CN116989789A - Composite traffic path planning method, device, electronic equipment and medium - Google Patents

Abstract

The present disclosure provides a method for planning a composite traffic path, which can be applied to the technical field of automobile control. The composite traffic path planning method comprises the following steps: acquiring at least one predicted path; dividing the predicted path into a plurality of sub-predicted paths; and calculating the composite predicted transit time of the predicted path according to the plurality of time histories of the sub-predicted path. And correcting the current planning path based on the composite predicted transit time of the predicted path. The method and the device can comprehensively consider historical data and time periodicity factors to improve the accuracy of path prediction planning.

Description

Composite traffic path planning method, device, electronic equipment and medium

Technical Field

The disclosure relates to the technical field of automobile control, in particular to a method, a device, electronic equipment and a medium for planning a composite traffic path.

Background

With the acceleration of the urban process, the problem of traffic jam is increasingly serious, so that a great deal of inconvenience is brought to the travel of people, and banks are taken as important urban infrastructures, and the periphery of the banks usually has higher passenger flow, so that the traffic jam is caused.

Currently, various navigation software is available to provide path planning services to assist users in selecting the best route. However, these navigation software often consider only the road conditions at the current moment, and ignore historical data and time periodicity factors, resulting in a non-optimal route being provided. Therefore, it is necessary to develop a traffic path planning system capable of comprehensively considering historical data, time periodicity factors and real-time road conditions, so as to alleviate traffic jam and improve traffic efficiency.

Disclosure of Invention

In view of the above, the present disclosure provides a method, an apparatus, an electronic device, and a medium for planning a composite traffic path to improve accuracy of route planning.

According to a first aspect of the present disclosure, there is provided a composite traffic path planning method comprising: acquiring at least one predicted path; dividing the predicted path into a plurality of sub-predicted paths; calculating the composite predicted transit time of the predicted path according to the plurality of time histories of the sub-predicted path; the current planned path is revised based on the composite predicted transit time.

According to an embodiment of the present disclosure, calculating a composite predicted transit time for a sub-predicted path includes: acquiring current time traffic data of the sub-prediction path; acquiring a plurality of non-current time historical traffic data of a sub-prediction path; acquiring a plurality of simultaneous historic data of the sub-prediction paths; and calculating the composite predicted traffic time of the predicted path according to the current time traffic data, the plurality of non-current time historical traffic data and the plurality of simultaneous historical data.

According to an embodiment of the present disclosure, the time-of-day history data includes at least one of time-of-day history data, time-of-week history data, time-of-month history data, and time-of-year history data.

According to an embodiment of the present disclosure, the non-current time historical traffic data includes historical traffic data for any time within ten minutes to twenty minutes prior to the current time.

According to an embodiment of the present disclosure, calculating a composite predicted transit time of a predicted path from current time transit data, a plurality of non-current time historical transit data, and a plurality of simultaneous historical data includes: multiplying the current time traffic data by the current time traffic weight to obtain a first sub-predicted traffic time; multiplying the non-current time historical traffic data by the non-current time traffic weight to obtain second sub-predicted traffic time; adding the first sub-predicted transit time of all sub-predicted paths of the predicted path and the second sub-predicted transit time of all sub-predicted paths of the predicted path to obtain a first predicted time; multiplying the history data at the same time by the history traffic weight to obtain second prediction time; adding the second prediction time of all sub-prediction paths of the prediction path to obtain a third prediction time of the prediction path; the first predicted time is multiplied by the first predicted weight and added to the third predicted time to obtain a composite transit time of the predicted path.

According to the embodiment of the present disclosure, the history passing weight decreases along with an increase in the time difference of the generation time of the time history data from the current time.

According to an embodiment of the present disclosure, correcting a current planned path based on a composite predicted transit time includes: acquiring the current position of a user; acquiring the composite passing time of the current planning route according to the current position; under the condition that the difference value between the composite predicted passing time and the composite passing time is larger than a preset threshold value, a route correction request is sent to a user; and correcting the current planned route based on the feedback result of the user on the route correction request.

A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the composite traffic path planning method described above.

A fourth aspect of the present disclosure also provides a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the above-described composite traffic path planning method.

On the basis of path planning, the current planned path transit time is corrected according to transit time data of the same day in the past and transit time data of 10-20 minutes before the current moment, historical data and time periodicity factors are comprehensively considered to optimize a recommended route, and the accuracy of path transit time prediction is improved so as to improve the reliability of route recommendation.

Drawings

The foregoing and other objects, features and advantages of the disclosure will be more apparent from the following description of embodiments of the disclosure with reference to the accompanying drawings, in which:

FIG. 1 schematically illustrates an application scenario diagram of a composite traffic path plan according to an embodiment of the present disclosure;

FIG. 2 schematically illustrates a flow chart of a composite traffic path planning method according to an embodiment of the present disclosure;

FIG. 3 schematically illustrates a flow chart of calculating a composite predicted transit time for a predicted path in accordance with an embodiment of the present disclosure;

FIG. 4 schematically illustrates a flow chart of modifying a current planned path in accordance with an embodiment of the present disclosure;

FIG. 5 schematically illustrates a block diagram of a composite traffic path planning apparatus according to an embodiment of the present disclosure;

fig. 6 schematically illustrates a block diagram of a computer system suitable for implementing the above-described method according to an embodiment of the present disclosure.

Detailed Description

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.

Fig. 1 schematically illustrates an application scenario diagram of a composite traffic path planning in accordance with an embodiment of the present disclosure.

As shown in fig. 1, an application scenario 100 according to this embodiment may include terminal devices 101, 102, 103. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as shopping class applications, web browser applications, search class applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only) may be installed on the terminal devices 101, 102, 103.

The terminal devices 101, 102, 103 may be a variety of electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like. The terminal device may be a separate application program in a smart phone, a tablet computer, a laptop portable computer, a desktop computer, or the like, or may be a functional module in a certain application program.

The server 105 may be a server providing various services, such as a background management server (by way of example only) providing support for websites browsed by users using the terminal devices 101, 102, 103. The background management server may analyze and process the received data such as the user request, and feed back the processing result (e.g., the web page, information, or data obtained or generated according to the user request) to the terminal device.

It should be noted that, the method for planning a composite traffic path provided by the embodiments of the present disclosure may be generally performed by the server 105. Accordingly, the composite traffic path planning apparatus provided by the embodiments of the present disclosure may be generally disposed in the server 105. The composite traffic path planning method provided by the embodiments of the present disclosure may also be performed by a server or cluster of servers other than the server 105 and capable of communicating with the terminal devices 101, 102, 103 and/or the server 105. Accordingly, the composite traffic path planning apparatus provided by the embodiments of the present disclosure may also be provided in a server or server cluster that is different from the server 105 and is capable of communicating with the terminal devices 101, 102, 103 and/or the server 105.

It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

The composite traffic path planning method of the disclosed embodiment will be described in detail below with reference to fig. 2 to 4 based on the scenario described in fig. 1.

Fig. 2 schematically illustrates a flow chart of a composite traffic path planning method according to an embodiment of the present disclosure.

As shown in fig. 2, the composite traffic path planning method of the embodiment includes operations S210 to S240, and the transaction processing method may be performed by a server or other computing device.

In operation S210, at least one predicted path is acquired.

Specifically, the user inputs a start point and a destination: the user enters a starting point (e.g., a current location or manually entered address) and a destination (e.g., an address, a landmark, or a point of interest) in a navigational map application; selecting an appropriate map data source, such as google map, hundred degree map, goldmap, etc., the navigation map application will query map data, which may be from an online map server or an offline map data package, based on the start point and destination of the user input. The map data comprises information such as roads, traffic rules, speed limits and the like; the navigation map application uses a path search algorithm (e.g., dijkstra algorithm, a-algorithm, etc.) to search map data for a path from a start point to a destination. These algorithms consider various factors, such as road length, road class, traffic rules, etc., to find one or more viable paths; based on real-time traffic information (e.g., congestion, road closure, accidents, etc.) and user demand, the navigation map application may optimize the searched route, such as avoiding toll road segments, selecting scenic route, etc., to obtain a final at least one predicted route.

In some embodiments, in the case that the user does not operate, the terminal automatically selects the predicted path with the highest probability according to the common route of the user, the quantity of the vehicle, the preference of the route of the user and other factors. The user may also manually select one of the provided at least one predicted path as the current planned path through the terminal.

In the presently disclosed embodiments, the predicted path is denoted as S _i (i=1, 2,., m); i is a counting unit, S _i Representing the i-th predicted path.

In operation S220, the predicted path is divided into a plurality of sub-predicted paths.

Specifically, the division of the predicted path into multiple sub-predicted paths may be performed according to several strategies:

based on distance segmentation: the predicted path is segmented according to a fixed distance or distance interval. For example, the path is divided into one sub-predicted path every 1 km or every 500 meters.

Based on road class segmentation: the predicted path is segmented according to road class, such as expressway, arterial road, secondary arterial road, branch road, etc. Therefore, the traffic time of the whole path can be predicted more accurately according to the characteristics of different road grades.

Based on traffic node segmentation: the predicted path is segmented according to traffic nodes, such as intersections, traffic lights, bridges, tunnels, and the like. The method can more accurately consider the influence of traffic nodes on the passing time, such as waiting for red lights, turning and the like.

Based on real-time traffic information segmentation: and segmenting the predicted path according to congestion conditions, road closure, accidents and the like according to the real-time traffic information. Therefore, the traffic time of the sub-prediction path can be dynamically adjusted according to the real-time traffic condition, so that the traffic time of the whole path can be predicted more accurately.

The prediction path may also be segmented based on a hybrid strategy, combining the various strategies described above. For example, the segments are first performed by road class, and then within each road class, the sub-predicted paths are further subdivided according to traffic nodes and real-time traffic information.

It should be noted that, since the existing roads are mostly double lanes, the traffic directions of the vehicles in the double lanes are opposite, so that the vehicles are exactly opposite at the start end and the end of each traffic segment, and the traffic congestion conditions of the double lanes are not consistent, so that it is necessary to distinguish whether the traffic directions of the vehicles in the divided sub-prediction paths are forward or reverse.

The sub-prediction path is denoted as S _ij (j=1, 2,., n); wherein j is a counting unit, S _ij The jth sub-predicted path representing the ith predicted path.

In operation S230, a composite predicted transit time of the predicted path is calculated from the plurality of time histories of the sub-predicted path.

In operation S240, the current planned path is corrected based on the composite predicted transit time of the predicted path.

Fig. 3 schematically illustrates a flow chart of calculating a composite predicted transit time for a predicted path in accordance with an embodiment of the present disclosure.

As shown in fig. 3, step S230 further includes steps S310 to S340.

In operation S310, current time traffic data of the sub-predicted path is acquired.

Specifically, first, a suitable real-time traffic data source needs to be selected. This may include real-time traffic data provided by government traffic authorities, real-time road condition information provided by third-party map service providers (e.g., google maps, hundred degree maps, etc.), data collected by car navigation systems, etc. Second, real-time traffic information is acquired from the selected data source by calling a corresponding API (application program interface) or using an SDK (software development kit). These interfaces typically return real-time data containing information about the time of travel, speed, congestion, etc. of the vehicle on a particular road segment. Again, the acquired real-time traffic data is processed as necessary to facilitate subsequent analysis and prediction. For example, the passage time may be converted to a passage speed, or the passage speed may be converted to a passage time. In addition, the data can be normalized to eliminate differences between different data sources. For example, a theoretical transit time may be calculated based on road length and speed limits, and then adjusted based on real-time traffic information. Based on the calculation result, the influence of some additional factors on the commute time, such as weather, holidays, etc., can be considered, and these factors can be adjusted by setting weight coefficients or other methods.

The acquired current time traffic data is recorded as t _ij0 。

In operation S320, a plurality of non-current time history traffic data of the sub-predicted path is acquired.

First, historical traffic data needs to be collected from multiple data sources. Such data sources may include government traffic departments, third party map service providers, car navigation systems, cell phone map applications, and the like. Such data typically contains information about the time of travel, speed, congestion, etc. of the vehicle on a particular road segment.

The collected historical traffic data is filtered and grouped according to the time period. For example, the data may be divided into rush hour, off-peak hours, and the like. On the basis of screening and grouping, a proper time period is selected as an acquisition time period of historical traffic data at the non-current time of the day, and as the historical traffic data possibly has certain fluctuation, the data can be subjected to smoothing processing except an average method so as to reduce the influence of the data fluctuation on a prediction result. Typical smoothing methods include a moving average method and an exponential smoothing method.

According to an embodiment of the present disclosure, the non-current time historical traffic data includes historical traffic data at any one of ten minutes to twenty minutes before the current time.

It should be noted that, in the selection of the history traffic data at the non-current time, consideration of specific factors may be added, for example, in the next sub-prediction path, a traffic accident occurs in the first twenty minutes, but in the first ten minutes of the current time, the traffic accident is processed, and the road resumes normal operation, so the history traffic data at the non-current time in the period of the first ten minutes to the first twenty minutes is not representative, and if calculation is added, the actual prediction result is greatly affected, so that data in the first ten minutes to the first twenty minutes of the current time need to be excluded, and traffic data at a time outside this range is selected as the history traffic data at the non-current time of the present disclosure.

According to the embodiment of the disclosure, 2 historical traffic data at non-current time are selected and are respectively the historical communication data t at ten minutes before the current time _ij10 And, history communication data t at a time twenty minutes before the present time _ij20 。t _ij10 Can be obtained by calculating the average value j of the traffic time of the vehicle in the sub-prediction path and the same thing can obtain t _ij20 。

In operation S330, a plurality of time histories of the sub-prediction paths are acquired.

In an embodiment of the present disclosure, the time-of-day history data includes at least one of time-of-day history data, time-of-week history data, time-of-month history data, and time-of-year history data.

It should be noted that, the selection of the historical data at the same time may take into consideration specific time, for example, the data at the same time on some specific days is not representative, for example, a traffic accident occurs on a sub-prediction path at the same time before seven days, a holiday at the same time before one month, and severe weather is sudden at the same time before one year. The passing data at the same time of the date is not representative, a day before or after the date can be selected as the taking date of the history data at the same time according to the actual situation, the special date can be marked, and the lower weight is set in the process of multiplying the weight later.

In the embodiment of the disclosure, four time histories are selected, which are respectively the time histories T before the day _i1 Historical data T of seven days ago _i7 Historical data T of a month before and at the same time _i30 Historical data T of one year ago _i365 。T _i1 For the average value of the communication time of all vehicles at the same time of the previous day in the sub-prediction path, T _i7 、T _i7 、T _i365 Is similar to the calculation method.

It should be noted that, the method for calculating the time history data is not limited thereto, and methods that may be used include, but are not limited to, a moving average method, an exponential smoothing method, and the like due to differences in vehicles, road conditions, weather, and the like.

It should be noted that, when acquiring the current time traffic data, the plurality of non-current time historical traffic data and the plurality of simultaneous time historical data, some data sources do not consider the situation that the vehicle will turn at the traffic light intersection, because the turning at the traffic light intersection does not affect the moving time on the traffic segment, but because the turning of the vehicle at the traffic light intersection is different, the waiting time of the vehicle at the traffic light intersection is different, therefore, the traffic vehicle with the same moving direction as the reference vehicle needs to be screened out, and the current time traffic data, the plurality of non-current time historical traffic data and the plurality of simultaneous time historical data of the traffic vehicle in the sub-prediction path including the traffic light intersection are acquired.

In operation S340, a composite predicted transit time of the predicted path is calculated from the current time transit data, the plurality of non-current time history transit data, and the plurality of time history data.

Multiplying the current time traffic data by current time traffic weight to obtain first sub-predicted traffic time; multiplying the non-current time historical traffic data by a non-current time traffic weight to obtain a second sub-predicted traffic time; adding the first sub-predicted transit time of all sub-predicted paths of the predicted path to the second sub-predicted transit time of all sub-predicted paths of the predicted path to obtain a first predicted time T _i0 。

It should be noted that, the non-current time passing weight corresponding to the non-current time historical passing data should show a decreasing trend along with the increase of the time difference between the generation time of the non-current time historical passing data and the current time.

In the embodiment of the present disclosure, the current time traffic weight is set to 85%, and the non-current time historical traffic data t _ij10 The non-current time passing weight of the traffic pattern is set to 10%, and the non-current time history passing data t _ij20 The non-current time pass weight of (1) is set to 5%, and therefore, the first prediction time T _i0 The calculation formula of (2) is as follows:

where j is a count unit, the value of j is taken from 0, and n is the number of sub-prediction paths.

Multiplying the history data at the same time by the history traffic weight to obtain second prediction time; adding the second prediction time of all sub-prediction paths of the prediction path to obtain a third prediction time of the prediction path; multiplying the first predicted time by a first predicted weight and adding the first predicted time to the third predicted time to obtain the composite transit time of the predicted path.

The history traffic weight shows a decreasing trend along with an increase in the time difference from the current time of the generation time of the time history data.

In the presently disclosed embodiment, historical data T is at the time of day _i1 The history traffic weight of (1) is set to 10%, and history data T is obtained at the moment seven days ago _i7 The history traffic weight of (1) was set to 10%, and the history data T was obtained at the same time as one month ago _i30 The history traffic weight of (1) is set to 5%, and the history data T is the same as the previous year _i365 The historical traffic weight of (1) is set to 5%, the first predicted weight is set to 70%, and the composite traffic time T _i The formula is as follows:

T _i ＝70％*T _i0 +10％*T _i1 +10％*T _i7 +5％*T _i30 +5％*T _i365 。

it should be noted that, the above weights may be weights preset in the program by a developer, or may be preferences manually set by a user. For example, when the life and driving habits of the user are regular, the influence of the periodic data on the traffic time prediction can be more emphasized, and the user can manually adjust down the first prediction weight and adjust up the historical traffic weight.

Fig. 4 schematically illustrates a flow chart of correcting a current planned path according to an embodiment of the present disclosure.

As shown in fig. 4, step S240 includes steps S410 to S440.

In operation S410, a current location of a user is acquired.

For example, methods of obtaining the current location of the user include, but are not limited to, the following:

positioning by GPS: a GPS receiver on the vehicle may receive signals from satellites and calculate the current location of the vehicle.

Cellular network positioning: the vehicle position is calculated by connecting to a cellular network through a mobile phone or a vehicle-mounted communication device and utilizing information such as signal intensity and distance of a base station.

Wi-Fi positioning: wi-Fi positioning is the determination of vehicle position by receiving signal strength and location information of surrounding Wi-Fi hotspots.

Inertial navigation system positioning: the motion state of the vehicle is measured by using sensors such as an accelerometer and a gyroscope, and the position of the vehicle is calculated through integration.

The multi-source fusion positioning method comprises the following steps: in order to improve the positioning accuracy and reliability, the positioning methods can be fused, and more accurate vehicle positioning is realized by utilizing the complementary advantages of the methods. For example, GPS, cellular network and Wi-Fi positioning data may be weighted fused or automatically switched to an inertial navigation system when GPS signals are disturbed.

It should be noted that the location of the user may be a location manually entered by the user in the map software, where the location of the user should be based on the user input, not the actual location of the user.

In operation S420, a composite transit time of the current planned route is acquired according to the current location. Concrete embodimentsIn the step (a), a current planning route is determined according to the current position, and the composite transit time T is obtained through the current planning route _s And the method for obtaining the predicted route and the composite predicted transit time T _i The method of (2) is the same and will not be described in detail herein.

In operation S430, a route correction request is issued to the user in case that the difference between the composite predicted transit time and the composite transit time is greater than a preset threshold.

The preset threshold may be a default threshold set by a developer in advance, and the user may manually adjust the preset threshold according to actual situations, for example, may be set to 5 minutes, 10 minutes, or the like.

According to the embodiment of the disclosure, the preset threshold is set to 8min, if T _s -T _f And sending a route correction request to the user if the time is more than 8 minutes, and inquiring whether the user changes the planned route into a route corresponding to Ts. Means of querying the user include, but are not limited to, the following:

the voice prompt informs the user of correcting the route in a voice broadcasting mode;

visual prompting: displaying a better route in the form of an arrow, a highlighted route and the like on a navigation interface, and prompting a user to correct the route;

and (5) character prompting: displaying route correction suggestions in a text form on a navigation interface;

vibration prompting: for wearable equipment such as a smart watch, a user can be reminded of correcting a route in a vibration mode;

meanwhile, in order to improve user experience, various modes can be combined, for example, voice prompt and visual prompt are combined, so that the user can understand conveniently, and driving safety is not affected.

In operation S440, the current planned route is corrected based on the feedback result of the user on the route correction request.

If the user agrees to modify the route, modifying the current planning path to be T _s And if the corresponding path is not agreed by the user, not executing modification.

The operation of obtaining the current user position can be performed in real time to obtain the current user position at any time; the method can also be carried out according to road sections, for example, according to a method of dividing a predicted path into sub-predicted paths, dividing the current planned path into a plurality of sub-planned paths, and acquiring a user position once when a user enters a new sub-predicted path. It is also possible to take the vehicle position in time, for example, every half a minute. The method can also be used for acquiring the user position in a fusion way, for example, the user position is acquired when a new sub-prediction path is entered, and the user position is acquired every half a minute, so that traffic accidents, congestion of the sub-prediction path can be avoided, and the operation of acquiring the position of the current user is triggered only when the sub-prediction path is entered.

Likewise, the current planned path is modified to be T _s The corresponding path may be modified before the user's vehicle enters the next sub-predicted path, or may be modified at the first time that user feedback is obtained.

In some embodiments, the location of the user may be unchanged, such as in a parking lot, or a pre-planned route at home, etc., but the road conditions may change over time, the obtained predicted path, the sub-predicted path may also change, and the obtained composite predicted time may also change over time. At this time, a method of acquiring a position by time may be adopted for the user.

In some embodiments, the user may select a default global optimal mode, i.e., automatically change the current planned route to a predicted route whenever a predicted route is found for which the difference between the composite predicted transit time and the composite transit time is greater than a preset threshold without issuing a path modification request to the user.

In some embodiments, when the user deviates from the current planned route, whether it is the next sub-predicted path or not, whether it is the next sampling time point or not, the operation of acquiring the current position of the user is triggered once, at least one predicted path is acquired again according to the current position, and one of the at least one predicted paths is selected as the current planned path.

According to the composite traffic route planning method, on the basis of route planning, the currently planned route passing time is corrected according to the passing time data of the same time of a certain day in the past and the passing time data of a period of time before the current time, the recommended route is optimized by comprehensively considering historical data and time periodicity factors, and the accuracy of route passing time prediction is improved, so that the reliability of route recommendation is improved.

Based on the composite traffic path planning method, the disclosure also provides a composite traffic path planning device. The device will be described in detail below in connection with fig. 5.

Fig. 5 schematically illustrates a block diagram of a composite traffic path planning apparatus according to an embodiment of the present disclosure.

As shown in fig. 5, the composite traffic path planning apparatus 800 of this embodiment includes an acquisition module 510, a path segmentation module 520, a calculation module 530, and a correction module 540.

The obtaining module 510 is configured to obtain at least one predicted path. In an embodiment, the obtaining module 510 may be configured to perform the operation S210 described above, which is not described herein.

The path segmentation module 520 is configured to divide the predicted path into a plurality of sub-predicted paths. In an embodiment, the path segmentation module 520 may be used to perform the operation S220 described above, which is not described herein.

The calculation module 530 is configured to calculate a composite predicted transit time of the predicted path according to the plurality of time histories of the sub-predicted path. In an embodiment, the calculating module 530 may be configured to perform the operation S230 described above, which is not described herein.

The correction module 540 is configured to calculate a composite predicted transit time of the predicted path according to the plurality of time histories of the sub-predicted path. In an embodiment, the correction module 540 may be used to perform the operation S240 described above, which is not described herein.

Any of the acquisition module 510, the path segmentation module 520, the calculation module 530, and the modification module 540 may be combined in one module to be implemented, or any of them may be split into a plurality of modules, according to an embodiment of the present disclosure. Alternatively, at least some of the functionality of one or more of the modules may be combined with at least some of the functionality of other modules and implemented in one module. According to embodiments of the present disclosure, at least one of the acquisition module 510, the path segmentation module 520, the calculation module 530, and the correction module 540 may be implemented at least in part as hardware circuitry, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware in any other reasonable way of integrating or packaging circuitry, or in any one of or a suitable combination of three of software, hardware, and firmware. Alternatively, at least one of the acquisition module 510, the path segmentation module 520, the calculation module 530, and the modification module 540 may be at least partially implemented as a computer program module, which when executed, may perform the corresponding functions.

Fig. 6 schematically illustrates a block diagram of a computer system suitable for implementing the above-described method according to an embodiment of the present disclosure.

In particular, the processor 610 may include, for example, a general purpose microprocessor, an instruction set processor and/or an associated chipset and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. The processor 610 may also include on-board memory for caching purposes. The processor 610 may be a single processing unit or multiple processing units for performing the different actions of the method flows according to embodiments of the disclosure.

Computer-readable storage medium 620, which may be, for example, a non-volatile computer-readable storage medium, specific examples include, but are not limited to: magnetic storage devices such as magnetic tape or hard disk (HDD); optical storage devices such as compact discs (CD-ROMs); a memory, such as a Random Access Memory (RAM) or a flash memory; etc.

The computer-readable storage medium 620 may include a computer program 621, which computer program 621 may include code/computer-executable instructions that, when executed by the process 610, cause the processor 610 to perform a method according to an embodiment of the present disclosure or any variation thereof.

The computer program 621 may be configured with computer program code comprising, for example, computer program modules. For example, in an example embodiment, code in computer program 621 may include one or more program modules, including 621A, modules 621B, … …, for example. It should be noted that the division and number of modules is not fixed, and that a person skilled in the art may use suitable program modules or combinations of program modules depending on the actual situation, which when executed by the processor 610, enable the processor 610 to perform the methods according to embodiments of the present disclosure or any variations thereof.

At least one of the acquisition module 510, the path segmentation module 520, the calculation module 530, and the correction module 540 may be implemented as computer program modules described with reference to fig. 6, which when executed by the processor 610, may implement the respective operations described above, in accordance with embodiments of the present disclosure.

The present disclosure also provides a computer-readable storage medium that may be embodied in the apparatus/device/system described in the above embodiments; or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs which, when executed, implement methods in accordance with embodiments of the present disclosure.

According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

Thus, embodiments of the present disclosure have been described in detail with reference to the accompanying drawings. It should be noted that, in the drawings or the text of the specification, implementations not shown or described are all forms known to those of ordinary skill in the art, and not described in detail. Furthermore, the above definitions of the components are not limited to the specific structures, shapes or modes mentioned in the embodiments, and may be simply modified or replaced by those of ordinary skill in the art.

Those skilled in the art will appreciate that the features recited in the various embodiments of the disclosure and/or in the claims may be provided in a variety of combinations and/or combinations, even if such combinations or combinations are not explicitly recited in the disclosure. In particular, the features recited in the various embodiments of the present disclosure and/or the claims may be variously combined and/or combined without departing from the spirit and teachings of the present disclosure. All such combinations and/or combinations fall within the scope of the present disclosure.

While the foregoing is directed to embodiments of the present disclosure, other and further details of the invention may be had by the present application, it is to be understood that the foregoing description is merely exemplary of the present disclosure and that no limitations are intended to the scope of the disclosure, except insofar as modifications, equivalents, improvements or modifications may be made without departing from the spirit and principles of the present disclosure.

Claims (10)

1. A method of composite traffic path planning, comprising:

acquiring at least one predicted path;

dividing the predicted path into a plurality of sub-predicted paths;

calculating the composite predicted transit time of the predicted path according to the plurality of time histories of the sub-predicted path;

and correcting the current planning path based on the composite predicted transit time.

2. The method of composite traffic path planning of claim 1, wherein the calculating the composite predicted transit time for the sub-predicted path comprises:

acquiring current time traffic data of the sub-prediction path;

acquiring a plurality of non-current time historical traffic data of the sub-prediction paths;

acquiring a plurality of the time history data of the sub-prediction paths;

and calculating the composite predicted traffic time of the predicted path according to the current time traffic data, the plurality of non-current time historical traffic data and the plurality of simultaneous historical data.

3. The composite traffic path planning method of claim 2 wherein the time history data comprises at least one of a day before time history data, a week before time history data, a month before time history data, and a year before time history data.

4. The composite traffic path planning method of claim 2 wherein the non-current time historical traffic data comprises historical traffic data at any time within ten minutes to twenty minutes prior to the current time.

5. The method of composite traffic path planning according to claim 2, wherein calculating the composite predicted transit time of the predicted path from the current time transit data, the plurality of non-current time historical transit data, and the plurality of simultaneous historical data comprises:

multiplying the current time traffic data by current time traffic weight to obtain first sub-predicted traffic time;

multiplying the non-current time historical traffic data by a non-current time traffic weight to obtain a second sub-predicted traffic time;

adding the first sub-predicted transit time of all sub-predicted paths of the predicted path and the second sub-predicted transit time of all sub-predicted paths of the predicted path to obtain a first predicted time;

multiplying the time history data by the history traffic weight to obtain second prediction time;

adding the second prediction time of all sub-prediction paths of the prediction path to obtain a third prediction time of the prediction path;

multiplying the first predicted time by a first predicted weight and adding the first predicted time to the third predicted time to obtain the composite transit time of the predicted path.

6. The composite traffic path planning method according to claim 5, wherein the historical traffic weight decreases as a time difference from a current time of generation time of the simultaneous historical data increases.

7. The composite traffic path planning method of claim 1 wherein said correcting the current planned path based on the composite predicted transit time comprises:

acquiring the current position of a user;

acquiring the composite transit time of the current planning route according to the current position;

sending a route correction request to a user under the condition that the difference value between the composite predicted passing time and the composite passing time is larger than a preset threshold value;

and correcting the current planned route based on a feedback result of the user on the route correction request.

8. A composite traffic path planning apparatus, comprising:

the acquisition module is used for acquiring at least one predicted path;

a path segmentation module for dividing the predicted path into a plurality of sub-predicted paths;

the calculation module is used for calculating the composite predicted passing time of the predicted path according to the plurality of time histories of the sub-predicted path; and

and the correction module is used for correcting the current planning path based on the composite predicted passing time.

9. An electronic device, comprising:

one or more processors;

storage means for storing one or more programs,

wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-7.

10. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method according to any of claims 1-7.