WO2019109645A1 - 一种路况预报方法、装置、存储介质和服务器 - Google Patents

一种路况预报方法、装置、存储介质和服务器 Download PDF

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Publication number
WO2019109645A1
WO2019109645A1 PCT/CN2018/097498 CN2018097498W WO2019109645A1 WO 2019109645 A1 WO2019109645 A1 WO 2019109645A1 CN 2018097498 W CN2018097498 W CN 2018097498W WO 2019109645 A1 WO2019109645 A1 WO 2019109645A1
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WIPO (PCT)
Prior art keywords
time
congestion
historical
road condition
information
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Application number
PCT/CN2018/097498
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English (en)
French (fr)
Inventor
孔令琛
Original Assignee
深圳壹账通智能科技有限公司
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Application filed by 深圳壹账通智能科技有限公司 filed Critical 深圳壹账通智能科技有限公司
Publication of WO2019109645A1 publication Critical patent/WO2019109645A1/zh

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Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34Route searching; Route guidance
    • G01C21/3453Special cost functions, i.e. other than distance or default speed limit of road segments
    • G01C21/3484Personalized, e.g. from learned user behaviour or user-defined profiles
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34Route searching; Route guidance
    • G01C21/3453Special cost functions, i.e. other than distance or default speed limit of road segments
    • G01C21/3492Special 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
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/09Arrangements for giving variable traffic instructions
    • G08G1/0962Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0968Systems involving transmission of navigation instructions to the vehicle
    • G08G1/096833Systems involving transmission of navigation instructions to the vehicle where different aspects are considered when computing the route

Definitions

  • the present application relates to the field of information monitoring, and in particular, to a road condition prediction method, apparatus, storage medium, and server.
  • the user can only provide route navigation and real-time road conditions when using navigation.
  • the road condition information is provided, and the route is planned for the user according to the road condition information at this time.
  • the road condition is found only during the driving process. The anomaly is too late, and the user usually does not need to use navigation for familiar routes, and thus cannot know the traffic information in time. Therefore, the existing navigation does not provide the user with relevant travel-related information in advance to facilitate the user's travel.
  • the embodiment of the present application provides a road condition prediction method, device, storage medium, and server, so as to solve the problem that the navigation cannot provide the user with relevant travel-related information in advance to facilitate the user to travel.
  • a first aspect of the embodiments of the present application provides a road condition prediction method, including:
  • Generating an abnormality reminding information to the intelligent terminal bound by the user according to the abnormality of the common travel route and the location of the abnormal road segment;
  • the predicted road condition of the common travel route is pushed to the smart terminal bound by the user.
  • a second aspect of an embodiment of the present application provides a server comprising a memory and a processor, the memory storing computer readable instructions executable on the processor, the processor executing the computer readable instructions The following steps are implemented:
  • Generating an abnormality reminding information to the intelligent terminal bound by the user according to the abnormality of the common travel route and the location of the abnormal road segment;
  • the predicted road condition of the common travel route is pushed to the smart terminal bound by the user.
  • a third aspect of embodiments of the present application provides a computer readable storage medium storing computer readable instructions that, when executed by a processor, implement the following steps:
  • Generating an abnormality reminding information to the intelligent terminal bound by the user according to the abnormality of the common travel route and the location of the abnormal road segment;
  • the predicted road condition of the common travel route is pushed to the smart terminal bound by the user.
  • the real-time road condition of the common travel route of the user is obtained before the travel time specified by the user arrives, and the real-time road condition is analyzed, and the travel route of the user is monitored in advance, if the commonly used If the real-time road condition of the travel route is abnormal, the location of the abnormal road segment is determined, and according to the abnormality of the common travel route and the location of the abnormal road segment, an abnormality reminding information is generated and pushed to the intelligent terminal bound by the user, if the If there is no abnormality in the real-time road condition of the commonly used travel route, the arrival time specified by the user is obtained, and the big data analysis and prediction is performed according to the traffic hotspot information and the historical road condition information, and the common travel route is obtained between the travel time and the arrival time.
  • the predicted road condition of the travel time period is to push the predicted road condition of the common travel route to the intelligent terminal bound by the user. Because the user-specified common travel route is monitored, the effectiveness of the road condition monitoring and forecasting can be improved. When the abnormality is monitored, the user is informed in advance that the road condition is abnormal before the user travels, and when the abnormality is not monitored, the future road condition is predicted and pushed to The user's smart terminal can facilitate the user to adjust the travel route in time to avoid delays in travel.
  • FIG. 1 is a flowchart of an implementation of a road condition prediction method provided by an embodiment of the present application
  • FIG. 3 is a specific implementation flowchart of a road condition prediction method A2 provided by an embodiment of the present application.
  • FIG. 5 is a flowchart of implementing a road condition prediction method according to another embodiment of the present application.
  • FIG. 6 is a structural block diagram of a road condition prediction apparatus according to an embodiment of the present application.
  • FIG. 7 is a structural block diagram of a road condition prediction apparatus according to another embodiment of the present application.
  • FIG. 8 is a schematic diagram of a server provided by an embodiment of the present application.
  • FIG. 1 shows an implementation flow of a road condition prediction method provided by an embodiment of the present application, and the method flow includes steps S101 to S106.
  • the specific implementation principles of each step are as follows:
  • S101 Before the arrival of the travel time specified by the user, obtain real-time road conditions of the common travel route of the user, and analyze the real-time road condition.
  • the user registers an account in advance on the smart terminal, and uploads information filled in when registering the account, and the information includes the travel information of the user and the mobile communication account, such as a mobile phone number, a micro signal, and the mobile communication account and the default.
  • the travel information includes a common travel route and a travel time specified by the common travel route, and the designated travel time includes a specified travel time and a specified travel time.
  • the cloud server monitors the real-time road conditions of the commonly-used travel routes uploaded by the user and analyzes the real-time road conditions within a certain period of time before the travel time specified by the user arrives.
  • the user selects one of them as the common travel route monitored by the cloud server by default, and at the same time, the user can specify the mobile communication account on the smart terminal to receive the traffic condition prediction by the server. Further, the user can set the priority of multiple common travel routes, and the cloud server monitors multiple common travel routes in priority order. Alternatively, the priority of a common travel route may be set according to the date.
  • an alternative travel route is generated according to the departure location and the target location of the common travel route specified by the user, or when the user uploads more than one common travel route, in addition to monitoring the common travel route specified by the user.
  • the common travel route and the alternate travel route are monitored at the same time, so as to provide timely preparation when the user-specified common travel route has abnormal traffic conditions.
  • the road condition of the selected route is predicted to the user.
  • S102 Determine an abnormal road segment position if an abnormality occurs in the real-time road condition of the common travel route.
  • the types of abnormal road conditions that occur in the common travel route include, but are not limited to, road congestion and road prohibition. If the real-time road condition of the common travel route is abnormal, the cloud server determines the type of the abnormal road condition and immediately locates the abnormal road section within a certain period of time before the user-specified travel time arrives.
  • S103 Generate an abnormality reminding information to be sent to the smart terminal bound by the user according to the abnormal road condition that occurs in the common travel route and the location of the abnormal road segment.
  • step S103 pushes the abnormality of the road condition that occurs in the common travel route in step S102 and the location abnormality alarm information of the determined abnormal road segment to the smart terminal that is pre-bound by the user.
  • the embodiment of the present application monitors the common travel route before the travel arrival time specified by the user, and pushes the monitored abnormal road condition and the abnormal road segment location abnormality reminding information to the user's smart terminal, thereby promptly reminding the user of the common travel.
  • the road condition information of the route so that the user can effectively adjust the travel plan before departure.
  • the foregoing S103 specifically includes:
  • A1 Find the cause of the congestion of the congested road section according to the traffic hotspot information.
  • A2 searching for a historical congestion event in the historical road condition information that is the same as the congestion reason of the congestion road segment in the preset time period, and calculating a congestion estimation duration in the common travel route according to the historical congestion event, where the commonly used The travel route includes the departure location and the destination location.
  • A3 determining a travel influence judgment according to the travel time, the arrival time, the departure position, the target location, the location of the congestion road segment, and the congestion estimation duration, and determining an abnormality of the common travel route Whether the road condition affects the user reaching the target location at the arrival time.
  • A4 Generate an abnormality reminding information to be pushed to the smart terminal according to the judgment result of the travel influence judgment.
  • the traffic hot spot information is traffic information that causes or may cause congestion of the road segment. Find the cause of congestion by analyzing the traffic hotspot time.
  • the causes of congestion include large-scale activities such as peak road congestion, traffic accidents, sports events or large-scale concerts, or congestion caused by natural disasters such as road collapse and typhoon weather.
  • the abnormality reminding information generated by the location of the congestion road section, the congestion cause, and the congestion estimation duration is pushed to the smart terminal of the user.
  • the cloud server re-plans a suggested travel route according to the travel time, the arrival time, and the departure location and the target location specified by the user, and generates the abnormality together with the location of the congestion road segment, the congestion cause, and the congestion estimation duration.
  • the reminder information is pushed to the smart terminal of the user, so as to provide effective travel information for the user to refer to, and facilitate the user to travel.
  • FIG. 3 shows a specific implementation process of the road condition prediction method step A2 provided by the embodiment of the present application, which is described in detail as follows:
  • A22 Calculate an average historical congestion duration of the historical congestion event.
  • A23 Determine, according to the average historical congestion duration, a congestion estimation duration of the congestion road segment.
  • the number of historical congestion events of the same congestion cause and the historical congestion time of each historical congestion event are determined, so as to be congested according to the historical road condition information and the congestion road section according to the preset time.
  • the average historical congestion duration of the same historical congestion event is the same, and the congestion estimation duration of the congestion road segment is determined.
  • the historical road condition information of the congestion road section of the historical road condition large database is searched for a similar traffic accident within one year from the date of the current day.
  • the resulting historical congestion event for example, 10 similar historical congestion events, the average historical congestion duration of the 10 historical congestion events is calculated, and the possible duration of the congestion road segment in the common travel route is estimated, combined with the user designation Travel time and arrival time to determine whether the congestion road will affect the user's travel.
  • step A1 finds that there is more than one cause of the congestion of the congestion road segment
  • step A2 the specific implementation process of the step A2 is as follows:
  • A22' calculate the average historical congestion duration of historical congestion events caused by each congestion cause.
  • the congestion cause of the shortest average historical congestion duration in the above ranking is the shortest historical congestion duration in the historical road condition information, as a preset estimated time.
  • the historical historical congestion information of the historical traffic congestion event caused by the traffic accident is determined respectively.
  • the average historical congestion duration of historical congestion events caused by natural disasters, and the average historical congestion duration of traffic accidents is compared with the average historical congestion duration of natural disasters. If the average historical congestion duration of traffic accidents is longer, then Obtain the shortest historical congestion duration of natural disasters in the historical road information of the past year, and determine the sum of the average historical congestion duration of the traffic accident and the historical congestion duration of the shortest natural disaster as the congestion estimate of the congested section continues. time.
  • the user-specified arrival time is obtained.
  • S105 Perform big data analysis and prediction according to the traffic hotspot information and the historical road condition information, and obtain a predicted road condition of the travel time of the common travel route between the travel time and the arrival time.
  • the traffic hotspot information is traffic information that causes or may cause congestion of the road section.
  • the traffic information caused or likely to cause the road section to be congested the big data analysis and prediction of the road condition of the common travel route during the travel time period is performed, so as to provide an effective road condition forecast for the user's reference.
  • the predicted road condition of the common travel route is pushed to the smart terminal bound by the user for reference by the user, and the user can obtain the effective road condition information in time without opening the navigation application. For easy planning and travel.
  • FIG. 4 shows a specific implementation flow of the road condition prediction method S105 provided by the embodiment of the present application, which is described in detail as follows:
  • the information data source includes the traffic information reported on the website of the ticketing website, the microblog, and the website of the transportation bureau, and the historical road condition information includes historical regularity information.
  • B2 extract hotspot information related to the common travel route in the traffic hotspot information, and historical regularity information related to the common travel route in the historical road condition information.
  • B3 predicting, according to the related hot spot information and the related historical regularity information, a road condition of the travel time of the common travel route between the travel time and the arrival time.
  • the route planning is re-created to generate a suggested travel route, and the predicted road condition is The suggested travel route is pushed to the smart terminal bound by the user.
  • the traffic hotspot information that may cause traffic congestion within a certain period of time (such as within 30 minutes), further, It also includes traffic information for the weather website.
  • the traffic hotspot information that may cause traffic congestion is obtained through webpage information capture, microblog application interface information capture, and database information capture.
  • the road condition information of the relevant road section in the common travel route of the user is obtained; the relevant road section of the common travel route of the user is obtained from the historical road condition information, the school or the enterprise Regular information such as the time of the large-scale unit to go to school or commute time; grab the traffic information of the relevant road sections in the common travel route from the microblog application interface.
  • the information content of the traffic information obtained above is classified and extracted, and the traffic hotspot prediction is performed based on the statistical model to predict the abnormal road conditions that may occur.
  • the accuracy of the road condition prediction can be improved, thereby facilitating the user's travel.
  • the real-time road condition of the common travel route of the user is obtained before the travel time specified by the user arrives, and the real-time road condition is analyzed, and the travel route of the user is monitored in advance, if the commonly used If the real-time road condition of the travel route is abnormal, the location of the abnormal road segment is determined, and according to the abnormality of the common travel route and the location of the abnormal road segment, an abnormality reminding information is generated and pushed to the intelligent terminal bound by the user, if the If there is no abnormality in the real-time road condition of the commonly used travel route, the arrival time specified by the user is obtained, and the big data analysis and prediction is performed according to the traffic hotspot information and the historical road condition information, and the common travel route is obtained between the travel time and the arrival time.
  • the predicted road condition of the travel time period is to push the predicted road condition of the common travel route to the intelligent terminal bound by the user. Because the user-specified common travel route is monitored, the effectiveness of the road condition monitoring and forecasting can be improved. When the abnormality is monitored, the user is informed in advance that the road condition is abnormal before the user travels, and when the abnormality is not monitored, the future road condition is predicted and pushed to The user's smart terminal can facilitate the user to adjust the travel route in time to avoid delays in travel.
  • the road condition prediction method further includes:
  • S108 If the real-time location of the smart terminal continues to change, obtain a moving speed of the smart terminal to determine a current vehicle speed traveled by the user.
  • the real-time location of the smart terminal is located within a certain time after the arrival of the travel time specified by the user, and the real-time location of the smart terminal is the current location of the user, so that the smart terminal is The moving speed determines the current vehicle speed of the user.
  • S109 Perform real-time road condition monitoring on the road segment within the preset distance before the driving according to the current position of the user, the current vehicle speed, and the common travel route.
  • the road section for real-time road condition monitoring within the preset distance ahead of the driving changes in real time.
  • S1010 If a sudden abnormality occurs in a section within a preset distance before the driving is detected, the possible duration of the sudden abnormality is calculated according to the historical road condition information of the section where the sudden abnormality occurs.
  • the cause of the sudden abnormality is determined, and each historical congestion information of the road section where the sudden abnormality occurs within a preset time is acquired, and each historical congestion is the same as the cause of the sudden abnormality.
  • the historical congestion duration of the event, the estimated duration of the sudden anomaly is estimated based on the average historical congestion duration of historical congestion events for these same reasons.
  • S1011 Determine, according to the current vehicle speed and the possible duration of the sudden abnormality, whether the sudden abnormality affects a target position of the user to reach the common route at the arrival time.
  • the current location of the user and the location of the sudden abnormal road segment are obtained, and according to the current location and the current vehicle speed, the travel time required for the sudden abnormal road segment position is calculated, thereby Determining the required travel time and the duration of the sudden abnormality, and determining whether the sudden abnormality affects the target of the user reaching the common route at the specified arrival time when the user travels to the sudden abnormal road position position.
  • the shortest travel time means that the time from the current position to the target position according to the current vehicle speed is the shortest.
  • the cloud server not only monitors the common travel route specified by the user, but also monitors the alternate travel route, according to the current location of the user, the alternate travel route, and the Retarget the route at the target location.
  • the user when the user uploads the information of the registered account, the user establishes a personal folder for the user in the cloud server, and after detecting that the user travels to reach the specified target location, ends the real-time road condition monitoring of the common travel route. Further, the actual travel route of the current time is recorded, and the road segment where the abnormality occurs in the common travel route is marked, and the recorded actual travel route and the mark of the abnormal road segment in the common travel route are stored to the user. Under the personal folder, so that users can follow up and reference.
  • the real-time location of the smart terminal is obtained to determine the current location of the user, and if the real-time location of the smart terminal continues to change, the smart terminal is acquired.
  • the moving speed is determined to determine the current vehicle speed traveled by the user, and the real-time road condition monitoring is performed on the road segment within the preset distance before the driving according to the current position of the user, the current vehicle speed, and the common travel route, so that the user can grasp the time in time.
  • the possible road duration may be calculated according to the historical road condition information of the road section where the sudden abnormality occurs, according to the current speed and the current speed Determining the possible duration of the sudden abnormality, determining whether the sudden abnormality affects the target position of the user at the arrival time, and if so, according to the current position of the user and the target position Plan the route and push the route with the shortest travel time according to the current speed
  • the intelligent terminal can improve the effectiveness of traffic forecasts, while the user time to change course to avoid late as possible.
  • FIG. 6 is a structural block diagram of the road condition prediction apparatus provided by the embodiment of the present application. For the convenience of description, only parts related to the embodiment of the present application are shown.
  • the road condition forecasting device includes: a road condition analyzing unit 61, an abnormal position determining unit 62, a first pushing unit 63, a time obtaining unit 64, a road condition predicting unit 65, and a second pushing unit 66, wherein:
  • the traffic condition analysis unit 61 is configured to acquire real-time road conditions of the common travel route of the user before the arrival of the travel time specified by the user, and analyze the real-time road condition;
  • the abnormal position determining unit 62 is configured to determine the position of the abnormal road segment if the real-time road condition of the common travel route is abnormal;
  • the first pushing unit 63 is configured to generate an abnormal reminding information and push the smart reminder information to the smart terminal bound by the user according to the abnormality of the common travel route and the location of the abnormal road segment;
  • the time obtaining unit 64 is configured to acquire the arrival time specified by the user if the real-time road condition of the common travel route does not have an abnormality
  • the road condition prediction unit 65 is configured to perform big data analysis and prediction according to the traffic hot spot information and the historical road condition information, and obtain a predicted road condition of the travel time period between the travel time and the arrival time of the common travel route;
  • the second pushing unit 66 is configured to push the predicted road condition of the common travel route to the smart terminal bound by the user.
  • the first pushing unit 63 includes:
  • a reason finding subunit configured to find a cause of the congestion of the congestion road section according to the traffic hot spot information
  • a time estimation subunit configured to search for a historical congestion event in the historical road condition information that is the same as the congestion reason of the congestion road segment in a preset time, and calculate a congestion estimation duration in the common travel route according to the historical congestion event Time, the common travel route includes a departure location and a target location;
  • the influence judging subunit is configured to perform a travel influence judgment according to the travel time, the arrival time, the departure position, the target location, the location of the congestion road segment, and the congestion estimation duration, and determine the commonly used Whether the abnormal road condition that occurs in the travel route affects the user reaching the target location at the arrival time;
  • the abnormal push subunit is configured to generate an abnormality reminding information to be pushed to the smart terminal according to the judgment result of the travel influence determination.
  • the time estimation subunit specifically includes:
  • a time acquisition sub-module configured to acquire a historical congestion duration of each historical congestion event in the historical road condition information in the preset time period that is the same as the congestion cause of the congestion road segment;
  • An average time calculation sub-module configured to calculate an average historical congestion duration of the historical congestion event
  • the time estimation submodule is configured to determine a congestion estimation duration of the congestion road segment according to the average historical congestion duration.
  • the road condition prediction unit 65 includes:
  • the information capture subunit is configured to obtain the traffic hotspot information from the information data source, where the information data source includes the ticket information, the microblog, and the traffic information reported on the website of the transportation bureau in real time, and the historical road condition information includes a historical law. Sexual information;
  • An information filtering subunit configured to extract hotspot information related to the common travel route in the traffic hotspot information, and historical regularity information related to the common travel route in the historical road condition information;
  • a road condition prediction subunit configured to predict, according to the related hot spot information and the related historical regularity information, a road condition of the travel time of the common travel route between the travel time and the arrival time;
  • a route planning sub-unit configured to re-route the route to generate a suggested travel route according to the departure location and the target location of the common travel route and the departure time and the arrival time, if it is predicted that congestion may occur during the travel time period, Pushing the predicted road condition and the suggested travel route to the smart terminal bound by the user.
  • the road condition prediction apparatus further includes:
  • the current location determining unit 71 is configured to acquire a real-time location of the smart terminal to determine a current location of the user after the travel time specified by the user arrives;
  • the current vehicle speed determining unit 72 is configured to acquire a moving speed of the smart terminal to determine a current vehicle speed traveled by the user if the real-time position of the smart terminal continues to change;
  • the current road condition monitoring unit 73 is configured to perform real-time road condition monitoring on the road segment within the preset distance before the driving according to the current position of the user, the current vehicle speed, and the common travel route;
  • the duration calculating unit 74 is configured to calculate a possible abnormality of the sudden abnormality according to the historical road condition information of the road segment where the sudden abnormality occurs if a sudden abnormality occurs in the link within the preset distance before the driving is monitored;
  • the abnormality influence judging unit 75 is configured to determine, according to the current vehicle speed and the possible duration of the sudden abnormality, whether the sudden abnormality affects a target position of the user to reach the common route at the arrival time;
  • the optimal route pushing unit 76 is configured to, if affected, re-route the route according to the current location of the user and the target location, and push the route with the shortest travel time according to the current vehicle speed to the smart terminal.
  • the real-time road condition of the common travel route of the user is obtained before the travel time specified by the user arrives, and the real-time road condition is analyzed, and the travel route of the user is monitored in advance, if the commonly used If the real-time road condition of the travel route is abnormal, the location of the abnormal road segment is determined, and according to the abnormality of the common travel route and the location of the abnormal road segment, an abnormality reminding information is generated and pushed to the intelligent terminal bound by the user, if the If there is no abnormality in the real-time road condition of the commonly used travel route, the arrival time specified by the user is obtained, and the big data analysis and prediction is performed according to the traffic hotspot information and the historical road condition information, and the common travel route is obtained between the travel time and the arrival time.
  • the predicted road condition of the travel time period is to push the predicted road condition of the common travel route to the intelligent terminal bound by the user. Because the user-specified common travel route is monitored, the effectiveness of the road condition monitoring and forecasting can be improved. When the abnormality is monitored, the user is informed in advance that the road condition is abnormal before the user travels, and when the abnormality is not monitored, the future road condition is predicted and pushed to The user's smart terminal can facilitate the user to adjust the travel route in time to avoid delays in travel.
  • FIG. 8 is a schematic diagram of a server according to an embodiment of the present application.
  • the server 8 of this embodiment includes a processor 80, a memory 81, and computer readable instructions 82 stored in the memory 81 and operable on the processor 80, such as a road condition prediction program.
  • the processor 80 executes the computer readable instructions 82, the steps in the embodiments of the various road condition prediction methods described above are implemented, such as steps 101 to 106 shown in FIG.
  • the processor 80 when executing the computer readable instructions 82, implements the functions of the various modules/units in the various apparatus embodiments described above, such as the functions of the modules 61-66 shown in FIG.
  • the computer readable instructions 82 may be partitioned into one or more modules/units that are stored in the memory 81 and executed by the processor 80, To complete this application.
  • the one or more modules/units may be a series of computer readable instruction instruction segments capable of performing a particular function for describing the execution of the computer readable instructions 82 in the server 8.
  • the server 8 can be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server.
  • the server may include, but is not limited to, a processor 80, a memory 81. It will be understood by those skilled in the art that FIG. 8 is merely an example of the server 8, does not constitute a limitation of the server 8, may include more or less components than those illustrated, or combine some components, or different components, such as
  • the server may also include an input and output device, a network access device, a bus, and the like.
  • the processor 80 can be a central processing unit (Central) Processing Unit, CPU), can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc.
  • the general purpose processor may be a microprocessor or the processor or any conventional processor or the like.
  • the memory 81 may be an internal storage unit of the server 8, such as a hard disk or a memory of the server 8.
  • the memory 81 may also be an external storage device of the server 8, such as a plug-in hard disk equipped with the server 8, a smart memory card (SMC), and a Secure Digital (SD) card. Flash card (Flash Card) and so on. Further, the memory 81 may also include both an internal storage unit of the server 8 and an external storage device.
  • the memory 81 is used to store the computer readable instructions and other programs and data required by the server.
  • the memory 81 can also be used to temporarily store data that has been output or is about to be output.

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Abstract

一种路况预报方法、装置、存储介质和服务器,包括:获取用户的常用出行路线的实时路况,对实时路况进行分析(S101);若常用出行路线的实时路况出现异常,则确定异常路段的位置(S102);根据常用出行路线出现的异常以及异常路段的位置,生成异常提醒信息推送至用户绑定的智能终端(S103);若常用出行路线的实时路况未出现异常,则获取用户指定的到达时间(S104);根据交通热点信息与历史路况信息进行大数据分析预测,获取常用出行路线在出行时间与到达时间之间的出行时段的预测路况(S105);将常用出行路线的预测路况推送至所述用户绑定的智能终端(S106)。通过对用户出行路线进行监控,提前告知用户路况信息,方便用户出行。

Description

一种路况预报方法、装置、存储介质和服务器
本申请要求于2017年12月08日提交中国专利局、申请号为CN 201711293930.0、发明名称为“一种路况预报方法、存储介质和服务器”的中国专利申请的优先权,其全部内容通过引用结合在本申请中。
技术领域
本申请涉及信息监控领域,尤其涉及一种路况预报方法、装置、存储介质和服务器。
背景技术
随着社会经济的蓬勃发展,城市人口和车辆迅速增加,导致城市交通状况逐渐恶化,交通拥堵现象时常发生。由定位系统结合电子地图数据形成的导航装置,也越来越多的应用到人们的日常生活中。
当前的导航产品中,仅能在使用导航时为用户提供路线导航及实时路况,开始导航时,提供路况信息,并根据此时的路况信息为用户规划路线,然而,在行驶过程中才发现路况异常已经晚了,并且,用户对熟悉的路线通常无需使用导航,从而也不能及时得知路况信息。因此,现有的导航并不能为用户提前提供有效地出行相关的信息以方便用户出行。
技术问题
本申请实施例提供了一种路况预报方法、装置、存储介质和服务器,以解决现有技术中,导航不能为用户提前提供有效地出行相关的信息以方便用户出行的问题。
技术解决方案
本申请实施例的第一方面提供了一种路况预报方法,包括:
在用户指定的出行时间到达之前,获取所述用户的常用出行路线的实时路况,并对所述实时路况进行分析;
若所述常用出行路线的实时路况出现异常,则确定异常路段的位置;
根据所述常用出行路线出现的异常以及所述异常路段的位置,生成异常提醒信息推送至所述用户绑定的智能终端;
若所述常用出行路线的实时路况未出现异常,则获取用户指定的到达时间;
根据交通热点信息与历史路况信息进行大数据分析预测,获取所述常用出行路线在所述出行时间与所述到达时间之间的出行时段的预测路况;
将所述常用出行路线的预测路况推送至所述用户绑定的智能终端。
本申请实施例的第二方面提供了一种服务器,包括存储器以及处理器,所述存储器存储有可在所述处理器上运行的计算机可读指令,所述处理器执行所述计算机可读指令时实现如下步骤:
在用户指定的出行时间到达之前,获取所述用户的常用出行路线的实时路况,并对所述实时路况进行分析;
若所述常用出行路线的实时路况出现异常,则确定异常路段的位置;
根据所述常用出行路线出现的异常以及所述异常路段的位置,生成异常提醒信息推送至所述用户绑定的智能终端;
若所述常用出行路线的实时路况未出现异常,则获取用户指定的到达时间;
根据交通热点信息与历史路况信息进行大数据分析预测,获取所述常用出行路线在所述出行时间与所述到达时间之间的出行时段的预测路况;
将所述常用出行路线的预测路况推送至所述用户绑定的智能终端。
本申请实施例的第三方面提供了一种计算机可读存储介质,所述计算机可读存储介质存储有计算机可读指令,所述计算机可读指令被处理器执行时实现如下步骤:
在用户指定的出行时间到达之前,获取所述用户的常用出行路线的实时路况,并对所述实时路况进行分析;
若所述常用出行路线的实时路况出现异常,则确定异常路段的位置;
根据所述常用出行路线出现的异常以及所述异常路段的位置,生成异常提醒信息推送至所述用户绑定的智能终端;
若所述常用出行路线的实时路况未出现异常,则获取用户指定的到达时间;
根据交通热点信息与历史路况信息进行大数据分析预测,获取所述常用出行路线在所述出行时间与所述到达时间之间的出行时段的预测路况;
将所述常用出行路线的预测路况推送至所述用户绑定的智能终端。
有益效果
本申请实施例中,通过在用户指定的出行时间到达之前,获取所述用户的常用出行路线的实时路况,并对所述实时路况进行分析,预先对用户的出行路线进行监控,若所述常用出行路线的实时路况出现异常,则确定异常路段的位置,根据所述常用出行路线出现的异常以及所述异常路段的位置,生成异常提醒信息推送至所述用户绑定的智能终端,若所述常用出行路线的实时路况未出现异常,则获取用户指定的到达时间,根据交通热点信息与历史路况信息进行大数据分析预测,获取所述常用出行路线在所述出行时间与所述到达时间之间的出行时段的预测路况,将所述常用出行路线的预测路况推送至所述用户绑定的智能终端。由于针对用户指定的常用出行路线进行监控,可提高路况监控预报的有效性,在监控到异常时,在用户出行前预先告知用户路况异常,未监控到异常时,对未来路况进行预测并推送至用户的智能终端,可方便用户及时调整出行路线,以免耽误出行。
附图说明
图1是本申请实施例提供的路况预报方法的实现流程图;
图2是本申请实施例提供的路况预报方法S103的具体实现流程图;
图3是本申请实施例提供的路况预报方法A2的具体实现流程图;
图4是本申请实施例提供的路况预报方法S105的具体实现流程图;
图5是本申请另一实施例提供的路况预报方法的实现流程图;
图6是本申请实施例提供的路况预报装置的结构框图;
图7是本申请另一实施例提供的路况预报装置的结构框图;
图8是本申请实施例提供的服务器的示意图。
本发明的实施方式
实施例1
图1示出了本申请实施例提供的路况预报方法的实现流程,该方法流程包括步骤S101至S106。各步骤的具体实现原理如下:
S101:在用户指定的出行时间到达之前,获取所述用户的常用出行路线的实时路况,并对所述实时路况进行分析。
在本申请实施例中,用户预先在智能终端上注册账户,并上传注册账户时填写的信息,信息包括用户的出行信息与移动通讯账户如手机号、微信号,默认所述移动通讯账户与所述智能终端绑定。所述出行信息包括常用出行路线以及所述常用出行路线指定的出行时段,所述指定的出行时段包括指定的出行时间与指定的到达时间。在用户指定的出行时间到达之前的一定时间内,云服务器监控所述用户上传的常用出行路线的实时路况,并对所述实时路况进行分析。其中,当所述常用出行路线不止一条时,用户选取其中一条为作为云服务器默认监控的常用出行路线,同时,用户可以指定智能终端上接收服务器发送路况预报的移动通讯账户。进一步地,用户可设置多条常用出行路线的优先级,云服务器按优先级顺序监控多条常用出行路线。可选地,常用出行路线的优先级可根据日期设置。
可选地,在出行时间到达之前,根据用户指定的常用出行路线的出发位置与目标位置生成备选出行路线,或者,当用户上传的常用出行路线不止一条时,除了监控用户指定的常用出行路线,同时还根据优先级监控一条常用出行路线作为备选出行路线,在出行时间到达之前,同时对常用出行路线与备用出行路线进行监控,以便在用户指定的常用出行路线出现异常路况时及时提供备选出行路线的路况预报给用户。
S102:若所述常用出行路线的实时路况出现异常,则确定异常路段的位置。
在本申请实施例中,所述常用出行路线的出现的路况异常的类型包括但不限于路段拥堵、路段禁行。云服务器在用户指定的出行时间到达之前的一定时间内,若监控到所述常用出行路线的实时路况出现异常,则确定异常路况的类型,并立即定位异常路段的位置。
S103:根据所述常用出行路线出现的异常路况以及所述异常路段的位置,生成异常提醒信息推送至所述用户绑定的智能终端。
具体地,步骤S103将步骤S102中所述常用出行路线出现的路况异常以及确定的异常路段的位置生成异常提醒信息推送至用户预先绑定的智能终端。本申请实施例通过在用户指定的出行到达时间之前对所述常用出行路线进行监控,将监控到的异常路况与异常路段的位置生成异常提醒信息推送至用户的智能终端,从而预先提醒用户常用出行路线的路况信息,以便用户在出发前可有效调整出行计划。
作为本申请的一个实施例,如图2所示,当所述异常路段为拥堵路段时,上述S103具体包括:
A1:根据交通热点信息查找所述拥堵路段拥堵的原因。
A2:查找预设时间内所述历史路况信息中与所述拥堵路段的拥堵原因相同的历史拥堵事件,并根据所述历史拥堵事件计算所述常用出行路线中的拥堵估计持续时间,所述常用出行路线包括出发位置与目标位置。
A3:根据所述出行时间、所述到达时间、所述出发位置、所述目标位置、所述拥堵路段的位置以及所述拥堵估计持续时间进行出行影响判断,判断所述常用出行路线出现的异常路况是否影响用户在所述到达时间到达所述目标位置。
A4:根据所述出行影响判断的判断结果,生成异常提醒信息推送至所述智能终端。
在本申请实施例中,当所述异常路段为拥堵路段时,上述交通热点信息即为造成或可能造成路段拥堵的交通信息。通过分析交通热点时间查找拥堵的原因。其中,拥堵的原因包括高峰道路拥堵、交通事故、体育赛事或大型演唱会等大型活动,或者是路面塌陷、台风天气积水等自然灾害导致的拥堵。
在确定拥堵的原因之后,在历史路况大数据库所述拥堵路段的历史路况信息中查找预设时间内(例如,当前日期起往前追溯一年内)与所述确定的拥堵原因相同的历史拥堵事件,参考历史拥堵事件的历史拥堵持续时间,确定所述常用出行路线中拥堵路段可能的持续时间,进而判断拥堵路段是否会影响用户在指定的出行时段的出行,并将判断结果生成异常提醒信息推送至所述用户的智能终端。通过对交通热点信息与历史路况信息的分析,大大提高异常影响判断的准确性,从而可提高路况预报的效率。
可选地,在本申请实施例中,当所述出行影响判断的判断结果为影响,则将拥堵路段的位置、拥堵原因以及拥堵估计持续时间生成异常提醒信息推送至所述用户的智能终端。进一步地,云服务器根据用户指定的出行时间、到达时间以及出发位置与目标位置,重新规划一条建议出行路线,将所述建议出行路线与拥堵路段的位置、拥堵原因以及拥堵估计持续时间一起生成异常提醒信息推送至所述用户的智能终端,以便提供有效地出行信息供用户参考,方便用户出行。
作为本申请的一个实施例,图3示出了本申请实施例提供的路况预报方法步骤A2的具体实现流程,详述如下:
A21、获取预设时间内所述历史路况信息中与所述拥堵路段的拥堵原因相同的每一件历史拥堵事件的历史拥堵持续时间。
A22、计算所述历史拥堵事件的平均历史拥堵持续时间。
A23、根据所述平均历史拥堵持续时间,确定所述拥堵路段的拥堵估计持续时间。
在本申请实施例中,确定相同拥堵原因的历史拥堵事件的件数以及每一件历史拥堵事件的历史拥堵时间,从而根据所述预设时间内所述历史路况信息中与所述拥堵路段的拥堵原因相同的历史拥堵事件的平均历史拥堵持续时间,确定所述拥堵路段的拥堵估计持续时间。
示例性地,当根据热点事件确定所述拥堵路段拥堵的原因是交通事故时,在历史路况大数据库所述拥堵路段的历史路况信息中查找当日起往前追溯一年内,发生类似交通事故的所导致的历史拥堵事件,例如有10件类似的历史拥堵事件,则计算该10件历史拥堵事件的平均历史拥堵持续时间,估计所述常用出行路线中拥堵路段可能的持续时间,结合所述用户指定的出行时间与到达时间判断拥堵路段是否会影响用户的出行。
可选地,当步骤A1查找出所述拥堵路段拥堵的原因不止一个时,所述步骤A2的具体实现流程如下:
A21’、获取预设时间内所述历史路况信息中与所述拥堵路段的多个拥堵原因分别相同的每一件历史拥堵事件的历史拥堵持续时间。
A22’、计算各个拥堵原因造成的历史拥堵事件的平均历史拥堵持续时间。
A23’、比较各个拥堵原因造成的历史拥堵事件的平均历史拥堵持续时间,根据比较结果中最长的平均历史拥堵持续时间估计所述拥堵路段的拥堵估计持续时间。具体地,将各个拥堵原因造成的历史拥堵事件的平均历史拥堵持续时间从长到短排序,确定所述拥堵路段的拥堵估计持续时间为最长的平均历史拥堵持续时间与预设的估计时间之和。可选地,将上述排序中最短的平均历史拥堵持续时间的拥堵原因在历史路况信息中最短的历史拥堵持续时间,作为预设的估计时间。
示例性地,当根据热点事件确定所述拥堵路段拥堵的原因是交通事故与自然灾害时,在所述拥堵路段的历史路况信息中,分别确定交通事故导致的历史拥堵事件的平均历史拥堵持续时间与自然灾害导致的历史拥堵事件的平均历史拥堵持续时间,并将交通事故的平均历史拥堵持续时间与自然灾害的平均历史拥堵持续时间进行比较,若交通事故的平均历史拥堵持续时间较长,则获取自然灾害在过去一年的历史路况信息中最短的历史拥堵持续时间,将交通事故的平均历史拥堵持续时间与自然灾害最短的历史拥堵持续时间之和,确定为所述拥堵路段的拥堵估计持续时间。
S104:若所述常用出行路线的实时路况未出现异常,则获取用户指定的到达时间。
在本申请实施例中,当监控到在用户指定的出行时间到达之前,所述常用出行路线路况正常,没有出现拥堵或者禁行等异常路况,则获取用户指定的到达时间。
S105:根据交通热点信息与历史路况信息进行大数据分析预测,获取所述常用出行路线在所述出行时间与所述到达时间之间的出行时段的预测路况。
在本申请实施例中,交通热点信息即为造成或可能造成路段拥堵的交通信息。将历史路况信息中的规律性异常信息结合造成或可能造成路段拥堵的交通信息对所述常用出行路线在所述出行时段的路况进行大数据分析预测,以便提供有效地路况预报供用户参考。
S106:将所述常用出行路线的预测路况推送至所述用户绑定的智能终端。
在本申请实施例中,在用户指定的出行时间到达之前,将所述常用出行路线的预测路况推送至用户绑定的智能终端供用户参考,用户无需打开导航应用即可及时获知有效地路况信息,方便计划出行。
作为本申请的一个实施例,图4示出了本申请实施例提供的路况预报方法S105的具体实现流程,详述如下:
B1:从信息数据源中获取所述交通热点信息,所述信息数据源包括票务网站、微博、交通局网站上实时上报的交通信息,所述历史路况信息包括历史规律性信息。
B2:提取所述交通热点信息中与所述常用出行路线相关的热点信息,以及所述历史路况信息中与所述常用出行路线相关的历史规律性信息。
B3:根据所述相关的热点信息与所述相关的历史规律性信息对所述常用出行路线在所述出行时间与所述到达时间之间的出行时段的路况进行预测。
B4:若预测在所述出行时段内可能出现拥堵,则根据所述常用出行路线的出发位置和目标位置以及所述出发时间与到达时间,重新进行路线规划生成建议出行路线,将预测的路况与所述建议出行路线推送至所述用户绑定的智能终端。
在本申请实施例中,通过挖掘票务网站、微博、交通局网站上实时上报的交通信息中,当前及未来一定时间内(如30分钟内)可能导致交通拥堵的交通热点信息,进一步地,还包括气象网站的交通信息。
具体地,通过网页信息抓取、微博应用接口信息抓取以及数据库信息抓取获取可能导致交通拥堵的交通热点信息。通过对票务网站、交通局发布的实时路况信息的网站的特定网页进行解析,获取用户常用出行路线中相关路段的路况信息;从历史路况信息中获取用户常用出行路线中相关路段上,学校或企业等大型单位上下学时间或者上下班时间等规律性信息;从微博应用接口抓取常用出行路线中相关路段的交通信息。对上述获取的交通信息的信息内容进行分类抽取和学习,并基于统计模型进行交通热点预测,预测可能出现的路况异常。通过实时抓取信息数据源中的实时交通信息,并结合历史规律性信息进行分析,可提高路况预测的准确性,从而更方便用户出行。
本申请实施例中,通过在用户指定的出行时间到达之前,获取所述用户的常用出行路线的实时路况,并对所述实时路况进行分析,预先对用户的出行路线进行监控,若所述常用出行路线的实时路况出现异常,则确定异常路段的位置,根据所述常用出行路线出现的异常以及所述异常路段的位置,生成异常提醒信息推送至所述用户绑定的智能终端,若所述常用出行路线的实时路况未出现异常,则获取用户指定的到达时间,根据交通热点信息与历史路况信息进行大数据分析预测,获取所述常用出行路线在所述出行时间与所述到达时间之间的出行时段的预测路况,将所述常用出行路线的预测路况推送至所述用户绑定的智能终端。由于针对用户指定的常用出行路线进行监控,可提高路况监控预报的有效性,在监控到异常时,在用户出行前预先告知用户路况异常,未监控到异常时,对未来路况进行预测并推送至用户的智能终端,可方便用户及时调整出行路线,以免耽误出行。
进一步地,基于上述图1实施例中所提供的路况预报方法,提出本申请的另一实施例。在本申请实施例中,在图1所示的步骤S101-S106的基础上,如图5所示,所述路况预报方法还包括:
S107:在用户指定的出行时间到达之后,获取所述智能终端的实时位置以确定所述用户的当前位置。
S108:若所述智能终端的实时位置持续变化,则获取所述智能终端的移动速度以确定所述用户行驶的当前车速。
在本申请实施例中,在用户指定的出行时间到达之后的一定时间内,定位智能终端的实时位置,默认所述智能终端的实时位置为所述用户的当前位置,从而根据所述智能终端的移动速度确定所述用户的当前车速。
S109:根据所述用户的当前位置、所述当前车速以及所述常用出行路线,对行驶前方预设距离内的路段进行实时路况监控。
在本申请实施例中,由于用户在行驶过程中,上述行驶前方预设距离是固定的,但行驶前方预设距离内进行实时路况监控的路段实时在变化。
S1010:若监控到行驶前方预设距离内的路段出现突发异常,根据出现突发异常的路段的历史路况信息计算所述突发异常可能的持续时间。
在本申请实施例中,确定所述突发异常的原因,获取预设时间内所述出现突发异常的路段的历史路况信息中,与所述突发异常的原因相同的每一件历史拥堵事件的历史拥堵持续时间,根据这些相同原因的历史拥堵事件的平均历史拥堵持续时间估计所述突发异常可能的持续时间。
S1011:根据所述当前车速与所述突发异常可能的持续时间,判断所述突发异常是否会影响用户在所述到达时间到达所述常用路线的目标位置。
在本申请实施例中,获取用户的当前位置与所述突发异常的路段位置,并根据当前位置与所述当前车速,计算到底所述突发异常的路段位置所需的行驶时间,从而根据所需的行驶时间与所述突发异常可能的持续时间,判断在用户行驶至所述突发异常的路段位置时,所述突发异常是否会影响用户在指定的到达时间到达常用路线的目标位置。
S1012:若影响,则根据所述用户的当前位置与所述目标位置重新规划路线,将根据所述当前车速行驶的行驶时间最短的路线推送至所述智能终端。
在本申请实施例中,行驶时间最短是指根据当前车速从当前位置行驶至目标位置的时间最短。通过将根据当前位置与所述目标位置重新规划的路线中,到达目标位置时间最短的路线推送至用户的智能终端,以便用户及时更改路线,尽可能避免迟到。
可选地,若在用户指定的出行时间到达之前,云服务器不仅监控了用户指定的常用出行路线,还监控了备用出行路线,可根据所述用户的当前位置、所述备用出行路线以及所述目标位置重新规划路线。
可选地,用户在上传注册账户的信息时,在云服务器中为所述用户建立个人文件夹,在检测到用户行驶到达指定的目标位置后,结束对所述常用出行路线的实时路况监控。进一步地,对当次的实际出行路线进行记录,并对所述常用出行路线中出现异常的路段进行标记,将记录的实际出行路线与常用出行路线中的异常路段的标记存储至所述用户对应的个人文件夹下,以便用户后续查询和参考。
本申请实施例中,通过在用户指定的出行时间到达之后,获取所述智能终端的实时位置以确定所述用户的当前位置,若所述智能终端的实时位置持续变化,则获取所述智能终端的移动速度以确定所述用户行驶的当前车速,根据所述用户的当前位置、所述当前车速以及所述常用出行路线,对行驶前方预设距离内的路段进行实时路况监控,方便用户及时掌握行驶前方的路况,若监控到行驶前方预设距离内的路段出现突发异常,根据出现突发异常的路段的历史路况信息计算所述突发异常可能的持续时间,根据所述当前车速与所述突发异常可能的持续时间,判断所述突发异常是否会影响用户在所述到达时间到达所述常用路线的目标位置,若影响,则根据所述用户的当前位置与所述目标位置重新规划路线,将根据所述当前车速行驶的行驶时间最短的路线推送至所述智能终端,可提高路况预报的有效性,同时方便用户及时更改路线,尽可能避免迟到。
应理解,上述实施例中各步骤的序号的大小并不意味着执行顺序的先后,各过程的执行顺序应以其功能和内在逻辑确定,而不应对本申请实施例的实施过程构成任何限定。
实施例2
对应于上文实施例所述的路况预报方法,图6示出了本申请实施例提供的路况预报装置的结构框图,为了便于说明,仅示出了与本申请实施例相关的部分。
参照图6,该路况预报装置包括:路况分析单元61,异常位置确定单元62,第一推送单元63,时间获取单元64,路况预测单元65,第二推送单元66,其中:
路况分析单元61,用于在用户指定的出行时间到达之前,获取所述用户的常用出行路线的实时路况,并对所述实时路况进行分析;
异常位置确定单元62,用于若所述常用出行路线的实时路况出现异常,则确定异常路段的位置;
第一推送单元63,用于根据所述常用出行路线出现的异常以及所述异常路段的位置,生成异常提醒信息推送至所述用户绑定的智能终端;
时间获取单元64,用于若所述常用出行路线的实时路况未出现异常,则获取用户指定的到达时间;
路况预测单元65,用于根据交通热点信息与历史路况信息进行大数据分析预测,获取所述常用出行路线在所述出行时间与所述到达时间之间的出行时段的预测路况;
第二推送单元66,用于将所述常用出行路线的预测路况推送至所述用户绑定的智能终端。
可选地,所述第一推送单元63包括:
原因查找子单元,用于根据交通热点信息查找所述拥堵路段拥堵的原因;
时间估计子单元,用于查找预设时间内所述历史路况信息中与所述拥堵路段的拥堵原因相同的历史拥堵事件,并根据所述历史拥堵事件计算所述常用出行路线中的拥堵估计持续时间,所述常用出行路线包括出发位置与目标位置;
影响判断子单元,用于根据所述出行时间、所述到达时间、所述出发位置、所述目标位置、所述拥堵路段的位置以及所述拥堵估计持续时间进行出行影响判断,判断所述常用出行路线出现的异常路况是否影响用户在所述到达时间到达所述目标位置;
异常推送子单元,用于根据所述出行影响判断的判断结果,生成异常提醒信息推送至所述智能终端。
可选地,所述时间估计子单元具体包括:
时间获取子模块,用于获取预设时间内所述历史路况信息中与所述拥堵路段的拥堵原因相同的每一件历史拥堵事件的历史拥堵持续时间;
平均时间计算子模块,用于计算所述历史拥堵事件的平均历史拥堵持续时间;
时间估计子模块,用于根据所述平均历史拥堵持续时间,确定所述拥堵路段的拥堵估计持续时间。
可选地,所述路况预测单元65包括:
信息抓取子单元,用于从信息数据源中获取所述交通热点信息,所述信息数据源包括票务网站、微博、交通局网站上实时上报的交通信息,所述历史路况信息包括历史规律性信息;
信息过滤子单元,用于提取所述交通热点信息中与所述常用出行路线相关的热点信息,以及所述历史路况信息中与所述常用出行路线相关的历史规律性信息;
路况预测子单元,用于根据所述相关的热点信息与所述相关的历史规律性信息对所述常用出行路线在所述出行时间与所述到达时间之间的出行时段的路况进行预测;
路线规划子单元,用于若预测在所述出行时段内可能出现拥堵,则根据所述常用出行路线的出发位置和目标位置以及所述出发时间与到达时间,重新进行路线规划生成建议出行路线,将预测的路况与所述建议出行路线推送至所述用户绑定的智能终端。
可选地,如图7所示,所述路况预测装置还包括:
当前位置确定单元71,用于在用户指定的出行时间到达之后,获取所述智能终端的实时位置以确定所述用户的当前位置;
当前车速确定单元72,用于若所述智能终端的实时位置持续变化,则获取所述智能终端的移动速度以确定所述用户行驶的当前车速;
当前路况监控单元73,用于根据所述用户的当前位置、所述当前车速以及所述常用出行路线,对行驶前方预设距离内的路段进行实时路况监控;
持续时间计算单元74,用于若监控到行驶前方预设距离内的路段出现突发异常,根据出现突发异常的路段的历史路况信息计算所述突发异常可能的持续时间;
异常影响判断单元75,用于根据所述当前车速与所述突发异常可能的持续时间,判断所述突发异常是否会影响用户在所述到达时间到达所述常用路线的目标位置;
最优路线推送单元76,用于若影响,则根据所述用户的当前位置与所述目标位置重新规划路线,将根据所述当前车速行驶的行驶时间最短的路线推送至所述智能终端。
本申请实施例中,通过在用户指定的出行时间到达之前,获取所述用户的常用出行路线的实时路况,并对所述实时路况进行分析,预先对用户的出行路线进行监控,若所述常用出行路线的实时路况出现异常,则确定异常路段的位置,根据所述常用出行路线出现的异常以及所述异常路段的位置,生成异常提醒信息推送至所述用户绑定的智能终端,若所述常用出行路线的实时路况未出现异常,则获取用户指定的到达时间,根据交通热点信息与历史路况信息进行大数据分析预测,获取所述常用出行路线在所述出行时间与所述到达时间之间的出行时段的预测路况,将所述常用出行路线的预测路况推送至所述用户绑定的智能终端。由于针对用户指定的常用出行路线进行监控,可提高路况监控预报的有效性,在监控到异常时,在用户出行前预先告知用户路况异常,未监控到异常时,对未来路况进行预测并推送至用户的智能终端,可方便用户及时调整出行路线,以免耽误出行。
实施例3
图8是本申请一实施例提供的服务器的示意图。如图8所示,该实施例的服务器8包括:处理器80、存储器81以及存储在所述存储器81中并可在所述处理器80上运行的计算机可读指令82,例如路况预报程序。所述处理器80执行所述计算机可读指令82时实现上述各个路况预报方法实施例中的步骤,例如图1所示的步骤101至106。或者,所述处理器80执行所述计算机可读指令82时实现上述各装置实施例中各模块/单元的功能,例如图6所示模块61至66的功能。
示例性的,所述计算机可读指令82可以被分割成一个或多个模块/单元,所述一个或者多个模块/单元被存储在所述存储器81中,并由所述处理器80执行,以完成本申请。所述一个或多个模块/单元可以是能够完成特定功能的一系列计算机可读指令指令段,该指令段用于描述所述计算机可读指令82在所述服务器8中的执行过程。
所述服务器8可以是桌上型计算机、笔记本、掌上电脑及云端服务器等计算设备。所述服务器可包括,但不仅限于,处理器80、存储器81。本领域技术人员可以理解,图8仅仅是服务器8的示例,并不构成对服务器8的限定,可以包括比图示更多或更少的部件,或者组合某些部件,或者不同的部件,例如所述服务器还可以包括输入输出设备、网络接入设备、总线等。
所述处理器80可以是中央处理单元(Central Processing Unit,CPU),还可以是其他通用处理器、数字信号处理器 (Digital Signal Processor,DSP)、专用集成电路 (Application Specific Integrated Circuit,ASIC)、现成可编程门阵列 (Field-Programmable Gate Array,FPGA) 或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
所述存储器81可以是所述服务器8的内部存储单元,例如服务器8的硬盘或内存。所述存储器81也可以是所述服务器8的外部存储设备,例如所述服务器8上配备的插接式硬盘,智能存储卡(Smart Media Card, SMC),安全数字(Secure Digital, SD)卡,闪存卡(Flash Card)等。进一步地,所述存储器81还可以既包括所述服务器8的内部存储单元也包括外部存储设备。所述存储器81用于存储所述计算机可读指令以及所述服务器所需的其他程序和数据。所述存储器81还可以用于暂时地存储已经输出或者将要输出的数据。

Claims (20)

  1. 一种路况预报方法,其特征在于,包括:
    在用户指定的出行时间到达之前,获取所述用户的常用出行路线的实时路况,并对所述实时路况进行分析;
    若所述常用出行路线的实时路况出现异常,则确定异常路段的位置;
    根据所述常用出行路线出现的异常以及所述异常路段的位置,生成异常提醒信息推送至所述用户绑定的智能终端;
    若所述常用出行路线的实时路况未出现异常,则获取用户指定的到达时间;
    根据交通热点信息与历史路况信息进行大数据分析预测,获取所述常用出行路线在所述出行时间与所述到达时间之间的出行时段的预测路况;
    将所述常用出行路线的预测路况推送至所述用户绑定的智能终端。
  2. 根据权利要求1所述的方法,其特征在于,当所述异常路段为拥堵路段时,所述根据所述常用出行路线出现的异常以及所述异常路段的位置,生成异常提醒信息推送至所述用户绑定的智能终端的步骤,包括:
    根据交通热点信息查找所述拥堵路段拥堵的原因;
    查找预设时间内所述历史路况信息中与所述拥堵路段的拥堵原因相同的历史拥堵事件,并根据所述历史拥堵事件计算所述常用出行路线中的拥堵估计持续时间,所述常用出行路线包括出发位置与目标位置;
    根据所述出行时间、所述到达时间、所述出发位置、所述目标位置、所述拥堵路段的位置以及所述拥堵估计持续时间进行出行影响判断,判断所述常用出行路线出现的异常路况是否影响用户在所述到达时间到达所述目标位置;
    根据所述出行影响判断的判断结果,生成异常提醒信息推送至所述智能终端。
  3. 根据权利要求2所述的方法,其特征在于,所述查找预设时间内历史路况信息中与所述拥堵路段的拥堵原因相同的历史拥堵事件,并根据所述历史拥堵事件计算所述常用出行路线中的拥堵估计持续时间的步骤,包括:
    获取预设时间内所述历史路况信息中与所述拥堵路段的拥堵原因相同的每一件历史拥堵事件的历史拥堵持续时间;
    计算所述历史拥堵事件的平均历史拥堵持续时间;
    根据所述平均历史拥堵持续时间,确定所述拥堵路段的拥堵估计持续时间。
  4. 根据权利要求1所述的方法,其特征在于,所述根据交通热点信息与历史路况信息进行大数据分析预测,获取所述常用出行路线在所述出行时间与所述到达时间之间的出行时段的预测路况的步骤,包括:
    从信息数据源中获取所述交通热点信息,所述信息数据源包括票务网站、微博、交通局网站上实时上报的交通信息,所述历史路况信息包括历史规律性信息;
    提取所述交通热点信息中与所述常用出行路线相关的热点信息,以及所述历史路况信息中与所述常用出行路线相关的历史规律性信息;
    根据所述相关的热点信息与所述相关的历史规律性信息对所述常用出行路线在所述出行时间与所述到达时间之间的出行时段的路况进行预测;
    若预测在所述出行时段内可能出现拥堵,则根据所述常用出行路线的出发位置和目标位置以及所述出发时间与到达时间,重新进行路线规划生成建议出行路线,将预测的路况与所述建议出行路线推送至所述用户绑定的智能终端。
  5. 根据权利要求1至4任一项所述的方法,其特征在于,还包括:
    在用户指定的出行时间到达之后,获取所述智能终端的实时位置以确定所述用户的当前位置;
    若所述智能终端的实时位置持续变化,则获取所述智能终端的移动速度以确定所述用户行驶的当前车速;
    根据所述用户的当前位置、所述当前车速以及所述常用出行路线,对行驶前方预设距离内的路段进行实时路况监控;
    若监控到行驶前方预设距离内的路段出现突发异常,根据出现突发异常的路段的历史路况信息计算所述突发异常可能的持续时间;
    根据所述当前车速与所述突发异常可能的持续时间,判断所述突发异常是否会影响用户在所述到达时间到达所述常用路线的目标位置;
    若影响,则根据所述用户的当前位置与所述目标位置重新规划路线,将根据所述当前车速行驶的行驶时间最短的路线推送至所述智能终端。
  6. 一种路况预报装置,其特征在于,包括:
    路况分析单元,用于在用户指定的出行时间到达之前,获取所述用户的常用出行路线的实时路况,并对所述实时路况进行分析;
    异常位置确定单元,用于若所述常用出行路线的实时路况出现异常,则确定异常路段的位置;
    第一推送单元,用于根据所述常用出行路线出现的异常以及所述异常路段的位置,生成异常提醒信息推送至所述用户绑定的智能终端;
    时间获取单元,用于若所述常用出行路线的实时路况未出现异常,则获取用户指定的到达时间;
    路况预测单元,用于根据交通热点信息与历史路况信息进行大数据分析预测,获取所述常用出行路线在所述出行时间与所述到达时间之间的出行时段的预测路况;
    第二推送单元,用于将所述常用出行路线的预测路况推送至所述用户绑定的智能终端。
  7. 根据权利要求6所述的方法,其特征在于,当所述异常路段为拥堵路段时,所述第一推送单元包括:
    原因查找子单元,用于根据交通热点信息查找所述拥堵路段拥堵的原因;
    时间估计子单元,用于查找预设时间内所述历史路况信息中与所述拥堵路段的拥堵原因相同的历史拥堵事件,并根据所述历史拥堵事件计算所述常用出行路线中的拥堵估计持续时间,所述常用出行路线包括出发位置与目标位置;
    影响判断子单元,用于根据所述出行时间、所述到达时间、所述出发位置、所述目标位置、所述拥堵路段的位置以及所述拥堵估计持续时间进行出行影响判断,判断所述常用出行路线出现的异常路况是否影响用户在所述到达时间到达所述目标位置;
    异常推送子单元,用于根据所述出行影响判断的判断结果,生成异常提醒信息推送至所述智能终端。
  8. 根据权利要求7所述的方法,其特征在于,所述时间估计子单元具体包括:
    时间获取子模块,用于获取预设时间内所述历史路况信息中与所述拥堵路段的拥堵原因相同的每一件历史拥堵事件的历史拥堵持续时间;
    平均时间计算子模块,用于计算所述历史拥堵事件的平均历史拥堵持续时间;
    时间估计子模块,用于根据所述平均历史拥堵持续时间,确定所述拥堵路段的拥堵估计持续时间。
  9. 根据权利要求6所述的方法,其特征在于,所述路况预测单元包括:
    信息抓取子单元,用于从信息数据源中获取所述交通热点信息,所述信息数据源包括票务网站、微博、交通局网站上实时上报的交通信息,所述历史路况信息包括历史规律性信息;
    信息过滤子单元,用于提取所述交通热点信息中与所述常用出行路线相关的热点信息,以及所述历史路况信息中与所述常用出行路线相关的历史规律性信息;
    路况预测子单元,用于根据所述相关的热点信息与所述相关的历史规律性信息对所述常用出行路线在所述出行时间与所述到达时间之间的出行时段的路况进行预测;
    路线规划子单元,用于若预测在所述出行时段内可能出现拥堵,则根据所述常用出行路线的出发位置和目标位置以及所述出发时间与到达时间,重新进行路线规划生成建议出行路线,将预测的路况与所述建议出行路线推送至所述用户绑定的智能终端。
  10. 根据权利要求6至9任一项所述的方法,其特征在于,所述路况预测装置还包括:
    当前位置确定单元,用于在用户指定的出行时间到达之后,获取所述智能终端的实时位置以确定所述用户的当前位置;
    当前车速确定单元,用于若所述智能终端的实时位置持续变化,则获取所述智能终端的移动速度以确定所述用户行驶的当前车速;
    当前路况监控单元,用于根据所述用户的当前位置、所述当前车速以及所述常用出行路线,对行驶前方预设距离内的路段进行实时路况监控;
    持续时间计算单元,用于若监控到行驶前方预设距离内的路段出现突发异常,根据出现突发异常的路段的历史路况信息计算所述突发异常可能的持续时间;
    异常影响判断单元,用于根据所述当前车速与所述突发异常可能的持续时间,判断所述突发异常是否会影响用户在所述到达时间到达所述常用路线的目标位置;
    最优路线推送单元,用于若影响,则根据所述用户的当前位置与所述目标位置重新规划路线,将根据所述当前车速行驶的行驶时间最短的路线推送至所述智能终端。
  11. 一种计算机可读存储介质,所述计算机可读存储介质存储有计算机可读指令,其特征在于,所述计算机可读指令被处理器执行时实现如下步骤:
    在用户指定的出行时间到达之前,获取所述用户的常用出行路线的实时路况,并对所述实时路况进行分析;
    若所述常用出行路线的实时路况出现异常,则确定异常路段的位置;
    根据所述常用出行路线出现的异常以及所述异常路段的位置,生成异常提醒信息推送至所述用户绑定的智能终端;
    若所述常用出行路线的实时路况未出现异常,则获取用户指定的到达时间;
    根据交通热点信息与历史路况信息进行大数据分析预测,获取所述常用出行路线在所述出行时间与所述到达时间之间的出行时段的预测路况;
    将所述常用出行路线的预测路况推送至所述用户绑定的智能终端。
  12. 根据权利要求11所述的计算机可读存储介质,其特征在于,当所述异常路段为拥堵路段时,所述根据所述常用出行路线出现的异常以及所述异常路段的位置,生成异常提醒信息推送至所述用户绑定的智能终端的步骤,包括:
    根据交通热点信息查找所述拥堵路段拥堵的原因;
    查找预设时间内所述历史路况信息中与所述拥堵路段的拥堵原因相同的历史拥堵事件,并根据所述历史拥堵事件计算所述常用出行路线中的拥堵估计持续时间,所述常用出行路线包括出发位置与目标位置;
    根据所述出行时间、所述到达时间、所述出发位置、所述目标位置、所述拥堵路段的位置以及所述拥堵估计持续时间进行出行影响判断,判断所述常用出行路线出现的异常路况是否影响用户在所述到达时间到达所述目标位置;
    根据所述出行影响判断的判断结果,生成异常提醒信息推送至所述智能终端。
  13. 根据权利要求12所述的计算机可读存储介质,其特征在于,所述查找预设时间内历史路况信息中与所述拥堵路段的拥堵原因相同的历史拥堵事件,并根据所述历史拥堵事件计算所述常用出行路线中的拥堵估计持续时间的步骤,包括:
    获取预设时间内所述历史路况信息中与所述拥堵路段的拥堵原因相同的每一件历史拥堵事件的历史拥堵持续时间;
    计算所述历史拥堵事件的平均历史拥堵持续时间;
    根据所述平均历史拥堵持续时间,确定所述拥堵路段的拥堵估计持续时间。
  14. 根据权利要求11所述的计算机可读存储介质,其特征在于,所述根据交通热点信息与历史路况信息进行大数据分析预测,获取所述常用出行路线在所述出行时间与所述到达时间之间的出行时段的预测路况的步骤,包括:
    从信息数据源中获取所述交通热点信息,所述信息数据源包括票务网站、微博、交通局网站上实时上报的交通信息,所述历史路况信息包括历史规律性信息;
    提取所述交通热点信息中与所述常用出行路线相关的热点信息,以及所述历史路况信息中与所述常用出行路线相关的历史规律性信息;
    根据所述相关的热点信息与所述相关的历史规律性信息对所述常用出行路线在所述出行时间与所述到达时间之间的出行时段的路况进行预测;
    若预测在所述出行时段内可能出现拥堵,则根据所述常用出行路线的出发位置和目标位置以及所述出发时间与到达时间,重新进行路线规划生成建议出行路线,将预测的路况与所述建议出行路线推送至所述用户绑定的智能终端。
  15. 根据权利要求11至14任一项所述的计算机可读存储介质,其特征在于,所述计算机可读指令被处理器执行时还实现如下步骤:
    在用户指定的出行时间到达之后,获取所述智能终端的实时位置以确定所述用户的当前位置;
    若所述智能终端的实时位置持续变化,则获取所述智能终端的移动速度以确定所述用户行驶的当前车速;
    根据所述用户的当前位置、所述当前车速以及所述常用出行路线,对行驶前方预设距离内的路段进行实时路况监控;
    若监控到行驶前方预设距离内的路段出现突发异常,根据出现突发异常的路段的历史路况信息计算所述突发异常可能的持续时间;
    根据所述当前车速与所述突发异常可能的持续时间,判断所述突发异常是否会影响用户在所述到达时间到达所述常用路线的目标位置;
    若影响,则根据所述用户的当前位置与所述目标位置重新规划路线,将根据所述当前车速行驶的行驶时间最短的路线推送至所述智能终端。
  16. 一种服务器,包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机可读指令,其特征在于,所述处理器执行所述计算机可读指令时实现如下步骤:
    在用户指定的出行时间到达之前,获取所述用户的常用出行路线的实时路况,并对所述实时路况进行分析;
    若所述常用出行路线的实时路况出现异常,则确定异常路段的位置;
    根据所述常用出行路线出现的异常以及所述异常路段的位置,生成异常提醒信息推送至所述用户绑定的智能终端;
    若所述常用出行路线的实时路况未出现异常,则获取用户指定的到达时间;
    根据交通热点信息与历史路况信息进行大数据分析预测,获取所述常用出行路线在所述出行时间与所述到达时间之间的出行时段的预测路况;
    将所述常用出行路线的预测路况推送至所述用户绑定的智能终端。
  17. 根据权利要求16所述的服务器,其特征在于,当所述异常路段为拥堵路段时,所述根据所述常用出行路线出现的异常以及所述异常路段的位置,生成异常提醒信息推送至所述用户绑定的智能终端,包括:
    根据交通热点信息查找所述拥堵路段拥堵的原因;
    查找预设时间内所述历史路况信息中与所述拥堵路段的拥堵原因相同的历史拥堵事件,并根据所述历史拥堵事件计算所述常用出行路线中的拥堵估计持续时间,所述常用出行路线包括出发位置与目标位置;
    根据所述出行时间、所述到达时间、所述出发位置、所述目标位置、所述拥堵路段的位置以及所述拥堵估计持续时间进行出行影响判断,判断所述常用出行路线出现的异常路况是否影响用户在所述到达时间到达所述目标位置;
    根据所述出行影响判断的判断结果,生成异常提醒信息推送至所述智能终端。
  18. 根据权利要求17所述的服务器,其特征在于,所述查找预设时间内历史路况信息中与所述拥堵路段的拥堵原因相同的历史拥堵事件,并根据所述历史拥堵事件计算所述常用出行路线中的拥堵估计持续时间的步骤,包括:
    获取预设时间内所述历史路况信息中与所述拥堵路段的拥堵原因相同的每一件历史拥堵事件的历史拥堵持续时间;
    计算所述历史拥堵事件的平均历史拥堵持续时间;
    根据所述平均历史拥堵持续时间,确定所述拥堵路段的拥堵估计持续时间。
  19. 根据权利要求16所述的服务器,其特征在于,所述根据交通热点信息与历史路况信息进行大数据分析预测,获取所述常用出行路线在所述出行时间与所述到达时间之间的出行时段的预测路况,包括:
    从信息数据源中获取所述交通热点信息,所述信息数据源包括票务网站、微博、交通局网站上实时上报的交通信息,所述历史路况信息包括历史规律性信息;
    提取所述交通热点信息中与所述常用出行路线相关的热点信息,以及所述历史路况信息中与所述常用出行路线相关的历史规律性信息;
    根据所述相关的热点信息与所述相关的历史规律性信息对所述常用出行路线在所述出行时间与所述到达时间之间的出行时段的路况进行预测;
    若预测在所述出行时段内可能出现拥堵,则根据所述常用出行路线的出发位置和目标位置以及所述出发时间与到达时间,重新进行路线规划生成建议出行路线,将预测的路况与所述建议出行路线推送至所述用户绑定的智能终端。
  20. 根据权利要求16至19任一项所述的服务器,其特征在于,所述处理器执行所述计算机可读指令时还实现如下步骤:
    在用户指定的出行时间到达之后,获取所述智能终端的实时位置以确定所述用户的当前位置;
    若所述智能终端的实时位置持续变化,则获取所述智能终端的移动速度以确定所述用户行驶的当前车速;
    根据所述用户的当前位置、所述当前车速以及所述常用出行路线,对行驶前方预设距离内的路段进行实时路况监控;
    若监控到行驶前方预设距离内的路段出现突发异常,根据出现突发异常的路段的历史路况信息计算所述突发异常可能的持续时间;
    根据所述当前车速与所述突发异常可能的持续时间,判断所述突发异常是否会影响用户在所述到达时间到达所述常用路线的目标位置;
    若影响,则根据所述用户的当前位置与所述目标位置重新规划路线,将根据所述当前车速行驶的行驶时间最短的路线推送至所述智能终端。
PCT/CN2018/097498 2017-12-08 2018-07-27 一种路况预报方法、装置、存储介质和服务器 WO2019109645A1 (zh)

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