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CN101894469B - Event processing method and device in historical traffic information - Google Patents

Event processing method and device in historical traffic information Download PDF

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Publication number
CN101894469B
CN101894469B CN2010102348891A CN201010234889A CN101894469B CN 101894469 B CN101894469 B CN 101894469B CN 2010102348891 A CN2010102348891 A CN 2010102348891A CN 201010234889 A CN201010234889 A CN 201010234889A CN 101894469 B CN101894469 B CN 101894469B
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traffic
event
road
abnormal
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CN101894469A (en
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魏俊华
贾学力
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Beijing Cennavi Technologies Co Ltd
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Beijing Cennavi Technologies Co Ltd
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Priority to PCT/CN2011/074002 priority patent/WO2012010004A1/en
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0129—Traffic data processing for creating historical data or processing based on historical data

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Abstract

本发明实施例公开了一种历史交通信息中事件处理方法及装置，涉及交通信息处理领域。为了能够提高交通信息预测结果的可靠性，本发明提供的技术方案如下：从交通流历史数据中获取异常交通数据和正常交通数据；根据规定条件从所述异常交通数据中获取交通事件数据；根据所述交通事件数据和所述正常交通数据获取事件信息。本发明适用于交通信息预测。

Figure 201010234889

The embodiment of the present invention discloses a method and device for processing events in historical traffic information, and relates to the field of traffic information processing. In order to improve the reliability of traffic information prediction results, the technical solution provided by the present invention is as follows: abnormal traffic data and normal traffic data are obtained from traffic flow historical data; traffic event data are obtained from the abnormal traffic data according to specified conditions; event information is obtained according to the traffic event data and the normal traffic data. The present invention is applicable to traffic information prediction.

Figure 201010234889

Description

Event-handling method and device in the historical traffic information
Technical field
The present invention relates to the transport information process field, relate in particular to event-handling method and device in a kind of historical traffic information.
Background technology
In recent years, urban traffic blocking, traffic hazard take place frequently and traffic problems such as traffic environment deterioration more and more severeer, become the current problem that presses for solution.Thus, people hope to predict following transport information, make people's trip obtain guiding, thereby traffic is improved.
Through traffic information data is carried out analyzing and processing, can count certain characteristic rule, and this characteristic rule can be used for the prediction of transport information.In traditional transport information analytical approach, can earlier the exceptional value in the historical traffic information data be filtered away, then the historical traffic information data after filtering carried out analytic statistics.Wherein, adopt the data detection method to remove exceptional value usually.
In realizing process of the present invention; The inventor finds to have following problem in the prior art at least: for the exceptional value in the historical traffic information data; It possibly comprise for example event information such as accident, control, and the sporadic influence to the traffic information predicting result of traffic events is very big, therefore; Characteristic rule according to being counted by traditional transport information analytical approach predicts that traffic information predicting result's reliability is lower.
Summary of the invention
Embodiments of the invention provide event-handling method and device in a kind of historical traffic information, can improve traffic information predicting result's reliability.
For achieving the above object, embodiments of the invention adopt following technical scheme:
Event-handling method in a kind of historical traffic information comprises:
From the traffic flow historical data, obtain unusual traffic data and normal traffic data;
Condition is obtained traffic event data from said unusual traffic data according to the rules;
Obtain event information according to said traffic event data and said normal traffic data.
Event processing apparatus in a kind of historical traffic information comprises:
The historical data acquiring unit is used for obtaining unusual traffic data and normal traffic data from the traffic flow historical data;
The event data acquiring unit, the condition that is used for is according to the rules obtained traffic event data from the unusual traffic data that said historical data acquiring unit obtains;
The event information acquiring unit, the normal traffic data that traffic event data that is used for obtaining according to said event data acquiring unit and said historical data acquiring unit obtain are obtained event information.
Event-handling method and device in the historical traffic information that the embodiment of the invention provides; Through from the traffic flow historical data, obtaining unusual traffic data and normal traffic data; Condition is obtained traffic event data from said unusual traffic data according to the rules, and obtains event information according to said traffic event data and said normal traffic data, and these event informations can well react the sporadic of transport information; Therefore; Can utilize the event information that obtains that the traffic parameter predicted value in the short-term forecasting is adjusted, thereby, traffic information predicting result's reliability can be improved.
Description of drawings
In order to be illustrated more clearly in the technical scheme of the embodiment of the invention; The accompanying drawing of required use is done an introduction simply in will describing embodiment below; Obviously, the accompanying drawing in describing below only is some embodiments of the present invention, for those of ordinary skills; Under the prerequisite of not paying creative work, can also obtain other accompanying drawing according to these accompanying drawings.
The schematic flow sheet of event-handling method in a kind of historical traffic information that Fig. 1 provides for the embodiment of the invention;
The formation synoptic diagram of event processing apparatus in a kind of historical traffic information that Fig. 2 provides for the embodiment of the invention;
A kind of traffic information predicting block diagram that Fig. 3 provides for the embodiment of the invention based on historical data;
A kind of schematic flow sheet that from unusual traffic data, extracts event information that Fig. 4 provides for the embodiment of the invention;
The application drawing of a kind of event information that Fig. 5 provides for the embodiment of the invention.
Embodiment
To combine the accompanying drawing in the embodiment of the invention below, the technical scheme in the embodiment of the invention is carried out clear, intactly description, obviously, described embodiment only is the present invention's part embodiment, rather than whole embodiment.Based on the embodiment among the present invention, those of ordinary skills are not making the every other embodiment that is obtained under the creative work prerequisite, all belong to the scope of the present invention's protection.
In order to improve traffic information predicting result's reliability, the embodiment of the invention provides event-handling method in a kind of historical traffic information, and is as shown in Figure 1, comprising:
101, from the traffic flow historical data, obtain unusual traffic data and normal traffic data;
Wherein, Said " traffic flow historical data " is main through resultant to handling like various raw data that collect such as floating car data, coil data, video datas, and its content is included in the data of reaction road travelling characteristics such as required time of certain time period certain road of interior vehicle process or travel speed.In addition; Back literary composition described " event history data " is through the traffic data through strict examination that obtains like manual types such as camera inquiry, broadcast listening, site searches, and it has comprised the information such as time, position, event type and event content that incident takes place.
For example; From the traffic flow historical data base, read the traffic flow historical data; According to characteristic day and time band the traffic characteristic information in the traffic flow historical data (like hourage and travel speed) is analyzed; The sample numerical value of traffic characteristic information is belonged to the outer traffic flow historical data of range of normal value as unusual traffic data, and the traffic flow historical data except that unusual traffic data is the normal traffic data.For example; Travel speed sample to road is analyzed; With the travel speed sample point of substantial deviation average as exceptional value; All the other travel speed sample points are as normal value, and the travel speed sample point is that the traffic flow historical data of exceptional value is unusual traffic data so, and the travel speed sample point is that the traffic flow historical data of normal value is the normal traffic data.
102, condition is obtained traffic event data from said unusual traffic data according to the rules;
In said unusual traffic data, some data is because of the causing of traffic events, and some data is because the precision problem that systematic error occurs.Because of the unusual traffic data that generation produced of traffic events has implied event information, be called traffic event data in these data with these implicit event informations.
For example,, and can there be certain duration and coverage, therefore, can judge traffic event data from these three aspects owing to happening suddenly of traffic events.
Sudden can the definition according to the saltus step of average overall travel speed on surrounding time of road.For example, obtain the unusual traffic data of investigating road R1 in the abnormal time section T1.Confirm first period of supervision in the abnormal time section T1, with first period of supervision as current period.Wherein, period of supervision is meant the unit interval band of gathering the traffic flow historical data.Confirm the period of supervision of the specified quantity before the current period, with the period of supervision of confirming as the historical cycle.Unusual traffic data according to the current period of investigating road R1 obtains the current average overall travel speed V 1 that investigates road R1, and, obtain the historical average overall travel speed V2 that investigates road R1 according to the normal traffic data in the historical cycle of investigating road R1.At V1-V2＞A, and (V1-V2)/V1＞B, it is sudden to think that the road traffic state of investigating road R1 occurs, and the unusual traffic data of promptly investigating road R1 in the abnormal time section T1 has sudden.Wherein, A is the relative changing value, and B is the absolute change value, and the value of A and B can change according to setting.
Continuation from the time viewpoint definition characteristic that occurs of incident, incident generally can continue a period of time.For example, suppose that incident generally can continue C minute, be higher than C minute, confirm that then the unusual traffic data of investigating road R1 in the abnormal time section T1 has continuation if investigate the unusual duration of sample value of road.If the duration is less than C minute, can think that then this unusual traffic data is because the data exception that systematic error or error originated from input cause.
Because city road network is an integral body, Traffic Anomaly appears in certain bar road, can the distribution of the flow of its adjacent road be exerted an influence; Form certain range of influence; Therefore, spatiality is meant the coverage the when time takes place, and can define through the quantity that influences adjacent road.For example; Obtain the unusual traffic data of investigating the adjacent road of road R1 in the abnormal time section T1; When the unusual traffic data of confirming these adjacent road according to said method has sudden and continuation, with said adjacent road as the road that influenced by said investigation road.And, when the road quantity of being investigated road R1 and influencing is higher than specified quantity D, confirm that the unusual traffic data of investigating road R1 in the abnormal time section T1 has spatiality.
Have suddenly during at the unusual traffic data of confirming in the abnormal time section T1 to investigate road R1 according to the following formula method simultaneously, this unusual traffic data is extracted as the traffic event data of investigating road R1 with continuation and spatiality.
In addition, the event history data are event informations of issuing out through after the audit, and reliability is higher, but because factors such as channel of gathering and cost, the coverage rate of event history data is limited.Yet the event history data can test, revise and replenish the traffic event data that extracts, thereby obtain traffic event data more accurately.
Specifically can for, obtain the event history data corresponding with said traffic event data, check said traffic event data whether consistent with said event history data content.When said traffic event data and said event history data content are inconsistent, the content of said traffic event data is revised according to the content of said event history data.Whether the content of check event history data all exists in said traffic event data.If there is not the content of event history data in the said traffic event data, with said non-existent event history data as traffic event data.
103, obtain event information according to said traffic event data and said normal traffic data.
Wherein, said event information comprises incident zero-time, road number, category of roads, events affecting degree, incident duration and events affecting scope etc.
For example, obtain the unusual zero-time of sample value according to said traffic event data, promptly road traffic state produces the time of saltus step, with the unusual zero-time of said sample value as incident zero-time T.Road number N and the category of roads L of the road that event information takes place of the road of event information confirm to take place according to said traffic event data; When the road that event information takes place is analyzed; Sudden change to the road traffic state that receives this events affecting was analyzed with the unusual duration, the road traffic state sudden change is created in downstream (time that road traffic state produces saltus step the earliest) and sudden change road the most serious (the historical average overall travel speed V2 of current average overall travel speed V1-is maximum with the value of (the historical average overall travel speed V2 of current average overall travel speed V1-)/current average overall travel speed V1), that the unusual duration is the longest as the incident occurrence positions.
Events affecting degree Y DegreeThe intensity of variation of the road traffic state that (t) causes for incident is the function of incident duration.It can obtain according to the unusual average velocity in the regulation moment that is obtained by traffic event data and by the regulation normal average velocity constantly that the normal traffic data obtain.For example, try to achieve events affecting degree Y with following formula Degree(t):
Figure BSA00000202892600051
Wherein, unusual average velocity v Incident(t) be the average velocity of investigating highway section R according to the t that said traffic event data is obtained constantly, promptly the average velocity that back t investigates highway section R constantly takes place in incident, and normal average velocity v Normally(t) investigate the average velocity of highway section R constantly for the t that obtains according to said normal traffic data, promptly no traffic events t investigates the average velocity of road R1 constantly under the normal condition.
Because the time of the road traffic state ANOMALOUS VARIATIONS that incident causes; And the duration of each road in the involved area is inconsistent; Therefore, need obtain incident generation time and incident resolution time, wherein; The incident generation time is the time that the traffic behavior of incident occurrence positions is undergone mutation, and the incident resolution time is the time that every road in the involved area all returns to normal condition.Just can obtain incident duration Y according to this incident generation time and incident resolution time Time(r).
Obtain affected road scope in the said traffic event data, use the radius that influences with respect to the incident occurrence positions of receiving to represent, with said road scope as events affecting scope Y Scope(t), and, events affecting scope Y Scope(t) also be incident duration Y Time(r) function.
Set up event information database by the way; Can adopt look-up table is condition query incident duration and events affecting scope with events affecting degree, incident zero-time and category of roads; It is event on corresponding different time, the different brackets road; Under certain influence degree, the duration and the coverage of this incident of inquiry from database.Can the traffic parameter predicted value in the short-term forecasting be adjusted according to these two values.
Event-handling method in the historical traffic information that the embodiment of the invention provides; Through from the traffic flow historical data, obtaining unusual traffic data and normal traffic data; Condition is obtained traffic event data from said unusual traffic data according to the rules, and obtains event information according to said traffic event data and said normal traffic data, and these event informations can well react the sporadic of transport information; Therefore; Can utilize the event information that obtains that the traffic parameter predicted value in the short-term forecasting is adjusted, thereby, traffic information predicting result's reliability can be improved.
With said method accordingly, the embodiment of the invention also provides event processing apparatus in a kind of historical traffic information, and is as shown in Figure 2, comprising:
Historical data acquiring unit 201 is used for obtaining unusual traffic data and normal traffic data from the traffic flow historical data;
Event data acquiring unit 202, the condition that is used for is according to the rules obtained traffic event data from the unusual traffic data that said historical data acquiring unit 201 obtains;
Event information acquiring unit 203, the normal traffic data that traffic event data that is used for obtaining according to said event data acquiring unit 202 and said historical data acquiring unit 201 obtain are obtained event information.
Further, said event data acquiring unit 202 specifically comprises:
Abnormal data obtains subelement, is used to obtain the unusual traffic data of investigating road in the abnormal time section;
Sudden judgment sub-unit; Be used for difference at the current average overall travel speed of said investigation road and historical average overall travel speed greater than the relative changing value; And the ratio of said difference and said current average overall travel speed judges that the unusual traffic data of said investigation road in the said abnormal time section has sudden during greater than the absolute change value;
The continuation judgment sub-unit is used for when the unusual duration of sample value of said investigation road is higher than the stipulated time, judging that the unusual traffic data of said investigation road in the said abnormal time section has continuation;
The spatiality judgment sub-unit is used for when the road quantity that influenced by said investigation road is higher than specified quantity, judges that the unusual traffic data of said investigation road in the said abnormal time section has spatiality;
Event data is confirmed subelement; Be used for judging that in said sudden judgment sub-unit said unusual traffic data has sudden; And said continuation judgment sub-unit judges that said unusual traffic data has continuation; And said continuation judgment sub-unit judges when said unusual traffic data has spatiality, with the unusual traffic data of said investigation road in the said abnormal time section traffic event data as said investigation road.
Further, said sudden judgment sub-unit comprises:
The current period determination module is used for confirming first period of supervision in the said abnormal time section, with said first period of supervision as current period;
Historical period determination module is used for confirming the period of supervision of the specified quantity before the said current period, with the period of supervision of confirming as the historical cycle;
The present speed acquisition module is used for obtaining according to the unusual traffic data of the current period of said investigation road the current average overall travel speed of said investigation road;
The historical speed acquisition module is used for obtaining according to the normal traffic data in historical cycle of said investigation road the historical average overall travel speed of said investigation road;
Sudden judge module; Be used for difference at the current average overall travel speed of said investigation road and historical average overall travel speed greater than the relative changing value; And the ratio of said difference and said current average overall travel speed judges that the unusual traffic data of said investigation road in the said abnormal time section has sudden during greater than the absolute change value;
Further, said spatiality judgment sub-unit comprises:
The adjacent data acquisition module is used to obtain the unusual traffic data of the adjacent road of said investigation road in the said abnormal time section;
The adjacent road determination module, be used for having at the unusual traffic data of confirming said adjacent road sudden during with continuation, with said adjacent road as the road that influenced by said investigation road;
The spatiality judge module is used for when the road quantity that influenced by said investigation road is higher than specified quantity, judges that the unusual traffic data of said investigation road in the said abnormal time section has spatiality.
Further, event processing apparatus also comprises in the said historical traffic information:
The event history data capture unit is used to obtain the event history data;
The event data amending unit is used for when said traffic event data and said event history data content are inconsistent, according to said event history data said traffic event data being revised;
The event data supplementary units is used for when there is not the content of event history data in said traffic event data, with said non-existent event history data as traffic event data.
Further, said event information acquiring unit 203 comprises:
Zero-time is obtained subelement, be used for obtaining the unusual zero-time of sample value according to said traffic event data, with the unusual zero-time of said sample value as the incident zero-time;
The numbering grade is obtained subelement, is used for confirming the numbering and the grade of said traffic event data road corresponding;
Influence degree is obtained subelement; Be used for obtaining the events affecting degree according to unusual average velocity and normal average velocity; Regulation constantly the average velocity of said unusual average velocity for obtaining according to said traffic event data, regulation constantly the average velocity of said normal average velocity for obtaining according to said normal traffic data;
Duration is obtained subelement, is used for obtaining incident duration according to incident generation incident and incident resolution time;
Coverage is obtained subelement, is used for obtaining the affected road scope of said traffic event data, with said road scope as the events affecting scope.
Event processing apparatus in the historical traffic information that the embodiment of the invention provides; Through from the traffic flow historical data, obtaining unusual traffic data and normal traffic data; Condition is obtained traffic event data from said unusual traffic data according to the rules, and obtains event information according to said traffic event data and said normal traffic data, and these event informations can well react the sporadic of transport information; Therefore; Can utilize the event information that obtains that the traffic parameter predicted value in the short-term forecasting is adjusted, thereby, traffic information predicting result's reliability can be improved.
Below, be that example describes the embodiment of the invention with the traffic information predicting.As shown in Figure 3; From the traffic flow historical data base, read the traffic flow historical data; And this traffic flow historical data carried out statistical study, the statistical nature that obtains unusual traffic data and obtained by the normal traffic data statistics, this statistical nature are mainly used in normal sending out in the property prediction.Wherein, unusual traffic data can extract according to method mentioned above, and can repeat no more at this referring to prior art according to the method that the normal traffic data obtain statistical nature.From the incident historical data base, read the event history data, the unusual traffic data that statistical study obtains is tested, revised and replenishes with these event history data.After unusual traffic data having been carried out correction and having replenished; As shown in Figure 4; According to method mentioned above judge whether unusual traffic data sudden, continuation and spatiality, if unusual traffic data satisfies this three conditions simultaneously, then unusual traffic data is a traffic event data; According to method mentioned above this traffic event data is carried out statistical study, obtain incident zero-time, category of roads, road number, events affecting degree, incident duration and events affecting scope.In the process of carrying out sporadic prediction, utilize incident zero-time, category of roads and events affecting degree query time duration and events affecting scope, as shown in Figure 5.
When carrying out traffic information predicting according to the method described above; Through from the traffic flow historical data, obtaining unusual traffic data, and utilize the event history data that said unusual traffic data is verified and revisal, condition is obtained traffic event data from said unusual traffic data according to the rules; And obtain event information according to said traffic event data and said normal traffic data; These event informations can well react the sporadic of transport information, therefore, can utilize the event information that obtains that the traffic parameter predicted value in the short-term forecasting is adjusted; Thereby, can improve traffic information predicting result's reliability.
One of ordinary skill in the art will appreciate that all or part of flow process that realizes in the foregoing description method; Be to instruct relevant hardware to accomplish through computer program; Described program can be stored in the computer read/write memory medium; This program can comprise the flow process like the embodiment of above-mentioned each side method when carrying out.Wherein, described storage medium can be magnetic disc, CD, read-only storage memory body (Read-Only Memory, ROM) or at random store memory body (Random Access Memory, RAM) etc.
The above; Be merely embodiment of the present invention, but protection scope of the present invention is not limited thereto, any technician who is familiar with the present technique field is in the technical scope that the present invention discloses; Can expect easily changing or replacement, all should be encompassed within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion by said protection domain with claim.

Claims (7)

1.一种历史交通信息中事件处理方法，其特征在于，包括：1. An event processing method in historical traffic information, characterized in that, comprising: 从交通流历史数据中获取异常交通数据和正常交通数据；Obtain abnormal traffic data and normal traffic data from traffic flow historical data; 根据规定条件从所述异常交通数据中获取交通事件数据；Obtaining traffic event data from the abnormal traffic data according to specified conditions; 根据所述交通事件数据和所述正常交通数据获取事件信息，acquiring event information according to the traffic event data and the normal traffic data, 所述根据规定条件从所述异常交通数据中获取交通事件数据包括：The obtaining traffic event data from the abnormal traffic data according to specified conditions includes: 获取异常时间段内考察道路的异常交通数据；Obtain the abnormal traffic data of the inspected road during the abnormal time period; 在根据所述考察道路的行驶速度确定所述异常时间段内所述考察道路的异常交通数据具有突发性，且根据所述考察道路的样本值异常持续时间确定所述异常时间段内所述考察道路的异常交通数据具有持续性，且根据受所述考察道路影响的道路数量确定所述异常时间段内所述考察道路的异常交通数据具有空间性时，将所述异常时间段内所述考察道路的异常交通数据作为所述考察道路的交通事件数据，The abnormal traffic data of the road under investigation is determined to be sudden during the abnormal time period according to the driving speed of the road under investigation, and the abnormal traffic data within the abnormal time period is determined according to the abnormal duration of the sample value of the road under investigation. The abnormal traffic data of the investigated road is persistent, and when it is determined that the abnormal traffic data of the investigated road in the abnormal time period is spatial in accordance with the number of roads affected by the investigated road, the abnormal traffic data of the investigated road in the abnormal time period is The abnormal traffic data of the investigated road is used as the traffic event data of the investigated road, 所述根据考察道路的行驶速度确定所述异常时间段内所述考察道路的异常交通数据具有突发性包括：The determining that the abnormal traffic data of the surveyed road within the abnormal time period according to the traveling speed of the surveyed road is sudden includes: 确定所述异常时间段内的第一个考察周期，将所述第一考察周期作为当前周期；determining the first investigation period within the abnormal time period, and using the first investigation period as the current period; 确定所述当前周期之前的规定数量的考察周期，将确定的考察周期作为历史周期；determining a specified number of investigation periods before the current period, and using the determined investigation period as a historical period; 根据所述考察道路的当前周期的异常交通数据获取所述考察道路的当前平均行驶速度；Acquiring the current average driving speed of the surveyed road according to the abnormal traffic data of the current period of the surveyed road; 根据所述考察道路的历史周期的正常交通数据获取所述考察道路的历史平均行驶速度；Obtaining the historical average driving speed of the surveyed road according to the normal traffic data of the historical period of the surveyed road; 在所述考察道路的当前平均行驶速度与历史平均行驶速度之差大于相对变化值，且所述差与所述当前平均行驶速度之比大于绝对变化值时，确定所述异常时间段内所述考察道路的异常交通数据具有突发性；When the difference between the current average driving speed of the investigated road and the historical average driving speed is greater than the relative change value, and the ratio of the difference to the current average driving speed is greater than the absolute change value, it is determined that the abnormal time period The abnormal traffic data of the inspected road is sudden; 所述根据所述考察道路的样本值异常持续时间确定所述异常时间段内所述考察道路的异常交通数据具有持续性包括：The determining that the abnormal traffic data of the investigated road within the abnormal time period has continuity according to the abnormal duration of the sample value of the investigated road includes: 在所述考察道路的样本值异常持续时间高于规定时间时，确定所述异常时间段内所述考察道路的异常交通数据具有持续性；When the abnormal duration of the sample value of the investigated road is higher than the specified time, it is determined that the abnormal traffic data of the investigated road within the abnormal time period has continuity; 所述根据受所述考察道路影响的道路数量确定所述异常时间段内所述考察道路的异常交通数据具有空间性包括：The determining that the abnormal traffic data of the surveyed road within the abnormal time period according to the number of roads affected by the surveyed road has spatial characteristics includes: 获取所述异常时间段内所述考察道路的相邻道路的异常交通数据；Obtain abnormal traffic data of adjacent roads of the investigation road within the abnormal time period; 在确定所述相邻道路的异常交通数据具有突发性和持续性时，将所述相邻道路作为受所述考察道路影响的道路；When it is determined that the abnormal traffic data of the adjacent road is sudden and continuous, the adjacent road is regarded as the road affected by the investigation road; 在受所述考察道路影响的道路数量高于规定数量时，确定所述异常时间段内所述考察道路的异常交通数据具有空间性。When the number of roads affected by the investigation road is higher than a predetermined number, it is determined that the abnormal traffic data of the investigation road within the abnormal time period has spatiality. 2.根据权利要求1所述的历史交通信息中事件处理方法，其特征在于，所述根据所述交通事件数据和所述正常交通数据获取事件信息的步骤之前还包括：2. The event processing method in historical traffic information according to claim 1, characterized in that, before the step of obtaining event information according to the traffic event data and the normal traffic data, the method also includes: 获取事件历史数据；Get event history data; 在所述交通事件数据与所述事件历史数据内容不一致时，根据所述事件历史数据对所述交通事件数据进行修正；When the traffic event data is inconsistent with the content of the event history data, correcting the traffic event data according to the event history data; 在所述交通事件数据中不存在事件历史数据的内容时，将所述不存在的事件历史数据作为交通事件数据。When the content of event history data does not exist in the traffic event data, the non-existing event history data is used as traffic event data. 3.根据权利要求1-2任一所述的历史交通信息中事件处理方法，其特征在于，所述根据所述交通事件数据和所述正常交通数据获取事件信息包括：3. The method for processing events in historical traffic information according to any one of claims 1-2, wherein said acquiring event information according to said traffic event data and said normal traffic data comprises: 根据所述交通事件数据获取样本值异常起始时间，将所述样本值异常起始时间作为事件起始时间；Obtaining the abnormal start time of the sample value according to the traffic event data, and using the abnormal start time of the sample value as the event start time; 确定所述交通事件数据对应的道路的编号和等级；Determining the number and grade of the road corresponding to the traffic event data; 根据异常平均速度和正常平均速度获取事件影响程度，所述异常平均速度为根据所述交通事件数据获取的规定时刻的平均速度，所述正常平均速度为根据所述正常交通数据获取的规定时刻的平均速度；Obtain the impact degree of the event according to the abnormal average speed and the normal average speed, the abnormal average speed is the average speed at a specified time obtained according to the traffic event data, and the normal average speed is the specified time obtained according to the normal traffic data average speed; 根据事件产生事件和事件消散时间获取事件持续时间；Obtain the event duration according to the event generation event and event dissipation time; 获取所述交通事件数据中受影响的道路范围，将所述道路范围作为事件影响范围。The affected road range in the traffic event data is obtained, and the road range is used as the event affected range. 4.根据权利要求3所述的历史交通信息中事件处理方法，其特征在于，所述根据异常平均速度和正常平均速度获取事件影响度包括：4. the event processing method in the historical traffic information according to claim 3, is characterized in that, said obtaining event influence degree according to abnormal average speed and normal average speed comprises: 根据异常平均速度v事件(t)和正常平均速度v正常(t)以及公式
Figure FDA0000127372390000021
获取事件影响度Y程度(t)；According to the abnormal mean speed v event (t) and the normal mean speed v normal (t) and the formula
Figure FDA0000127372390000021
Obtain the event influence degree Y degree (t); 其中，所述异常平均速度v事件(t)为根据所述交通事件数据获取的t时刻的平均速度，所述正常平均速度v正常(t)为根据所述正常交通数据获取的t时刻的平均速度。Wherein, the abnormal average speed vevent (t) is the average speed at time t obtained according to the traffic event data, and the normal average speed vnormal (t) is the average speed at time t obtained according to the normal traffic data speed. 5.一种历史交通信息中事件处理装置，其特征在于，包括：5. An event processing device in historical traffic information, characterized in that it comprises: 历史数据获取单元，用于从交通流历史数据中获取异常交通数据和正常交通数据；A historical data acquisition unit, configured to acquire abnormal traffic data and normal traffic data from traffic flow historical data; 事件数据获取单元，用于根据规定条件从所述历史数据获取单元获取的异常交通数据中获取交通事件数据；An event data acquisition unit, configured to acquire traffic event data from the abnormal traffic data acquired by the historical data acquisition unit according to specified conditions; 事件信息获取单元，用于根据所述事件数据获取单元获取的交通事件数据和所述历史数据获取单元获取的正常交通数据获取事件信息，an event information acquisition unit, configured to acquire event information according to the traffic event data acquired by the event data acquisition unit and the normal traffic data acquired by the historical data acquisition unit, 所述事件数据获取单元包括：The event data acquisition unit includes: 异常数据获取子单元，用于获取异常时间段内考察道路的异常交通数据；The abnormal data acquisition subunit is used to acquire the abnormal traffic data of the inspected road during the abnormal time period; 突发性判断子单元，用于在所述考察道路的当前平均行驶速度与历史平均行驶速度之差大于相对变化值，且所述差与所述当前平均行驶速度之比大于绝对变化值时，判定所述异常时间段内所述考察道路的异常交通数据具有突发性；The abruptness judgment subunit is used for when the difference between the current average driving speed and the historical average driving speed of the investigated road is greater than the relative change value, and the ratio of the difference to the current average driving speed is greater than the absolute change value, It is determined that the abnormal traffic data of the investigated road within the abnormal time period is sudden; 持续性判断子单元，用于在所述考察道路的样本值异常持续时间高于规定时间时，判定所述异常时间段内所述考察道路的异常交通数据具有持续性；A continuity judging subunit, configured to determine that the abnormal traffic data of the road under investigation within the abnormal time period is persistent when the abnormal duration of the sample value of the road under investigation is longer than a specified time; 空间性判断子单元，用于在受所述考察道路影响的道路数量高于规定数量时，判定所述异常时间段内所述考察道路的异常交通数据具有空间性；A spatial judgment subunit, configured to determine that the abnormal traffic data of the surveyed road within the abnormal time period has spatiality when the number of roads affected by the surveyed road is higher than a specified number; 事件数据确定子单元，用于在所述突发性判断子单元判定所述异常交通数据具有突发性，且所述持续性判断子单元判定所述异常交通数据具有持续性，且所述持续性判断子单元判定所述异常交通数据具有空间性时，将所述异常时间段内所述考察道路的异常交通数据作为所述考察道路的交通事件数据，The event data determination subunit is used to determine that the abnormal traffic data has a suddenness in the suddenness judgment subunit, and the persistence judgment subunit judges that the abnormal traffic data has continuity, and the persistence When the sex judging subunit judges that the abnormal traffic data is spatial, the abnormal traffic data of the road under investigation within the abnormal time period is used as the traffic event data of the road under investigation, 所述突发性判断子单元包括：The burst judgment subunit includes: 当前周期确定模块，用于确定所述异常时间段内的第一个考察周期，将所述第一考察周期作为当前周期；The current period determination module is configured to determine the first investigation period within the abnormal time period, and use the first investigation period as the current period; 历史周期确定模块，用于确定所述当前周期之前的规定数量的考察周期，将确定的考察周期作为历史周期；A historical cycle determination module, configured to determine a specified number of investigation periods before the current period, and use the determined investigation period as a historical period; 当前速度获取模块，用于根据所述考察道路的当前周期的异常交通数据获取所述考察道路的当前平均行驶速度；The current speed acquisition module is used to acquire the current average driving speed of the investigation road according to the abnormal traffic data of the current cycle of the investigation road; 历史速度获取模块，用于根据所述考察道路的历史周期的正常交通数据获取所述考察道路的历史平均行驶速度；A historical speed acquisition module, configured to acquire the historical average driving speed of the investigated road according to the normal traffic data of the historical period of the investigated road; 突发性判断模块，用于在所述考察道路的当前平均行驶速度与历史平均行驶速度之差大于相对变化值，且所述差与所述当前平均行驶速度之比大于绝对变化值时，判定所述异常时间段内所述考察道路的异常交通数据具有突发性；A suddenness judging module, used to judge when the difference between the current average driving speed and the historical average driving speed of the investigated road is greater than a relative change value, and the ratio of the difference to the current average driving speed is greater than an absolute change value The abnormal traffic data of the investigated road in the abnormal time period is sudden; 所述空间性判断子单元包括：The spatial judgment subunit includes: 相邻数据获取模块，用于获取所述异常时间段内所述考察道路的相邻道路的异常交通数据；Adjacent data acquisition module, used to acquire the abnormal traffic data of the adjacent roads of the investigation road within the abnormal time period; 相邻道路确定模块，用于在确定所述相邻道路的异常交通数据具有突发性和持续性时，将所述相邻道路作为受所述考察道路影响的道路；An adjacent road determination module, configured to use the adjacent road as a road affected by the investigation road when it is determined that the abnormal traffic data of the adjacent road is sudden and continuous; 空间性判断模块，用于在受所述考察道路影响的道路数量高于规定数量时，判定所述异常时间段内所述考察道路的异常交通数据具有空间性。The spatiality judging module is configured to determine that the abnormal traffic data of the surveyed road within the abnormal time period has spatiality when the number of roads affected by the surveyed road is higher than a prescribed number. 6.根据权利要求5所述的历史交通信息中事件处理装置，其特征在于，还包括：6. The event processing device in historical traffic information according to claim 5, further comprising: 事件历史数据获取单元，用于获取事件历史数据；An event history data acquisition unit, configured to acquire event history data; 事件数据修正单元，用于在所述交通事件数据与所述事件历史数据内容不一致时，根据所述事件历史数据对所述交通事件数据进行修正；An event data correction unit, configured to correct the traffic event data according to the event history data when the traffic event data is inconsistent with the content of the event history data; 事件数据补充单元，用于在所述交通事件数据中不存在事件历史数据的内容时，将所述不存在的事件历史数据作为交通事件数据。The event data supplementing unit is configured to use the non-existing event history data as the traffic event data when the content of the event history data does not exist in the traffic event data. 7.根据权利要求5-6任一所述的历史交通信息中事件处理装置，其特征在于，所述事件信息获取单元包括：7. The device for processing events in historical traffic information according to any one of claims 5-6, wherein the event information acquisition unit includes: 起始时间获取子单元，用于根据所述交通事件数据获取样本值异常起始时间，将所述样本值异常起始时间作为事件起始时间；The start time acquisition subunit is used to acquire the abnormal start time of the sample value according to the traffic event data, and use the abnormal start time of the sample value as the event start time; 编号等级获取子单元，用于确定所述交通事件数据对应的道路的编号和等级；The numbering level acquisition subunit is used to determine the number and level of the road corresponding to the traffic event data; 影响程度获取子单元，用于根据异常平均速度和正常平均速度获取事件影响程度，所述异常平均速度为根据所述交通事件数据获取的规定时刻的平均速度，所述正常平均速度为根据所述正常交通数据获取的规定时刻的平均速度；The impact degree acquisition subunit is used to acquire the impact degree of the event according to the abnormal average speed and the normal average speed, the abnormal average speed is the average speed at a specified time obtained according to the traffic event data, and the normal average speed is according to the The average speed at the specified time for normal traffic data acquisition; 持续时间获取子单元，用于根据事件产生事件和事件消散时间获取事件持续时间；The duration acquisition subunit is used to acquire the event duration according to the event generation event and event dissipation time; 影响范围获取子单元，用于获取所述交通事件数据中受影响的道路范围，将所述道路范围作为事件影响范围。The influence range obtaining subunit is used to obtain the affected road range in the traffic event data, and use the road range as the event influence range.
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