Prediction method of traffic parameters

• US 5,822,712 A
• Filed: 09/29/1997
• Issued: 10/13/1998
• Est. Priority Date: 11/19/1992
• Status: Expired due to Fees

• Current Assignee: Kjell Olsson
• Original Assignee: Kjell Olsson
• Inventors: Olsson, Kjell
• Primary Examiner(s): Chin, Gary
• Application Number:
  US08/939,580
• Time in Patent Office: 379 Days
• Field of Search: 701/117, 701/118, 701/119, 340/933, 340/934, 340/936, 340/937, 340/942
• US Class Current: 701/117
• CPC Class Codes: G08G 1/0104 Measuring and analyzing of parameters relative to traffic conditions
• CPC Class Codes: G08G 1/08 according to detected number or speed of vehicles

Abstract

The invention relates to a method for predicting the traffic flow in a road network. Sensors in the road network register the passage of vehicles and two of the parameters, flow, density, speed enable all three parameters to be calculated. The correlation between the traffic at a point X at a certain time and the traffic at another point Y some period τ later can in certain cases and under certain conditions provide good values. In these cases, the traffic can also be predicted with good precision. The invention utilizes this fact and relates the prediction factor to the correlation coefficient. The invention also uses the methods to divide a traffic parameter into various frequency components to be used in various situations and improves the prediction by using the corresponding prediction factor for the corresponding frequency components of the traffic parameters. For the prediction, sensor information from different links is used in some cases to provide a quicker and more effective prediction by means of cooperation. The method for providing this cooperating also belongs to the invention. In certain sensor-lean situations, the prediction factor described previously is supplemented with a propagation factor W that describes the traffic changes along a traffic link, and where W can be defined and adapted to the various frequency components of a traffic parameter.

Claims

1. For a traffic system having routes formed by links, said links in combination forming a link network, a method for predicting a time-dependent value of a first traffic parameter at location Y in said system at time t from at least one time-dependent value of a second traffic parameter at location X in said system, the method predicting the traffic parameter at said location Y at said time t as a function of the traffic parameter at said location X at a time τ

earlier than time t;

the method comprising the steps of;

(a) deploying various sensors at the measuring sites, each of the sensors generating a raw signal measuring traffic parameter at said location X as a function of said time τ

;

(b) filtering each raw signal through a lower frequency band-pass filter to obtain a respective low-frequency filtered signal from the associated raw signal, and

(c) filtering each raw signal through a higher frequency band-pass filter to obtain a respective high-frequency filtered signal from the associated raw signal, and

(d) from the low-frequency filtered signals, selectively calculating the traffic parameter at said location Y at said time t while predicting traffic at one of said routes from traffic on at least one other of said routes; and

(e) from the high-frequency filtered signals, calculating the traffic parameter at said location Y at said time t while predicting near time traffic variation along a selected one of said routes.

2. For traffic management, information and control in a traffic system having a plurality of routes formed by links, said links in combination forming a link network, a method for determining values of traffic parameters utilizing sensor information obtained from sensors at different measurement sites in said link network, wherein a number of the sensors produce measurement values, from which are obtained at least two traffic parameters selected from the group consisting of traffic flow, traffic density and vehicle speed or alternatively link travel time, the method comprising:

predicting a time-dependent value of first traffic parameter Y, at a time t, from at least one time-dependent value of a second traffic parameter X, at a time t-τ

;

the first parameter Y and the second parameter X being selected from the parameter group consisting of traffic flow I, traffic densities, vehicle speed v, travel time and products and quotients thereof;

said sensors generating measurement values, from which said first parameter Y and said second parameter X are obtained as functions of time;

filtering a filtrant selected from the group consisting of said measurement values and values derived from said measurement values, obtained from a number of the sensors, through a frequency filtering process and thereby separating time-dependent variations of said filtrant into at least two frequency regions including first frequency region components exhibiting a first time variation and second frequency region components exhibiting a second time variation, said first time variation being faster than said second time variation, and from said first frequency region components and said second frequency region components obtaining at least two filtered components selected from the group consisting of high-frequency X components which comprise high-frequency components of said second parameter X, and high-frequency Y components which comprise high-frequency components of said first parameter Y, said high-frequency X components and said high-frequency Y components being obtained from said first frequency region components, and low-frequency X components which comprise low-frequency components of said second parameter X, and low-frequency Y components which comprise low-frequency components of said first parameter Y, said low-frequency X components and said low-frequency Y components being obtained from said second frequency region components, wherein the selected combinations of said filtered components each exhibit a covariance, respectively;

calculating a number of prediction factors employing covariance inherent factors for the selected combinations of said filtered components;

(a1) predicting near time traffic parameters on a first route of said plurality of routes, using said high-frequency X components on said first route and said calculated prediction factors from said selected combinations of high-frequency X components and said high-frequency Y components, on said first route to predict future high-frequency Y components on said first route; and

selecting predicting future low-frequency Y components for at least one of (a2), (b), (c) and (d);

(a2) combining the predicted future high-frequency Y components from (a1) with selected low-frequency Y components;

(b) predicting future low-frequency Y components on one of said routes, using the low-frequency X components from at least one other of said routes, and said calculated prediction factor from the said selected combination of low-frequency Y components on said one of said routes and the low-frequency X components from said at least one other of said routes;

(c) predicting future low-frequency Y components from first average values of said second parameter X, where the said first average values are averages over time periods equivalent to low-frequency time periods of said low-frequency regions;

(d) predicting future low-frequency Y components from said second average values of said second parameter X, where said second average values are obtained from said first average values, representing a selected time period of the day, by averaging the values of said time period of the day for more than one day, the second averages being referred to as historical average values.

3. The method according to claim 2, further comprising

(a) updating stored average values of said second parameter X, said first parameter Y and said prediction factors by the steps of:

storing an historical average value X_H of said second parameter X and an historical average value Y_H, of said first parameter and the prediction factors in a data storage;

collecting the historical average values X_H and Y_H from the data storage;

generating updated values of said average values X_H and Y_H and said prediction factors by calculating new average values X_H and Y_H including at least one new second parameter X and at least one new first parameter Y obtained from new measured values from said sensors;

storing the updated values of said average values X_H and Y_H and said prediction factors in the data storage; and

(b) predicting deviations from the historical average value Y_H by the steps of;

calculating a deviation dX between at least on current value X obtained from the sensors and said average value X_H and a deviation of dY between at least one current value of said first parameter Y obtained from the sensors and said average value Y_H ;

calculating values of the new prediction factors from said deviations dY and dX;

updating and storing the new prediction factors;

predicting a future value of said deviation dY from said deviation dX and the new prediction factors;

predicting a future value of the first parameter Y by combining Y_H and the predicted future value of the deviation dY.

4. The method according to claim 2, further comprising at least one step selected from the group consisting of:

determining a prediction factor Γ

directly as an expected value for a product of said second parameter X and said first parameter Y in relation to the expected value for a square of said second parameter X;

determining a prediction factor Γ

for deviations of said first parameter Y from a mean value related to deviations of said second parameter X from the mean value, said prediction factor Γ

being equal to β

*σ

_y σ

_x, wherein σ

_x and σ

_y are standard deviations for said second parameter X and said first parameter Y, respectively, and β

is the correlation coefficient; and

calculating a prediction factor Γ

for a time derivative of said first parameter Y related to a time derivative of said second parameter X, by replacing said second parameter X with a first derivative thereof with respect to time and replacing said first parameter Y with a first derivative thereof with respect to time in all steps; and

combining the prediction factors obtained using said first derivatives with respect to time with the prediction factors obtained using said second parameter X and said first parameter Y.

5. The method according to claim 2, further comprising the steps of:

obtaining and storing an historical average X_H (0) of values of said second parameter X;

combining new values of said second parameter X, obtained from said sensors with said historical average X_H (0) to obtain an updated historical average X_H (1) according to an equation X_H (1)=X_H (0)+(X-X_H (0))/k, where k is a constant which determines sensitivity to changes;

storing said updated historical average X_H (0); and

successively applying said equation for successively updating and replacing X_H (0).

6. The method according to claim 2, further comprising the step of:

predicting a Y-value of the first parameter Y from selected separate X-values of the second parameter X, by calculating and updating the separate X, Y prediction factors one by one, before combining the separate predictions of Y from each of selected, separate values of said second parameter X, respectively predicting a future value of said first parameter Y, and thereby obtaining a plurality of separate predicted values of said first parameter Y, by calculating and updating separate ones of said prediction factors individually, and subsequently combining said plurality of predicted values of said first parameter Y to obtain a final prediction of said first parameter Y.

7. The method according to claim 2, further comprising the steps of:

determining at least one of said prediction factors and at time shift τ

between respective values of said second parameter X and said first parameter Y, when the correlation coefficient is at a maximum; and

relating the time shift τ

, when said second parameter X and said first parameter Y are defined at respective different selected positions along one route in said plurality of routes, to a time different selected positions along one route time estimate for travel between said selected positions.

8. The method according to claim 2, further comprising the steps of:

determining at least one of said prediction factors and a time shift τ

between respective values of said second parameter X and said first parameter Y, when the correlation coefficient is at a maximum; and

relating the time shift τ

, when said second parameter X and said first parameter Y are defined respectively at selected positions on different routes in said plurality of routes, to a time difference comprising a traffic variation time difference estimate for traffic variations between said selected positions.

9. The method according to claim 2, further comprising:

calculating a first propagation function W(z,t) for a value X(z,t) of said second parameter X from measured values of sensors separated in a traffic propagation direction by a distance z and by a time t according to W(z,t)=X(z,t)/X(0,0), where X(0,0) is a starting value at z=0 and t=0;

defining a second propagation function W2=f₁ (t)*f₂ (z-v*t) as a product of a time dependent function f₁, and a separate traffic propagation dependent function f₂, where growth and decay of W along the propagation direction is described by f₁, and the traffic propagation with a velocity v is described by f₂ ;

approximating W2 to W(z,t), including adapting f₁ to said measurement values, by using one of a least square method or by approximating he growth or decay of the measured values by a linear function, based on (1+α

t), which is a small scale linearization |α

t|<

1, of an exponential large scale function, exp (α

t), which is used for large |α

t|;

using W2=f₁ *f₂ to predict the traffic parameter X(z,t) along a route from said starting value X(0,0), according to X(z,t)=W2*X(0,0);

updating W2 to new traffic situations on selected routes by calibrating f₁ and f₂ against measurements from the sensors on said selected routes;

selectively calculating a third propagation function W3, obtained for a selected case where W3=W2, when adapted to measurements from sensors at several different routes, and using W3 for predicting traffic parameters along any of said plurality of routes where direct sensor data from the sensors are missing;

selecting using W2 for predicting variations in traffic parameter values during high traffic flows, close to maximum values, and congestion conditions.

10. The method according to claim 2, further comprising:

predicting the first parameter Y for a selected link according to the sensors on a selected number of different links including a link on the same route as the selected link, and a link on another routed identified as a sister route.

11. The method according to claim 2, further comprising:

obtaining a separate value XX of the second parameter X from each of a number of the sensors to obtain a plurality of values XX;

respectively predicting a separate prediction value YY of the first parameter Y from each separate XX-value to obtain a plurality of prediction values YY;

combining the prediction values YY to form a single prediction value of said first parameter Y at a selected position of said first parameter Y, in conditions where mutual correlations between the separate XX values;

applying weighting factors to the prediction values YY, in combining the prediction values YY to form said single prediction value of said first parameter Y;

relating the weighting factors to squares of signal-to-noise ratios, (S/N)², for the respective prediction values YY, with N being a difference between values of said first parameter Y obtained from the sensors in reality and the prediction values YY for a same position and time;

selectively approximately (S/N)² with R₁ *β

² /(1-β

²), where β

is a separate correlation factor for said first parameter Y and the separate value XX from which it was predicted, and R₁ is a correction factor dependent on noise referred to the separate values XX, with R₁ =1, when noise is referred to only to the prediction values YY.

12. The method according to claim 2, further comprising the steps of:

identifying routes among said plurality of routes as being sister routes and comparing respective predictions at said sister routes;

identifying at least one of said sister routes on which said predictions significantly differ from said predictions on other sister routes, indicating an unusual traffic situation on said at least one of said sister routes; and

executing at least one of;

triggering detection alarms;

triggering predetermined activities;

predicting the unusual traffic situation in the said at least one of said sister routes, by predicting the first parameter Y from the second parameter X on said at least one of said sister routes.

13. The method according to claim 2, comprising the step of predicting from the second parameter X the first parameter Y at a same link or spot as a link or spot where said second parameter X is defined.

14. The method according to claim 2, further comprising the steps of:

calculating a growth function U(t) including

at least one growth factor selected from the group consisting of exponential exp t/τ and

linear t/τ

growth factors from measurements on growth or decay of traffic parameters at selected parts of the traffic system;

measuring respective time constants τ

for said growth factors under different circumstances and for different ratios of I/C values, where I is a flow value related to C and C is capacity values for I comprising a maximum possible flow value for I;

using the time constants τ

for predicting a traffic course produced by traffic-affecting events selected from the group of events consisting of a traffic jam, an accident and a public gathering, each of said events having a growth factor (U(t) associated therewith;

predicting a traffic course produced by a new occurrence of an event in said group, by means of interpolating or extrapolating U(t) for one of said events in said group to said new occurrence of an event;

updating and storing U(t) for at least one new occurrence of an event as said at least one new occurrence of an event occurs.

15. The method according to claim 2, further comprising the steps of:

identifying a nearest entrance link upstream of a selected link among said links;

using said traffic parameters determining conditions for traffic on the selected link reaching a capacity value;

determining if the entrance link implies a narrower section for the traffic flow than the selected link;

determining a risk of traffic jam dependent on at least one of connecting flows, and vehicles from different connecting flows weaving by a common flow into the selected link;

analyzing effects of said traffic jam at the selected link, on traffic at neighboring upstream and downstream links; and

predicting the traffic parameters downstream of the traffic jam.

16. The method according to claim 2, further comprising the steps of:

controlling traffic by executing traffic control actions dependent on a first prediction of said traffic parameters, and, making a second prediction of a response to said traffic control actions, using stored results from earlier events, when said traffic control actions were performed;

calculating a correlation between each selected predicted response to said traffic control actions and actually measured traffic parameters from the sensors occurring as a result of said traffic control actions;

updating and storing a list of selected responses selected from the group consisting of predicted responses, measured responses, and combinations of predicted responses and measured responses to respective traffic control actions including values on prediction accuracy dependent on said correlation;

updating and storing average values selectively combined with variances of the responses and relations among responses for the respective traffic control actions;

using the list of responses related to the respective traffic control actions for implementation in future traffic situations.

17. The method according to claim 2, further comprising the steps of:

predicting said traffic parameters, when a short time incident partly or totally blocks a link, and where the incident can be reported by means of external sources or detected by sensors;

using sensors including at least one of a sensor downstream of the incident and a sensor upstream of the incident;

indicating a possible incident by the downstream sensor presenting a relative abrupt decreased of traffic flow;

estimating a new incident reduced capacity value indicative of traffic flow maximum from new traffic flow measures;

identifying exit routes upstream and entrance routes downstream of the incident within a limited local area around an incident location;

determining exits and entrances for alternative routes around the link of the incident;

ordering said alternative routes by means of valuating a cost function for each route, including selective costs of travel-time, route length, and road-size;

predicting a traffic distribution according to a principle of filling a first best alternative route with traffic until the costs are increasing due to heavy traffic or queues, whereafter a second best alternative route also is filled with traffic until the costs for said second best alternative route are increasing;

successively repeating said principle, for further alternative routes in order by, increasing the traffic successively on each of said alternative routes, while balancing the costs of traffic at the same level for the different alternative routes;

making measurements of traffic to obtain actual traffic distributions on the alternative routes; and

updating the traffic predictions along the alternative routes according to the traffic measurements.

18. The method according to claim 17, wherein the step of ordering said alternative routes further comprises directly choosing measurements from the alternative routes for the new traffic distribution prediction.

19. The method according to claim 2, further comprising the steps of:

calculating values of said first high frequency X to component for values of said second parameter X, obtained from measurements by said sensors on a selected link and on each of connected downstream alternative links;

correlating said calculated values of said high frequency X component from the selected link, with the respective calculated values of said high frequency X component, and thereby obtaining correlation values, from the downstream alternative links to determine a first traffic distribution at the different downstream alternative links due to the traffic at the selected link; and

predicting a second traffic distribution, subsequent to said first distribution using said correlation values.

20. The method according to claim 2, further comprising using said high frequency Y component in the steps of:

determining a traffic distribution from a selected link to a number of downstream alternative routes, by using a correlation between the selected link and each of said alternative routes;

selectively storing and using said traffic distribution for rerouting traffic at incidents;

defining alternative routes matching an existing traffic distribution downstream of an incident; and

predicting initially a traffic distribution on each of said alternative routes.

21. The method according to claim 2, further comprising the steps of:

providing traffic prediction for a selected one of the links from measurement values from another one of the links;

calculating a predicting using at least one of said low frequency X component and said low frequency Y component traffic parameter;

adding first frequency components I₂ (I₀,C) obtained from second values of traffic parameter relations, where I₂ is a first frequency traffic flow component, I₀ is a second frequency traffic flow component and C is a capacity value, equal to a maximum flow value;

determining I₂ by at least one of;

obtaining I₂ from measurements of traffic parameters on the selected link; and

estimating I₂ from standard deviations, σ

, of traffic variations from the I₀ value during time periods T₂, where T₂ is related to the period of the first frequency component;

providing predicted traffic flow values I, where I is a combination of said traffic flow values I₀ and I₂ ; and

judging risks for traffic congestions by comparing the predicted traffic flow values with criteria for traffic jam.

22. The method according to claim 2 further comprising:

estimating a prediction accuracy of at least some of said prediction factors, in terms of a correlation factor, using the medium of said covariance in calculating the correlation factor associated with the selected combination of filtered X and Y components with the time difference τ

, inherent in the covariance, being a correlation time.

23. The method according to claim 2, further comprising:

predicting traffic Y at a selected sub-area of the link network from measured traffic X in at least one neighboring sub-area;

selecting a number of selected sensors at various measuring sites in the said neighboring sub-area;

calculating prediction factors for selected X, Y combinations, related to the said selected sensors;

predicting Y from selected values of X and respective said prediction factors;

combining the predictions of Y from the selected values of X.

24. A method as claimed in claim 2 wherein each of said steps (a1), (a2), (b), (c) and (d) produces a respective prediction result, and said method comprising the additional step of combining at least two of said respective results to obtain a final prediction result.

25. A method as claimed in claim 2 comprising the additional steps of:

obtaining a least two different respective sets of said second parameter X using measurement values respectively from at least two different one of said sensors; and

predicting said first parameter Y by combining said sets of said second parameter X.

26. A method as claimed in claim 25 wherein each combination of said sets of said second parameter X produces a prediction of said first parameter Y, and comprising the additional step of producing a final prediction of said first parameter Y by combining the predictions of said first parameter Y respectively obtained using said respective sets of said second parameter X.

27. A method as claimed in claim 2 comprising the additional steps of:

obtaining at least two different respective sets of said second parameter X using measurement values respectively from at least two different one of said sensors at respective different measuring sites; and

predicting said first parameter Y by combining said sets of said second parameter X.

28. A method as claimed in claim 2 wherein each of said steps (a2), (b), (c) and (d) produces a predicted low frequency Y component, and comprising the additional step of producing a final prediction of said low frequency Y component by combining at least two of said predicted low frequency Y components.

29. A method as claimed in claim 2 comprising the additional step of producing a final predicted high frequency Y component by combining at least two predictions of high frequency Y components obtained respectively using sets of said second parameter X obtained from different ones of said sensors.

30. A method as claimed in claim 2 comprising additional step of predicting a plurality of different sets of said first parameter Y respectively representing different localizations of said link network, using a single value of said second parameter X.

31. A method as claimed in claim 2 comprising the additional step of predicting respective values of said first parameter Y, respectively representing at least two of said links, by combining predictions of said first parameter Y obtained for each of said at least two of said links.