CN102737502A - Method for predicting road traffic flow based on global positioning system (GPS) data - Google Patents
Method for predicting road traffic flow based on global positioning system (GPS) data Download PDFInfo
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Abstract
The invention belongs to the technical field of traffic management and relates to a method for predicting road traffic flow based on global positioning system (GPS) data. The method comprises the following steps of: dividing time during which the traffic flow is required to be predicted and monitored in a day into different periods of time; in each observation period of time, monitoring the positions of taxis loaded with vehicle-mounted GPSs to acquire GPS position data in each period of time, monitoring road traffic flow data in each period of time, and counting; calculating the speed corresponding to the position of each vehicle in the GPS position data at each historical moment and the average speed of all vehicles on the whole main road; and monitoring real-time position data of each vehicle in each period of time in real time, calculating the instantaneous speed of each vehicle and the average traffic flow speed of the road, predicting future traffic flow data, and judging whether traffic jams occur or not. By the method, the capital and manpower investment of road detection equipment can be reduced, and a traffic congestion problem can be solved in an auxiliary mode.
Description
Technical field
The invention belongs to the traffic management technology field, relate to a kind of traffic flow forecasting method.
Background technology
Current, along with society and rapid development of economy, traffic congestion becomes the serious problems of restriction social development, is the puzzlement of citizens' activities, also is the difficult problem that traffic control department of government carries out traffic administration.Simultaneously, when Emergent Public Events took place, traffic congestion also can have a strong impact on the response speed of emergency personnel to accident, causes many unnecessary economic losses.Therefore, can in time dope the magnitude of traffic flow efficiently through effective means, be the effective means that transport solution blocks up.Intensive checkout equipment is installed is certainly effective method the most on road, but the input of its fund and manpower is very huge, obviously its operability is poor.
Summary of the invention
The objective of the invention is to overcome the above-mentioned deficiency of prior art, providing a kind of can in time predict and the monitoring and controlling traffic method of flow efficiently.Technical scheme of the present invention is following:
A kind of road traffic flow Forecasting Methodology based on gps data is used for certain road traffic delay prediction of major urban arterial highway, and this method may further comprise the steps:
1) time that need carry out forecasting traffic flow and monitoring in one day is divided into the different time section;
2) in the observation stage, for each period of being observed, monitor through position to the taxi that is mounted with vehicle GPS, obtain the GPS position data of each time period, and monitoring and add up the road traffic flow data on each time period;
3) calculate the GPS position data in speed and the average velocity of all cars on the whole piece major trunk roads of pairing each historical juncture of position of each car;
4) at forecast period, monitor the real time position data of each car of each time period in real time, calculate the average wagon flow speed of the instantaneous velocity and the road of each car;
5) data of utilizing step 4 to calculate; Predict the traffic flow data constantly in future in this highway section; If dope the predicted value G (N) of the speed on the major trunk roads of following a certain moment N; If predicted value G (N) is very little,, judge that then this highway at moment N a large amount of blocking up will take place less than the value of setting.
As preferred implementation, in the step 4), utilize the wagon flow speed in following certain this highway section constantly of method prediction that returns and the average velocity of driving, obtain following certain traffic flow data constantly.
The present invention utilizes observation data to obtain the real-time GPS position data of a part vehicle on the highway section, utilizes the method that returns to predict the wagon flow speed of following certain highway constantly and the average velocity of driving, and then judges the jam situation of road.Method of the present invention can reduce the fund of Road Detection equipment and the input of manpower, for realizing social concerns such as municipal intelligent traffic, transport solution block up effective solution is provided.
Embodiment
In city with certain vehicle scale, utilize the collection real time position data of GPS equipment to carry out forecasting traffic flow, be one of important channel that solves the prediction magnitude of traffic flow.The present invention utilizes data in mobile phone prediction and monitoring road traffic flow.
Traffic flow forecasting method of the present invention is divided into two stages, and the first step is the observation stage, and second step was the real-time estimate stage.
The present invention is at first with needing in one day the time of prediction to be divided into each time period, such as from 6 of mornings to point in evenings 10, be divided into 80 time periods according to the number of minutes.In the observation stage,, collect respectively and add up by each time period with the position of the vehicle of this road and the sum of vehicle.And,, calculate average speeds according to the average wagon flow speed of each time period and the instantaneous velocity of each car at forecast period, method is following:
G(n)=K(n|(n-1))C
T[C(n)K(n|n-1)C
T+R(n)]
-1
a(n)=Y(n)-C(n)X(n|n-1)
K(n|n-1)=F(n,n-1)K(n-1，n-1)F
T(n,n-1)+Q(n-1)
K(n?n)=(I-G(n)C(n))K(n，n-1)
X(n+1，n)=F(n+1,n)X(n，n)
Y(n+1，n)=C(n+1)X(n+1，n)
Y(n，n)=C(n)X(n，n)
With symbol description in the following formula:
X (n+1|n)---represent given observed reading;
Y (1), Y (2) ..., Y (n) estimates in the n+1 prediction of state constantly.
G (n)---filter gain matrix;
F (n|n1)---be carved into n transition matrix constantly during from n1;
K (n|n1)---X (n+1, n) correlation matrix of middle error;
C (n)---n measurement matrix constantly;
Q (n)---be the correlation matrix of process noise;
R (n)---measure the correlation matrix of noise;
Y (n+1|n)---n+1 prediction estimated value constantly.
Regression coefficient in the equation is time dependent, and prediction is whenever extended forward a step, all will predict the outcome and observed result compares, and its difference (predicated error) will feed back to by rights in the equation of change of regression coefficient and go.Come in time to revise predictive equation through the feedback information that utilizes the previous moment predicated error, to improve next precision of prediction constantly.
When carrying out real-time estimate, in real time, dope the speed of any time moment in future with above-mentioned regression algorithm minute be the real time position of each vehicle of unit record; Ask the mean value Y1 (n of average velocity in the following a certain moment of all vehicle; N), obtain the mean value Y2 (N) of all vehicles speed of a certain moment, utilize above-mentioned regression algorithm calculate following certain constantly average velocity Y2 (m; N); Through weighted calculation go out final predicted value Y (N)=[mY2 (and n, N)+nY (m, N)]/(m+n)
Through the current mobile communication network data that gets access to is carried out Data Receiving, computing, data analysis step, can draw current road traffic condition information.Obtain Y (N) through said process,, avoid traffic congestion if Y (N) less than certain value then judge that this highway at moment N a large amount of blocking up can take place, should pay close attention to and handle.
Claims (2)
1. the road traffic flow Forecasting Methodology based on gps data is used for the road traffic delay prediction of major urban arterial highway, and this method may further comprise the steps:
1) time that need carry out forecasting traffic flow and monitoring in one day is divided into the different time section;
2) in the observation stage, for each period of being observed, monitor through position to the taxi that is mounted with vehicle GPS, obtain the GPS position data of each time period, and monitoring and add up the road traffic flow data on each time period;
3) calculate the GPS position data in speed and the average velocity of all cars on the whole piece major trunk roads of pairing each historical juncture of position of each car;
4) at forecast period, monitor the real time position data of each car of each time period in real time, calculate the average wagon flow speed of the instantaneous velocity and the road of each car;
5) data of utilizing step 4 to calculate; Predict the traffic flow data constantly in future in this highway section; If dope the predicted value G (N) of the speed on the major trunk roads of following a certain moment N; If predicted value G (N) is very little,, judge that then this highway at moment N a large amount of blocking up will take place less than the value of setting.
2. the road traffic flow Forecasting Methodology based on gps data according to claim 1 is characterized in that, utilizes the wagon flow speed in following certain this highway section constantly of method prediction that returns and the average velocity of driving, obtains following certain traffic flow data constantly.
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Cited By (8)
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| CN103116983A (en) * | 2013-01-17 | 2013-05-22 | 清华大学 | Method for obtaining traffic information of plug-in hybrid power bus routes |
| CN104408924A (en) * | 2014-12-04 | 2015-03-11 | 深圳北航新兴产业技术研究院 | Detection method for abnormal traffic flow of urban road based on coupled hidden markov model |
| CN104778834A (en) * | 2015-01-23 | 2015-07-15 | 哈尔滨工业大学 | Urban road traffic jam judging method based on vehicle GPS data |
| CN104933860A (en) * | 2015-05-20 | 2015-09-23 | 重庆大学 | GPS data-based prediction method for predicting traffic jam-resulted delay time of bus |
| CN105185107A (en) * | 2015-07-23 | 2015-12-23 | 合肥革绿信息科技有限公司 | GPS-based traffic running tendency prediction method |
| CN108961762A (en) * | 2018-08-24 | 2018-12-07 | 东北林业大学 | A kind of urban road traffic flow amount prediction technique based on multifactor fusion |
| CN110619748A (en) * | 2019-10-22 | 2019-12-27 | 江苏广宇协同科技发展研究院有限公司 | Traffic condition analysis and prediction method, device and system based on traffic big data |
| CN113345225A (en) * | 2021-05-24 | 2021-09-03 | 郑州航空工业管理学院 | Method and system for predicting real-time road conditions of front roads of logistics vehicles based on V2V communication |
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| CN108961762A (en) * | 2018-08-24 | 2018-12-07 | 东北林业大学 | A kind of urban road traffic flow amount prediction technique based on multifactor fusion |
| CN110619748A (en) * | 2019-10-22 | 2019-12-27 | 江苏广宇协同科技发展研究院有限公司 | Traffic condition analysis and prediction method, device and system based on traffic big data |
| CN113345225A (en) * | 2021-05-24 | 2021-09-03 | 郑州航空工业管理学院 | Method and system for predicting real-time road conditions of front roads of logistics vehicles based on V2V communication |
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Application publication date: 20121017 |