JP2002170190A - Abnormality detection device in traffic stream and method therefor - Google Patents

Abstract

PROBLEM TO BE SOLVED: To examine changes of the arrangement of a group of vehicles in one traffic lane (alignment) to detect the occurrence of an unexpected event on a road because, if an unexpected event such as an accident or a disaster occurs on the road, changes of traffic lanes are frequently performed, and as a result, a vehicle moves onto the other land and looks as if it disappears, another vehicle enters the lane from the other lane and it looks that the unexpected event occurs, and the order of vehicles is changed as a result of passing so that the alignment is changed, although the alignment is not changed unless the unexpected even occurs on the road. SOLUTION: An abnormality detection device in a traffic stream is provided with vehicle sensors 3, 5 installed at a plurality of vehicle observation points to observe a feature amount of passing vehicles, a matching decision part 22 deciding a degree of agreement of the alignment of a group of vehicles by corresponding vehicles passing respective vehicle observation points mutually, and an abnormality decision part 24 detecting the occurrence of the unexpected event on the road based on the results of judgement of the matching decision part 22.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention collects traffic measurement data by installing a vehicle sensor or the like on a road, and uses the traffic measurement data to detect a traffic flow abnormality caused by an unexpected event. And a method and apparatus for detecting an abnormality.

[0002]

2. Description of the Related Art When an unexpected event such as a traffic accident or disaster occurs on a road, an abnormality in traffic flow based on the incident is detected promptly to notify a following vehicle or guide a following vehicle. There is a need to. 2. Description of the Related Art Conventionally, a camera is installed on a road and image processing is performed to detect an abnormality in a traffic flow (see JP-A-7-21488, JP-A-10-40490, etc.). Installing cameras over a wide area is costly and difficult to detect at night or in bad weather.

[0003] In view of this, it has been practiced to measure the traffic volume of a road, the speed of a vehicle, and the like using a vehicle sensor installed at various places on the road, and to monitor the abnormality of the traffic flow based on these measured values. . According to this monitoring device, it is determined that an accident has occurred when the speed drops sharply despite a small traffic volume and the state continues for a certain period of time.

[0004]

However, in the above-described monitoring device, the determination is made based on the traveling speed of the vehicle.
There is a problem that it is difficult to distinguish when an unexpected event occurs during a natural traffic jam, and the detection accuracy is reduced. Therefore,
The inventor has noted that lane changes are frequently performed when an unexpected event occurs on a road. If you focus on one lane, as a result of the lane change, the vehicle may appear to have disappeared by moving to another lane, may appear to be coming from another lane, or the order of vehicles may be changed as a result of passing May be.

On the other hand, there has been developed a technology for identifying a vehicle by installing a vehicle detector and a camera on the upper and lower sides of a road. The present invention regards a plurality of vehicles traveling on a road as a vehicle group, and can detect the occurrence of an unexpected event on the road based on a change in the arrangement in the vehicle group using the vehicle identification technology. It is an object of the present invention to implement a traffic flow abnormality detection device and method.

[0006]

The traffic flow abnormality detecting device according to the present invention is installed at a plurality of vehicle observation points, and measures vehicle characteristic amount observing means for observing characteristic amounts of passing vehicles. By associating the vehicles that have passed with each other, a matching judgment unit for judging the degree of coincidence of the group of vehicles (alignment), that is, a matching ratio, and the occurrence of a sudden event on the road is detected based on the judgment result of the matching judgment unit. Abnormality determination means for performing the determination (claim 1).

[0007] If there is no sudden event on the road, the alignment of the group of vehicles does not change much. However, when a sudden event occurs on the road, lane changes are frequently performed.
The alignment changes because the vehicle appears to have disappeared as it moves to another lane, appears to have originated from another lane, or as a result of overtaking. Therefore, by observing the feature amount of the vehicle and associating the vehicles that have passed through the plurality of vehicle observation points with each other, the degree of coincidence of the alignment, that is, the matching rate, is examined. Can be detected.

The decrease in the degree of coincidence can be determined by comparison with a threshold value. This threshold is
The degree of coincidence before and after the actual occurrence of the sudden event on the road is recorded, and the value may be selected so that the occurrence of the sudden event on the road can be detected with the highest accuracy by implementing the present invention. The vehicle length, the vehicle height, the image of the vehicle, and the like can be used as the vehicle feature amount (claims 3 to 5).

The threshold value is obtained by accumulating detection data under various conditions in the past, such as a time zone, a day of the week, and a day without an event.
An optimum threshold value may be set (claim 6). The threshold value may be a value that is automatically determined according to a traffic condition and stored.
In the traffic flow abnormality detection device of the present invention, the matching rate naturally decreases when no sudden event on the road occurs, when the road is not congested, and when the road is congested, the matching rate decreases. Rises naturally. Therefore, when the road is not congested, the threshold value is automatically lowered to reduce the false detection rate, and when the road is congested, the threshold value is automatically raised to reduce detection omission. I do.

[0010] The traffic flow abnormality detecting device of the present invention comprises:
When a sudden event on the road is detected, the information processing device may further include information providing means for notifying the outside of the occurrence of the sudden event (claim 8). For example, information can be provided to the driver of the vehicle using communication means such as a roadside beacon, a mobile radio telephone, and broadcasting. The information providing means may provide information to a vehicle traveling in the area or the section or a vehicle expected to travel in the area or the section (claim 9). The purpose is to limit the vehicles that provide the information to certain related vehicles.

The information providing means does not have to detect the occurrence of the event on the road already scheduled or notify it to the outside (claim 10). For example, when it is known that the lane is restricted from a certain time due to road construction or the like, even if an event is detected on the road in the construction section at that time, it is not notified to the outside. This is to prevent confusion by notifying the outside. The traffic flow monitoring means makes a stepwise determination according to the magnitude (likelihood) of the evaluation value on which the determination is based, and the information providing means makes a stepwise determination by the traffic flow monitoring means. It is preferable to change the content of the abnormality information depending on the result.
1). By changing the content of information provision such as “accident / stop ahead” or “caution ahead” according to the likelihood (certainty) of the occurrence of the sudden event, more appropriate information can be given to the driver or the like.

[0012] When the traffic flow monitoring means detects a traffic flow abnormality in a plurality of road sections, the traffic flow monitoring means may specify the abnormality occurrence section in accordance with the magnitude of the evaluation value serving as the basis of the determination. Item 12). By setting the section having the highest likelihood (certainty) of the occurrence of the sudden event as the abnormality occurrence section, it is possible to inform the subsequent driver or the like of the information of the occurrence section and the information of the avoidance route. The traffic flow abnormality detection method according to the present invention (claim 13) is the same method as the traffic flow abnormality detection device according to claim 1.

[0013]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The embodiments of the present invention will be described below in detail with reference to the accompanying drawings, taking monitoring of a traffic flow on a highway as an example. FIG. 1 is a schematic diagram showing a traffic flow monitoring system for detecting a traffic flow abnormality. On a two-lane highway 1, a vehicle sensor 5 embedded in a loop
It is installed in each lane at intervals. An ultrasonic vehicle sensor 3 for measuring the vehicle height from above the vehicle is also provided for each lane. These vehicle sensors 3,5
Are connected to the primary processing unit 4. Primary processing unit 4
Detects the speed and height of each vehicle.

The highway 1 is provided with a variable display panel 6 for notifying the vehicle of accident information and road surface information. In addition, a roadside beacon 7 that performs bidirectional communication with a vehicle.
Is provided. Further, on the general road 2 connected to the highway 1, accident information and road surface information of the highway 1 are provided.
A variable display board 9 is provided to notify a vehicle entering the highway 1.

The computer in the traffic management center 10 is connected to the primary processing unit 4, the roadside beacon 7, the variable display panel 6, etc. installed in each section through a wired communication network 12 (which may be a wireless communication network). Have been. Computers are also used by related organizations such as the Ministry of Construction, the National Police Agency, and the Fire Service.
3 via a communication line, and is also connected to the broadcasting station 14 via a communication line. FIG. 2 is a functional block diagram of the computer 11 of the traffic management center 10.

The input processing unit 21 of the computer 11 includes:
The sensing signals of the vehicle sensors 3 and 5 are input via the matching determination unit 22. The input processing unit 21 calculates the maximum vehicle height of each vehicle based on the output of the vehicle sensor 3,
The speed of the vehicle is measured based on the output time difference between the two loops of the vehicle sensor 5, and the vehicle length is calculated based on the measured speed and the vehicle sensing time. Since the vehicle height and vehicle length are calculated each time the vehicle passes, one or a plurality of vehicle height and vehicle length data strings are obtained for each lane.

The matching determining section 22 of the computer 11
Will be described with reference to a flowchart (FIG. 3). Various methods are known as the vehicle group matching processing method in addition to the method described below. In the following, as an example, a method of associating vehicles with a p-dimensional alignment problem is performed. (Kobayashi et al., "Global Traffic Flow Analysis Algorithm from Two-Point Vehicle Observation Information" (published by Information Processing Society of Japan 72nd Algorithm Study Group, March 21, 2000)).

Hereinafter, attention will be focused on only one lane. A vehicle that has entered the lane from another lane or a vehicle that has exited from the lane to another lane is treated in the same manner as a vehicle that has entered the lane from an intersection or exited from the lane to an intersection. The vehicle height detected by the vehicle detectors 3, 5 upstream of the traveling direction,
The vehicle length data string is input (step S1), and the vehicle height and vehicle length data strings detected by the vehicle sensors 3, 5 downstream in the traveling direction are input (step S2).

The number of vehicles to be processed detected by the upstream vehicle detector is defined as n, and the vehicle height data sequence is defined as hA1, hA2, hA3,.
The vehicle length data sequence is represented by lA1, lA2, lA3,..., lAn (representative is represented by "lA"). The number of vehicles to be processed detected by the downstream vehicle sensor is m, and the vehicle height data sequence is hB1,
hB2, hB3,..., hBm (represented by “hB” when represented), and the vehicle length data sequence is represented by lB1, lB2, lB3,.
‥, IBm (representatively represented by IB).

The probability that a vehicle having the vehicle height h detected by the upstream vehicle sensor as the vehicle height hA is observed as the vehicle height hB by the downstream vehicle sensor is based on the observation error of the vehicle sensor following a Gaussian distribution. If it is assumed, it is expressed by the following equation (1).

[0021]

(Equation 1)

Here, σ ² h, A is the variance of the vehicle height observed by the upstream vehicle sensor, and σ ² h, B is the variance of the vehicle height observed by the upstream vehicle sensor. Similarly, the probability that the vehicle having the vehicle length l detected by the upstream vehicle detector as the vehicle length lA is observed as the vehicle length IB by the downstream vehicle detector is expressed by the following equation (2).

[0023]

(Equation 2)

Here, σ ² l, A is the variance of the vehicle length observed by the upstream vehicle sensor, and σ ² l, B is the variance of the vehicle length observed by the upstream vehicle sensor. (a) When the difference in vehicle height or vehicle length is used as a cost evaluation criterion The posterior probability density Ph in which a vehicle detected as a vehicle height hA by an upstream vehicle sensor is detected as a vehicle height hB by a downstream vehicle sensor
(ha, hb) is represented by the following equation (3).

[0025]

(Equation 3)

Similarly, the posterior probability density Pl (la, lb) that the vehicle detected as the vehicle length IA by the upstream vehicle sensor is detected as the vehicle length IB by the downstream vehicle sensor is represented by the following (4). It is expressed by an equation.

[0027]

(Equation 4)

Σ ² h, A + σ ² h, B = σ ² h, σ ² l, A + σ
² l, B = Writing and sigma ² l, wherein (3) (4), respectively, (5)
It can be rewritten as in equation (6).

[0029]

(Equation 5)

[0030]

(Equation 6)

(B) When the ratio of vehicle height or vehicle length is used as a cost evaluation criterion The following expressions (7) and (8) are used instead of the expressions (5) and (6).

[0032]

(Equation 7)

[0033]

(Equation 8)

The natural logarithm of the probability density listed above is called "match cost". When only the vehicle height is used, the match cost d (h) between the vehicle detected as the vehicle height hA by the upstream vehicle sensor and the vehicle detected as the vehicle height hB by the downstream vehicle sensor.
a, hb) becomes d (ha, hb) = ln [Ph (ha, hb)] (9) When only the vehicle length is used, the match cost d (la, lb) between the vehicle detected as the vehicle length IA by the upstream vehicle detector and the vehicle detected as the vehicle length IB by the downstream vehicle detector is: d (la, lb) = ln [Pl (la, lb)] (10)

When both the vehicle height and the vehicle length are used, the vehicle height hA and the vehicle length IA are detected by the upstream vehicle sensor, and the vehicle height hB and the vehicle length IB are detected by the downstream vehicle sensor. The match cost d (ha, la; hb, lb) for the vehicle is d (ha, la; hb, lb) = ln [Ph (ha, hb)] + ln [Pl (la, lb)] (11) Becomes

The matching judgment section 22 performs the operations (9), (10), (1)
The match cost is calculated for all combinations of the vehicle sensed by the upstream vehicle sensor and the vehicle sensed by the downstream vehicle sensor by one of the formulas (Step S3). Next, the matching determination unit 22 acquires “gap cost”. This gap cost is stored as a constant corresponding to the characteristics of the vehicle sensor. The match cost and the gap cost are collectively referred to as “score”.

There are two types of gap costs, an internal gap cost (IGC) and an external gap cost (EXGC). In the IGC, an overtaking occurs between the two points and the order is changed,
This is set when no response can be made. This means that either the vehicle height or the vehicle length will not be more than 3σ apart, and 3σ
It is based on the assumption that if you leave more than that, you consider it another vehicle.

When only the vehicle height is used, the specific value of IGC is expressed by the following equation (12). d (ha,-) = d (-, hb) = (1/2) ln [Ph (0,3.sigma.h)] =-(1/2) ln ((2.pi.) ^{1 /} 2.sigma.h) -9/4 ( 12) The reason why 1/2 is used in the equation is that the cost is equivalent to one branch because two branches are used when the gap cost is used (the same applies hereinafter). When only the vehicle length is used, the specific value of the IGC is expressed by Expression (13).

D (la, −) = d (−, lb) = (1/2) ln [Pl (0,3σl)] = − (1/2) ln ((2π) ^{1 /} 2σl) -9 / 4 (13) When both the vehicle height and the vehicle length are used, the specific value of the IGC is expressed by the equation (14). d (ha, −; −, −) = d (−, la; −, −) = d (−, −; hb, −) = d (−, −; −, lb) = (1/2) ｛ ln [Ph (0,3σh)] + ln [Pl (0,0)]｝ = (1/2) ｛ln [Ph (0,0)] + ln [Pl (0,3σl)]｝ = − (1 / 2) ln (2πσhσl) -9/4 (14) EXGC is set when a vehicle that has passed the upstream point has not yet passed the downstream point and cannot be associated with a vehicle.
The value of this EXGC is calculated by calculating the maximum score of the alignment when it is confirmed by visual observation or the like that the optimal matching is actually obtained at the time of initial setting after installation of the apparatus. Is determined based on

Furthermore, it is conceivable that the correspondence score between vehicles that cannot be handled in reality is set to -∞ (for example, when the travel time becomes negative, as compared with the travel time estimated from experience, Too short or long, etc.).
When the above costs are obtained, the n vehicles detected by the upstream vehicle detector and the m vehicles detected by the downstream vehicle detector are set so that the sum of the match costs or the gap costs is maximized.
It associates with one vehicle. For this reason, it is formulated as a two-dimensional character string alignment problem.

The vehicles passing the upstream point are referred to as a1, a2, a
The vehicle passing through the downstream point is denoted by b1,
Expressed as b2, b3,..., bn. Matrix (aibj) (1 <i <n, 1 <
j <m) is called alignment. The two-dimensional character string alignment problem can be reduced to the longest path problem on a two-dimensional grid-like directed graph (the general term is "shortest path problem,
Here we are looking for the longest path of the sum of the costs,
"The longest road problem"). FIG. 4 is a diagram illustrating a two-dimensional grid-like directed graph. In FIG. 4, diagonal branches that correspond to the vehicle ai and the vehicle bj are represented by solid lines. This branch length corresponds to the above-mentioned match cost d (any one of equations (9) to (11)). The vertical and horizontal broken lines inside the frame indicate the vehicle ai
And the vehicle bj do not correspond, and the branch length corresponds to the above-mentioned IGC. The dashed-line branch outside the frame indicates a case where there is no corresponding vehicle, and the branch length corresponds to the above-described EXGC.

The longest path of the sum of the costs from the upper left point to the lower right point in FIG. 4 is the solution indicating the plausible association of the vehicle. The longest path problem is based on dynamic programming (DP; D
dynamic programming) (step S4) (see [1] and [2] below). [1] D. Gusfield. "Aigorithms on Strings, Trees,
and Sequences. "Cambridge University Prass, 1997. [2] SBNeedleman and CDWunsch." A general meth
od applicable to thesearch for similarities in the
amino acid sequences of two proteins. "Journal of
Molecular Biology, 48, pp. 443-453, 1970. For example, it is assumed that n = 3 and m = 4, and a route (thick solid line) as shown in FIG. 5 is obtained as a result of solving the longest path problem. From FIG. 5, vehicle a1 corresponds to b1, vehicle a2 does not correspond to vehicle b2 (internal gap), and vehicle a3
Corresponds to b3 and there is no corresponding to vehicle b4 (external gap). It is considered that this is because vehicles a2 and a3 have been switched after passing the upstream point, and vehicle b4 has entered from an intersection on the way or has entered due to movement between lanes.

The above analysis results are output (step S
5). Then, based on the output result, the travel time can be estimated by focusing on the matching vehicles.
Here, a method of determining the value of EXGC will be described. EX
Let the value of GC be g. It is assumed that n vehicles are observed upstream and m vehicles are observed downstream. Note that n <m. Vehicles a1-observed at the upstream point by visual inspection
An is assumed to correspond to the vehicles b1 to bn observed at the downstream point. However, vehicles an + 1 to am observed at a relatively late time at the upstream point
Has not reached downstream yet at the time of observation of the downstream point, and there is no corresponding one.

In this case, the score of the matching portion of the alignment is expressed as S '(A), and the score of the non-matching portion is expressed as g · x (A). S '(A) is a value calculated from the measured value of the vehicle sensor. g is the value of EXGC to be obtained, x (A) indicates the number of non-matching units, and x (A) =
nm. The value g of the EXGC is changed to “the score S ′ of the matching portion when the optimal matching is obtained.
(A) divided by "n + mx (A)".

G = S ′ (A) / [n + mx (A)] (15) For example, if a two-dimensional lattice-like directed graph where n = 4 and m = 3 is drawn, it becomes as shown in FIG. In FIG. 6, the score S '(A) of the matching part and the value g · x of the EXGC
(A) is shown. Where x (A) = nm = 4-
Since 3 = 1, g.x (A) in FIG. 6 indicates g itself. In the example of FIG.
The value g of GC is g = S '(A) / [4 + 3-1] = S' (A) / 6 based on (15).

In practice, a process of observing a group of vehicles for which matching has been confirmed upstream and downstream to obtain the EXGC value g is performed a plurality of times, and the average of the obtained plurality of g is calculated. May be finally determined as the EXGC value g. In the embodiment using the IGC and the EXGC, the vehicle association portion (data with the largest m) on the latest time point (latest observation time) is EXGC
Greatly depends on the value of. The value of EXGC fluctuates because it is a value obtained statistically as described above.

Therefore, a method of extending the algorithm so as not to require the EXGC at the latest time point in the time series will be described. As described above, the value g of EXGC is represented by g = S '(A) / [n + mx (A)] (15). On the other hand, the total score S (A, g) is represented by S (A, g) = S '(A) + g.x (A) (17) By substituting equation (15) into equation (17), S (A, g) = S '(A) + S' (A) x (A) / [n + mx (A)] = (n + m) S '( A) / [n + mx (A)] (18) Since n + m is constant, S '(A) / [n + m-
x (A)] is the optimal solution.

The above is illustrated as follows. FIG. 7 is a diagram illustrating a two-dimensional lattice-like directed graph excluding the external gap at the latest time point in the time series. In the graph of FIG. 7, the upstream passing vehicles a1,
The subscripts of ‥‥ and an are i (1 ≦ i ≦ n), and the corresponding point on the latest time point is vi. The maximum score of the alignment at each point vi is obtained, and the point at which the value obtained by dividing the score by (i + m) -x (A) becomes the maximum is defined as the latest corresponding point vi ₀ .

Although the embodiment of the present invention has been described above, the embodiment of the present invention is not limited to the above embodiment. For example, in the present invention, in addition to vehicle length and vehicle height data, camera image data can be acquired by a camera and used for alignment. In this case, it is necessary to determine a score between images, but the “distance between images” devised in fields such as image search, specifically, match distance (see [3] below), EMD (Earth Mover's Dista
nce) (see [4] below).

[3] M. Werman, S. Peieg, and A. Rosenfe
ld. "A distance metric for multi-dimensional histgr
ams. "Computer, Vision, Graphics, and Image Proces
sing, 32, pp.328-336, 1985. [4] Y. Rubner, C Tomasi, and LJ Guibas. "The Eart
h Mover's Distance as a Metric for Image Retrieva
l. "Technical Report STAN-CS-TN-98-86, Department
of Computer Science, Stanford University, Septembe
r 1998. The match distance is a distance using color information of a pixel in an image, and is given as an L1 distance between cumulative histograms of two image histograms. Specifically, a histogram of the luminance and a histogram of the hue are created from an image photographed from behind each vehicle. In the luminance histogram, all pixels are divided into 32 levels from the luminance, and the frequency is the number of pixels at each level. The hue histogram divides the hue into 30 levels, and the frequency is the sum of the saturations of the pixels included in each level. The image distance was a weighted sum of the distances based on the histogram of luminance and hue.

The EMD is a generalized distance of the match distance, and uses position information in addition to pixel color information. The pixels of each image are clustered by color and proximity (see [5] below) and given as the least cost stream between them. This time, each image was clustered into 16 clusters. [5] M. Inaba, H. Imai, and N. Katoh. "Application of
weighted Voronoi diagrams and randomization to va
riance-based k-clastering. "In Proceedingsof the 1
0th ACM Symposium on Computational Geometry, pp.33
2-339, 1994 The distance between the image of the vehicle ai and the image of the vehicle bj is Dij
Then, as the alignment score Dtotal, a linear sum of any of the expressions (9), (10), and (11) and the distance Dij between images is adopted.

Dtotal = d (ha, la; hb, lb) + λDij (18) Here, the constant λ is a weighting coefficient. Further, assuming that the gap cost of the vehicle length / height is G and the maximum value of the distance between the corresponding vehicles is M, the total gap cost Gtotal is given as Gtotal = G−λM / 2 (19).

In the above-described embodiment, the number of vehicle observation points is two. However, the number of vehicle observation points can be extended to arbitrary plural points. If the vehicle observation point is a point p (p is an integer of 2 or more), the association of the vehicle can be reduced to a p-dimensional alignment problem. As described above, the association between the n vehicles passing the upstream point and the m vehicles passing the downstream point was performed.

Next, the traffic flow abnormality detection processing performed by the abnormality determination unit 24 will be described with reference to a flowchart (FIG. 8).
This abnormality detection processing, in synchronization with the vehicle that has passed the upstream point and the vehicle that has passed the downstream point,
Do. The abnormality determination unit 24 inputs the result of association at the upstream and downstream points (step T1). Then, of the vehicles that have passed the downstream point, it is checked what percentage is associated with the vehicle that has passed the upstream point. This ratio is called a matching ratio. For example, out of m vehicles that passed the downstream point,
Assuming that p vehicles correspond to vehicles that passed the upstream point,
The matching rate is p / m. This matching ratio is compared with a threshold.

As the threshold value, data of the matching rate before and after the time when the sudden event on the road actually occurs is recorded, and the occurrence of the sudden event on the road is detected with the highest accuracy by implementing the present invention. Choose a value that can be used. In addition, such as time of day, day of the week, days with no events,
It is also possible to accumulate data on lane utilization rates under various conditions in the past, and set an optimal threshold value according to the time zone, the day of the week, the presence or absence of an event, and the like.

Further, the travel time is measured, and when the travel time is relatively short, that is, when it is close to free flow, the threshold value is made relatively small. When the travel time is relatively long, that is, when traffic is congested, the threshold is set. Make the value relatively large (step T2
T4). Instead of the travel time, it may be determined whether or not there is a traffic jam based on the traffic volume and the occupancy. When the flow is close to the free flow, the matching rate is relatively low even in a normal time when no sudden event occurs. Therefore, the possibility of erroneous detection increases unless the threshold is lowered. On the other hand, during a traffic jam, the matching rate is relatively high even in normal times. Therefore, if the threshold value is not increased, detection omission increases.

The increase or decrease of the threshold value in steps T2 to T4 may be set artificially, but it is desirable to automatically perform the increase or decrease in order to save labor. Abnormality determination unit 2
4 compares the matching rate with the threshold (step T
5) If it is lower than the threshold value, it is determined that a sudden event has occurred between the upstream and downstream points (step T7). If it is higher than the threshold value, it is further checked whether or not the matching rate obtained by associating the vehicles that have passed the past (for example, one time before) upstream and downstream points was higher than the threshold value (step T6).
If it is higher, it is determined that there is no abnormality. If it is low, it is determined that the sudden event has continued.

As described above, when the matching rate is higher than the threshold value twice or more consecutively, it is determined that there is no abnormality in order to avoid an erroneous determination in the case of a sudden match even if an unexpected event occurs. It is. The occurrence of the sudden event described above may be determined in a plurality of sections. In this case, by comparing the matching rates in the sections, the section having the lowest matching rate can be specified as the sudden event occurrence section.

FIG. 9 is a flowchart for explaining the process of specifying the sudden event occurrence section. First, the sudden event occurrence determination processing described so far is performed in each of the monitored road sections 1, 2,.
1). If the processing is completed in all the monitored road sections 1, 2,..., I (YES in step W2), it is checked whether or not there is a section determined to have an unexpected event (step W).
3). Then, the matching rates calculated in the respective sections are compared (step W4). The section in which the matching rate shows the minimum is specified as the sudden event occurrence section (step W
5).

Hereinafter, the reciprocal of the matching ratio is referred to as “evaluation value”. That is, the evaluation value = 1 / (matching rate). FIG. 10 is a graph showing the time transition of the evaluation values obtained from the detection processing results in five sections. According to this graph, the accident occurred at 8:20, and the evaluation values in the sections 1 to 3 have increased. In particular, since the evaluation value of the section 2 is the maximum, the section 2 can be specified as the sudden event occurrence section.

When the occurrence of the sudden event and the section where the sudden event has occurred are determined as described above, the traffic management center 10 displays the occurrence of the sudden event on the variable display boards 6 and 9, and transmits the information to the vehicle through the roadside beacon 7. Notifies the occurrence of a sudden event. In this notification, the evaluation value is compared with a plurality of thresholds. One is a “detection threshold” and the other is a “attention threshold”. There is a relationship of “detection threshold”> “attention threshold”.

If the evaluation value exceeds the detection threshold value, the likelihood of occurrence of a sudden event is sufficiently high and it is determined that "the occurrence of a sudden event". If this detection threshold is too high, detection omissions will increase, and if the detection threshold is too low, false detections will increase. This detection threshold is based on the results of detection omission rate and false detection rate,
It may be determined automatically. If the detection threshold is not exceeded, this evaluation value is compared with the caution threshold. If the caution threshold value is exceeded, the likelihood of the occurrence of the sudden event is moderately high, and it is determined that "the attention state in which the possibility of occurrence of the sudden event is high".

If the caution threshold value is not exceeded, the likelihood of occurrence of a sudden event is low, and it is determined that "no sudden event has occurred". If it is determined that “an unexpected event has occurred”, the output processing unit 25 of the traffic management center 10 displays a message giving a driver warning such as “accident / stop ahead” on the variable display plates 6 and 9, The vehicle is also notified of the dangerous section through the roadside beacon 7.

If it is determined that the caution state is highly likely to cause a sudden event, the output processing unit 25 prompts the driver to pay attention to the variable display plates 6 and 9 as "forward warning". Such a message is displayed, and the vehicle is notified through the roadside beacon 7 to the effect that the section is a traveling caution section. Then, the information is transmitted to the related organization 13 or the broadcasting station 1 through a communication line.
Contact 4

If road construction or the like has already been scheduled and traffic flow abnormalities are expected, the output processing unit 25
Does not display the occurrence of the catastrophic event on the variable display boards 6 and 9 based on this judgment even if the occurrence of the catastrophic event is determined at the time, and may contact the related organization 13 or the broadcasting station 14. Absent. Although the embodiment of the present invention has been described above, the embodiment of the present invention is not limited to the above. In the above example, a highway is assumed, but a general road may be used. Assuming a road with two lanes, the number of lanes is
The number of lanes is not limited to two, and may be one lane or three or more lanes.

Further, a Doppler type vehicle sensor installed beside a road may be used in place of the plural embedded type vehicle sensor 5. Alternatively, a television camera may be installed on the road to detect the number of vehicles passing, vehicle height, vehicle length, passing speed, and the like by image processing. Further, as a method of calculating the matching ratio, another known method may be employed. For example, a method of taking an image of a vehicle and recognizing a feature of the vehicle, for example, a color or a plate number, using image processing may be adopted.

[0067]

As described above, according to the traffic flow abnormality detecting device of the present invention, it is possible to accurately detect the occurrence of a sudden event on a road.

[Brief description of the drawings]

FIG. 1 is a schematic diagram showing a traffic flow monitoring system for detecting a traffic flow abnormality.

FIG. 2 is a functional block diagram of a computer 11 of the traffic management center 10.

FIG. 3 is a flowchart for explaining a vehicle group matching processing method performed by a matching determination unit 22;

FIG. 4 is a diagram illustrating a two-dimensional grid-like directed graph.

FIG. 5 is a diagram of a two-dimensional lattice-like directed graph for showing the result of solving the longest path problem with n = 3 and m = 4.

FIG. 6 is a two-dimensional grid-like directed graph when n = 4 and m = 3 and only vehicle b4 is not matched.

FIG. 7 is a diagram illustrating a two-dimensional grid-like directed graph for explaining an algorithm that does not require an external gap at the latest time point in a time series.

FIG. 8 is a flowchart illustrating a process performed by the abnormality determination unit for monitoring occurrence of a sudden event.

FIG. 9 is a flowchart illustrating a process of specifying a sudden event occurrence section.

FIG. 10 is a graph showing a time transition of an evaluation value obtained from a detection processing result in five sections.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 Expressway 2 General road 3 Vehicle detector 4 Primary processing unit 5 Vehicle detector 6 Variable display board 7 Roadside beacon 9 Variable display board 10 Traffic management center 11 Computer 13 Related organization 14 Broadcasting station 22 Matching determination unit 24 Abnormality determination unit

Continued on the front page (51) Int.Cl. ⁷ Identification code FI Theme coat II (Reference) G08G 1/04 G08G 1/04 D (72) Inventor Kenji Tenme 1-3-1 Shimaya, Konohana-ku, Osaka Sumitomo Electric Energy F-term (reference) in Osaka Seisakusho Co., Ltd.

Claims (13)

[Claims] 1. A vehicle feature amount observing means installed at a plurality of vehicle observing points and observing a feature amount of a passing vehicle, and the vehicles passing through each vehicle observing point are associated with each other, so that the arrangement of the vehicle groups is matched. A traffic flow abnormality detection device, comprising: a matching determination unit that determines a degree; and an abnormality determination unit that detects occurrence of a sudden event on a road based on a determination result of the matching determination unit. 2. The system according to claim 1, wherein said abnormality judging means detects occurrence of a sudden event on the road when the degree of coincidence of the group of vehicles becomes smaller than a threshold value.
Abnormality detection device for traffic flow described. 3. The traffic flow abnormality detecting device according to claim 1, wherein the vehicle characteristic amount observing means observes a vehicle length. 4. The traffic flow abnormality detecting device according to claim 1, wherein the vehicle characteristic amount observing means observes a vehicle height. 5. The traffic flow abnormality detecting device according to claim 1, wherein the vehicle characteristic amount observing means captures an image of the vehicle. 6. The traffic flow abnormality detecting device according to claim 2, wherein the threshold value is a value statistically obtained and stored according to a time zone, a day of the week, the presence or absence of an event, and the like. 7. The traffic flow abnormality detecting device according to claim 2, wherein said threshold value is a value automatically determined and stored according to a traffic condition. 8. The traffic flow abnormality according to claim 1, further comprising information providing means for notifying the occurrence of the sudden event to the outside when the sudden event on the road is detected by the traffic flow monitoring means. Detection device. 9. The information providing means provides information to a vehicle traveling in the area or the section or a vehicle expected to travel in the area or the section. 9. The traffic flow abnormality detection device according to claim 8, wherein: 10. The traffic flow according to claim 8, wherein the information providing means does not notify the outside of the event on the road which has already been scheduled even if it detects the occurrence of the event. Anomaly detection device. 11. The traffic flow monitoring means makes a stepwise determination according to the magnitude of the evaluation value on which the determination is based, and the information providing means makes a stepwise determination by the traffic flow monitoring means. 9. The traffic flow abnormality detection device according to claim 8, wherein the content of the abnormality information is changed according to the result of the above. 12. When the traffic flow monitoring means detects a traffic flow abnormality in a plurality of road sections, the traffic flow monitoring means specifies the abnormality occurrence section in accordance with the magnitude of the evaluation value on which the determination is based. The traffic flow abnormality detection device according to claim 1, wherein: 13. A vehicle traveling on a road is measured at a plurality of vehicle observation points, a characteristic amount of a passing vehicle is observed based on the measurement result, and each vehicle observation point is identified based on the characteristic amount. A traffic flow abnormality detection method characterized by determining the degree of coincidence of a group of vehicles by associating vehicles that have passed with each other, and detecting occurrence of a sudden event on a road based on the determination result.