JP3399401B2 - Traffic information management device and traffic information management method - Google Patents

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a traffic information management apparatus and method, and in particular, collects characteristic quantities of vehicles passing at an upstream point and a downstream point of a road, and performs matching of the collected characteristic quantities of a group of vehicles. The present invention relates to a traffic information management device and method for calculating travel time between an upstream point and a downstream point by performing the calculation and estimating the number of vehicles existing between the upstream point and the downstream point based on the result.

[0002]

2. Description of the Related Art Conventionally, a vehicle detector provided at a point on a road determines the number Q [vehicles / h] of a vehicle passing in a unit time and a vehicle speed V [km / h]. Q /
V is used to determine the vehicle density K [vehicles / km].

By multiplying this density by the length of the road [km], the number of vehicles existing on the road can be obtained.

[0004]

However, the number of existing vehicles thus obtained is low in reliability because it is based on the information obtained by the vehicle detector provided at one point on the road. There was a problem.

Therefore, an object of the present invention is to provide a traffic information management device capable of obtaining the number of existing vehicles with high reliability.

[0006]

[0007]

[0008]

[0009]

[0010]

[0011]

[0012]

[0013]

[0014]

[0015]

According to one aspect of the present invention, a traffic information management device is installed at an upstream point of a road,
First collection means for collecting the traffic volume, the characteristic amount of the passing vehicle and the passing time of the vehicle, and the traffic amount, the characteristic amount of the passing vehicle and the passing time of the vehicle installed at a downstream point of the road. Second collecting means, an acquiring means for acquiring a reference time estimated to be a travel time between the upstream point and the downstream point, the acquired reference time and the vehicle collected by the first collecting means. A predicting unit that predicts a time when the vehicle that has passed through the upstream point reaches the downstream point based on the passing time of the second point, and the characteristic amount collected by the first collecting unit of the characteristic amounts collected by the second collecting unit. Deviation from the second
Information of the group of vehicles collected by the first collecting means and the group of vehicles collected by the second collecting means based on the deviation of the passing time of the vehicle from the predicted time collected by the collecting means Collecting means for performing matching with information of the vehicle, calculating means for calculating travel time between the upstream point and the downstream point based on the matching result, and collecting by at least one of the first and second collecting means. Estimating means for estimating the number of vehicles present between the upstream point and the downstream point from the calculated traffic volume and the calculated travel time, and the matching means collects the second collecting means.
The amount of deviation from the predicted time of passing the vehicle
An evaluation value is calculated based on this .

According to the present invention, the travel time between the upstream point and the downstream point is calculated based on the matching result, so that the travel time of the vehicle between the upstream point and the downstream point can be calculated accurately and efficiently. It is possible to provide a traffic information management device that can perform calculation and can determine the number of vehicles with high reliability based on the result.

Preferably, each of the first and second collecting means includes a loop type sensor and an identifying means for identifying the vehicle length of the vehicle as a characteristic amount based on the output of the sensor.

When the loop type sensor is adopted as the collecting means in this way, it becomes easy to collect the characteristic amount of the vehicle.

Preferably, each of the first and second collecting means comprises an ultrasonic sensor.

When the ultrasonic type sensor is adopted as the collecting means in this way, the characteristic amount can be collected easily.

[0021] Preferably, each of the first and second collecting means includes a sensor for collecting a running sound of the vehicle.

In this way, when the sensor for collecting the traveling sound is adopted as the collecting means, the collection of the characteristic amount becomes easy.

Preferably, each of the first and second collecting means is a camera for obtaining an image of the vehicle, and a single number from a group of vehicle width, vehicle height, vehicle length, vehicle color, brightness and pattern from the image. It also includes an image processing device that extracts a plurality of selected feature amounts.

If the camera is adopted as the collecting means in this way, the collection of the characteristic amount becomes easy.

Preferably, each of the first and second collection means comprises an optical vehicle sensor.

When the optical vehicle detector is adopted as the collecting means in this way, the characteristic amount can be collected easily.

Preferably, each of the first and second collection means comprises a microwave vehicle detector.

When the microwave type vehicle detector is adopted as the collecting means in this way, the characteristic amount can be collected easily.

Preferably, the first and second collecting means collect a plurality of types of characteristic quantities, and the matching means performs matching by weighting a plurality of types of characteristic quantities.

In this way, a plurality of types of feature quantities are collected,
If matching is performed by weighting a plurality of types of feature amounts, more accurate travel time calculation can be performed.

Preferably, the matching means determines a deviation from the predicted time of the passing time of the vehicles collected by the second collecting means by determining a deviation of the number of vehicles.

In this way, if the deviation of the number of vehicles is determined, the deviation can be easily determined.

Preferably, the matching means uses only the characteristic amount of the vehicle satisfying a specific criterion and the passing time of the vehicle for matching.

As described above, when only the characteristic amount of the vehicle satisfying the specific criterion and the passing time of the vehicle are used for the matching, the processing in the traffic information management device becomes easy.

Preferably, the first and second collecting means collect only the characteristic amount of the vehicle satisfying a specific criterion and the passing time of the vehicle.

As described above, by collecting only the characteristic amount of the vehicle satisfying the specific criterion and the passing time of the vehicle, the processing in the traffic information management device becomes easy.

Preferably, the traffic information management device further comprises reliability calculation means for calculating the reliability of the travel time calculated by the calculation means.

By calculating the reliability in this way, the travel time can be easily evaluated. Preferably, the reliability calculation means calculates reliability based on the variance of travel times,
By using the Kalman filter, the accuracy of estimating the number of existing vehicles is improved.

By using the dispersion of travel times and the Kalman filter in this way, it becomes possible to more accurately estimate the number of existing vehicles.

Preferably, the estimating means makes an estimation in consideration of the calculated reliability. If the estimation is performed in consideration of the reliability calculated in this way, it becomes possible to more accurately estimate the number of existing vehicles.

According to another aspect of the present invention, a traffic information management device is installed at an upstream point of a road and collects a traffic volume, a feature quantity of a passing vehicle and a passing time of the vehicle.
Collecting means, a second collecting means installed at a downstream point of the road and collecting traffic volume, a characteristic amount of a passing vehicle, and a passing time of the vehicle, and a first collecting means.
Collected by the feature quantity of one vehicle and the second collecting means
The deviation of the feature amount of one vehicle, and a passage time of one vehicle collected by the first collecting means, and passage time of one vehicle collected by the second collecting means, these Based on the reference time, which is the estimated travel time between both points,
An evaluation value is calculated for each of a number of vehicles of interest, and
Matching means for matching the group of vehicle information collected by the first collecting means with the group of vehicle information collected by the second collecting means based on the plurality of obtained evaluation values ; Based on the result of the above, the calculation means for calculating the travel time between the upstream point and the downstream point, the traffic volume collected by at least one of the first and second collection means, and the calculated travel time Estimating means for estimating the number of vehicles existing between the point and the downstream point.

According to the present invention, the travel time between the upstream point and the downstream point is calculated based on the result of matching, so that the travel time of the vehicle can be calculated accurately and efficiently, and based on the result. Therefore, it is possible to provide a traffic information management device capable of obtaining the number of vehicles with high reliability.

According to another aspect of the present invention, a traffic information management method collects a traffic amount, a feature amount of a passing vehicle and a passing time of the vehicle at an upstream point of a road.
And the traffic volume at the downstream of the road.
A second collecting step of collecting a characteristic amount of a passing vehicle and a passing time of the vehicle; an obtaining step of obtaining a reference time estimated to be a travel time between an upstream point and a downstream point; Based on the reference time and the passing time of the vehicle collected in the first collecting step, the predicting step of predicting the time when the vehicle passing through the upstream point reaches the downstream point and the collecting time collected in the second collecting step The first collection step based on the deviation of the feature quantity from the feature quantity collected in the first collection step and the deviation of the passing time of the vehicle collected in the second collection step from the predicted time Based on the matching result, the matching step of matching the information of the group of vehicles collected by and the information of the group of vehicles collected by the second collecting step. From the calculation step for calculating the travel time between the upstream point and the downstream point, the traffic volume collected by at least one of the first and second collection steps, and the calculated travel time, the upstream point and the downstream point are obtained. and a estimation step of estimating the number of existing vehicles during the match
In the second collecting step,
The amount of deviation of the passing time of the vehicle from the predicted time
An evaluation value is calculated based on this .

According to the present invention, the travel time of a vehicle between the upstream point and the downstream point can be calculated accurately and efficiently, and the highly reliable number of existing vehicles can be obtained based on the result. It becomes possible to provide a traffic information management method.

According to another aspect of the present invention, a traffic information management method collects a traffic volume, a feature quantity of a passing vehicle and a passing time of the vehicle at an upstream point of a road.
And the traffic volume at the downstream of the road.
A second collecting step of collecting the features and passing time of the vehicle passing vehicles, one that has been collected by the feature amount and the second collection step of one vehicle collected by the first collecting step Deviation from the feature amount of the vehicle, and
And passing time of one vehicle collected by the first collecting step, one and passing time of the vehicle <br/> both of which are collected by the second collecting step, the estimated travel time between these two points based on the reference time becomes, commentary in a plurality of target vehicles each
Value is calculated and based on the multiple evaluation values
There are a matching step of performing a set of vehicle information collected by the first collecting step, matching with a group of vehicle information collected by the second collecting step,
A calculation step of calculating a travel time between the upstream point and the downstream point based on the result of the matching;
The estimation step of estimating the number of vehicles existing between the upstream point and the downstream point from the traffic volume collected by at least one of the collecting steps and the calculated travel time.

According to the present invention, the travel time of a vehicle between the upstream point and the downstream point can be calculated accurately and efficiently, and the highly reliable number of vehicles existing can be calculated based on the result. It becomes possible to provide a traffic information management method.

[0047]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS [First Embodiment] FIG. 1 is a block diagram showing the configuration of a road traffic control system according to a first embodiment of the present invention. Referring to the figure, the road traffic control system is roughly divided into a traffic control center 100.
A traffic information collecting device 200 installed on the road, a traffic information providing device 300 installed on the road, a CRT 400 displaying video data and a characteristic amount of a vehicle, and information obtained from the traffic information providing device 300 and displayed. Information display board 50 for performing
0 and a vehicle 6 that obtains information from the traffic information providing device 300
00 and.

The traffic control center 100 is a device that performs traffic information processing and system monitoring, and is composed of a traffic information processing device and an information monitoring device.

The traffic information processing apparatus measures traffic volume, speed and occupancy rate, collects OD information, measures congestion degree and congestion length, compression wave analysis, unexpected event detection, runaway vehicle detection, Specific vehicle tracking, earthquake detection, travel time measurement, existing vehicle number estimation, travel time prediction, inflow control processing, and traffic flow simulation processing are performed.

The information monitoring device comprises a system display board, a system monitoring device, and a system intervention device.
The information monitoring device allows the user to obtain traffic information.

The traffic information collecting device 200 is composed of ultrasonic type vehicle detectors and the like installed at at least two locations (upstream and downstream) on the road, and collects the characteristic quantities of the vehicle.

The upstream point and the downstream point mentioned here are as shown in FIGS. Here, the white triangle indicates the upstream point, the black triangle indicates the downstream point, and the arrow indicates the flow of the vehicle. 2 shows a case of a single road, FIG. 3 shows a case of a branch, and FIG. 4 shows a case of an intersection.

Alternatively, even if there is a branch or an intersection,
The upstream and downstream points may be set along the route, such as Tomei Expressway, National Route 2, Koshu Kaido, and Meiji Dori.

The traffic information providing device 300 includes the information display board 5
00 and automobile 600 are provided with information.

FIG. 5 is a view showing the external appearance of the road traffic control system. Referring to the figure, the traffic information collecting device includes ultrasonic vehicle detectors 201a and 201b and primary processing devices 203a and 203b that process signals from the ultrasonic vehicle detectors 201a and 201b.

Ultrasonic vehicle detectors 201a, 201b
Is provided on the road R and detects that the vehicle V passing through the road has passed the position and the height of the vehicle (vehicle height). The detection signal is the primary processing devices 203a and 203b.
Entered in. From the primary processing devices 203a and 203b, the time when the vehicle passes through the upstream point or the downstream point and the vehicle height of the vehicle are output and sent to the traffic control center 100.

Here, the ultrasonic type vehicle detector 201 is used.
It is assumed that a is provided downstream of the road from the ultrasonic vehicle detector 201b. Ultrasonic vehicle detector 201
The point where "a" is installed is called "downstream point", and the point where the ultrasonic vehicle detector 201b is installed is called "upstream point".

In this embodiment, a vehicle group including a certain number (N) of vehicles (which are defined as a target vehicle) that satisfy a certain standard at the upstream point is measured as one vehicle group. Subject to. At the downstream point, it is considered that the number of vehicles that make up this vehicle group and the order of the vehicles have changed, so the characteristic amount of the vehicle group obtained at the upstream point and the characteristic amount of the vehicle obtained at the downstream point By evaluating and as a whole according to the predetermined matching evaluation criteria,
Correlate vehicle groups.

FIG. 6 is a graph in which the vertical axis indicates the vehicle height (feature amount) detected by the ultrasonic vehicle detector 201b provided at the upstream point, and the horizontal axis indicates the time when the detection is performed. is there. As shown in the figure, the characteristic amount of the vehicle detected with the passage of time is recorded.

In order to improve the performance of the matching process, the vehicles to be matched (vehicles of interest) are limited to those having a feature amount with a relatively low appearance frequency (here, the vehicle height is relatively high). . Further, in the present embodiment, N = 10 vehicles (1) to (10) are considered as vehicles of interest.
Is to be used. 10 vehicles are detected at time t
_It is assumed that the period is from _u to time T.

FIG. 7 is a graph in which the vertical axis indicates the vehicle height (feature amount) detected by the ultrasonic type vehicle detector 201a provided at the downstream point, and the horizontal axis indicates the time when the detection is performed. is there. The detection here is performed from the reference time t _d . The reference time t _d is determined by the time t _u and the travel time measured last time.

As shown in FIG. 7, at the downstream point, the order of the vehicles of interest and the number of vehicles of interest constituting the vehicle group are different from those of the upstream point (FIG. 6). For more details, see FIG.
In comparison with FIG. 7, vehicles (2) and (5) are not detected in FIG. This is because the detection error of the sensor occurs, the lanes of the vehicles (2) and (5) are changed, the vehicle moves to another branch road, or the vehicle stops.

At the downstream point, a vehicle not detected at the upstream point (vehicle indicated by ●) is newly detected.

Matching is performed based on the data shown in FIGS. 6 and 7, and the vehicle group (1) shown in FIG.
The time (travel time) for (10) to move to the downstream point is determined.

For example, considering each vehicle, it is as follows whether the vehicle (7) passing through the upstream point at time t ₇ in FIG. 6 corresponds to the vehicle detected at the downstream point. Will be judged.

First, the reference time t _d is obtained by adding the travel time between the upstream point and the downstream point obtained previously to the time t _u at which the vehicle detection is started at the upstream point. Then, by adding the reference time t _d a (t ₇ -t _u), determining an expected time for the vehicle (7) (time considered will pass through a point downstream).

T before and after the expected time for this vehicle (7)
_A vehicle that is considered to be closest to the vehicle (7) is determined using a predetermined function within a time range of ₀ (t ₀ is a predetermined time). Here, t ₀ is determined by how much the characteristics of travel time between the upstream point and the downstream point of the vehicle of interest vary on average.

The following process is performed to determine the vehicle that is considered to be closest to the vehicle (7).

First, the time (t _d + (t ₇ −t _u )) ± t
_In the range of ₀ , a candidate vehicle corresponding to the target vehicle (here, vehicle (7) in FIG. 6) is obtained. In this, a vehicle having a characteristic amount relatively close to the characteristic amount of the vehicle of interest is set as a candidate vehicle. However, the number m of candidate vehicles is a constant value m
If there are ₀ or more vehicles, it is considered that there are too many candidate vehicles and reliable handling is not possible, and it is handled in the same manner as when there is no candidate vehicle. Note that m ₀ is determined here by the detection error of the feature amount and the number N of vehicles of interest.

Then, for each candidate vehicle, the deviation (difference in characteristic quantity) x of the characteristic quantity from the vehicle of interest is obtained. Further, the deviation y of the time when each candidate vehicle is detected at the downstream point from the expected time is calculated.

Then, the vehicle having the smallest value of a | x | + b | y | is regarded as the vehicle closest to the vehicle of interest, and the processing is performed. Here, the variables a and b are predetermined constants.

One vehicle group (for example, (1) to FIG. 6)
The minimum value of a | x | + b | y | is obtained for each of the target vehicles included in (10)) that have candidate vehicles, and the average value thereof is the reference time (here, t _d ) Is evaluated value.

However, when the number of vehicles of interest having no candidate vehicle exceeds a fixed value n ₀ , it is determined that vehicles cannot be associated at the reference time, and the evaluation value is set to infinity. Here, n ₀ is substantially determined by the probability of vehicle inflow and outflow between N and the upstream and downstream points.

For example, referring to FIG. 8, vehicles A and C at downstream points are considered as candidate vehicles with respect to vehicle A at an upstream point, and vehicle C at a downstream point with respect to vehicle C of interest.
Is a candidate vehicle, and the target vehicle D at the upstream point
Assuming that the vehicles E, F, and G at the downstream point are candidate vehicles and the vehicle E at the downstream point is a candidate vehicle with respect to the target vehicle E at the upstream point, the target vehicles A to The number m of each candidate vehicle of E is 2, 0,
It becomes 1, 3, 1. In addition, the number of target vehicles without candidate vehicles is 1 (only the target vehicle B). Further, the number n of vehicles of interest having candidate vehicles is four.

The reference time t _d is gradually shifted as shown in I to V of FIG. 9, and the evaluation value is calculated at each reference time. The one with the best evaluation value is determined as the time when the vehicle arrives.

10 to 12 show the traffic control center 100.
3 is a flowchart showing a process performed in.
Referring to the figure, the system is initialized in step S101. In this initial setting, conditions such as the vehicle of interest, the characteristic amount, and the processing cycle are set. Also, various constants are set.

In step S103, the traffic control center 100 refers to the current time. Step S10
At 5, it is determined whether or not the current timing is to start the periodic processing based on the referred time. Until YES, the processing from step S103 is repeated.

If YES in step S105, in step S107, the latest N vehicles of interest (here, N = 10) are selected based on the data obtained at the upstream point. Specifically, the vehicle (1) to (10) shown in FIG. 6 is selected.

In step S109, the passage time and the feature amount of each of the vehicles of interest (1) to (10) are detected and set. In step S111, the reference times are sequentially shifted, and matching with the data obtained at the downstream point is started. For example, the reference time is assumed in the initial state is a reference time t _d as shown in FIG.

The vehicle of interest selected in step S113 is sequentially referred to. In step S115, the expected time of passage of each vehicle of interest at the downstream point is calculated. In step S117, the area around the expected time (± t ₀ ) is searched, and candidate vehicles for the vehicle of interest are searched. In step S119, it is determined whether or not there is a candidate vehicle, and if YES, step S1
If NO in step 21, the process returns to step S113 to process the next vehicle of interest.

In step S121, it is determined whether the number of candidate vehicles is a certain number or more. If YES, the process returns to step S113, and if NO, the process proceeds to step S123.

In step S123, the deviation amount x of the characteristic amount from the vehicle of interest and the deviation y from the expected time are recorded for each candidate vehicle. Next, in step S125, the evaluation value is calculated and recorded by the formula a | x | + b | y | for each candidate vehicle.

In step S127, the minimum evaluation value obtained for each candidate vehicle is accumulated in the accumulated evaluation value. In step S129, it is determined whether the processing has been completed for all the target vehicles. If NO, the process returns to step S113 to start the processing of the next target vehicle. If YES, the process proceeds to step S131.

At step S131, the target vehicle in which the upstream point and the downstream point are associated (the target vehicle with the candidate vehicle)
The number n of is referred to. In step S133, it is determined whether or not the number is equal to or less than the constant value n _0. If YES, the evaluation value is set to infinity in step S157. On the other hand, if NO, then in step S135 the cumulative evaluation value is divided by the corresponding number n of the target vehicles. Then, a value obtained by dividing the infinity evaluation value or the cumulative evaluation value in step S137 by the number n of the corresponding attention vehicles is recorded as the evaluation value at the reference time.

In step S139, it is determined whether or not the processing has been completed for all reference times to be investigated. If NO, the process returns to step S111 to start matching at the next reference time. If YES, step S
Proceed to 141.

In step S141, the recorded evaluation value for each reference time is referred to. In step S143, it is checked whether there is a time zone in which the evaluation value suddenly decreases. If it is determined in step S145 that there is a time zone in which the evaluation value suddenly decreases, the process proceeds to step S147. If NO in step S145, the flow advances to step S151 to refer to the time series data of travel times measured up to the previous processing cycle. The tendency of the change in travel time measured in step S153 is grasped, and the travel time in this cycle is estimated. The process is terminated with the travel time estimated in step S155 as the travel time of the cycle.

In step S147, it is determined whether or not there is only one time zone when the evaluation value suddenly decreases. If NO, the process proceeds to step S151. If YES, in step S149, the travel time of the cycle is determined from the reference time when the evaluation value is the minimum, and the process ends.

The determinations in steps S143 and S145 are to select a time zone (a plurality of reference times) in which the vehicle may have passed from the obtained evaluation value for each reference time. In this case, the evaluation value of this possible time zone needs to be sufficiently small compared to all the time zones. If it is not small enough, matching is not possible for this train of interest and travel time cannot be measured, and the travel time for that cycle is calculated based on the travel time data up to the previous cycle. (S151 to S155).

In the present embodiment, the reference time having the best evaluation value within the time zone thus obtained is selected, and the travel time is determined based on the reference time ( S149).

For example, when the reference time is shifted with reference to FIG. 13, the evaluation value in a certain specific time zone is steeply smaller than that in other time zones (a steep valley occurs. It is a condition for adopting the reference time obtained by matching.

On the other hand, as shown in FIG. 14, when there is no time zone in which the evaluation value is sufficiently small (A) or when there are a plurality of valleys of the same level (B), the measurement is performed only by the periodic processing. Is determined to be insufficient, and the travel time in this cycle is determined by comprehensively evaluating travel time measured in time series up to that time.

As described above, in the present embodiment, the following data is used as the data related to the matching evaluation standard.

(1) Data relating to comparison between each vehicle of interest and its corresponding candidate vehicle Deviation of feature amount (x) Deviation from expected time (y) Number of candidate vehicles (m) (2) Attention Data in consideration of vehicle sequence Number of vehicles of interest with corresponding candidate vehicles (n) Note that, based on the above data, the evaluation value for each reference time is obtained by the function shown in the following equation (1). You may do it.

[0094]

[Equation 1]

In the equation (1), Σ is a process for obtaining the total amount in the vehicle of interest, and min is a process for comparing the evaluation values between the candidate vehicles.

Alternatively, the following equation (2) may be used instead of the above equation (1).

[0097]

[Equation 2]

In the equation (2), a and b are constants for adjusting numerical values between variables.

[Second Embodiment] Since the configuration of the road traffic control system in the second embodiment is the same as that in the first embodiment, the description thereof will not be repeated here. The road traffic control system according to the second embodiment is characterized in that more detailed matching is performed in consideration of the change in the rank of the vehicle of interest. That is, in the road traffic control system according to the second embodiment, the data described below is used as the data used for matching.

(1) Data relating to comparison between each target vehicle and candidate vehicle corresponding thereto Deviation of feature amount (x) Deviation from expected time (y) (2) Data considering the train of target vehicle Corresponding candidate Number of target vehicles in which vehicles exist (n) Time difference between target vehicles or difference in the number of vehicles (y
₂ ) Rank change of the vehicle of interest (z) FIG. 15 is a diagram for explaining the rank change of the vehicle of interest. In the figure, A to F indicate the vehicle of interest, and ∘ indicates a vehicle that is not the vehicle of interest. At the upstream point, attention vehicle A
It is assumed that the ranks of to F are the first to the sixth.

It is assumed that the target vehicle B is not detected at the downstream point, but the target vehicles A, D, C, E, and F are detected in this order. That is, the target vehicles A, C, D, E, F
The respective rankings are 1, 3, 2, 4, and 5. As a result, the rank change Z of the vehicle of interest is A, C, D, E, F.
0, 0, 2, 1, 1, respectively.

16 to 18 are flowcharts showing the processing performed by the road traffic control system according to the second embodiment. Differences between this flowchart and those shown in FIGS. 10 to 12 will be described below.

First, after the process of setting the passage time and the feature amount of the vehicle of interest in FIG. 16 (S209), the process of determining and setting the number of vehicles between the vehicle of interest is performed (S210). This is specifically performed to obtain the time difference (y ₂ ) between the vehicles of interest.

When it is determined in step S247 in FIG. 18 that there is a time zone in which the evaluation value suddenly decreases at only one location, the processing from step S257 is performed. This is to perform more detailed matching while sequentially shifting the reference time in the time zone in which it is determined that the evaluation value suddenly decreases.

In step S257, the process of starting the matching is performed while shifting the reference time. Step S
At 259, the data of each target vehicle and each candidate vehicle is read.

In step S261, the process of calculating the evaluation value at the reference time is performed by the following equation (3) or (4).

[0107]

[Equation 3]

In the expressions (3) and (4), the variables a, b, c and d are constants for adjusting the numerical values among the variables.

In step S263, it is determined whether the processing has been completed for all candidate vehicles. If NO, the process proceeds to step S259, and if YES, step S259.
Proceed to 265.

In step S265, it is determined whether the processing is completed for all the reference times to be investigated,
If NO, the process returns to step S257, and if YES, the process proceeds to step S267.

In step S267, the travel time of the cycle is determined from the reference time with the smallest evaluation value.

As described above, in the present embodiment, more detailed matching is performed in the time zone in which the evaluation value suddenly decreases, so that the travel time can be determined more accurately.

In the present embodiment, detailed matching is performed only in the time zone when the evaluation value suddenly decreases, but detailed matching may be performed at all reference times.

[Third Embodiment] FIG. 19 is a diagram showing the structure of a road traffic control system according to a third embodiment of the present invention. Referring to the figure, in the present embodiment, traffic information collecting device 200 is a loop type vehicle detector (coil) 231a, 231b, 233 provided on a road.
a, 233b.

In this embodiment, the passing time of the vehicle is obtained at the upstream point and the downstream point by the loop type vehicle detector. Further, the vehicle speed and the vehicle length are required as the characteristic quantities of the vehicle.

For example, the loop type vehicle detector 233a or 233b is used as the first loop, the loop type vehicle detector 231.
When a or 231b is the second loop, the pulse shown in FIG. 20 is generated in the first or second loop when the vehicle passes the upstream point or the downstream point.

Time t ₁ shows the rising point of the pulse of the first loop, and time t ₂ shows the falling point. Also, at time t
₃ indicates the rising point of the pulse of the second loop, and time t ₄ indicates the falling point.

These times t _{1 to} t ₄ indicate that the vehicle is in the position shown in FIG. That is, time t ₁ indicates the time when the vehicle V is approaching the first loop, and time t ₂ indicates the time when the vehicle V has passed the first loop. Further, time t ₃ indicates the time when the vehicle V approaches the second loop, and time t ₄ indicates the time when the vehicle V passes the second loop.

Here, let the diameter of the loop type vehicle detector be r _l
And the length of the vehicle and l _v, and the distance between the first and second loop vehicle detectors and l _l, the velocity of the vehicle V and _{v, v = l l / (} t 3 -t 1 ) (7) l _v = {v (t ₂ −t ₁ ) −r _l } / 2 (8) holds. As a result, the vehicle speed and the vehicle length can be obtained as the characteristic amounts of the vehicle. Then, similarly to the first and second embodiments, the travel time can be calculated.

Since a plurality of feature amounts are used for matching in this embodiment, the feature amount shift (x)
It is necessary to use a vector as the value of. Further, the following formula (9) may be used instead of the formula (2), and the formula (10) may be used instead of the formula (4).

[0121]

[Equation 4]

In these equations, x ^T represents the transpose of the vector x. [Example of Actual Field Data] Below is shown an example of field data actually obtained at the upstream point and the downstream point when only the vehicle length is selected as a feature amount for reference.

FIG. 22 is a graph showing the relationship between the detection result of the characteristic amount and the time at the upstream point (in front of the Shibuya ramp of the Metropolitan Expressway No. 3), and FIG. 23 is at the downstream point (Roppongi of the Metropolitan Expressway No. 3). It is a graph which shows the relationship between a feature-value and time. Shibuya on-ramp, Shibuya off-ramp, and Takagi-machi on-ramp exist between these upstream points and downstream points. In addition, this data was obtained from the driving lane during heavy traffic on weekdays, and there are many data for large trucks. The distance between the upstream and downstream points is 4
It is a little less than km.

As can be seen from the figure, it is apparent that pattern matching of a vehicle having a long vehicle length (large vehicle) is easy at first glance. Further, in this case, it is considered that the travel time can be measured with high accuracy even if the detection accuracy of the feature amount is somewhat poor.

Originally, on a highway in a city, a large vehicle does not change lanes except for an off-ramp that goes down to a branch or a general road, and there is a small probability of overtaking other large vehicles. Further, it is considered that the number of lane changes is particularly small during heavy traffic congestion. Therefore, if the location is the same, it is considered that the detection accuracy of a large vehicle improves the accuracy of travel time measurement.

Further, in front of a branching point of an expressway, in front of an off-ramp with a large amount of outflow traffic, and in front of an on-ramp with a large inflow traffic, traffic is disturbed due to many lane changes, which is not preferable as a measurement point. Also, on holidays, the running probability of large vehicles is quite low, which is not desirable for measurement. In this case, it is conceivable to lengthen the unit of measurement, but if the detection accuracy of the feature quantity of the vehicle is good, or if there are multiple types of feature quantities to be adopted, even if such measurement conditions are bad It may be possible to detect travel time.

In the measurement on the intercity expressway, it is considered that the distance between the upstream point and the downstream point becomes long (a few km to 10 km), and the configuration of the vehicle of interest is likely to change. However, on an inter-city expressway, there is at most one inflow and outflow, and it is considered that the probability of inflow and outflow is not so high, and the running probability of large vehicles is very high. It is considered that the travel time can be measured without any problem by adopting a logic that considers the driving characteristics of a large vehicle.

However, in the time zone when the traffic volume becomes large and the traffic lane or the overtaking lane starts to become congested, the lane change of the vehicle is generally large, and the extent to which a large vehicle contributes to the travel It has some influence on the accuracy of time detection.
However, large vehicles are more likely to maintain their own lanes than ordinary vehicles and tend to return to their original lanes immediately after changing lanes. This is convenient for improving the detection accuracy of travel time.

Further, with respect to the ordinary road, generally, there is a large amount of vehicle inflow and outflow between the upstream point and the downstream point (assuming about 2 km). Therefore, the distribution of the traveling characteristics of how much a large-sized vehicle travels straight without turning right and left and the limit condition of the matching probability are related to the measurement accuracy.

[Fourth Embodiment] FIG. 24 is a diagram showing the structure of a road traffic control system according to a fourth embodiment of the present invention. Referring to the figure, in the present embodiment, traffic information collection device 200 is configured with cameras 251a and 251b provided on the road.

In this embodiment, the camera 251
The passing times of the vehicle are obtained at the upstream point and the downstream point by a and 251b. Further, vehicle length, vehicle width, vehicle height, vehicle speed, vehicle color, and other data are obtained as the characteristic amount of the vehicle.

On the basis of these plural characteristic amounts or a single characteristic amount among them, the vehicle group matching is performed as in the first and second embodiments, and the travel time is determined. To be done.

[Fifth Embodiment] FIG. 25 is a block diagram showing the structure of a road traffic control system according to the fifth embodiment of the present invention. Referring to the drawings, in the present embodiment, a voice recognition device is adopted as traffic information collecting device 200, as compared with the road traffic control system shown in FIG.

FIG. 26 is a diagram showing the structure of the road traffic control system in the present embodiment. Referring to the figure, the traffic information collecting device 200 is provided with a microphone 27 provided on a road R.
1a and 271b and primary processing devices (speech recognition devices) 272a and 272b that process voice from a microphone. The passing time of the vehicle is obtained at the upstream point and the downstream point from the voices from the microphones 271a and 271b. In addition, a voice when the vehicle passes is required as a feature amount of the vehicle.

In the traffic control center 100, the frequency characteristic (frequency spectrum) is recognized from the running sound of the vehicle (engine sound, wind noise, tire sound, etc.), and matching is performed using the frequency characteristic as a feature amount.

In the present embodiment, since the matching can be performed by the voice when the vehicle is running,
The travel time can be calculated accurately and efficiently.

[Sixth Embodiment] The appearance of a road traffic control system according to the sixth embodiment is the same as that shown in FIG. In the present embodiment, an optical vehicle detector is used instead of the ultrasonic vehicle detectors 201a and 201b of FIG. The optical vehicle detector determines the passing time of the vehicle at the upstream point and the downstream point.

Further, the vehicle height is obtained as a characteristic amount of the vehicle. Matching is performed based on this feature amount.

[Seventh Embodiment] The external appearance of the road traffic control system in the seventh embodiment is the same as that in FIG. In the present embodiment, a microwave type vehicle detector is used instead of the ultrasonic type vehicle detectors 201a and 201b in FIG.

The microwave type vehicle detector determines the passing time of the vehicle at the upstream point and the downstream point.

Further, the vehicle speed and the vehicle height obtained by the Doppler effect can be obtained as the characteristic amount of the vehicle. Also, as with the case of using the loop type vehicle detector,
The vehicle length can also be obtained as a feature amount. In other words, the output of the vehicle detector turned off when the output of the vehicle detector turned on because the front of the vehicle approached the vehicle detector, and the output of the vehicle detector turned off when the rear part of the vehicle passed the vehicle detector. The time until time is obtained, and the vehicle length is obtained from the time and the vehicle speed.

[Eighth Embodiment] In the above-described embodiment, the travel time is calculated by matching the information of a group of vehicles, and the number of existing vehicles is estimated based on the calculated travel time. By identifying the license plate of the vehicle between the point and the downstream point, the time (travel time) during which the vehicle moves from the upstream point to the downstream point may be calculated, and the number of existing vehicles may be estimated based on the calculated time (travel time) ( License plate matching).

[Ninth Embodiment] As shown in FIG. 27, the vehicle V to the ground systems 201a and 201b are connected.
The travel time may be calculated based on the uplink information sent to the vehicle, and the number of existing vehicles may be estimated based on the travel time. That is, using a device capable of two-way communication such as an optical beacon between the vehicle and the ground system, measure the time when the uplink information from the vehicle was obtained at the upstream point and the downstream point, and calculate the travel time from the time difference. It may be calculated.

[Modifications of the Above Embodiment] In the above embodiment, the passing vehicle at the upstream point is used as a reference for matching with the passing vehicle at the downstream point. Instead, conversely, the passing vehicle at the downstream point may be used as a reference to match the past passing vehicle at the upstream point. By doing so, it is not necessary to wait for the vehicle to be detected at the downstream point, and real-time processing becomes possible.

Further, in the third and fourth embodiments, the vehicle length is detected as the feature amount. However, in order to improve the performance of the matching process, the matching target is set to a vehicle length with a relatively low frequency of appearance. It is efficient to limit to the vehicles that have. Specifically, a vehicle having a vehicle length equal to or greater than a certain value (for example, a large truck) or a vehicle having a vehicle length less than or equal to a certain value (such as a light vehicle) may be a vehicle of interest.

When the device is activated,
The range of the reference time to be evaluated is set to be sufficiently large, and the travel time measured in the previous cycle and the maximum value of the travel time that fluctuates in one cycle are used as a reference to determine the reference time to be evaluated. The range may be narrowed.

Further, the reliability of the calculated travel time (variation error from the average) may be calculated based on the evaluation value.

Further, the cycle of measuring the travel time may be changed in response to changes in traffic conditions such as congestion and non-congestion.

Further, the cycle of measuring the travel time may be changed by the ratio of the number of passing vehicles of interest to the total number of vehicles.

In addition, the cycle of measuring the travel time is such that the number of vehicles flowing into the road to be measured between the upstream point and the downstream point is the same as the number of vehicles flowing out of the target road, and the vehicle travels on the target road. You may make it change with the ratio with respect to the number of vehicles.

The period for measuring the travel time may be changed depending on the distance between the upstream point and the downstream point.

Furthermore, the processing described in the first to fifth embodiments may be performed for each lane of the road to be measured.

In the above-mentioned road traffic control system, the information obtained from the traffic information collecting device 200 is transmitted to the traffic control center 100 so that the traffic control center 10
Although the travel time and the like are measured at 0, information may be exchanged between the traffic information collecting devices 200 and the travel time may be measured by the traffic information collecting device 200. Further, the processing may be performed by a distributed processing device separately installed on the road.

[0154] Information may be transmitted by using a dedicated wired line, a public line, or a wireless line.

When using a plurality of types of feature amounts for matching, each feature amount may be weighted.

Although the deviation from the expected time is used for matching in the embodiment, the deviation of the number of vehicles from the expected time may be used instead of this.

In addition, for example, both the ultrasonic type sensor and the optical type vehicle detector can determine the vehicle height. Also,
Both the loop type sensor and the microwave type vehicle detector can determine the vehicle length. Further, the camera (image input device) can determine the vehicle height and the vehicle length. In this way, different sensors can collect the same feature amount, so that matching can be performed even if different sensors are used at the upstream point and the downstream point.

The reliability of the calculated travel time may be calculated. This is the end of matching,
When the travel time is finally obtained, the reliability of the travel time is also obtained at the same time. For example, if the travel time finally obtained is Td and the time difference (or the number of vehicles) of each vehicle with respect to it is y _i , the reliability V (y) corresponds to the variation, and the following equation (11) ).

[0159]

[Equation 5]

In the equation (11), n indicates the number of vehicles. The smaller the reliability obtained by the equation (11), the higher the reliability is (reliable).

[Effects of the Embodiment] The above-described embodiments have the following advantageous effects as compared with the related art.

(1) In the case where there is a large amount of inflow / outflow traffic or overtaking because the evaluation criteria for matching as a whole vehicle group are set based on the ambiguous characteristic amount of each vehicle in the vehicle group of the upstream point and the downstream point. However, it becomes possible to associate vehicle groups. Further, even if the matching of the individual vehicles cannot be determined, highly accurate measurement values can be obtained in terms of average travel time.

(2) Since the characteristic amount is not a rough numerical value such as a large or small vehicle type, but an analog numerical value, it is possible to improve the measurement accuracy.

(3) Since the deviation of the feature amount and the deviation from the reference time are mainly considered as evaluation criteria, highly accurate measurement is possible.

(4) To enhance the matching effect,
Vehicles that have a high frequency of appearance, such as feature quantities of ordinary vehicles, are excluded from the matching target, and therefore accurate measurement is possible.

(5) If the reliability of the travel time is calculated along with the measurement of the travel time, the value obtained as the system can be used easily.

(6) By using the image processing apparatus as in the fifth embodiment, a plurality of ambiguous feature quantities can be extracted. Then, by performing matching as the entire vehicle group, it is possible to further improve the measurement accuracy.

[Tenth Embodiment] A road traffic control system according to an embodiment described below is a travel time between an upstream point and a downstream point obtained by the above-described first to seventh embodiments. The number of vehicles existing between the upstream point and the downstream point is calculated based on T. Further, as in the eighth and ninth embodiments, the travel time may be calculated using a license plate or an uplink. The reliability of travel time in this case is calculated as the variation.

Referring to FIG. 28, if there are sensors at the upstream point i and the downstream point i + 1 and the travel time Δt between them can be estimated, the upstream point i is passed between times t-T (t) and t. The number of vehicles (upstream traffic QtT _{(t), ti} ) corresponds to the number of vehicles existing in the sections ^{i to i} + 1 at time t.

Similarly, the number of vehicles passing through the downstream point i + 1 (downstream traffic _{Qt, t + T (t)} ^{i + 1} ) between times _{t and t + T (t)} can also be obtained.

That is, the traffic volume used to determine the number of vehicles may be that at the upstream point or at the downstream point.

FIG. 29 is a plan view for explaining the configuration and operation of the road traffic control system in this embodiment. Referring to the figure, traffic information collecting devices 200a and 200b are provided at upstream and downstream points of road R, respectively.

The traffic information collecting devices 200a, 200
Any of the above-described first to seventh embodiments may be used as the device configuration including b.

The traffic information collecting devices 200a and 200b are
The characteristic amount of the vehicle passing through the point and the vehicle speed V _u ,
V _d and the number of vehicles passing (traffic volume) Q _u , Q _d are measured.

The travel time T from the upstream point to the downstream point is obtained based on the characteristic amount of the vehicle. Then, the number E of vehicles existing between the upstream point and the downstream point is obtained from the traffic volumes Q _u and Q _d and the travel time T.

If the traffic information collecting devices 200a and 200b in the first embodiment described above are used, the passing speed of the vehicle cannot be obtained. Therefore, the traffic information collection devices 200a and 200b according to the first embodiment.
When is adopted, it is necessary to provide a vehicle speed detector for detecting the vehicle speed at the upstream point and the downstream point.

FIG. 30 is a flow chart showing a process performed by the road traffic control system in the present embodiment. Referring to the figure, initial setting is performed in step S100. In step S102, E (0), which is the initial value of the number of existing vehicles E, is obtained.

Since the initial value is usually unknown, for example, from the traffic volumes Q _u and Q _d at the upstream point and the downstream point, the vehicle speeds V _u and V _d, and the distance L between the upstream point and the downstream point. , Which is obtained by the following equation (12). Also, the variance at that time is set to ∞ (a very large value).

E (0) = (Q _u / V _u + Q _d / V _d ) L / 2 (12) Alternatively, an appropriate value may be substituted as the value of E (0). In any case, since the number of existing vehicles is fed back based on the observed value and is estimated at each time, the information of this initial value (preliminary information) becomes meaningless after some time. Therefore, the initial value is not so important.

In step S104, the time is referred to and it is determined whether or not a predetermined measurement cycle has been reached. And yes
Wait until. In step S106, the traffic information Q _u and Q _d at the upstream point and the downstream point are measured by the traffic information collecting devices 200a and 200b. Next, in step S108, the travel time T between the upstream point and the downstream point is calculated by the processing disclosed in any of the first to seventh embodiments.

In step S110, the Kalman filter is used to estimate the number of vehicles existing at that time.

The method of estimating the number of existing vehicles, which is performed in step S110, will be described below.

Assuming a road having only one lane, the traffic volume Q _u (a, b), Q _d (a, b) from time a to time b at the upstream point and the downstream point at time t, respectively. The travel time T (t) (based on the time when the vehicle reaches the downstream point) and the number E (t) of vehicles existing between the upstream point and the downstream point at the time t are as follows. Holds.

E (t) = E (t−dt) + Q _u (t−dt, t) −Q _d (t−dt, t) (13) E (t) = Q _u (t−T (t ), T) or ... (14) E (t-T (t)) = _Qd (t-T (t), t) (15) Relatively frequently, the travel time T and its reliability (accuracy or (Variation) is obtained by filtering from the dynamic characteristics including the traffic volume measurement error related to the number of existing vehicles according to the above equation (13) and the observation equation including the reliability according to the above equation (14) or (15). By the processing, the number of existing vehicles can be easily estimated.

In the present embodiment, the case where the number of lanes is one is assumed, but in the case of a plurality of lanes, the total number of vehicles for each lane may be considered.

Here, for example, a Kalman filter can be used for the filtering process. That is, when both the state equation and the observation equation include time distribution independent and uncorrelated errors of the normal distribution, the optimum estimated value can be obtained by the recurrence formula.

It is assumed that the state method and the observation method are respectively given as follows. x (t) = x (t−1) + u (t) + ξ (t) y (t) = v (t) + η (t) (16) where x (t) is the state variable at time t Indicates the true value (unknown) of (variable to be estimated).

Y (t) represents the observed value (known) of the state variable at time t. u (t) indicates some known variable (not a controlled variable here) up to time t.

V (t) represents some known variable up to time t. ξ (t) represents a state-systematic error (unknown) at time t.

Η (t) indicates an observation method error (unknown) at time t. At this time, assuming the error characteristics of ξ and η, the optimum estimated value x of the state variable x (t) at time t
(T) and its error (variance) Σ (t) are obtained as follows.

X (t) = x (t−1) + u (t) + K (t) {y (t) − ( x (t−1) + u (t))} K (t) = {M (t ) + Σ (t−1)} / {M (t) + Σ (t−1) + N (t)} = Σ (t) / N (t) Σ (t) = N (t) {M (t) + Σ (T-1)} / {M (t) + Σ (t-1) + N (t)} x (0) = {αN (0) + y (0) B} / {N (0) + B} Σ (0 ) = N (0) B / {N (0) + B} (17) Here, the error characteristic is assumed as follows. In addition,
N (*, **) indicates the mean value * and the variance ** of the normal distribution.

Initial value of x (0): N (α, B) ξ (t): N (0, M (t)) η (t): N (0, N (t)) K (t ) Is called Kalman gain, and is a weight for the predicted value from the time before the observed value.

From the above, the estimation formula (13) for
The equations (15) can be applied as follows. Since only one of equations (14) and (15) needs to be used, equation (14) is used here.

Dt → 1 [sec] E (t) → x (t) Left side of Eq. (14) (E (t) observed value) → y (t) Q _u (t-dt, t) -Q _d ( t−dt, t) → u (t) Q _u (t−T (t), t) → v (t) The error characteristics are considered as follows.

When the traffic volume Q _d (t−T (t), t) measured at the downstream point is used as v (t), the calculated number of existing vehicles is calculated by the equation (14). It must be taken into account that it is offset by T (t).

When both the traffic volume on the upstream side and the traffic volume on the downstream side are observed, the one which is considered to have a small error is used. If there is no difference, use the upstream traffic volume.

(1) Error in state equation: ξ (t) This error is a measurement error of traffic volume at each point (for example, sensor error). This error is set by investigation at the time of initial setting. Alternatively, since the difference between the inflow and outflow of the daily traffic volume is 0, if it is not 0, this error may be distributed to the measurement error at each point. Further, the error may be set in proportion to the traffic volume, or may be set in consideration of the information on the measurement uncertainty at each point obtained by the survey.

(2) Error of observation equation η (t) This error is an estimation error of the number of existing vehicles. In the present embodiment, since the travel time is estimated, the travel time estimation error is set as this error. For example, if the reliability of the travel time obtained (accuracy, variation, or the cumulative total of both, such as standard deviation, standard deviation, coefficient multiple of standard deviation, maximum deviation from average value, etc.), is set. do it.

For example, as a specific example of accuracy, when the travel time is measured only for special vehicles, the correlation between these vehicles and the entire traveling vehicle is obtained in advance,
Consider this error.

As an example of the variation, when a plurality of travel times can be obtained in almost the same time zone, the variation may be converted into the variation of the number of existing vehicles, and this may be used as an error.

In any case, it is not necessary to set the error so strictly, and therefore it is a usual method to set it slightly larger.

[Eleventh Embodiment] The road traffic control system in the eleventh embodiment measures the number of vehicles existing between the upstream point and the downstream point, as in the tenth embodiment. To do. However, in the present embodiment, it is assumed that measurement is performed when there is a branch road (outflow portion or inflow portion) at the upstream point and the downstream point of the road.

If there is an inflow section or an outflow section between the upstream point and the downstream point, it is necessary to make an assumption such as uniform running.
The estimation accuracy of the number of existing vehicles decreases slightly. However, if the traffic volume at the inflow and outflow points is smaller than that on the main line (between the upstream point and the downstream point), it will be a secondary error, and the overall error will be small.

Referring to FIG. 31, the road traffic control system according to the present embodiment is different from the system configuration according to the tenth embodiment in that the traffic information for measuring the traffic volume at the outflow portion and the inflow portion is added. Collection devices 200c, 200d
Equipped with. The traffic information collection devices 200a and 200b measure the traffic amounts Q _o and Q _{i at the} outflow portion and the inflow portion and the characteristic amount of the vehicle.

In this embodiment, the distance from the outflow portion to the downstream point is L _o , the distance from the inflow portion to the downstream point is L _i, and the distance from the upstream point to the downstream point is L. Further, the travel time from the outflow part to the downstream point is T _o, and the travel time from the inflow part to the downstream point is T _i.
And T is the travel time from the upstream part to the downstream part.

T _i , T _o , and T are obtained by the methods of the first to ninth embodiments. In the present embodiment, the number E of vehicles existing is obtained by approximating the travel time T from the upstream point, the downstream point, the positional relationship between the inflow section and the outflow section, or the traffic conditions between the upstream point and the downstream point.

Specifically, the following equations (18) to (20) are used in this embodiment in place of the above equations (13) to (15).

E (t) = E (t-dt) + Q _u (t-dt, t) -Q _d (t-dt, t) + Q _i (t-dt, t) -Q _o (t-dt, t) (18) E (t) = Q _u (t−T (t), t) + Q _i (t−T _i (t), t) −Q _o (t−T _o (t), t) ... (19) E (t- T (t)) = Q d (t-T (t), t) -Q i (t-T (t), t-T i (t)) + Q o (t- T (t), t−T _o (t)) (20) Note that T _i (t) and T _o (t) in formulas (19) and (20) are represented by the following formulas (21) and (22). Can be approximated by

T _o (t) ≈T (t) · L _o / L or ≈T (t) · a / (a + b) a = L _o Q _d / V _d , b = (L−L _o ) Q _u / V _u or _{≒ L o / V d (T} (t) ≒ (L-L o) / V u + L when the _{o / V d) ... (21} ) T i (t) ≒ T (t) · L i / L or ≈T (t) · a / (a + b) a = L _i Q _d / V _d , b = (L−L _i ) Q _u / V _u or ≈L _i / V _d (T (t) ≈ (When L−L _i ) / V _u + L _i / V _d ) (22) In the present embodiment, the number of existing vehicles may be determined in consideration of vehicles passing through the inflow section and the outflow section. it can.

FIG. 32 is a flow chart showing the processing performed by the road traffic control system in the present embodiment. In the figure, steps S200 to S206 are the same as those in FIG.
0 corresponds to steps S100 to S106. Also, steps S208 and S2 in FIG.
10 indicates steps S108 and S11 in FIG.
Corresponds to 0.

In this embodiment, step S20.
After measuring the traffic volume in 6, the traffic volumes Q _i and Q _o in the inflow section and the outflow section (interchange) are measured in step S207.

After the processing in step S208,
Travel time T from the inflow portion to the downstream portion in step S209
_i and the travel time T _o from the outflow portion to the downstream point are obtained by the processing described in the first to seventh embodiments.

In step S210, the existing number of vehicles at the time is estimated by using the Kalman filter.

Note that a plurality of feature quantities may be obtained by using a plurality of traffic information collection devices according to the first to seventh embodiments. In addition, the feature quantity is individually communicated (two-way beacon, ET
C)). Furthermore, one or more of license plate, vehicle shape, pattern, color, color balance, vehicle shade, and shade balance are extracted from the image data,
These may be used as the characteristic amount.

The embodiments disclosed this time are to be considered as illustrative in all points and not restrictive. The scope of the present invention is shown not by the above description but by the claims, and is intended to include meanings equivalent to the claims and all modifications within the scope.

[Brief description of drawings]

FIG. 1 is a block diagram showing a configuration of a road traffic control system according to a first embodiment of the present invention.

FIG. 2 is a first diagram showing specific examples of upstream points and downstream points.

FIG. 3 is a second diagram showing specific examples of upstream points and downstream points.

FIG. 4 is a third diagram showing specific examples of upstream points and downstream points.

FIG. 5 is a diagram showing a configuration of a road traffic control system according to the first embodiment.

FIG. 6 is a diagram showing a feature amount of a vehicle group obtained at an upstream point.

FIG. 7 is a diagram showing a feature amount of a vehicle group obtained at a downstream point.

FIG. 8 is a diagram for explaining a relationship between a vehicle of interest and a candidate vehicle at an upstream point and a downstream point.

FIG. 9 is a diagram for explaining how to shift the reference time.

FIG. 10 is a flowchart showing a process performed by the road traffic control system according to the first embodiment.

11 is a flowchart following FIG.

FIG. 12 is a flowchart following FIG. 11.

FIG. 13 is a diagram showing a result of matching when a valley of evaluation values exists.

FIG. 14 is a diagram showing a result of matching in which an evaluation value cannot be adopted.

FIG. 15 is a diagram for explaining the processing of the road traffic control system in the second embodiment.

FIG. 16 is a flowchart showing a process performed by the road traffic control system according to the second embodiment.

FIG. 17 is a flowchart following FIG.

FIG. 18 is a flowchart following FIG.

FIG. 19 is a diagram showing a configuration of a road traffic control system according to a third embodiment of the present invention.

FIG. 20 is a diagram showing pulses obtained with the system of FIG.

21 is a diagram showing the relationship between the pulse of FIG. 20 and the position of the vehicle.

FIG. 22 is a diagram showing a measurement result of a vehicle length (upstream point).

FIG. 23 is a diagram showing a measurement result of a vehicle length (downstream point).

FIG. 24 is a diagram showing a configuration of a road traffic control system according to a fourth embodiment of the present invention.

FIG. 25 is a block diagram showing a configuration of a road traffic control system according to a fifth embodiment of the present invention.

FIG. 26 is a diagram showing a configuration of a road traffic control system according to a fifth embodiment.

FIG. 27 is a diagram showing a configuration of a road traffic control system according to a ninth embodiment.

FIG. 28 is a diagram for explaining the principle of determining the number of vehicles present from the travel time and the traffic volume.

FIG. 29 is a diagram showing a configuration of a road traffic control system in the tenth embodiment.

FIG. 30 is a flowchart showing a process of the road traffic control system in the tenth embodiment.

FIG. 31 is a diagram showing a configuration of a road traffic control system according to an eleventh embodiment.

FIG. 32 is a flowchart showing processing of the road traffic control system in the eleventh embodiment.

[Explanation of symbols]

100 Traffic Control Center 200 Traffic Information Collection Devices 201a, 201b Ultrasonic Vehicle Detectors 231a, 231b, 233a, 233b Loop Vehicle Detectors 251a, 251b Cameras (Image Processing Devices) 271a, 271b Microphones

Continuation of the front page (56) Reference JP-A-8-161686 (JP, A) JP-A-11-39587 (JP, A) JP-A-9-91588 (JP, A) JP-A-7-262488 (JP , A) JP-A-8-16975 (JP, A) JP-A-9-167296 (JP, A) (58) Fields investigated (Int.Cl. ⁷ , DB name) G08G 1/00 G08G 1/01 G08G 1/015 G08G 1/04

Claims (17)

(57) [Claims] 1. A first collecting means installed at an upstream point of a road for collecting traffic volume, a characteristic amount of a passing vehicle and a passing time of the vehicle, and a first collecting means installed at a downstream point of the road for passing and passing traffic. Second collecting means for collecting a feature amount of a vehicle and a passing time of the vehicle, acquiring means for acquiring a reference time estimated to be travel time between the upstream point and the downstream point, Prediction means for predicting the time when the vehicle passing through the upstream point arrives at the downstream point, based on the acquired reference time and the passing time of the vehicle collected by the first collecting means; The first of the feature quantities collected by the collecting means
The deviation from the feature quantity collected by the collecting means of
Information of the group of vehicles collected by the first collecting means and the second collecting means based on the deviation of the passing time of the vehicle collected by the collecting means from the predicted time. Matching means for performing matching with the information of the group of vehicles that have been generated, calculating means for calculating travel time between the upstream point and the downstream point based on the result of the matching, and the first and second Traffic amount collected by at least one of the collecting means, and an estimating means for estimating the number of vehicles existing between the upstream point and the downstream point from the calculated travel time , the matching means, Collected by the second collecting means
Deviation of the passing time of the collected vehicles from the predicted time
A traffic information management device that calculates an evaluation value based on the amount of traffic. The method according to claim 2 wherein each of the first and second collection means includes a loop sensor, the identifying means for identifying a feature value of the car length of the vehicle based on an output of the sensor, according to claim 1 Traffic information management device described in. 3. The traffic information management device according to claim 1 , wherein each of the first and second collecting means includes an ultrasonic sensor. Wherein each of said first and second collection means includes a sensor for collecting running noise of the vehicle, according to claim 1
Traffic information management device described in. 5. Each of the first and second collecting means includes a camera for obtaining an image of a vehicle, and a group of a vehicle width, a vehicle height, a vehicle length, a vehicle color, a luminance and a pattern from the image. The traffic information management device according to claim 1 , further comprising an image processing device that extracts a single or a plurality of selected feature amounts. 6. The traffic information management device according to claim 1 , wherein each of the first and second collection means includes an optical vehicle detector. 7. The traffic information management device according to claim 1 , wherein each of the first and second collecting means includes a microwave type vehicle detector. Wherein said first and second collection means collects feature amounts of a plurality of types, the matching means performs the matching by performing the weighting of the plurality of types of characteristic amounts, of claims 1 to 7 The traffic information management device according to any one. Wherein said matching means, by determining the number displacement of the vehicle, determining a deviation from the predicted time of passing time of the vehicle collected by said second collecting means, according to claim 1 The traffic information management device according to any one of 1 to 8 . Wherein said matching means, utilizes only passage time of the feature and the vehicle in the vehicle that meet certain criteria for matching, the traffic information management apparatus according to any one of claims 1 to 9. Wherein said first and second collection means collects only passage time of the feature and the vehicle in the vehicle that meet certain criteria, the traffic information management according to any one of claims 1 9 apparatus. 12. A reliability calculation means for calculating the reliability of the travel time calculated by the calculation means is further provided.
Traffic information management apparatus according to any one of claims 1 to 11. 13. The reliability calculation means calculates reliability based on the variance of travel times, and improves the accuracy of estimating the number of existing vehicles by using a Kalman filter.
The traffic information management device according to item 12 . 14. The traffic information management device according to claim 13 , wherein the estimation means performs estimation in consideration of the calculated reliability. 15. The traffic volume, which is installed at an upstream point on the road,
A first collecting unit that collects a characteristic amount of a passing vehicle and a passing time of the vehicle, and a second collecting unit that is installed at a downstream point of a road and that collects a traffic volume, a characteristic amount of a passing vehicle and a passing time of the vehicle And a deviation between the characteristic amount of one vehicle collected by the first collecting unit and the characteristic amount of one vehicle collected by the second collecting unit, and the first and passing time of one vehicle collected by the collecting means, the passage time of one vehicle collected by said second collecting means, based on the reference time as the estimated travel time between these two points , Multiple
The evaluation value is calculated for each of the
Matching means for matching the group of vehicle information collected by the first collecting means with the group of vehicle information collected by the second collecting means based on the plurality of evaluated values obtained ; Based on the result of matching, a calculating means for calculating a travel time between the upstream point and the downstream point, a traffic volume collected by at least one of the first and second collecting means, and the calculated A traffic information management device, comprising: an estimation unit that estimates the number of vehicles existing between the upstream point and the downstream point from the travel time. 16. A first collecting step of collecting a traffic volume, a feature quantity of a passing vehicle and a passing time of the vehicle at an upstream point of the road, and a traffic volume and a characteristic of a passing vehicle at a downstream point of the road. A second collecting step of collecting an amount and a passing time of the vehicle, an acquiring step of acquiring a reference time estimated to be a travel time between the upstream point and the downstream point, and the acquired reference A predicting step of predicting a time when a vehicle passing through the upstream point reaches the downstream point, based on time and a passing time of the vehicle collected in the first collecting step, and collected by the second collecting step. The deviation of the generated feature amount from the feature amount collected in the first collecting step,
The information of the group of vehicles collected in the first collecting step and the second collection based on the deviation of the passing time of the vehicle collected in the second collecting step from the predicted time. A matching step of performing matching with the information of the group of vehicles collected by the step; a calculating step of calculating a travel time between the upstream point and the downstream point based on a result of the matching; a traffic volume collected by at least one of the second collection step, and a estimation step of estimating the number of existing vehicles between said downstream point and the upstream point of travel times and the calculated, the matching In the step, the second collecting step
Said predicted time of passage of the vehicle collected by
A traffic information management method that calculates an evaluation value based on the amount of deviation from . 17. A first collecting step of collecting traffic, a characteristic amount of a passing vehicle and a passing time of the vehicle at an upstream point of the road, and a characteristic of a traffic amount and a passing vehicle at a downstream point of the road. amount and a second collecting step of collecting the pass time of the vehicle, one collected by the feature and the second collection step of the first one vehicle collected by the collecting step of
The deviation of the feature amount of the vehicle, and the passage time of the first one vehicle collected by the collecting step, the passing time of the one vehicle collected by the second collecting step, these Calculate the evaluation value for each of the multiple vehicles of interest based on the reference time that is the estimated travel time between the two points
Then, the group of vehicle information collected in the first collecting step and the group of vehicle information collected in the second collecting step are matched based on the plurality of evaluation values obtained thereby. A matching step; a calculating step of calculating a travel time between the upstream point and the downstream point based on the result of the matching; and a traffic volume collected by at least one of the first and second collecting steps. And an estimating step of estimating the number of vehicles existing between the upstream point and the downstream point from the calculated travel time.