CN113358130A - Method, device and equipment for acquiring planned path and readable storage medium - Google Patents
Method, device and equipment for acquiring planned path and readable storage medium Download PDFInfo
- Publication number
- CN113358130A CN113358130A CN202110600232.0A CN202110600232A CN113358130A CN 113358130 A CN113358130 A CN 113358130A CN 202110600232 A CN202110600232 A CN 202110600232A CN 113358130 A CN113358130 A CN 113358130A
- Authority
- CN
- China
- Prior art keywords
- candidate
- path
- preset
- route
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 74
- 230000000903 blocking effect Effects 0.000 claims abstract description 136
- 238000001595 flow curve Methods 0.000 claims description 41
- 238000012163 sequencing technique Methods 0.000 claims description 6
- 238000004590 computer program Methods 0.000 claims description 5
- 238000004891 communication Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000010276 construction Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
- G01C21/3492—Special cost functions, i.e. other than distance or default speed limit of road segments employing speed data or traffic data, e.g. real-time or historical
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Navigation (AREA)
- Traffic Control Systems (AREA)
Abstract
The embodiment of the application provides a method, a device and equipment for acquiring a planned path and a readable storage medium, wherein a candidate path set is acquired, the road blocking probability of each candidate path is acquired according to historical traffic data, the road blocking probability of the candidate paths indicates the probability that the candidate paths are in a blocking state at a preset moment, the traffic risk of each candidate path is acquired according to the predicted traffic time of each candidate path and the road blocking probability of each candidate path, and if the candidate paths meet preset conditions, the candidate paths are used as the planned paths of reservation tasks. The higher the traffic risk is, the longer the traffic time from the starting place to the destination of the reserved task or the higher the probability that the candidate path is in the blocked state is, the candidate path with the risk smaller than the preset threshold value is selected as the planned path, and the accuracy of the planned path is improved by considering the traffic time of the candidate path and the probability that the candidate path is in the blocked state at the preset moment.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method, an apparatus, a device, and a readable storage medium for acquiring a planned path.
Background
At present, most banks add the home service to improve the experience of customers, the home service needs to meet the reservation time of the customers on one hand, and the efficiency of the home service needs to be improved on the other hand.
The current road planning method is to select a path with the shortest transit time as a planned path, but in reality, short-time special road conditions (such as road construction or road blockage due to other reasons) often exist to seriously affect the efficiency of the home service, and it is seen that the accuracy of the planned path only taking the transit time as a reference is low, which often results in low efficiency of the home service of the reservation task.
Disclosure of Invention
The application provides a method, a device, equipment and a readable storage medium for acquiring a planned path, which are used for improving the accuracy of the planned path, and comprise the following steps:
a planned path acquisition method comprises the following steps:
acquiring a candidate path set, wherein the candidate path set comprises at least two candidate paths, and the candidate paths are paths from a starting place of a reserved task to a destination of the reserved task;
acquiring the road blocking probability of each candidate path according to historical traffic flow data, wherein the road blocking probability indicates the probability that the candidate path is in a blocking state at a preset time, and the preset time is the reservation time of the reservation task;
the method comprises the steps that at preset time, according to the predicted passing time of each candidate route and the road blocking probability of each candidate route, the passing risk of each candidate route is obtained, and the longer the predicted passing time is, and/or the larger the road blocking probability is, the larger the passing risk is;
and if the candidate path meets a preset condition, taking the candidate path as a planning path of the reservation task, wherein the preset condition comprises that the passing risk of the candidate path is smaller than a preset threshold value.
Optionally, before obtaining the road blocking probability of each candidate route according to the historical traffic data, the method further includes:
generating a traffic flow curve of each candidate path according to the traffic flow of each candidate path in each preset time period on a preset date, wherein the preset time period is before the preset time, and the preset date is the reservation date of the reservation task;
generating a traffic flow curve of each preset sample path according to the traffic flow of each preset sample path in each preset time period of a preset sample date;
and taking a traffic flow curve of each candidate path, a traffic flow curve of each preset sample path and a road state of each preset sample path as the historical traffic flow data, wherein the road state of the preset sample path indicates that the preset sample path is in the blocking state or the non-blocking state.
Optionally, the process of obtaining the road blocking probability of the target candidate route according to the historical traffic flow data includes:
if the similarity of the traffic flow curve of the preset sample path and the traffic flow curve of the target candidate path exceeds a preset similarity, taking the preset sample path as a first sample path, and taking the target candidate path as any candidate path of the candidate path set;
if the road state of the first sample path indicates that the preset sample path is in the blocking state, taking the first sample path as a second sample path;
and taking the ratio of the number of the second sample paths to the number of the first sample paths as the road blocking probability of the target candidate path.
Optionally, before obtaining the traffic risk of each of the candidate routes according to the predicted traffic time of each of the candidate routes and the road blocking probability of each of the candidate routes, the method further includes:
acquiring attribute information, wherein the attribute information comprises: a preset attribute of each preset sample date and a preset attribute of the preset date, wherein the preset attributes indicate holidays and/or weather;
according to the attribute information and a preset corresponding relationship, obtaining the matching degree of the preset attribute of each preset sample date and the preset attribute of the preset date, wherein the preset corresponding relationship comprises: matching degrees among different preset attributes;
if the preset attributes of the preset sample date are matched with the preset attributes of the preset date, taking the preset sample date as a target date;
and acquiring the passing time of the candidate route at the preset moment of the target date as the predicted passing time of the candidate route.
Optionally, obtaining the traffic risk of each of the candidate routes according to the predicted traffic time of each of the candidate routes and the road blocking probability of each of the candidate routes, including:
and acquiring the product of the predicted passing time of each candidate route and the road blocking probability of each candidate route as the passing risk of each candidate route.
Optionally, after obtaining the traffic risk of each of the candidate routes according to the predicted traffic time of each of the candidate routes and the road blocking probability of each of the candidate routes, the method further includes:
sequencing the candidate paths from small to large according to the predicted passing time to obtain a time sequence;
taking the predicted passing time of a first candidate route as a first numerical value of the first candidate route, and taking the product of the road blocking probability of the first candidate route and the predicted passing time of a second candidate route as a second numerical value of the first candidate route, wherein the second candidate route is a candidate route positioned one bit behind the first candidate route in the time sequence, and the first candidate route is any one of candidate routes except the candidate route positioned the last bit in the time sequence;
acquiring the sum of a first numerical value of the first candidate path and a second numerical value of the first candidate path as a risk cost value of the first candidate path;
and judging whether the risk cost value of the first candidate route is smaller than the predicted transit time of the second candidate route, if so, determining that the first candidate route belongs to a low risk route.
If the candidate path meets the preset condition, taking the candidate path as a planning path of the reservation task, including:
if the candidate path meets a preset condition, taking the candidate path as a planning path of the reservation task, wherein the preset condition further comprises: the candidate path belongs to the low risk path.
An acquisition device for a planned path, comprising:
a path set acquiring unit, configured to acquire a candidate path set, where the candidate path set includes at least two candidate paths, and the candidate path is a path from a starting point of a reservation task to a destination of the reservation task;
a probability obtaining unit, configured to obtain a road blocking probability of each candidate path according to historical traffic flow data, where the road blocking probability indicates a probability that the candidate path is in a blocking state at a preset time, and the preset time is a reservation time of the reservation task;
a traffic risk obtaining unit, configured to obtain a traffic risk of each candidate route according to a predicted traffic time of each candidate route and a road blocking probability of each candidate route, where the traffic risk is greater as the predicted traffic time is longer and/or the road blocking probability is greater;
and the planning result acquisition unit is used for taking the candidate path as a planning path of the reservation task if the candidate path meets a preset condition, wherein the preset condition comprises that the passing risk of the candidate path is smaller than a preset threshold value.
Optionally, the traffic risk obtaining unit is configured to obtain a traffic risk of each candidate route according to the predicted traffic time of each candidate route and the road blocking probability of each candidate route, and includes:
the traffic risk acquiring unit is specifically configured to: and acquiring the product of the predicted passing time of each candidate route and the road blocking probability of each candidate route as the passing risk of each candidate route.
An acquisition device of a planned path, comprising: a memory and a processor;
the memory is used for storing programs;
the processor is used for executing the program and realizing each step of the acquisition method of the planned path.
A readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method of acquiring a planned path.
It can be seen from the foregoing technical solutions that, in the method, the apparatus, the device, and the readable storage medium for obtaining a planned path provided in the embodiments of the present application, a candidate path set is obtained, where the candidate path set includes at least two candidate paths, the candidate path is a path from a departure point of a reserved task to a destination of the reserved task, a road blocking probability of each candidate path is obtained according to historical traffic data, the road blocking probability of the candidate path indicates a probability that the candidate path is in a blocking state at a preset time, a traffic risk of each candidate path is obtained according to a predicted traffic time of each candidate path and the road blocking probability of each candidate path, and if the candidate path meets a preset condition, the candidate path is used as the planned path of the reserved task, where the preset condition includes that the traffic risk of the candidate path is less than a preset threshold. The longer the predicted traffic time is and/or the larger the road blocking probability is, the larger the traffic risk is, and the road blocking probability is obtained according to the historical traffic data, which is objective data, so that the larger the traffic risk is, the longer the traffic time from the departure place to the destination of the reservation task or the larger the probability that the candidate route is in the blocking state is, according to the candidate route, the candidate route with the risk less than the preset threshold value is selected as the planned route, on one hand, the traffic time of the candidate route is considered, on the other hand, the road state of the candidate route is considered, that is, the probability that the candidate route is in the blocking state at the preset time is considered, the accuracy of the planned route is improved, and the improvement of the completion efficiency of the reservation task is further promoted.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, 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 specific implementation of a method for acquiring a planned path according to an embodiment of the present application;
fig. 2 is a schematic flowchart of a method for acquiring a planned path according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an apparatus for acquiring a planned path according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an acquisition device for a planned path according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The method for acquiring the planned path provided by the embodiment of the application is not limited to a scheduling method for the reserved tasks, and can be particularly applied to a task scheduling system, wherein the task scheduling system is pre-configured in a server and comprises a task list generating module, a path planning module and a task issuing module, wherein the task list generating module is used for acquiring the reserved tasks in real time and generating the travel information of the reserved tasks, and the travel information comprises the corresponding relation between the starting place and the destination of each reserved task. And the path planning module is used for executing the acquisition method of the planned path to obtain the planned path of the reserved task. And the task issuing module is used for issuing the reservation task and the planning path of the reservation task to the client bound by the account number of the service personnel meeting the preset requirement. It should be noted that, specifically, a specific implementation manner of the method for acquiring a planned path provided in the embodiment of the present application in fig. 1 may specifically include S101 to S112:
s101, acquiring a candidate path set.
In this embodiment, the candidate route set includes at least two candidate routes, where a candidate route is a route from a starting point of the reserved task to a destination of the reserved task, and each candidate route includes at least one segment. The reservation task is a task of a path to be planned.
It should be noted that, the method for obtaining the candidate path may be referred to in the prior art.
And S102, acquiring historical traffic flow data.
In this embodiment, the historical traffic data includes a traffic curve of each candidate route, a traffic curve of each preset sample route, and a road state of each preset sample route. The preset sample path comprises paths with the similarity degree with the candidate paths larger than a first similarity threshold value, and the similarity degree of each path with the candidate paths is obtained according to the path information of the paths and the path information of the candidate paths, wherein the path information comprises at least one of the number of road sections, the number of intersections, the number of traffic lights and the type of roads. The road state of the preset sample path indicates that the preset sample path is in a blocking state or a non-blocking state at the preset time of the preset sample date, and the specific similarity calculation method may refer to the prior art, which is not described in detail in this embodiment.
In this embodiment, the specific process of acquiring the historical traffic data includes: 1. and generating a traffic flow curve of each candidate route according to the traffic flow of each candidate route in each preset time period on the preset date. The preset date is a reservation date of the reserved service, optionally, the preset time period is a time period before a reservation time point, the duration of each preset time period is half an hour, for example, the reservation time point is 16 points, and each half hour before 16 points is taken as a reservation time period.
2. And generating a traffic flow curve of each preset sample path according to the traffic flow of each preset sample path in each preset time period of the preset sample date.
The number of the preset sample paths is multiple, and the preset sample date is the date before the preset date. In order to improve the accuracy of the probability prediction, the data amount of the preset sample path and the preset sample date is as large as possible in practical application.
The prior art is referred to as a method for calculating the traffic flow in the reserved time zone and a method for generating the traffic flow curve.
3. And acquiring the road state of each preset sample path. The road state of the preset sample path indicates whether the preset sample path is in a blocking state on a preset sample date. The specific acquisition method is referred to in the prior art.
And S103, if the curve similarity of the traffic flow curve of the candidate path and the traffic flow curve of the preset sample path exceeds the preset similarity, taking the preset sample path as a first sample path.
In this embodiment, the method for obtaining the curve similarity between the traffic flow curve of the candidate path and the traffic flow curve of the preset sample path includes multiple methods, for example, a point-based curve similarity calculation method and a shape-based curve similarity calculation method, which may be referred to in the prior art specifically, and this embodiment is not described in detail.
It can be understood that, if the curve similarity between the traffic flow curve of the preset sample and the traffic flow curve of the candidate route is high, the similarity between the traffic flow of the preset sample route in each preset time period on the preset sample date and the traffic flow of the candidate route in each preset time period on the preset sample date is higher, that is, the road condition similarity is high.
And S104, if the road state of the first sample path indicates that the preset sample path is in a blocking state, taking the preset sample path as a second sample path.
It should be noted that, since the preset sample path includes a plurality of paths, the present embodiment performs S103 to S104 for each preset sample path, and obtains all the first sample path and the second sample path.
And S105, taking the ratio of the number of the second sample paths to the number of the first sample paths as the road blocking probability of the candidate paths.
In this embodiment, the second sample path satisfies that the curve similarity between the traffic flow curve and the candidate path is greater than the preset threshold and is in a blocking state at the preset time of the preset sample date, and the first sample path at least satisfies that the curve similarity between the traffic flow curve and the candidate path is greater than the preset threshold. That is, the second sample path is a path in a blocked state at a preset time of a preset sample date among the plurality of first sample paths.
Therefore, the ratio of the number of the second sample paths to the number of the first sample paths is the proportion of the road blockage paths in the paths similar to the road condition of the candidate paths, and since the preset sample paths are the similar paths of the candidate paths and the historical traffic data is the objective data, the proportion of the road blockage paths can be used as the road blockage probability of the candidate paths.
It should be noted that the candidate route set includes a plurality of candidate routes, and the road blocking probability of each candidate route is obtained according to the above steps.
And S106, acquiring the predicted passing time of each candidate route.
In this embodiment, the method for obtaining the predicted passing time of each candidate route includes multiple types, and the following is a selectable predicted passing time of a candidate route, specifically including a1 to a4, as follows:
a1, acquiring attribute information, wherein the attribute information comprises: a preset attribute for each preset sample date and a preset attribute for the preset date, the preset attributes indicating date, holidays, and weather.
For example, the preset date is 2021 year 4 month 15 day, and the preset sample date is 2021 year 4 month 13 day, and the attribute information of the preset date includes 2021 year 4 month 15 day (date), non-holiday (holiday), light rain (weather). The attribute information of the preset sample date includes 13 days (date) of 4 months in 2021, holidays (holidays), cloudy days (weather)
And A2, acquiring the matching degree of the attribute information of each preset sample date and the attribute information of the preset date according to the attribute information and the preset corresponding relation.
In this embodiment, the preset corresponding relationship includes: and matching degree between different preset attributes. For example, the degree of matching between holidays and non-holidays is 0.2, the degree of matching between holidays and holidays is 0.6, the degree of matching between cloudy days and light rains is 0.4, and the degree of matching between sunny days and cloudy days is 0.8. It should be noted that, the matching degrees between different preset attributes are configured in advance, specifically, the matching degrees between the preset attributes are determined according to the similarity of the influence degrees of each preset attribute on the traffic flow, for example, the influence of cloudy days and sunny days on the traffic flow is very small, but the influence of light rain and thunderstorm rain on the traffic flow is relatively large, so the matching degree of cloudy days and sunny days is greater than the matching degree of light rain and sunny days. The specific method for presetting the matching degree can be referred to in the prior art.
In this embodiment, the matching degrees of the attribute information of the preset sample date and the attribute information of the preset date are obtained by adding the matching degrees of the preset sample date and the corresponding preset attributes of the preset date.
And A3, if the preset attributes of the preset sample date and the preset attributes of the preset date are larger than the preset matching degree threshold value, taking the preset sample date as a target date.
And A4, acquiring the passing time of the candidate route at the preset moment of the target date as the predicted passing time of the candidate route.
It should be noted that, for the method for acquiring the transit time of the candidate route at the preset time of the target date, reference is made to the prior art.
According to the target date obtained according to the similarity of the attribute information of the candidate date, the road condition similarity of the candidate path on the target date and the preset date is high, and the accuracy of the obtained predicted passing time of each candidate path is high.
And S107, acquiring the product of the predicted passing time of the candidate route and the road blocking probability of the candidate route as the passing risk of the candidate route.
It will be appreciated that the longer the predicted transit time and/or the greater the probability of road congestion, the greater the risk of transit.
And S108, sequencing the candidate paths from small to large according to the predicted passing time of the candidate paths to obtain a time sequence.
S109, taking the predicted passing time of the first candidate route as a first numerical value of the first candidate route, and taking the product of the road blocking probability of the first candidate route and the predicted passing time of the second candidate route as a second numerical value of the first candidate route.
In this embodiment, the first candidate path is any one of the candidate paths except for the candidate path at the last bit of the sequence in the time series, and the second candidate path is a candidate path at a bit after the first candidate path in the time series.
For example, the candidate path set includes three candidate paths, which are a first path L1, a second path L2, and a third path L3, respectively. The predicted transit time of the first path is T1, the predicted transit time of the second path is T2, the predicted transit time of the third path is T3, the probability of road blocking probability of the first path is P1, the probability of road blocking probability of the second path is P2, and the probability of road blocking probability of the third path is P3. Wherein T1 is more than T2 and more than T3, and the time sequence is { L1, L2, L3 }.
Taking the first candidate route as L1 for example, the second candidate route is L2, the first value of the first candidate route is T1, and the second value is P1 × T2.
And S110, acquiring the sum of the first numerical value of the first candidate route and the second numerical value of the first candidate route as the risk cost value of the first candidate route.
That is, the risk cost value of the candidate path L1 is equal to T1+ P1 × T2. The risk cost value of the candidate path L2 is equal to T2+ P2 × T3.
And S111, judging whether the risk cost value of the first candidate route is smaller than the predicted passing time of the second candidate route, and if so, determining that the first candidate route belongs to the small risk route.
Specifically, with the candidate route L1 as the first candidate route, it is determined whether the risk cost value of the candidate route L1 is smaller than that of the second candidate route, i.e., the candidate route L2. If so, the candidate path L1 is taken as the low risk path.
And S112, if the candidate path meets the preset condition, taking the candidate path as a planning path of the reservation task.
In this embodiment, the preset conditions include: the traffic risk of the candidate path is smaller than a preset threshold value, and the candidate path belongs to a small risk path.
It should be noted that, if a plurality of candidate routes satisfying the preset condition are included, the candidate route with the shortest preset transit time may be selected.
According to the technical scheme, the traffic risk of each candidate route is determined according to the predicted traffic time of the candidate route and the road blocking probability of the candidate route, the longer the predicted traffic time is, the higher the road blocking probability is, the greater the traffic risk is, and the road blocking probability is obtained according to historical traffic flow data, the historical traffic flow data is objective data, therefore, the greater the traffic risk is, the longer the traffic time from the starting point to the destination of the reservation task or the greater the probability that the candidate route is in the blocked state is, the candidate route with the risk less than the preset threshold is selected as the planned route, on one hand, the traffic time of the candidate route is considered, on the other hand, the road state of the candidate route is considered, that is, the probability that the candidate route is in the blocked state at the preset moment is considered, the accuracy of the planned route is improved, and the completion efficiency of the reservation task is further improved.
Further, according to the attribute information of the preset date and the sample preset date, the sample preset date with high matching degree with the attribute information of the preset date is determined as the target date, and it can be understood that the road condition of the target date is high in similarity with the road condition of the preset date, and the passing time of the candidate route at the preset moment of the target date is high in objectivity and accuracy as the predicted passing time of the candidate route, so that the accuracy of route planning is further improved.
Further, the risk cost value of the first candidate route is determined according to the predicted transit time of the first candidate route, the road blocking probability and the predicted transit time of a candidate route (second candidate route) shorter than the predicted transit time of the first candidate route, and when the risk cost value of the first candidate route is smaller than the predicted transit time of the second candidate route, the risk cost value of selecting the second candidate route after the first candidate route is selected is smaller than the opportunity cost for directly selecting the second candidate route when the first candidate route is in a blocking state. Since the predicted transit time and the road blocking probability of the candidate route are predicted according to the objective data, the risk cost value of the first candidate route has objectivity.
It should be noted that the flow illustrated in fig. 1 is only a specific implementation of an optional method for acquiring a planned path provided in the embodiment of the present application, and the present application also includes other specific implementations. For example, S102 is an optional step and is only an optional method for acquiring historical traffic data, and for example, S103 to S105 are only optional methods for acquiring the road congestion probability of each candidate route, and the present application also includes other optional methods for acquiring the road congestion probability of each candidate route according to the historical traffic data.
In summary, the method for acquiring a planned path provided in the embodiment of the present application is summarized as a flow shown in fig. 2, and as shown in fig. 2, the method specifically includes:
s201, acquiring a candidate path set.
In this embodiment, the candidate path set includes at least two candidate paths, where a candidate path is a path from a starting point of the reserved task to a destination of the reserved task, and the candidate path includes at least one road segment.
S202, acquiring the road blocking probability of each candidate path according to the historical traffic flow data.
In this embodiment, the road blocking probability of the candidate route indicates a probability that the candidate route is in a blocking state at a preset time.
In this embodiment, the obtaining of the historical traffic data includes multiple methods, and an alternative method may be referred to as S102. The obtaining of the road blocking probability of each candidate route includes various methods, and an alternative method may be referred to in S103 to S104 described above.
S203, acquiring the traffic risk of each candidate route according to the predicted traffic time of each candidate route and the road blocking probability of each candidate route.
In this embodiment, the longer the predicted transit time and/or the greater the road blocking probability, the greater the transit risk.
And S204, if the candidate path meets the preset condition, taking the candidate path as a planning path of the reservation task.
In this embodiment, the preset condition includes that the traffic risk of the candidate route is smaller than a preset threshold.
It can be seen from the above technical solutions that, in the method for obtaining a planned path provided in the embodiments of the present application, the traffic risk of each candidate path is determined according to the predicted traffic time of the candidate path and the road blocking probability of the candidate path, the longer the predicted traffic time is and/or the greater the road blocking probability is, the greater the traffic risk is, and the road blocking probability is obtained according to the historical traffic data, and the historical traffic data is objective data, so that the greater the traffic risk indicates that according to the candidate path, the longer the traffic time from the departure place to the destination of the reserved task is or the greater the probability that the candidate path is in the blocking state is, the candidate path with the risk less than the preset threshold is selected as the planned path, on one hand, the traffic time of the candidate path is considered, on the other hand, the road state of the candidate path is considered, that is, the probability that the, the accuracy of planning the path is improved, and the completion efficiency of the reservation task is further improved.
Fig. 3 shows a schematic structural diagram of an obtaining apparatus for planning a path according to an embodiment of the present application, and as shown in fig. 3, the apparatus may include:
a route set obtaining unit 301, configured to obtain a candidate route set, where the candidate route set includes at least two candidate routes, and the candidate route is a route from a starting point of a reserved task to a destination of the reserved task;
a probability obtaining unit 302, configured to obtain a road blocking probability of each candidate route according to historical traffic data, where the road blocking probability indicates a probability that the candidate route is in a blocking state at a preset time, and the preset time is a reservation time of the reservation task;
a traffic risk obtaining unit 303, configured to obtain a traffic risk of each candidate route according to a predicted traffic time of each candidate route and a road blocking probability of each candidate route, where the traffic risk is greater if the predicted traffic time is longer and/or the road blocking probability is greater;
a planning result obtaining unit 304, configured to take the candidate path as a planning path of the reserved task if the candidate path meets a preset condition, where the preset condition includes that a passing risk of the candidate path is smaller than a preset threshold.
Optionally, the apparatus further comprises: a historical data obtaining unit, configured to generate a traffic flow curve of each candidate route according to a traffic flow of each candidate route in each preset time period on a preset date before obtaining a road blocking probability of each candidate route according to historical traffic flow data, where the preset date is a reservation date of the reserved task and the preset time period is before the preset time;
generating a traffic flow curve of each preset sample path according to the traffic flow of each preset sample path in each preset time period of a preset sample date;
and taking a traffic flow curve of each candidate path, a traffic flow curve of each preset sample path and a road state of each preset sample path as the historical traffic flow data, wherein the road state of the preset sample path indicates that the preset sample path is in the blocking state or the non-blocking state.
Optionally, the probability obtaining unit is configured to, when obtaining the road blocking probability of each candidate route according to the historical traffic flow data, specifically: and acquiring the road blocking probability of the target candidate path according to the historical traffic flow data.
The process of obtaining the road blocking probability of the target candidate path according to the historical traffic flow data comprises the following steps: if the similarity of the traffic flow curve of the preset sample path and the traffic flow curve of the target candidate path exceeds a preset similarity, taking the preset sample path as a first sample path, and taking the target candidate path as any candidate path of the candidate path set;
if the road state of the first sample path indicates that the preset sample path is in the blocking state, taking the first sample path as a second sample path;
and taking the ratio of the number of the second sample paths to the number of the first sample paths as the road blocking probability of the target candidate path.
Optionally, the apparatus further comprises: a predicted transit time obtaining unit, configured to obtain attribute information before obtaining a transit risk of each of the candidate routes according to the predicted transit time of each of the candidate routes and a road blocking probability of each of the candidate routes, where the attribute information includes: a preset attribute of each preset sample date and a preset attribute of the preset date, wherein the preset attributes indicate holidays and/or weather;
according to the attribute information and a preset corresponding relationship, obtaining the matching degree of the preset attribute of each preset sample date and the preset attribute of the preset date, wherein the preset corresponding relationship comprises: matching degrees among different preset attributes;
if the preset attributes of the preset sample date are matched with the preset attributes of the preset date, taking the preset sample date as a target date;
and acquiring the passing time of the candidate route at the preset moment of the target date as the predicted passing time of the candidate route.
Optionally, the traffic risk obtaining unit is configured to obtain a traffic risk of each candidate route according to the predicted traffic time of each candidate route and the road blocking probability of each candidate route, and includes: the traffic risk acquiring unit is specifically configured to:
and acquiring the product of the predicted passing time of each candidate route and the road blocking probability of each candidate route as the passing risk of each candidate route.
Optionally, the apparatus further comprises: a risk comparison unit, configured to, after obtaining a traffic risk of each candidate route according to the predicted traffic time of each candidate route and the road blocking probability of each candidate route:
sequencing the candidate paths from small to large according to the predicted passing time to obtain a time sequence;
taking the predicted passing time of a first candidate route as a first numerical value of the first candidate route, and taking the product of the road blocking probability of the first candidate route and the predicted passing time of a second candidate route as a second numerical value of the first candidate route, wherein the second candidate route is a candidate route positioned one bit behind the first candidate route in the time sequence, and the first candidate route is any one of candidate routes except the candidate route positioned the last bit in the time sequence;
acquiring the sum of a first numerical value of the first candidate path and a second numerical value of the first candidate path as a risk cost value of the first candidate path;
and judging whether the risk cost value of the first candidate route is smaller than the predicted transit time of the second candidate route, if so, determining that the first candidate route belongs to a low risk route.
The planning result obtaining unit is configured to, if the candidate path meets a preset condition, use the candidate path as a planning path for the reservation task, and includes: the planning result obtaining unit is specifically configured to:
if the candidate path meets a preset condition, taking the candidate path as a planning path of the reservation task, wherein the preset condition further comprises: the candidate path belongs to the low risk path.
Fig. 4 shows a schematic structural diagram of the planned path obtaining device, which may include: at least one processor 401, at least one communication interface 402, at least one memory 403 and at least one communication bus 404;
in the embodiment of the present application, the number of the processor 401, the communication interface 402, the memory 403 and the communication bus 404 is at least one, and the processor 401, the communication interface 402 and the memory 403 complete communication with each other through the communication bus 404;
the memory 403 may include a high-speed RAM memory, and may further include a non-volatile memory (non-volatile memory) or the like, such as at least one disk memory;
the storage stores a program, and the processor can execute the program stored in the storage, so as to implement the steps of the method for acquiring the planned path provided by the embodiment of the application, as follows:
a planned path acquisition method comprises the following steps:
acquiring a candidate path set, wherein the candidate path set comprises at least two candidate paths, and the candidate paths are paths from a starting place of a reserved task to a destination of the reserved task;
acquiring the road blocking probability of each candidate path according to historical traffic flow data, wherein the road blocking probability indicates the probability that the candidate path is in a blocking state at a preset time, and the preset time is the reservation time of the reservation task;
the method comprises the steps that at preset time, according to the predicted passing time of each candidate route and the road blocking probability of each candidate route, the passing risk of each candidate route is obtained, and the longer the predicted passing time is, and/or the larger the road blocking probability is, the larger the passing risk is;
and if the candidate path meets a preset condition, taking the candidate path as a planning path of the reservation task, wherein the preset condition comprises that the passing risk of the candidate path is smaller than a preset threshold value.
Optionally, before obtaining the road blocking probability of each candidate route according to the historical traffic data, the method further includes:
generating a traffic flow curve of each candidate path according to the traffic flow of each candidate path in each preset time period on a preset date, wherein the preset time period is before the preset time, and the preset date is the reservation date of the reservation task;
generating a traffic flow curve of each preset sample path according to the traffic flow of each preset sample path in each preset time period of a preset sample date;
and taking a traffic flow curve of each candidate path, a traffic flow curve of each preset sample path and a road state of each preset sample path as the historical traffic flow data, wherein the road state of the preset sample path indicates that the preset sample path is in the blocking state or the non-blocking state.
Optionally, the process of obtaining the road blocking probability of the target candidate route according to the historical traffic flow data includes:
if the similarity of the traffic flow curve of the preset sample path and the traffic flow curve of the target candidate path exceeds a preset similarity, taking the preset sample path as a first sample path, and taking the target candidate path as any candidate path of the candidate path set;
if the road state of the first sample path indicates that the preset sample path is in the blocking state, taking the first sample path as a second sample path;
and taking the ratio of the number of the second sample paths to the number of the first sample paths as the road blocking probability of the target candidate path.
Optionally, before obtaining the traffic risk of each of the candidate routes according to the predicted traffic time of each of the candidate routes and the road blocking probability of each of the candidate routes, the method further includes:
acquiring attribute information, wherein the attribute information comprises: a preset attribute of each preset sample date and a preset attribute of the preset date, wherein the preset attributes indicate holidays and/or weather;
according to the attribute information and a preset corresponding relationship, obtaining the matching degree of the preset attribute of each preset sample date and the preset attribute of the preset date, wherein the preset corresponding relationship comprises: matching degrees among different preset attributes;
if the preset attributes of the preset sample date are matched with the preset attributes of the preset date, taking the preset sample date as a target date;
and acquiring the passing time of the candidate route at the preset moment of the target date as the predicted passing time of the candidate route.
Optionally, obtaining the traffic risk of each of the candidate routes according to the predicted traffic time of each of the candidate routes and the road blocking probability of each of the candidate routes, including:
and acquiring the product of the predicted passing time of each candidate route and the road blocking probability of each candidate route as the passing risk of each candidate route.
Optionally, after obtaining the traffic risk of each of the candidate routes according to the predicted traffic time of each of the candidate routes and the road blocking probability of each of the candidate routes, the method further includes:
sequencing the candidate paths from small to large according to the predicted passing time to obtain a time sequence;
taking the predicted passing time of a first candidate route as a first numerical value of the first candidate route, and taking the product of the road blocking probability of the first candidate route and the predicted passing time of a second candidate route as a second numerical value of the first candidate route, wherein the second candidate route is a candidate route positioned one bit behind the first candidate route in the time sequence, and the first candidate route is any one of candidate routes except the candidate route positioned the last bit in the time sequence;
acquiring the sum of a first numerical value of the first candidate path and a second numerical value of the first candidate path as a risk cost value of the first candidate path;
and judging whether the risk cost value of the first candidate route is smaller than the predicted transit time of the second candidate route, if so, determining that the first candidate route belongs to a low risk route.
If the candidate path meets the preset condition, taking the candidate path as a planning path of the reservation task, including:
if the candidate path meets a preset condition, taking the candidate path as a planning path of the reservation task, wherein the preset condition further comprises: the candidate path belongs to the low risk path.
An embodiment of the present application further provides a readable storage medium, where the readable storage medium may store a computer program suitable for being executed by a processor, and when the computer program is executed by the processor, the computer program implements the steps of the method for acquiring a planned path provided in the embodiment of the present application, and the method includes:
a planned path acquisition method comprises the following steps:
acquiring a candidate path set, wherein the candidate path set comprises at least two candidate paths, and the candidate paths are paths from a starting place of a reserved task to a destination of the reserved task;
acquiring the road blocking probability of each candidate path according to historical traffic flow data, wherein the road blocking probability indicates the probability that the candidate path is in a blocking state at a preset time, and the preset time is the reservation time of the reservation task;
the method comprises the steps that at preset time, according to the predicted passing time of each candidate route and the road blocking probability of each candidate route, the passing risk of each candidate route is obtained, and the longer the predicted passing time is, and/or the larger the road blocking probability is, the larger the passing risk is;
and if the candidate path meets a preset condition, taking the candidate path as a planning path of the reservation task, wherein the preset condition comprises that the passing risk of the candidate path is smaller than a preset threshold value.
Optionally, before obtaining the road blocking probability of each candidate route according to the historical traffic data, the method further includes:
generating a traffic flow curve of each candidate path according to the traffic flow of each candidate path in each preset time period on a preset date, wherein the preset time period is before the preset time, and the preset date is the reservation date of the reservation task;
generating a traffic flow curve of each preset sample path according to the traffic flow of each preset sample path in each preset time period of a preset sample date;
and taking a traffic flow curve of each candidate path, a traffic flow curve of each preset sample path and a road state of each preset sample path as the historical traffic flow data, wherein the road state of the preset sample path indicates that the preset sample path is in the blocking state or the non-blocking state.
Optionally, the process of obtaining the road blocking probability of the target candidate route according to the historical traffic flow data includes:
if the similarity of the traffic flow curve of the preset sample path and the traffic flow curve of the target candidate path exceeds a preset similarity, taking the preset sample path as a first sample path, and taking the target candidate path as any candidate path of the candidate path set;
if the road state of the first sample path indicates that the preset sample path is in the blocking state, taking the first sample path as a second sample path;
and taking the ratio of the number of the second sample paths to the number of the first sample paths as the road blocking probability of the target candidate path.
Optionally, before obtaining the traffic risk of each of the candidate routes according to the predicted traffic time of each of the candidate routes and the road blocking probability of each of the candidate routes, the method further includes:
acquiring attribute information, wherein the attribute information comprises: a preset attribute of each preset sample date and a preset attribute of the preset date, wherein the preset attributes indicate holidays and/or weather;
according to the attribute information and a preset corresponding relationship, obtaining the matching degree of the preset attribute of each preset sample date and the preset attribute of the preset date, wherein the preset corresponding relationship comprises: matching degrees among different preset attributes;
if the preset attributes of the preset sample date are matched with the preset attributes of the preset date, taking the preset sample date as a target date;
and acquiring the passing time of the candidate route at the preset moment of the target date as the predicted passing time of the candidate route.
Optionally, obtaining the traffic risk of each of the candidate routes according to the predicted traffic time of each of the candidate routes and the road blocking probability of each of the candidate routes, including:
and acquiring the product of the predicted passing time of each candidate route and the road blocking probability of each candidate route as the passing risk of each candidate route.
Optionally, after obtaining the traffic risk of each of the candidate routes according to the predicted traffic time of each of the candidate routes and the road blocking probability of each of the candidate routes, the method further includes:
sequencing the candidate paths from small to large according to the predicted passing time to obtain a time sequence;
taking the predicted passing time of a first candidate route as a first numerical value of the first candidate route, and taking the product of the road blocking probability of the first candidate route and the predicted passing time of a second candidate route as a second numerical value of the first candidate route, wherein the second candidate route is a candidate route positioned one bit behind the first candidate route in the time sequence, and the first candidate route is any one of candidate routes except the candidate route positioned the last bit in the time sequence;
acquiring the sum of a first numerical value of the first candidate path and a second numerical value of the first candidate path as a risk cost value of the first candidate path;
and judging whether the risk cost value of the first candidate route is smaller than the predicted transit time of the second candidate route, if so, determining that the first candidate route belongs to a low risk route.
If the candidate path meets the preset condition, taking the candidate path as a planning path of the reservation task, including:
if the candidate path meets a preset condition, taking the candidate path as a planning path of the reservation task, wherein the preset condition further comprises: the candidate path belongs to the low risk path.
Finally, it should also be 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 an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for obtaining a planned path is characterized by comprising the following steps:
acquiring a candidate path set, wherein the candidate path set comprises at least two candidate paths, and the candidate paths are paths from a starting place of a reserved task to a destination of the reserved task;
acquiring the road blocking probability of each candidate path according to historical traffic flow data, wherein the road blocking probability indicates the probability that the candidate path is in a blocking state at a preset time, and the preset time is the reservation time of the reservation task;
the method comprises the steps that at preset time, according to the predicted passing time of each candidate route and the road blocking probability of each candidate route, the passing risk of each candidate route is obtained, and the longer the predicted passing time is, and/or the larger the road blocking probability is, the larger the passing risk is;
and if the candidate path meets a preset condition, taking the candidate path as a planning path of the reservation task, wherein the preset condition comprises that the passing risk of the candidate path is smaller than a preset threshold value.
2. The method according to claim 1, wherein before obtaining the road blocking probability of each candidate route according to the historical traffic data, the method further comprises:
generating a traffic flow curve of each candidate path according to the traffic flow of each candidate path in each preset time period on a preset date, wherein the preset time period is before the preset time, and the preset date is the reservation date of the reservation task;
generating a traffic flow curve of each preset sample path according to the traffic flow of each preset sample path in each preset time period of a preset sample date;
and taking a traffic flow curve of each candidate path, a traffic flow curve of each preset sample path and a road state of each preset sample path as the historical traffic flow data, wherein the road state of the preset sample path indicates that the preset sample path is in the blocking state or the non-blocking state.
3. The method according to claim 2, wherein the step of obtaining the road blocking probability of the target candidate route according to the historical traffic data comprises:
if the similarity of the traffic flow curve of the preset sample path and the traffic flow curve of the target candidate path exceeds a preset similarity, taking the preset sample path as a first sample path, and taking the target candidate path as any candidate path of the candidate path set;
if the road state of the first sample path indicates that the preset sample path is in the blocking state, taking the first sample path as a second sample path;
and taking the ratio of the number of the second sample paths to the number of the first sample paths as the road blocking probability of the target candidate path.
4. The method of claim 1, wherein before obtaining the traffic risk of each of the candidate routes according to the predicted traffic time of each of the candidate routes and the road blocking probability of each of the candidate routes, further comprising:
acquiring attribute information, wherein the attribute information comprises: a preset attribute of each preset sample date and a preset attribute of the preset date, wherein the preset attributes indicate holidays and/or weather;
according to the attribute information and a preset corresponding relationship, obtaining the matching degree of the preset attribute of each preset sample date and the preset attribute of the preset date, wherein the preset corresponding relationship comprises: matching degrees among different preset attributes;
if the preset attributes of the preset sample date are matched with the preset attributes of the preset date, taking the preset sample date as a target date;
and acquiring the passing time of the candidate route at the preset moment of the target date as the predicted passing time of the candidate route.
5. The method of claim 1, wherein the obtaining the traffic risk of each of the candidate routes according to the predicted traffic time of each of the candidate routes and the road blocking probability of each of the candidate routes comprises:
and acquiring the product of the predicted passing time of each candidate route and the road blocking probability of each candidate route as the passing risk of each candidate route.
6. The method according to claim 1, further comprising, after obtaining the traffic risk of each of the candidate routes according to the predicted traffic time of each of the candidate routes and the road blocking probability of each of the candidate routes, the steps of:
sequencing the candidate paths from small to large according to the predicted passing time to obtain a time sequence;
taking the predicted passing time of a first candidate route as a first numerical value of the first candidate route, and taking the product of the road blocking probability of the first candidate route and the predicted passing time of a second candidate route as a second numerical value of the first candidate route, wherein the second candidate route is a candidate route positioned one bit behind the first candidate route in the time sequence, and the first candidate route is any one of candidate routes except the candidate route positioned the last bit in the time sequence;
acquiring the sum of a first numerical value of the first candidate path and a second numerical value of the first candidate path as a risk cost value of the first candidate path;
judging whether the risk cost value of the first candidate route is smaller than the predicted passing time of the second candidate route, if so, determining that the first candidate route belongs to a small risk route;
if the candidate path meets the preset condition, taking the candidate path as a planning path of the reservation task, including:
if the candidate path meets a preset condition, taking the candidate path as a planning path of the reservation task, wherein the preset condition further comprises: the candidate path belongs to the low risk path.
7. An apparatus for obtaining a planned route, comprising:
a path set acquiring unit, configured to acquire a candidate path set, where the candidate path set includes at least two candidate paths, and the candidate path is a path from a starting point of a reservation task to a destination of the reservation task;
a probability obtaining unit, configured to obtain a road blocking probability of each candidate path according to historical traffic flow data, where the road blocking probability indicates a probability that the candidate path is in a blocking state at a preset time, and the preset time is a reservation time of the reservation task;
a traffic risk obtaining unit, configured to obtain a traffic risk of each candidate route according to a predicted traffic time of each candidate route and a road blocking probability of each candidate route, where the traffic risk is greater as the predicted traffic time is longer and/or the road blocking probability is greater;
and the planning result acquisition unit is used for taking the candidate path as a planning path of the reservation task if the candidate path meets a preset condition, wherein the preset condition comprises that the passing risk of the candidate path is smaller than a preset threshold value.
8. The apparatus of claim 7, wherein the traffic risk obtaining unit is configured to obtain the traffic risk of each of the candidate routes according to the predicted traffic time of each of the candidate routes and the road blocking probability of each of the candidate routes, and comprises:
the traffic risk acquiring unit is specifically configured to: and acquiring the product of the predicted passing time of each candidate route and the road blocking probability of each candidate route as the passing risk of each candidate route.
9. An acquisition device for a planned path, comprising: a memory and a processor;
the memory is used for storing programs;
the processor is configured to execute the program to implement the steps of the planned path obtaining method according to any one of claims 1 to 6.
10. A readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for obtaining a planned path according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110600232.0A CN113358130A (en) | 2021-05-31 | 2021-05-31 | Method, device and equipment for acquiring planned path and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110600232.0A CN113358130A (en) | 2021-05-31 | 2021-05-31 | Method, device and equipment for acquiring planned path and readable storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113358130A true CN113358130A (en) | 2021-09-07 |
Family
ID=77530426
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110600232.0A Pending CN113358130A (en) | 2021-05-31 | 2021-05-31 | Method, device and equipment for acquiring planned path and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113358130A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115798216A (en) * | 2023-02-03 | 2023-03-14 | 以萨技术股份有限公司 | Data processing system for acquiring target traffic flow |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1734238A (en) * | 2005-09-15 | 2006-02-15 | 北京工业大学 | Two-step multi-path optimization method for central controlled vehicle information system |
| CN102288193A (en) * | 2011-07-06 | 2011-12-21 | 东南大学 | Motor vehicle travel route determination method based on historical data |
| CN106248096A (en) * | 2016-09-29 | 2016-12-21 | 百度在线网络技术（北京）有限公司 | The acquisition methods of road network weight and device |
| CN110702129A (en) * | 2019-05-31 | 2020-01-17 | 北京嘀嘀无限科技发展有限公司 | System and method for path planning |
| CN110730271A (en) * | 2019-10-12 | 2020-01-24 | 深圳酷派技术有限公司 | Schedule reminding method and device, storage medium and terminal |
| JP2021038964A (en) * | 2019-09-02 | 2021-03-11 | アイシン・エィ・ダブリュ株式会社 | Driving determination system, and driving determination program |
-
2021
- 2021-05-31 CN CN202110600232.0A patent/CN113358130A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1734238A (en) * | 2005-09-15 | 2006-02-15 | 北京工业大学 | Two-step multi-path optimization method for central controlled vehicle information system |
| CN102288193A (en) * | 2011-07-06 | 2011-12-21 | 东南大学 | Motor vehicle travel route determination method based on historical data |
| CN106248096A (en) * | 2016-09-29 | 2016-12-21 | 百度在线网络技术（北京）有限公司 | The acquisition methods of road network weight and device |
| CN110702129A (en) * | 2019-05-31 | 2020-01-17 | 北京嘀嘀无限科技发展有限公司 | System and method for path planning |
| JP2021038964A (en) * | 2019-09-02 | 2021-03-11 | アイシン・エィ・ダブリュ株式会社 | Driving determination system, and driving determination program |
| CN110730271A (en) * | 2019-10-12 | 2020-01-24 | 深圳酷派技术有限公司 | Schedule reminding method and device, storage medium and terminal |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115798216A (en) * | 2023-02-03 | 2023-03-14 | 以萨技术股份有限公司 | Data processing system for acquiring target traffic flow |
| CN115798216B (en) * | 2023-02-03 | 2023-04-28 | 以萨技术股份有限公司 | Data processing system for obtaining target traffic flow |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109559512B (en) | Regional traffic flow prediction method and device | |
| AU2020244388B2 (en) | Method and server for providing fare availabilities, such as air fare availabilities | |
| Chen et al. | Reliable shortest path finding in stochastic networks with spatial correlated link travel times | |
| US9776512B2 (en) | Methods, circuits, devices, systems and associated computer executable code for driver decision support | |
| US9726502B2 (en) | Route planner for transportation systems | |
| Thomas et al. | The dynamic shortest path problem with anticipation | |
| CN103984735A (en) | Method and device for generating recommended delivery place name | |
| US20130024404A1 (en) | Flight caching methods and apparatus | |
| US9978090B2 (en) | Shopping optimizer | |
| AU2020244405A1 (en) | Method and server for providing a set of price estimates, such as air fare price estimates | |
| CA2468923A1 (en) | Method and system for origin-destination passenger demand forecast inference | |
| CN104050512A (en) | Transport time estimation based on multi-granular map | |
| CN110288205B (en) | Traffic influence evaluation method and device | |
| Amirgholy et al. | Analytical equilibrium of bicriterion choices with heterogeneous user preferences: application to the morning commute problem | |
| US9366541B2 (en) | AMI visual routing | |
| CN113358130A (en) | Method, device and equipment for acquiring planned path and readable storage medium | |
| JP2016537693A (en) | Hierarchical hidden variable model estimation device, hierarchical hidden variable model estimation method, payout amount prediction device, payout amount prediction method, and recording medium | |
| CN111815047A (en) | Path planning method based on user behavior analysis | |
| Lu et al. | Prediction-based parking allocation framework in urban environments | |
| Ni et al. | DEPART: Dynamic route planning in stochastic time-dependent public transit networks | |
| US20140074400A1 (en) | Method and system for generating fixed transit routes | |
| CN113159457A (en) | Intelligent path planning method and system and electronic equipment | |
| CN108256707B (en) | Policy return visit management method and device | |
| CN115660157A (en) | Smart city charging pile construction planning method, internet of things system, device and medium | |
| CN110956299A (en) | Arrival time estimation method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |