DETECTION AND PREDICTION OF TRAFFIC DISTURBANCES

Abstract: The invention relates to a method for detection and prediction of incidents and traffic queues formed by overloading. This is done in real time with use of sensors in a road network. Predictions are used also to reach a faster and more reliable detection. Sensor measurements are also used in the process, where the comparison with expected values are used for successively updating stored parameter values for the involved algorithms. By this, the system can succeedingly adapt itself for changed situations. The strong traffic variations, that are naturally occurring at short time intervals are treated with the use of noise-based methods. By this, there are formed distribution related measures as e.g. the standard deviation, which can be estimated from measurements, and submit a base for estimating probabilities for deviations of a certain size, e.g. related to the standard deviation. Automatic incident detection (AID) is based on determination of the desired false-alarm rate, and the related threshold level. The method includes accumulated measurements. Faster and more reliable incident detections are received with the use of the invented prediction process method.

Description: Detection and prediction of traffic disturbances. The present invention relates to a method for detection and prediction of disturbances in the road traffic, e g the forming of traffic queues depending on overloading the road-net or incidents. In traffic management systems an important task is to avoid overloading, where traffic breakdown is introducing queues with reduced passability, increased rise for accidents and increased environmental problems. Incidents should be detected early to be able to reduce the damages. The object is to get wounded people to hospitals, to reduce the secondary related accidents and to manage the traffic in such a way that no unnecessary blockings arise, but that the road-net will be efficiently utilized. A background for basic technologies is given in the Swedish patent 9203474-3. The present invention presupposes the existence of knowledge of that technology. To-day there is technology for detection of queues and incidents. Traditionally one has detected incidents using a method, which measures "occupancy" and possibly traffic flow or velocity and possibly differencies of the parameter between the the two recent time periods. Those measures have been studied at one sensor, and if the values put together according to any special algorithm have exceeded a threshold value, one has detected an incident. There are also examples, where one has used the method at two succeeding sensors and then has calculated the differences between the sensor values, before the values have been put together in an algorithm. The algorithms have been formed by "trial and error", i e one has tested and changed untill one has no longer got a lot of false alarms, at the same time as one has not missed detection of many real incidents. The drawbacks with those traditional methods compared to the present invention are the following: -1 A. Traditional methodology. The free-flow traffic over a sensor contains very large statistical variations, why measured values in one measuring period naturally might differ very much from the next period, i e without being a sign of any incident. At the use of two sensors (A and B), which are very close, one can understand the process as follows; if one during the same time period measures traffic at A and B and it takes a short time for traffic to move from A to B, there would be almost the same traffic measured at A and B, i e the parameters cannot change very much. If traffic anyhow have changed very much, that would be a sign of something unnormal, that an incident has occurred between A and B. If it is a longer distance between A and B, however, then one is measuring different traffic and then there are natural large variations. Since one wants to detect incidents as fast as possible, one wants to measure during a very short time period. At the same time one wants to keep a long distance between the sensors to keep a low number, saving costs. This is a dilemma for the function. -IB. The invention. In the present invention, prediction of traffic is used. As said above, the traffic at sensor B might vary much. If e g during one period, there is not a single car passing, although there were many cars passing during the period before, that might indicate that an incident has occurred, which prevents traffic to pass. But it can also be a natural gap in the traffic. If one by measuring traffic upstream, finds that there is a gap in the traffic, which will be measured later on at B, that can be predicted for B, - and then the measurement of 0 cars passing at B will not be a sign of an incident between A and B, but a confirmation that the traffic is as expected. This method increases the freedom to measure during short time periods, and since one predicts, one is not losing time. Directly after the measurement at B, differences between predicted and measured values are obtained and conclusive differencies indicate an incident. The distances between the sensors A and B can be increased too, and the number of sensors reduced. The requirement here is instead that there are possibilities to do reasonable predictions. Roughly speaking however,even weaker predictions should mean improvements, e g also if one couldn't predict exact 0 cars in the example above, anyhow a prediction of a reduction of flow would mean a less deviation from the measured value, than if one knows nothing, and by that one can reduce the rise for false alarms. - 2A. Traditional methods. Another drawback with the traditional methods are that they build very much on experimenting to find suitable algorithms with balanced parameters. It is very often difficult to understand why one way would be better than anyone of the others, and it is also difficult to prove. Neither does one know if it is possible to improve the method, by adding more tests and trimming. - 2B. The invention. In the present invention, the understanding of the traffic processes are utilized, and that in a direct way. An important part is the understanding of large traffic variations. Those can be regarded as results of stochastic processes, and if one measures e g flow at a sensor, then one experiences those as noise. Using short measurement periods, one obtaines relatively large variations around a given average. By utilizing knowledge about "noise", one can understand how to make use of the information in those "noise-variations", and not only regard those as something that destroy the possibilities to perform simple detections of incidents. By measurements one can get means and standard deviations and from theory and measurements one can create approximative distributive functions, i e one knows statistically rather much about the traffic variations. E g assuming a normal distribution, a measured standard deviation can give information about the probability for a variation being larger than a given value. One also obtains an understanding about what one does not know and a measure of the uncertainty. ( And that it is not of any use with never so long and complex parameterfilled algorithms, in accordance with the traditional methodology.) In the present invention the knowledge is utilized about probability for deviations of a certain order to set thresholds, which by that give the desired false alarm rate. It might also be that a deviation that originates from an incident is not large enough to exceed the threshold. Then one can wait untill the next measured deviation is received and examine if those two values together are that large that the probability requirement now is fulfilled, i e that one is now exceeding the corresponding threshold. This can be repeated successively, and if the natural variations are very large, the threshold will be large, and there might be required more incident- caused deviations for them to exceed their threshold. However time is running, the more measurement periods there are needed, and the incident detection should be fast to prevent serious secondary effects. In the invention e g the threshold can be automatically set in that way that a minimum of extra measurement periods need to be used. It is also by this reason important to keep the influence of the natural traffic variations at a low level, and that is done in the invention, as said above, by the process, where it is not the variations from an average or the former value, which is regarded, - but the much smaller deviation between the predicted and measured values, which is determining the threshold level. By that the threshold level can be reduced significantly without increased false alarm rate, and an incident-originated deviation is then more easily exceeding the threshold and the incident will be detected faster. - 3 A. Traditional methods. A third drawback with the traditional methodology is, that it is difficult to transfer from one situation, where it finally with trial and error, has been adapted to operation, to another situation. It might mean geographically, positions, eg transfer to another road section, where access-roads, intersections or number of lanes offer other traffic situations. It might mean changes of measuring time periods or other parameters. This effort can be very time-wasting and resource-consuming. One needs to observe the traffic in parallall with other accurate measuring means to get a key answer to compare with, giving possibility to change parameters in the algorithm towards better agreement with the reality. Also changes in the traffic situation might result in working through the process again, to get new better adapted parameters to put into the algorithm. - 3B. The invention. In the present invention much of the adaption can be done automatically. Besides that, the starting values can be well chosen from the origin. Independent of implementation, the topical deviations are measured, and the corresponding statistical measures are obtained, e g the standard deviation of the traffic deviations. Based on those measures the respective threshold values can be set automatically, and the method starts to generate incident detections, which the operator can observe are true or false. Since the method continuously measures the deviations, the statistical parameters can be successively updated and adapted to changes in the traffic situations. Traffic breakdown and queues. Overloading of the road-net, also if only for a short term traffic peak, is enough for generating traffic breakdown and queue build up. Those queues might then be maintained by a somewhat lower traffic flow, as the road-net capacity usually decreases by the queue-forming. E g if there is a traffic peak on the motorway at the same time as there arrives a traffic peak on the access- road, not offering space enough for all the cars, the cars have to break to increase their respective gaps during the trial to merge the two traffic flows. Then the velocity might be decreased to very low values with small gaps between the cars, resulting in a low traffic flow. In some cases it might be significantly lower than the maximum flow, obtainable at higher speeds, and which is regarded as the road capacity. Thus the result of overloading is queue-forming, which in principle reduce the road-net capacity. In the large cities this is occurring daily at rush-hours at mornings and afternoons, which implicates that the road-net is forced to its lowest capacity, when it is needed to be largest. So it is very valuable to be able to manage traffic in such a way that traffic breakdown is avoided. Especially important is preventing traffic breakdown at critical places, where queues give rise to serious disturbances on one or another of the large traffic arterials. Preventing that, might be one task for the invention. A key-function is prediction of traffic breakdown and queue-forming. By prediction a time- margin is obtained before the predicted problem really is happening. That time-margin can be used to implement actions, which prevent that the problem arise in the real world. Also in the detection process of queue-forming, it is interesting to utilize prediction. E g if free-flow is predicted and a queue anyhow forms, then the sensors offer values, showing the real traffic situation (queue). The deviations between the predicted free-flow values and the measured values can therefore be used as an indication on the forming of a queue. In this desciption of the invention, sometimes other words are used than "prediction", e g the word "expected". In general it is meant, that if a method contains a process, where a measured value shall be compared with another, e g earlier known, "corresponding value", then the notation of "corresponding value" often implies an association of a time direction of changed knowledge of the parameter, also if the value just have been obtained from historical values. In this paper therefore the notation "predict" is used including also estimations, that is not direct predictions, but is fulfilling a corresponding object. E g the comparison value might be a mean- value or a mean-value plus a value based on a standard deviation, historically estimated value etc. Independent of which way that has been used to get the comparison value, the object is anyhow that this value constitutes a type of expected comparison value, by which the measured value can reach criteria for detection of a queue. Hereby the expected value has got a forward- associated function towards the measured value, and might be estimated in an equivalent process of a prediction, also when the expected value is estimated afterwards, i e after that the the measured value has been obtained. A queue-detection according to the invention, can also be performed when queues are formed on links between sensors. This is also valid for the use of video-sensors, IR-sensors , radar and similar sensors, which e g with an image can cover a longer road distance than those few meters that traditional loop-sensors cover. However, in practice the video-sensor range is much shorter than the distance one "can see". The limitations in height-positions of the cameras implies e g that a bus can hide a long row of cars. Video-sensors, positioned at 0,5 to 1 km interval, therefore might only have a guaranteed coverage of their respective close area, and the larger part of the distance in between, has to be treated in the corresponding way as with loop-sensors. Detections can be performed at downstream as well as upstream sensor. At the upstream sensor the queue is detected by the fact that the queue is within the direct measuring area of the sensor. Characteristics of a queue is that traffic is dense and the speed is lower than at the free-flow mode. It is known, when the flow is approaching the capacity limit of the road, that the velocity is decreasing, e g at an access-road, where the speed limit at the motorway is 70 km/h, the motorway speed might drop to 55 km/h, because of the increased traffic density. At further increase of traffic density, the traffic breaks down to a queue, which might got still lower speeds. According to the invention, the later traffic state might be surveyed by measurements for at least two measuring periods. It appears, when it is queue caused by traffic collaps, that the velocity and the flow are in-phase, i e both flow and velocity are increasing respectively decreasing together. When it is dense traffic however, including queues with moving queue-fronts at high speeds, then the velocity and flow are changing in reverse phases. Using this method, one can also define at which velocity dense traffic typically is transferred into traffic collaps. Measurements by the innovator in Gothenburg, indicated the velocity breakpoint at the level of 55 km/h. That was well reproducable at the typical roadnet velocity limitation to 70 km/h . There are other defimtions of a queue-state, e g where already one car is considerred building a queue if the time gap to the car in front is less than 2 seconds and the following car has a higher velocity need. Queues and queue-forming also get different process courses on ordinary roads with one lane, compared to two lanes and compared to motorways. Those queues that are most interesting for this patent, are such that are appearing on motorways and similar arterial roads for larger cities. From the view of traffic management, the essential queues are those creating large problems. Therefore small groups of cars driving close, are considerred as dense traffic. Also longer packets of cars are here considerred as dense traffic, when driving in somewhat reduced velocities compared with the free-flow velocity ( often the given speed-limit on signs ). Usually those car-packets are characterized in that the front of the packet is moving forward along the road ( "moving queue"). At velocities above the break-point, the traffic in such a packet is characterized by high flow and reasonable high velocity, why a calm (homogeneous) driving in such a packet might not constitute a direct traffic problem. In near ranges of cities there are however a high density of on- and off-flows of the motorways, why a calm dense traffic is seldom appearing. Instead the traffic is characterized by transfers of lanes, "weaving", which rather cause a dense traffic to collapse, and result in queues with low velocity in the unstable queue-forrning state of traffic. On motorways there have been used "stretch-systems". Those systems are traditionally based on variable speed-limit signs, which are installed in intervals of 500 m along a motorway link. Sensors are measuring the traffic situation, and when traffic is considerred inhomogeneous, varying and reduced velocities etc., then the system is putting on the signs showing decreased speed-limits for a number of intervals. The purpose is that the traffic should be "calmed" and the velocity homogeneous at the now present lower speed limit. There are reports saying that large improvements have been achieved, in respect of both reductions of accidents and increased capacities. However there is a problem in the traditional methods in that, when the inhomogeneity in traffic is occurring, then the traffic collapse is already on its way, and when the measurements are obtained and detection achieved, then the collapse is a fact. Different trials might be done to improve the situation, e g by using much shorter measuring periods to get information faster. In the present invention the traffic is instead successively predicted, and when the probability of collapse is above a certain given value, then the corresponding speed-limits are reduced on the signs. By this there are created time-margins for avoiding the traffic collapse, and the action influence on the traffic might be kept at a lower level. The method is the same as that used for queue- and incident detection. The present invention can also be used for control of on-flow traffic, e g for control of "ramp- metering". In city-areas on-flow and off-flow traffic is a serious source of disturbances on the motorways, as is described above. There are often occurring overloading and traffic breakdown, and troublesome queues are forming. Prediction of the rise for overloading can be used for reduction of on-flow traffic on the considerred ramp, and for reducing of traffic on the motorway, e g by reduction of of on-flow traffic on ramps upstream of the original on-flow ramp. The prediction of traffic collapse at an on-ramp can be based on measurements at upstream sensors e g a sensor at the main road and a sensor at the access-road. Measurements of traffic by respective sensor can be used to predict the traffic a certain time-interval later on, equal to the travel time to the weaving area at the connection. By matching or synchronizing of measurements can e g occasions be predicted, when coinciding traffic peaks reach the access connection. The predicted flows are compared with the threshold values to obtain the prediction of overloading. One way to estimate the threshold value for the main road is illustrated as follows. The weaving capacity Cv = Co - a * I-, where Co is a constant and k is the flow on the access road. The factor a shows that the capacity on the main road is not determined by a simple sum of the two flows. Both Co and a should be calibrated for the present access road. In an example the value for a two-lane motorway is Co = 4200/h and a = 2,5, i e a differs significantly from the value = 1. That fact is also observed from the value of a for a turn-off road, where e g a = 1,5. In both cases it is the weaving that causes the smaller total capacity at off- respectively on-flow. Those present algorithms have been shown good agreement down to small on-flow values. When traffic has broken down, other conditions are valid. At established queue-situations e g, the weaving is by each second, which consequently gives a corresponding relation between the traffic on the motorway and the access road. When queues are growing, reaching the upstream sensor, the algorithms, as those given above, and Co respectively a can be successively updated. The queue-growth is determined by the difference between the flows behind and in front of the queue. The flow in front of the queue, might be estimated when needed, from a model for queue off-flow at the front of the queue. An example of a simple algorithm is Iaq = b * (g) "0 ' 5 , where g is the gap between the cars at the queue-front and b is a constant, which value can be approximately stated and updated by measurements. Iaq can also be obtained from Iaq = (Na - Vq) / (Da - Dq), where V is velocity and D is the periodic distance for the cars, and a indicates parameters in front of the queue and q in the queue. Together with the relation I = V / D, the off-flow at the queue-front and the flow downstream the queue can be determined, and with information on the flow and the related velocity downstream the queue, also the growth and decay of the queue can be determined. The queue off-flow algorithm is valid for many usual situations, and the gap g can be obtained typically from relations between gap, flow and velocity at queue-states. Determination of probabilities for queues and incidents. At prediction of an event, the most interesting is not always to judge, if it would be the most probable outcome that the event occurs, i e if that probability is above 50%. If the rise for queue-forming is 30 % or the rise for an accident is 10 %, then that might be enough for actions to be taken to prevent the event from occurring, i e in spite of the largest probability being neither a queue nor an accident. Below, examples are given for the way to work with the probability determination according to the invention. A typical distribution function within statistics is the Normal or Gaussian distribution. Assuming that one as approximately valid for the traffic on a certain part of the road-net, then the function can be calibrated from measurements and estimations of the variance of traffic around the average value. The probability for obtaining a certain value can be calculated or usually fetched from tables. Depending on the detection process, there might be a need for modifications of the distributions, or adaptions with the use of other distribution functions. The Rayleigh-distribution e g is interesting at envelope detection and filtered noise deviations. For illustrating the invention we use an approximative distribution of the "noise"- deviations according to P{y(t)>x} = exp(-x 2 /cy 2 ), i e the probability that a deviation y(t) is is larger than a given value x is exp^x cr 2 ). The amplitude normation is neglected. Then we find that the probability during one measurment period for a deviation larger than the standard deviation, is exp(-l) = 37 %. Also we can ask, how large the deviation need to be for the probability to be as small as 10 "4 . From 10 "4 = exp(-9,2) it is obtained x = 3σ. This also implicates, that for 10 4 measurement periods, there is a probability of 37 % to find a deviation above the 3σ value. If a large deviation indicates the possibility for an incident, then a threshold setting on x = 3σ means a false alarm rate of 10 "4 . In the example above x and y can be flows. Then incidents are expected to give rise to lower flow values downstream the queue, and by that, it is only single sided deviations that are needed to be considered, whereby the probability values only are 50 % of those above. At accumulation of several measurement periods, the noise deviations are added squared, i e after n periods σ(n) = (n) 0 ' 5 * σ(l). The signal is added linearily, i e in comparison s(n) = n * s(l). Thereby an integration effect is obtained s(n)/σ(n) = = n°' 5 * s(l)/σ(l). At false alarm rate 10 "4 , it is now obtained that the threshold related to one period, can be decreased at accumulated measurement periods according to (9,2/n) 0,5 , i e x = 3 * n "0 ' 5 * σ. Thus the accumulated mean value has got a lower threshold. In an applicable example e g, the flow of cars during a 30 seconds period is in average 16 cars and the related σ-value is 4. If an incident is blocking one of two lanes and queue-forming is reducing the flow to 8 cars, then the deviation is 8 cars = 2σ, and the condition is, for exceeding the threshold at an false alarm rate about 10 "4 , that 2σ is larger than (9,2/n) 05 * σ. The number of measurement periods thus needs to be above 9,2/4, i e larger than 3. If the distribution instead had been simply linear, i e exp(-x/σ), then there had been needed more than 20 periods. According to the invention one can instead use the deviations between the predicted and measured value as the base for the distribution function. Thereby the needed deviations can be considerable reduced. E g assume that 70 % of the deviation size can be predicted, then σ = 30 % of 4 giving the new σ = 1,2. Then it is required that the signal is larger than 9,2 0 ' 5 * 1,2 = 3,6 cars. With the incident-caused deviation of 8 cars, there is now obtained an acceptable incident detection already at the first measurement period. Giving several sensors in a large road network, it is even more important to keep the false alarm rate low. If with 100 sensors, one wants to limit the number of false alarms to one per day, one get in total 100 * 24 * 60 * 2 periods, and the corresponding requirement on false alarm is 3,5 * 10 "6 . Traditional methods, according to reports, have problems with long detection times, which also was illustrated above. The innovation offers as shown very large savings of time. Some examples are given below, for the use of the invented method in the traffic management process. In comparison, traditional methods for control of traffic e g at an access road, would mean an isolated pointlike control of traffic at the ramp. With the invention and its use of prediction, there is created time-margins for control of traffic as connected in a road net-work. This is a large step forward, as traffic inherently is a network phenomenon. Problems arise in narrow sections, bottle-necks, - but the solution of the problem should be network based, otherwise the problem often is only transfered to another point. Closely related to network control is managing of traffic between different routes. If.at prediction of traffic on a road-network, traffic problems are predicted e g overloading, then this problem often might be avoided, by taking actions in time, e g traffic can be guided to another route, whereby the traffic volume on the original route is decreased to a level below overloading. Route guidance might e g be performed by the use of "VMS", variable message signs. The message might e g contain information about different grades of problems on the given route. The larger the problem the larger the number of drivers that will consider choosing an alternative route. By sensor measurements directly connected to the position for the choice of routes, a fast feedback is obtained of the share of drivers, who chosed the alternative road route. That measure is also used for updating the value of strongness of the presently shown message, whereby the system successively stores an updated measure of the strongness for the respective messages. Thus the system beforehand can choose a message matching that share of the drivers, which is desireable for choosing a new route. It is an ingredient of the invention to predict the result of the actions. That is important as no action should be chosen giving rise to new problems. Calibration and updating is performed by successively measuring the consequences of the actions, and then matching the stored value of strongness for a message to the actions. In this process a slower rate of updating is preferrably chosen, in a way that deviations are only partially changing the former value. The innovation is also suitable for management of "park and ride", e g parking the car and taking the train or bus, - where the control information partly is based on predicted problems at the road net-work. Another area of use is the control of departure, e g information about traffic problems might influence some drivers to choose another transportation means or to delay the travel.

Claims: Claims. 1. A method for deterrnjning queues of vehicles in a road network using se orinformation from several sensors, where also queuefoπning on a route between two sensors can be detected with the use of at least one of upstream or downstream sensor respectively, characterized by using downstream sensor for detecting the arisen difference in traffic flows at queue-situation compared with no queue, and where the difference in flow at queue-situations is governed by the off-flow at the front of the queue; and when using upstream sensor for detecting a queue, this is performed when the queue has grown upstream to the sensor position, and that the queue-situation then is determined by the speed-decrease from the free¬ flow situation preferably using measurements giving at least one of the parameters flow and traffic density in addition to the velocity; and when using prediction, queue-forming is predicted by the use of both upstream and downstream sensors, by determining flow-relations and using those together with topical or present flows to predict queue-formation at least at one narrow section along the given route; and dividing the queue-forming process into two steps, where the first step is the traffic breakdown, which starts the queue-forming, and step two is the growth and decay of the queue, and that the prediction of the first step is performed by predicting the flow on the route and the breakdown is determined by the condition that the flows including possible on- and off-flows, exceed the corresponding threshold values; and that successive calibration and updating of parameters of the algorithms of the queue-forming process are performed by comparing predicted values with corresponding values obtained from measured values of the sensors, and are the parameters successively corrected by preferably slow, filtered or scaled changes. 2. A method according to claim 1 , characterized in , when predicting the flow at the downstream sensor position, comparing that flow with the corresponding later on measured flow and hereby a queue-detecting process utilizes the difference between predicted and measured values, possibly accumulated over several measuring periods, for determining if a queue has been formed; and hereby one or both of the following cases, queue or not queue is predicted and used in the comparison with the measured values. 3. A method according to claim 1 or 2, where the differences between the expected and measured values form stochastic distributions, dependent on the topical traffic situation e g free-flow traffic or traffic containing queue, and where differences larger than the standard deviation, σ, grow more and more improbable, if they are not signs of corresponding changes in the traffic situation, characterized by relating a single deviation or accumulated deviations to the mentioned distribution, and when deviations are larger than a choosen threshold, that is judged as a criterion of the occurrence of a corresponding change in the traffic situation, or the probability of the occurrence. 4. A method according to claim 1,2 or 3, characterized by estimating the probability for queue- forming in the queue-detection process, and performing that by calculating a measure on the deviations e g the standard deviation, and that measure or measures might be related to a given or determined distribution, obtained by use of measurements, and when obtaining measured or accumulated measured deviations, a measure on the probability of queue-forming might be estimated. 5. A method according to claims 1 ,2, 3 or 4, characterized by detecting incidents by use of the corresponding principles, which are valid for queue detection with the added main principle that when a queue is detected and the probability is small that it is created by anything but incidents, that is reason for judging that an incident might have occurred; and in case of traffic with low traffic flows, an unexpected stationary queue-front has probably been caused by an incident, and in cases of such traffic situations, that a queue might have been caused by overloading, added information is needed to decide if the cause might have been an incident instead, e g by detecting an almost blocked traffic for a certain time interval, when an overloading instead would give rise to a queue with limited but nevertheless significantly larger passability. 6. A method according to claim 5, characterized by using standard deviation or corresponding measures on deviation between predicted or in other ways expected values and corresponding measured values multiplied by a factor q to obtain a measure of the probability that a single or accumulated measured deviations with the value ( q * the measure of deviation) is representing the presupposed traffic situation; and is q governed by the assumed distribution; and might q be defined by use of a function of the probability or be stored in tables related to its corresponding probability value. 7. A method according to claim 6, where there is a desire or requirements to keep the number of false alarms of incidents below a certain false alarm rate, characterized in that the corresponding probability for false alarm is determined and associated q-value is used to determine the threshold level e g q*σ ; and are the deviations between the predicted and measured flow- values compared with the threshold value for acceptance of a possible incident detection; and when accumulated deviations are used, those deviations are compared using a q- value, related to the number of accumulated values; and this can e g be performed by an approximative method, where the accumulated value divided with the root-square of the number of accumulated values is compared using such a q-value, which then can be regarded mainly independent from variations of the number of accumulated values. 8. A method according to claim 5,6 or 7, characterized in that traffic also is predicted as if an incident has happened and that also measured deviations from this situation are used for judgement if an incident has occurred. 9. A method according to claim 7 or 8, characterized in that queue-detection is treated in a corresponding process as incidents. 10. A method according to claim 1, characterized in that determination or prediction on a given road-link is performed using flow relations, which are connected to upstream sensors, e g a sensor on the main road and a sensor on the access road; and that prediction of the flow at the connection mainly is done for the same time period, i e synchronized, e g that the prediction is matching two traffic flow peaks merging in the connection point, and by that obtaining the larger rise for traffic breakdown and queue-forming, and are the predicted flows compared with the threshold values; and that the threshold values for the main road e g might be estimated approximately as Cv= C 0 - a * Ie , where I e is the flow on the access road or on the turn-off road and a is the related factor and C 0 is a constant; and might C c and a be obtained from experience and / or possibly updated for respective road link, in that the predicted values are compared with the real ones obtained from measurments at the sensors. 11. A method according to claim 1 or 10, characterized in that the off-flow in the front of a queue is approximated with b * (g) "0 ' 5 , where g is the gap between cars at the queue-front and b is a constant, that might be updated by comparison with measured values; and that the off- flow also might be determined from (Va - Vq)/(Da - Dq), where V is the velocity and D is the periodic distance for the cars and a indicates the situation downstream the queue and q in the queue; and that those relations are used together with the traffic flows, I = V/D, for determination of the off-flow at the queue-front and with information about the flow also behind the queue, also for determination of growth and decay of the queue. 12. A method according to one of the claims 1 up to 11 for use in motorway systems, where signs can be controlled presenting warnings for traffic problems, possibly with speed limits, characterized in; that when traffic disturbances are predicted, the present cars in the disturbed area are given a sign message on speed decrease, and in applicable cases also upstream signs are changed, giving warnings with possibly speed decrease, alarming those cars of the beforehand problem situation; and when traffic disturbances are detected, upstream cars are alarmed, possibly with lower speed limits, and the sign information might also concern individual lanes. 13. A method according to one of the claims 1 up tol 1 for use at control of access traffic, e g with use of "ramp-metering", where light signs at access roads are controlled, characterized in; that by predicting of traffic disturbances, light signs are controlled to change its red-green time intervals to decrease the on-flow during the topical traffic load time interval, whereby the rise is reduced that traffic disturbances occur in reality; and that by detection of traffic disturbances, light signs are controlled to change the on-flow, reducing the traffic load and and by that reducing or dissolving the disturbances. 14. A method according to claim 13, where several access roads along the same road or chosen route can be controlled, characterized in; that by prediction of traffic disturbances at a certain time, at some on-ramp or another place, one or more of upstream accesses are controlled giving reduced on-flow during an early time period, corresponding to the downstream traffic load time period, and thereby reducing the rise that traffic disturbances arise in reality; and that the choice in what way the on-flow is reduced also is determined by the consequences for the traffic on the roadnet at respectively access road; and that the result of upstream on-flow control at one access road, might be corrected at downstream accesses at a corresponding later time; and at detection of traffic disturbances, a corresponding method can be used at upstream accesses to reduce the traffic load and by that reduce or dissolve the disturbance. 15. A method according to one of the claims 1 up to 11 for use at route guidance, where the road user might choose route based on information about predicted or detected downstream traffic disturbances , characterized in; that the response of the message for the users is predicted with prediction factors determined according to a feed-back method, wherein downstream sensors give measurement values, that are related to the messages; and that the prediction factors successively are updated during operation, and thus messages can be chosen beforehand, which give changes in route -choices, which reduces or prevent that predicted traffic disturbances arise in reality; and that the sensors also give feed-back of the result of the message, whereby corrections can be executed by choice of a new message. 16. A method according to one of the claims 1 up to 15 for use of traffic control in a road network, characterized in; that responses of actions can be predicted by use of prediction factors, which relate the action to the response of the action, in which the succeeding measurements of responses are used to update the prediction factors and possibly corrections of actions; and that this method also can be used for actions, which work on several links in a roadnet; and that also several actions can be connected and the combined result be predicted by use of respective prediction factors.

Inventors: OLSSON KJELL, OLSSON, KJELL
Applicants: DINBIS AB, OLSSON KJELL, OLSSON, KJELL
Application year: 1996
Main classification: G06F19
IPC codes: G08G1