Classifications

• • G—PHYSICS
  • G08—SIGNALLING
  • G08G—TRAFFIC CONTROL SYSTEMS
  • G08G1/00—Traffic control systems for road vehicles
  • G08G1/01—Detecting movement of traffic to be counted or controlled

Definitions

• 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.
• traffic management systems an important task is to avoid overloading, where traffic breakdown is introducing queues with reduced passability, increased risk 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 published international patent application WO 94/11839.
• the present invention presupposes the existence of knowledge of that technology.
• the algorithms have been formed by "trial and error", i e one has tested and changed until 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 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 differences 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 could'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 risk for false alarms.
• a measured standard deviation can give information about the probability for a variation being larger than a given value.
• 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, ie that one is now exceeding the corresponding threshold.
• 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.
• 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.
• the starting values can be well chosen from the origin.
• 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.
• 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. For example, 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.
• a key-function is prediction of traffic breakdown and queue-forming.
• 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.
• the detection process of queue-forming it is interesting to utilize prediction. For example, 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".
• 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.
• the notation "predict” is used including also estimations, that is not direct predictions, but is fulfilling a corresponding object.
• the comparison value might be a mean-value or a mean-value plus a value based on a standard deviation, historically estimated value etc.
• this value constitutes a type of expected comparison value, by which the measured value can reach criteria for detection of a queue.
• 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.
• 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.
• 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 considered 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").
• 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.
• a calm dense traffic 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-forming state of traffic.
• 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.
• 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".
• on-flow traffic e g for control of "ramp-metering".
• On-flow traffic e g for control of "ramp-metering”.
• 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 C v C 0 - a * I e , where C 0 is a constant and I e 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 C 0 and a should be calibrated for the present access road.
• Those present algorithms have been shown good agreement down to small on-flow values. When traffic has broken down, other conditions are valid.
• the queue-growth is determined by the difference between the flows behind and in front of the queue.
• the flow in from of the queue might be estimated when needed, from a model for queue off-flow at the front of the queue.
• 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.
• 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 risk for queue-forming is 30 % or the risk 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.
• the accumulated mean value has got a lower threshold.
• 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/ ⁇ ), then there had been needed more than 20 periods.
• That measure is also used for updating the value of strongness ofthe presently shown message, whereby the system successively stores an updated measure of the strongness for the respective messages.
• 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.

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• 1995
  • 1995-05-19 SE SE9501919A patent/SE9501919L/sv unknown
• 1996
  • 1996-05-13 DE DE69631629T patent/DE69631629T2/de not_active Expired - Fee Related
  • 1996-05-13 WO PCT/SE1996/000620 patent/WO1996036929A1/en active IP Right Grant
  • 1996-05-13 EP EP96914510A patent/EP0771447B1/en not_active Expired - Lifetime

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