithm can be determined.
In the embodiment of the invention, the target road network comprises a plurality of road segments, and the target function is the sum of a first distance estimation value from an origin to one of the road segments and a second distance estimation value from one of the road segments to a destination; the first distance estimation value is determined according to the initial distance estimation value, the crossing passing time, the first weight of the crossing passing time, the road section passing time and the second weight of the road section passing time. It will be understood by those skilled in the art that the estimate may also be referred to as a cost value, a first distance estimate from the origin to one of the road segments, i.e. the consumption required from the origin to one of the road segments; a second distance estimate from one of the road segments to the destination, i.e. the consumption required from one of the road segments to the destination.
Specifically, the objective function is, for example, f (n) ═ g (n) + h (n). Wherein g (n) is a first distance estimate from the origin to one of the segments, and h (n) is a second distance estimate from one of the segments to the destination.
In this way, the recommended path is searched from the target road network, the path search on the multi-time point network traffic flow speed sequence is realized, and the space-time path search in time and space dimensions is realized.
In order to improve the accuracy of the path recommendation, in the embodiment of the invention, the first distance estimation value is determined according to the initial distance estimation value, the intersection passing time, the first weight of the intersection passing time, the road section passing time and the second weight of the road section passing time. Illustratively, g (nt) is g (BT) + α T + β T ", where g (nt) is a first distance estimate from the origin to one of the links, BT is an initial distance estimate, T is a transit time of the link, α is a first weight of the transit time at the intersection, T" is the transit time at the intersection, and β is a second weight of the transit time at the link. It is understood that α and β take the values of [0,1], and α + β is 1.
In some embodiments of the present application, in order to better adapt to different complex road networks, optionally, the value of α may be determined by probability density estimation for predicting road section transit time, and the value of β may be determined by probability density estimation for predicting intersection transit time.
In addition, the values of α and β can also be preset empirically. In order to better adapt to different complex road networks, a first weight alpha of intersection passing time and a second weight of road section passing time beta are determined according to the ratio of all intersection passing time to all road section passing time of a target road network. Therefore, the recommended path is searched from the target road network, the path search on the multi-time point network traffic flow speed sequence is realized, the space-time path search in time and space dimensions is realized, the reliability of the recommended path is further improved, and the risk of secondary congestion is reduced.
In addition, when the user drives a vehicle and runs on a road, at this time, a road section where the user is located may be changed, and therefore, in order to improve reliability of a recommended route and implement dynamic recommendation of a route for the user, in S230 of the embodiment of the present invention, S231 to S233 may be specifically included to obtain a plurality of first vehicle flow speeds.
S231, determining the minimum speed corresponding to each road section from the second traffic flow speed corresponding to each road section at different sampling moments and the third traffic flow speed of each road section at the current moment.
For clarity of description of the embodiments of the present invention, the minimum speed corresponding to each road segment may be determined according to equation (1).
min(v1,v2……,vp,vg) (1)
Exemplary, v1To vpP sampling moments correspond to the second vehicle flow speed v of each road section in the p target road networksgAnd the third traffic speed of each road section in the target road network. The minimum speed corresponding to each road segment can be determined according to formula (1).
And S232, determining the fourth vehicle flow speed of each road section in the target road network according to the minimum speed corresponding to each road section.
That is, according to the minimum speed corresponding to each road segment, the fourth vehicle flow speed in the target road network can be obtained.
And S233, inputting the second vehicle flow speed and the fourth vehicle flow speed in the second time period into a preset road network vehicle flow speed prediction model to obtain a plurality of first vehicle flow speeds of the target road network in the first time period.
After obtaining a plurality of first traffic flow speeds according to embodiments S231 to S233 of the present invention, dynamic update of the first traffic flow speed may be implemented, and then, a transit schedule of the target road network in the first time period may be obtained. And searching for the recommended path from the target road network according to the traffic schedule and a preset space-time path search algorithm, so that the accuracy of the predicted recommended path is ensured.
According to the path recommendation method provided by the embodiment of the invention, after the navigation request information is received, the passing time table of the target road network in the first time period is generated according to the navigation request information and the preset road network traffic speed prediction model, wherein the target road network comprises intersection information and road section information from the starting place to the destination, and the first time period is the passing time from the starting place to the destination, so that the accuracy of identification of periodic congestion and sudden congestion can be effectively improved. And then, searching for a recommended path from a target road network according to the traffic schedule and a preset spatio-temporal path search algorithm, realizing path search according to a multi-time point network traffic flow speed sequence, realizing spatio-temporal path search in time and space dimensions, improving the reliability of the recommended path and reducing the risk of secondary congestion.
Fig. 4 is a schematic structural diagram of a route recommendation apparatus according to an embodiment of the present invention, and as shown in fig. 4, the route recommendation apparatus 400 may include: a receiving module 410 and a data processing module 420.
A receiving module 410, configured to receive navigation request information, where the navigation request information includes an origin and a destination;
the data processing module 420 is configured to generate a traffic schedule of a target road network in a first time period according to the navigation request information and a preset road network traffic speed prediction model, where the target road network includes intersection information and road segment information from an origin to a destination, and the first time period is a traffic time from the origin to the destination;
the data processing module 420 is further configured to search for a recommended path from the target road network according to the transit time table and a preset spatio-temporal path search algorithm.
In some embodiments, the link information includes link position information and link length information of the plurality of links.
The data processing module 420 is further configured to determine, according to the navigation request information and a preset road network traffic speed prediction model, a first traffic speed of each road segment in the target road network in a first time period; determining the passing time of each road section according to the first vehicle flow speed of each road section and the road section length information corresponding to the road section; and obtaining a passing time table according to the passing time of each road section.
The data processing module 420 is further configured to determine a target road network according to the origin and the destination; acquiring a second vehicle flow speed of each road section in the target road network at different sampling moments in a second time period; acquiring a third traffic flow speed of each road section in the target road network at the current moment based on the positioning information of the preset floating car; and inputting a preset road network traffic speed prediction model into a second traffic speed and a third traffic speed in a second time period to obtain a plurality of first traffic speeds of each road section in the target road network in the first time period.
The data processing module 420 is further configured to determine a minimum speed corresponding to each road segment from the second traffic flow speed corresponding to each road segment at different sampling times and the third traffic flow speed of each road segment at the current time; determining a fourth vehicle flow speed according to the minimum speed corresponding to each road section; and inputting the second vehicle flow speed and the fourth vehicle flow speed in the second time period into a preset road network vehicle flow speed prediction model to obtain a plurality of first vehicle flow speeds of the target road network in the first time period.
In some embodiments, each sampling instant corresponds to a preset time interval.
The data processing module 420 is further configured to obtain signaling data of a plurality of mobile terminals of the target road network in each preset time interval; inputting signaling data of a plurality of mobile terminals into a preset semi-supervised classification model to obtain at least one signaling sequence corresponding to each road section; acquiring time length information of each signaling sequence; determining the moving speed of each mobile terminal passing through the road section according to the time length information and the length information of each road section; acquiring the average value of the moving speeds of a plurality of mobile terminals passing through a road section to obtain the traffic flow speed of the road section in each preset time interval; and taking the traffic flow speed of each road section in each preset time interval as a second traffic flow speed of each road section at different sampling moments.
In some embodiments, the transit schedule includes intersection transit times and link transit times. The data processing module 420 is further configured to determine an objective function of a preset spatio-temporal path search algorithm according to the intersection passing time and the road section passing time; and searching a recommended path from the target road network according to the target function.
In some embodiments, the target road network comprises a plurality of road segments, the objective function being the sum of a first distance estimate from an origin to one of the road segments and a second distance estimate from one of the road segments to a destination; the first distance estimation value is determined according to the initial distance estimation value, the crossing passing time, the first weight of the crossing passing time, the road section passing time and the second weight of the road section passing time.
In some embodiments, the first weight of the crossing transit time and the second weight of the road segment transit time are determined according to the ratio of the transit time of all the crossings of the target road network to the transit time of all the road segments.
It is to be understood that the path recommendation apparatus 400 in the embodiment of the present invention may correspond to an execution subject of the path recommendation method provided in the embodiment of the present invention, and specific details of operations and/or functions of each module/unit of the path recommendation apparatus 400 may refer to the descriptions of corresponding parts in the path recommendation method provided in the embodiment of the present invention, which are not described herein again for brevity.
The route recommendation device of the embodiment of the invention generates the traffic schedule of the target road network in the first time period, wherein the target road network comprises the intersection information and the road section information from the starting place to the destination, and the first time period is the traffic time from the starting place to the destination, so that the accuracy of identifying the periodic congestion and the sudden congestion can be effectively improved. And then, searching for a recommended path from a target road network according to the traffic schedule and a preset spatio-temporal path search algorithm, realizing path search according to a multi-time point network traffic flow speed sequence, realizing spatio-temporal path search in time and space dimensions, improving the reliability of the recommended path and reducing the risk of secondary congestion.
Fig. 5 is a schematic diagram of a hardware structure of a path recommendation device according to an embodiment of the present invention. As shown in fig. 5, the apparatus may include a processor 501 and a memory 502 storing computer program instructions.
Specifically, the processor 501 may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement the embodiments of the present invention.
In one example, the Memory 502 may be a Read Only Memory (ROM). In one example, the ROM may be mask programmed ROM, programmable ROM (prom), erasable prom (eprom), electrically erasable prom (eeprom), electrically rewritable ROM (earom), or flash memory, or a combination of two or more of these.
The processor 501 reads and executes the computer program instructions stored in the memory 502 to implement the method described in the embodiment of the present invention, and achieves the corresponding technical effects achieved by the method executed in the embodiment of the present invention, which are not described herein again for brevity.
In one example, the path recommendation device may also include a communication interface 503 and a bus 510. As shown in fig. 5, the processor 501, the memory 502, and the communication interface 503 are connected via a bus 510 to complete communication therebetween.
The communication interface 503 is mainly used for implementing communication between modules, apparatuses, units and/or devices in the embodiments of the present invention.
The path recommendation device can execute the recommendation method of the enterprise user service in the embodiment of the invention, thereby realizing the corresponding technical effect of the path recommendation method described in the embodiment of the invention.
In addition, in combination with the path recommendation method in the foregoing embodiments, the embodiments of the present invention may be implemented by providing a readable storage medium. The readable storage medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any of the path recommendation methods in the above embodiments.
It is to be understood that the invention is not limited to the specific arrangements and instrumentality described above and shown in the drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present invention are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications and additions or change the order between the steps after comprehending the spirit of the present invention.
The functional blocks shown in the above-described structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic Circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the invention are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor Memory devices, Read-Only memories (ROMs), flash memories, Erasable Read-Only memories (EROMs), floppy disks, Compact disk Read-Only memories (CD-ROMs), optical disks, hard disks, optical fiber media, Radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.
It should also be noted that the exemplary embodiments mentioned in this patent describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.
Aspects of the present disclosure are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware for performing the specified functions or acts, or combinations of special purpose hardware and computer instructions.
As described above, only the specific embodiments of the present invention are provided, and it can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the module and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present invention, and these modifications or substitutions should be covered within the scope of the present invention.
Claims (11)
1. A method for path recommendation, comprising:
receiving navigation request information, wherein the navigation request information comprises an origin and a destination;
generating a traffic schedule of a target road network in a first time period according to the navigation request information and a preset road network traffic speed prediction model, wherein the target road network comprises intersection information and road section information from the starting place to the destination, and the first time period is the traffic time from the starting place to the destination;
and searching for a recommended path from the target road network according to the passing time table and a preset space-time path search algorithm.
2. The method of claim 1, wherein the link information comprises link position information and link length information of a plurality of links, and the generating a traffic schedule of the target road network in the first time period according to the navigation request information and a preset road network traffic speed prediction model comprises:
determining a first vehicle flow speed of each road section in the target road network in a first time period according to the navigation request information and the preset road network vehicle flow speed prediction model;
determining the passing time of each road section according to the first vehicle flow speed of each road section and the road section length information corresponding to the road section;
and obtaining the passing time table according to the passing time of each road section.
3. The method of claim 2, wherein said determining a first vehicle flow speed for each road segment in said target road network during a first time period based on said navigation request information and said predetermined road network vehicle flow speed prediction model comprises:
determining a target road network according to the origin and the destination;
acquiring a second vehicle flow speed of each road section in the target road network at different sampling moments in a second time period; and the number of the first and second groups,
acquiring a third traffic flow speed of each road section in the target road network at the current moment based on the positioning information of the preset floating car;
and inputting the second traffic flow speed and the third traffic flow speed in the second time period into the preset road network traffic flow speed prediction model to obtain a plurality of first traffic flow speeds of each road section in the target road network in the first time period.
4. The method of claim 3, wherein said inputting said second vehicle flow speed and said third vehicle flow speed at said second time interval into said predetermined road network vehicle flow speed predictive model to obtain a plurality of first vehicle flow speeds for each road segment in the target road network during said first time interval comprises:
determining the minimum speed corresponding to each road section from the second traffic flow speed corresponding to each road section at different sampling moments and the third traffic flow speed of each road section at the current moment;
determining a fourth vehicle flow speed according to the minimum speed corresponding to each road section;
and inputting the second vehicle flow speed and the fourth vehicle flow speed in the second time period into the preset road network vehicle flow speed prediction model to obtain a plurality of first vehicle flow speeds of the target road network in the first time period.
5. The method according to any one of claims 3 or 4, wherein each sampling time corresponds to a preset time interval, and the obtaining of the second vehicle flow speed of each road segment in the target road network at different sampling times in the second time period comprises:
acquiring signaling data of a plurality of mobile terminals of a target road network in each preset time interval;
inputting the signaling data of the mobile terminals into a preset semi-supervised classification model to obtain at least one signaling sequence corresponding to each road section;
acquiring time length information of each signaling sequence;
determining the moving speed of each mobile terminal passing through the road section according to the time length information and the length information of each road section;
obtaining the average value of the moving speeds of a plurality of mobile terminals through the road section to obtain the traffic flow speed of the road section in each preset time interval;
and taking the traffic flow speed of each road section in each preset time interval as a second traffic flow speed of each road section at different sampling moments.
6. The method according to claim 1, wherein the transit time schedule comprises intersection transit time and section transit time, and the searching for the recommended path from the target road network according to the transit time schedule and a preset spatiotemporal path search algorithm comprises:
determining an objective function of the preset space-time path search algorithm according to the intersection passing time and the road section passing time;
and searching the recommended path from the target road network according to the target function.
7. The method of claim 6, wherein said target road network comprises a plurality of road segments, and said objective function is a sum of a first distance estimate from said origin to one of said road segments and a second distance estimate from said one of said road segments to said destination;
the first distance estimation value is determined according to an initial distance estimation value, intersection passing time, a first weight of the intersection passing time, the road section passing time and a second weight of the road section passing time.
8. The method of claim 7, wherein the first weight of the intersection transit time and the second weight of the link transit time are determined according to a ratio of all intersection transit times to all link transit times of the target road network.
9. A path recommendation device, characterized in that the device comprises:
the system comprises a receiving module, a sending module and a receiving module, wherein the receiving module is used for receiving navigation request information which comprises an origin and a destination;
the data processing module is used for generating a passing time table of a target road network in a first time period according to the navigation request information and a preset road network traffic speed prediction model, wherein the target road network comprises intersection information and road section information from the starting place to the destination, and the first time period is the passing time from the starting place to the destination;
and the data processing module is also used for searching a recommended path from the target road network according to the traffic schedule and a preset spatio-temporal path search algorithm.
10. A path recommendation device, characterized in that the device comprises: a processor, and a memory storing computer program instructions;
the processor reads and executes the computer program instructions to implement the path recommendation method of any one of claims 1-8.
11. A readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the path recommendation method of any one of claims 1-8.
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