CN103280109A - Obtaining method, obtaining device and prediction system of travel time - Google Patents
Obtaining method, obtaining device and prediction system of travel time Download PDFInfo
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Abstract
The invention provides an obtaining method, an obtaining device and a prediction system of travel time. The obtaining method of the travel time comprises the steps of (1) obtaining appointed parameters from a traffic device monitoring terminal of a high way, wherein the appointed parameters comprise charging data of a road charging system and other data besides the charging data; (2) obtaining the travel time between every two adjacent charging stations of the high way in a prediction mode through a preset travel time model according to the charging data; (3) carrying out verification correction on the travel time predicted by the travel time model according to monitoring data; (4) outputting the travel time after the verification correction. According to the scheme, the technical problem that in the prior art, accurate travel time data cannot be obtained is solved, and thus prediction accuracy of the travel time is improved.
Description
Technical field
The present invention relates to intelligent public transport field, in particular to the acquisition methods of a kind of hourage and device, prognoses system.
Background technology
Along with traffic fast development in recent years, shipping and the volume of passenger traffic significantly increase on the highway, and private car is constantly popularized, and make the current pressure of highway increase day by day, and Operation and Management of Expressway and information service level have been proposed new challenge.At present, by to expressway construction, run, raise financing and related industry operation, progressively formed the management of scale, normalized Mechatronic Systems through informatization for many years.System has produced a large amount of information datas in operational process, the data resource of accumulation has contained abundant potential information and clue.How these information being used better, and then predicting the traffic circulation trend that some are following, is the problem that is worth discussion for the public provides better service.
In numerous traffic trip information, the most effective no more than hourage to the public, and other traffic trip information such as speed, flow finally all is reflected on hourage.Therefore, be the important indicator of reflection traffic circulation situation hourage, and simple, intuitive is convenient to understand, and generally accepted by traffic professional and the public and use, and is public's decision-making foundation the most intuitively of going on a journey.And comprise highway section running time and toll plaza parking stand-by period hourage, when the highway vehicle flowrate was tending towards saturated, indivedual gateways, toll plaza queuing phenomena was serious, caused the waiting vehicle delay time at stop to increase greatly, even it is current to influence the main road wagon flow.Therefore, the research of the current level in toll plaza is the important means that the supvr improves service quality, the indirect induction public routing of going on a journey.From international trend, along with the enforcement of intelligent transportation system, the current level in hourage information and square has obtained application more and more widely at aspects such as traffic monitoring management, traveler information service, communications policy support and evaluations.But the traffic that can only obtain highway in the correlation technique but obtains less than data hourage accurately, has influenced user's experience.
At the problems referred to above in the correlation technique, effective solution is not proposed as yet at present.
Summary of the invention
At the problems referred to above in the correlation technique, the invention provides the acquisition methods of a kind of hourage and device, prognoses system, to address the above problem at least.
According to an aspect of the present invention, the acquisition methods of a kind of hourage is provided, comprise: obtain designated parameter from the transit equipment monitoring terminal of highway, wherein, described designated parameter comprises: the charge data of highroad toll collection system, other data except described charge data; Obtain hourage between described freeway toll station point according to default model prediction hourage of described charge data utilization; According to the described monitor data that utilizes to carrying out the verification correction hourage that hourage, model was predicted; Output verification revised hourage.
Preferably, from the transit equipment monitoring terminal of highway, obtain designated parameter according to predetermined period, comprising: obtain the data that all or part transit equipment monitoring terminal of highway collects in real time; From the described data of obtaining, extract described designated parameter according to predetermined period.
Preferably, described hourage, model comprised: based on model hourage of Kalman filtering algorithm.
Preferably, described other data comprise following one of at least: the toll plaza data on flows of highroad toll collection system, E-payment system ETC track data on flows, weather and/or traffic event data, outfield vehicle checker data on flows.
Preferably, utilize described hourage model to carrying out the verification correction described hourage according to described other data, also comprise: obtain in described other data through the result of calculation corresponding with described other data after described hourage, model was handled; According to described result of calculation to carrying out the verification correction described hourage.
Preferably, said method also comprises: export described result of calculation.
According to another aspect of the present invention, the deriving means of a kind of hourage is provided, comprise: acquisition module, be used for obtaining designated parameter from the transit equipment monitoring terminal of highway, wherein, described designated parameter comprises: the charge data of highroad toll collection system, other data except described charge data; Prediction module is for the hourage that obtains according to default model prediction hourage of described charge data utilization between described freeway toll station point; Correcting module is used for utilizing described hourage model to carrying out the verification correction described hourage according to described other data; Output module is used for output verification revised hourage.
Preferably, described acquisition module comprises: acquiring unit is used for obtaining in real time the data that all or part transit equipment monitoring terminal of highway collects; Extracting unit is used for extracting described designated parameter from the described data of obtaining according to predetermined period.
Preferably, described acquisition module, be used for comprising in described other data following one of at least the time, obtain described designated parameter: the toll plaza data on flows of highroad toll collection system, E-payment system ETC track data on flows, weather and/or traffic event data, outfield vehicle checker data on flows.
According to a further aspect of the invention, provide the prognoses system of a kind of hourage, having comprised: the data pick-up subsystem is used for extracting desired data from external system and is saved in the formal database of system after the ephemeral data banked cache is handled; The predicting travel time subsystem is used for utilizing the highway website charge data of described formal database to carry out predicting travel time between the highway website, and with other monitor datas to revising described hourage; Information issue subsystem is used for showing the hourage that predicts.
By the present invention, employing utilizes model prediction hourage to obtain hourage according to charge data, and according to the technological means of other data to revising hourage except charge data, solved in the correlation technique, can not obtain comparatively accurately technical matterss such as data hourage, thereby improve the predicting travel time precision.
Description of drawings
Accompanying drawing described herein is used to provide further understanding of the present invention, constitutes the application's a part, and illustrative examples of the present invention and explanation thereof are used for explaining the present invention, do not constitute improper restriction of the present invention.In the accompanying drawings:
Fig. 1 is the process flow diagram according to the acquisition methods of the hourage of the embodiment of the invention 1;
Fig. 2 is the structured flowchart according to the deriving means of the hourage of the embodiment of the invention 1;
Fig. 3 is another structured flowchart according to the deriving means of the hourage of the embodiment of the invention 1;
Fig. 4 is the structured flowchart according to the prognoses system of the hourage of the embodiment of the invention 1;
Fig. 5 is the prediction principle synoptic diagram according to the hourage of the embodiment of the invention 2;
Fig. 6 is the principle schematic according to the predicting travel time subsystem of the embodiment of the invention 2;
Fig. 7 is the principle schematic according to the current index subsystem of the embodiment of the invention 2.
Embodiment
Hereinafter will describe the present invention with reference to the accompanying drawings and in conjunction with the embodiments in detail.Need to prove that under the situation of not conflicting, embodiment and the feature among the embodiment among the application can make up mutually.
Embodiment 1
Fig. 1 is the process flow diagram according to the acquisition methods of the hourage of the embodiment of the invention 1.As shown in Figure 1, this method comprises:
Step S102 obtains designated parameter from the transit equipment monitoring terminal of highway, wherein, this designated parameter comprises: the charge data of highroad toll collection system, other data except above-mentioned charge data;
Step S104 obtains hourage between freeway toll station point according to default model prediction hourage of above-mentioned charge data utilization;
Step S106 utilizes the hourage model to carrying out the verification correction above-mentioned hourage according to above-mentioned other data;
Step S108, output verification revised hourage.
By above-mentioned each step, be master's prediction hourage owing to adopt with the charge data, be auxilliary to carrying out the verification correction this hourage with above-mentioned other data, therefore, can predict hourage exactly.
In the present embodiment, the implementation of step S102 has multiple, for example can lay new monitoring terminal and obtain above-mentioned designated parameter, also can utilize existing monitoring equipment to realize, for a kind of implementation in back: obtain the data that all or part transit equipment monitoring terminal of highway collects in real time; From the data of obtaining, extract designated parameter according to predetermined period, utilize existing information management system (comprising various monitor terminals) through the mode of data mining like this, greatly reduce the detection cost.
Step S104 can realize by following processing procedure, at first carry out pre-service to charge data, carries out the optimization pre-service of data by methods such as rejecting abnormalities data, inquartation, data interpolating (for example uniformly-spaced difference).The charge data that will clean is brought the prediction that Kalman filter equation is carried out hourage into afterwards, its principle is: utilize the optimal estimation value of t-1 moment state variable and t-1 observed reading constantly to upgrade the optimal filtering estimated value that obtains t-1 moment state variable, and then the optimal estimation value of prediction t moment state variable
Step S106 can realize by following processing procedure: consider some not networking in real time of monitor data, monitor message lags behind event, report form embodies afterwards often, therefore predicting travel time is based on charge data, and monitor data, section detect data (be that above-mentioned other data comprise: monitor data and section detect data) and predict hourage for assisting.It is the instantaneous velocity of vehicle that section detects data, can draw the average velocity in this highway section by hourage and distance between sites, the speed collection of bringing respective stretch section detection data afterwards into carries out difference calculating and checking, thereby reaches the correction to predicting travel time.Under accident event, if utilizing the normal Kalman filtering factor calculates, error is often bigger, enter the event prediction pattern so utilize the event information of monitor data can trigger prediction algorithm, utilize the calculated factor under the event to predict, to guarantee the precision of prediction under improper situation.
In the present embodiment, above-mentioned hourage, model comprised: based on model hourage of Kalman filtering algorithm.
In the present embodiment, above-mentioned other data include but not limited to following one of at least: the toll plaza data on flows of highroad toll collection system, E-payment system ETC track data on flows, weather and/or traffic event data, outfield vehicle checker data on flows.
In the present embodiment, step S106 can show as following way of realization: obtain in above-mentioned other data through the result of calculation corresponding with above-mentioned other data after hourage, model was handled; According to result of calculation to carrying out the verification correction hourage.
In the present embodiment, can also export result of calculation, for example with to be presented at display screen together hourage first-class.
The deriving means of a kind of hourage also is provided in the present embodiment, has been used for realizing above-described embodiment and preferred implementation, carried out repeating no more of explanation, below the module that relates in this device has been described.As used below, the combination of software and/or the hardware of predetermined function can be realized in term " module ".Although the described device of following examples is preferably realized with software, hardware, perhaps the realization of the combination of software and hardware also may and be conceived.Fig. 2 is the structured flowchart according to the deriving means of the hourage of the embodiment of the invention 1.As shown in Figure 2, this device comprises:
In the present embodiment, as shown in Figure 3, above-mentioned acquisition module 20 comprises: acquiring unit 200, be connected to extracting unit 202, and be used for obtaining in real time the data that all or part transit equipment monitoring terminal of highway collects; Extracting unit 22 is used for extracting designated parameter from the data of obtaining according to predetermined period.
Preferably, acquisition module 20, be used for comprising in other data following one of at least the time, obtain designated parameter: the toll plaza data on flows of highroad toll collection system, E-payment system ETC track data on flows, weather and/or traffic event data, outfield vehicle checker data on flows.
As mentioned above, above-mentioned each module that relates in the present embodiment both can realize by software, also can realize by hardware correspondingly.For example, above-mentioned each module all can be in the processor, for example: above-mentioned each module all is in the processor, and perhaps wherein two modules in above-mentioned four modules are arranged in a processor, and remaining module is arranged in the another one processor, etc.
Fig. 4 is the structured flowchart according to the prognoses system of the hourage of the embodiment of the invention 1.As shown in Figure 4, this system comprises:
Data pick-up subsystem 40 is used for extracting desired data from external system and is saved in the formal database of system after the ephemeral data banked cache is handled;
Predicting travel time subsystem 42 is used for utilizing the highway website charge data of formal database to carry out predicting travel time between the highway website, and with other monitor datas to revising hourage;
Embodiment 2
Present embodiment utilizes the existing freeway information management system of highway information center to adopt and a kind ofly predicts hourage based on data mining technology and Kalman filtering algorithm, and a kind of hourage of the system by special-purpose travel time sign issuing time information, this system can be the public information hourage between any two gateways of highway is provided.Having solved the traffic that can only obtain highway in the prior art but obtains less than data and the problem of unpredictable and issue highway hourage hourage accurately.
Highway system prediction hourage sees also Fig. 5, is made of data pick-up subsystem, predicting travel time subsystem, information issue subsystem three parts.
The data pick-up subsystem extracts desired data to the ephemeral data banked cache from external system, carries out data cleansing and repairing through data conversion module, and the data after the conversion are saved in the formal database of system by the data loading module.The data that external system extracts comprise toll plaza MTC, the ETC track data on flows of highway information charging system, supervisory system special weather and traffic event data, outfield vehicle checker data on flows etc.
As shown in Figure 6, traffic data in the formal database of predicting travel time subsystem utilization carries out computation processes such as predicting travel time, the calculating of square queuing index, parameter correction, for the external call system provides real-time hourage, the result of calculation of the current index in square.Be main prediction hourage with the charge data, revise for assisting with monitor data, section Monitoring Data.
Information issue subsystem responsible is externally issued predicting travel time subsystem result of calculation, and hourage, published method was mainly outfield travel time sign equipment, and current index is issued to administration and supervision authorities by inner WEB.
Step 1: data pick-up: extraction mode: utilize the ELT instrument to adopt the increment extraction mode, think 7 * 24 hours and carry out the extraction task for the cycle is uninterrupted, the cycle of at every turn extracting new data is 2 minutes.
Step 2: the predicting travel time subsystem is by each site parameter information of configuration high-speed highway, realize that simultaneously charge data, section detect the real-time extraction of data and monitor data, analyze and handle, and with data importing Kalman filtering algorithm model, but hourage between real-time estimate highway station.This subsystem mainly comprises manual intervention, raw data analysis and processing and calculates three modules hourage.
As shown in Figure 7, current index computing subsystem is gathered and analyzing and processing the queueing message data on square by reading the essential information of toll plaza, and the gained data calculate the index that blocks up of square specific period by exponentiation algorithm.This subsystem comprises that raw data acquisition, current index calculate and three modules of information issue.
Step 3: information issue hourage: mainly by the travel time sign issue, travel time sign has two kinds: fastlink travel time sign such as Fig. 3 and toll plaza travel time sign such as Fig. 4.The fastlink travel time sign is installed on charge station's Entrance ramp and main road intersection, the toll plaza travel time sign is installed on the charge station porch, travel time sign can show the hourage apart from following three websites simultaneously, and release cycle is elected 10 minutes as.Time figure display screen on the special-purpose travel time sign can show green, yellow, red prompting information of road condition respectively according to the congestion in road situation
In sum, the embodiment of the invention has realized following beneficial effect:
Data mining theories being introduced in the operation of highway tolling system, proposed in predicting travel time based on based on charge data, is the new approaches that the thinking of assisting is predicted and realized with the supervisory system data.
Utilize the existing information management system through the mode of data mining, greatly reduce the cost of traditional technique in measuring device detection method.
With charge data prediction, with the bigger raising of the mode of supervisory system data check the predicting travel time precision.
Obviously, those skilled in the art should be understood that, above-mentioned each module of the present invention or each step can realize with the general calculation device, they can concentrate on the single calculation element, perhaps be distributed on the network that a plurality of calculation elements form, alternatively, they can be realized with the executable program code of calculation element, thereby, they can be stored in the memory storage and be carried out by calculation element, and in some cases, can carry out step shown or that describe with the order that is different from herein, perhaps they are made into each integrated circuit modules respectively, perhaps a plurality of modules in them or step are made into the single integrated circuit module and realize.Like this, the present invention is not restricted to any specific hardware and software combination.
Be the preferred embodiments of the present invention only below, be not limited to the present invention, for a person skilled in the art, the present invention can have various changes and variation.Within the spirit and principles in the present invention all, any modification of doing, be equal to replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (10)
1. the acquisition methods of a hourage is characterized in that, comprising:
Obtain designated parameter from the transit equipment monitoring terminal of highway, wherein, described designated parameter comprises: the charge data of highroad toll collection system, other data except described charge data;
Obtain hourage between described freeway toll station point according to default model prediction hourage of described charge data utilization;
According to the described monitor data that utilizes to carrying out the verification correction hourage that hourage, model was predicted;
Output verification revised hourage.
2. method according to claim 1 is characterized in that, obtains designated parameter according to predetermined period from the transit equipment monitoring terminal of highway, comprising:
Obtain the data that all or part transit equipment monitoring terminal of highway collects in real time;
From the described data of obtaining, extract described designated parameter according to predetermined period.
3. method according to claim 1 is characterized in that, described hourage, model comprised: based on model hourage of Kalman filtering algorithm.
4. method according to claim 1 is characterized in that, described other data comprise following one of at least:
The toll plaza data on flows of highroad toll collection system, E-payment system ETC track data on flows, weather and/or traffic event data, outfield vehicle checker data on flows.
5. according to each described method of claim 1 to 4, it is characterized in that, utilize described hourage model to carrying out the verification correction described hourage according to described other data, also comprise:
Obtain in described other data through the result of calculation corresponding with described other data after described hourage, model was handled;
According to described result of calculation to carrying out the verification correction described hourage.
6. method according to claim 5 is characterized in that, also comprises:
Export described result of calculation.
7. the deriving means of a hourage is characterized in that, comprising:
Acquisition module is used for obtaining designated parameter from the transit equipment monitoring terminal of highway, and wherein, described designated parameter comprises: the charge data of highroad toll collection system, other data except described charge data;
Prediction module is for the hourage that obtains according to default model prediction hourage of described charge data utilization between described freeway toll station point;
Correcting module is used for utilizing described hourage model to carrying out the verification correction described hourage according to described other data;
Output module is used for output verification revised hourage.
8. device according to claim 7 is characterized in that, described acquisition module comprises:
Acquiring unit is used for obtaining in real time the data that all or part transit equipment monitoring terminal of highway collects;
Extracting unit is used for extracting described designated parameter from the described data of obtaining according to predetermined period.
9. device according to claim 7 is characterized in that, described acquisition module is used for comprising in described other data following one of at least the time, obtains described designated parameter:
The toll plaza data on flows of highroad toll collection system, E-payment system ETC track data on flows, weather and/or traffic event data, outfield vehicle checker data on flows.
10. the prognoses system of a hourage is characterized in that, comprising:
The data pick-up subsystem is used for extracting desired data from external system and is saved in the formal database of system after the ephemeral data banked cache is handled;
The predicting travel time subsystem is used for utilizing the highway website charge data of described formal database to carry out predicting travel time between the highway website, and with other monitor datas to revising described hourage;
Information issue subsystem is used for showing the hourage that predicts.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104900061A (en) * | 2015-05-29 | 2015-09-09 | 内蒙古工业大学 | Road section travel time monitoring method and device |
| CN112820103A (en) * | 2020-12-31 | 2021-05-18 | 山东奥邦交通设施工程有限公司 | Integrated dynamic and static combined marker management and control method and system |
| CN113345220A (en) * | 2021-05-11 | 2021-09-03 | 山西省交通科技研发有限公司 | Highway inspection vehicle tracking and prediction analysis system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002216192A (en) * | 2001-01-16 | 2002-08-02 | Toshiba Corp | Variable toll accounting system and storage medium |
| CN1434946A (en) * | 2000-07-21 | 2003-08-06 | 交通预测.Com公司 | Method of providing travel time predictions |
| CN101794512A (en) * | 2009-12-30 | 2010-08-04 | 北京世纪高通科技有限公司 | Travel time predicting method and device |
| US20100273508A1 (en) * | 2007-12-20 | 2010-10-28 | Telecom Italia S.P.A. | Method and System for Forecasting Travel Times on Roads |
| CN102081846A (en) * | 2011-02-22 | 2011-06-01 | 交通运输部公路科学研究所 | Expressway charge data track matching based traffic state recognition method |
| EP2447924A1 (en) * | 2010-10-29 | 2012-05-02 | Siemens Aktiengesellschaft | System for detecting the traffic situation on a stretch of road |
| CN102800198A (en) * | 2012-08-15 | 2012-11-28 | 重庆大学 | Measuring and calculating method for traffic flow of section of expressway |
-
2013
- 2013-06-08 CN CN201310227307.0A patent/CN103280109B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1434946A (en) * | 2000-07-21 | 2003-08-06 | 交通预测.Com公司 | Method of providing travel time predictions |
| JP2002216192A (en) * | 2001-01-16 | 2002-08-02 | Toshiba Corp | Variable toll accounting system and storage medium |
| US20100273508A1 (en) * | 2007-12-20 | 2010-10-28 | Telecom Italia S.P.A. | Method and System for Forecasting Travel Times on Roads |
| CN101794512A (en) * | 2009-12-30 | 2010-08-04 | 北京世纪高通科技有限公司 | Travel time predicting method and device |
| EP2447924A1 (en) * | 2010-10-29 | 2012-05-02 | Siemens Aktiengesellschaft | System for detecting the traffic situation on a stretch of road |
| CN102081846A (en) * | 2011-02-22 | 2011-06-01 | 交通运输部公路科学研究所 | Expressway charge data track matching based traffic state recognition method |
| CN102800198A (en) * | 2012-08-15 | 2012-11-28 | 重庆大学 | Measuring and calculating method for traffic flow of section of expressway |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104900061A (en) * | 2015-05-29 | 2015-09-09 | 内蒙古工业大学 | Road section travel time monitoring method and device |
| CN112820103A (en) * | 2020-12-31 | 2021-05-18 | 山东奥邦交通设施工程有限公司 | Integrated dynamic and static combined marker management and control method and system |
| CN113345220A (en) * | 2021-05-11 | 2021-09-03 | 山西省交通科技研发有限公司 | Highway inspection vehicle tracking and prediction analysis system |
| CN113345220B (en) * | 2021-05-11 | 2022-08-16 | 山西省交通科技研发有限公司 | Highway inspection vehicle tracking and prediction analysis system |
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