JP2000306190A - Method and device for traffic information management - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a traffic information management device which precisely and efficiently measures the number of vehicles existing between an upstream point and a downstream point. SOLUTION: The traffic information management device is provided with traffic information collection devices 300a and 200b provided at an upstream point and a downstream point of a road respectively. Each traffic information collection device obtains the feature values of passing vehicles, the traffic volume, and speeds of vehicles at the upstream point or the downstream point. Matching is performed on the basis of the feature values of vehicles to measure the time required for running of vehicles from the upstream point to the downstream point. Based on these measured time and traffic volume, the number of vehicles existing between the upstream point and the downstream point is estimated by processing with a Kalman filter.

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 more particularly, to collecting feature amounts of vehicles passing at an upstream point and a downstream point of a road, and matching the collected feature amounts of a group of vehicles. The present invention relates to a traffic information management apparatus and method for calculating a travel time between an upstream point and a downstream point, and estimating the number of vehicles existing between the upstream point and the downstream point based on the calculation result.

[0002]

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

[0003] By multiplying this density by the length [km] of the road, the number of vehicles existing on the road can be obtained.

[0004]

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

Accordingly, an object of the present invention is to provide a traffic information management device capable of determining the number of vehicles having high reliability.

[0006]

According to one aspect of the present invention, there is provided a traffic information management device comprising: a first measuring means for measuring a travel time between an upstream point and a downstream point of a road; And second measuring means for measuring the traffic volume at at least one of the upstream point and the downstream point; and the traffic volume at at least one of the upstream point and the downstream point and the travel time to determine the vehicle traffic between the upstream point and the downstream point. Estimating means for estimating the number of vehicles present.

According to the present invention, since the number of vehicles existing is estimated from the traffic volume and travel time, it is possible to provide a traffic information management device capable of obtaining a highly reliable number of vehicles existing.

[0008] Preferably, the traffic information management device further comprises a calculating means for calculating the reliability of the measured travel time,
The estimating means performs estimation in consideration of the calculated reliability.

According to the present invention, it is possible to perform an estimation in consideration of the reliability, so that it is possible to provide a traffic information management device capable of obtaining a more reliable number of vehicles.

[0010] More preferably, the calculation means calculates the reliability based on the variance of the travel time, and the traffic information management device improves the accuracy of estimating the number of vehicles by using a Kalman filter.

According to the present invention, the use of the travel time dispersion and the Kalman filter makes it possible to provide a traffic information management device capable of obtaining a more reliable number of vehicles.

[0012] Preferably, the first measuring means measures the travel time by license plate matching.

When the travel time is measured by license plate matching as described above, a more accurate travel time can be calculated, and a traffic information management device capable of obtaining a more reliable number of existing vehicles can be obtained. Can be provided.

Preferably, the first measuring means measures the travel time based on the uplink information. To provide a traffic information management device capable of measuring a more accurate travel time and obtaining a more reliable number of existing vehicles by measuring the travel time based on the uplink information as described above. Becomes possible.

According to another aspect of the present invention, the traffic information management device is installed at an upstream point of a road, and collects a traffic volume, a characteristic value of a passing vehicle, and a passing time of the vehicle.
Collecting means, and a second collecting means installed at a downstream point of the road and collecting the traffic amount, the characteristic amount of the passing vehicle and the passing time of the vehicle, and the travel time between the upstream point and the downstream point. Acquiring means for acquiring a reference time estimated to be present, and time at which the vehicle passing the upstream point reaches the downstream point based on the acquired reference time and the passing time of the vehicle collected by the first collecting means. Prediction means for predicting the deviation of the feature quantity collected by the second collection means from the feature quantity collected by the first collection means, and the passing time of the vehicle collected by the second collection means Matching means for matching the information of the group of vehicles collected by the first collection means with the information of the group of vehicles collected by the second collection means based on the deviation from the set time; Matching result Based, calculation means for calculating the travel time between the upstream point and the downstream point, first and second
And estimating means for 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 means and the calculated travel time.

According to the present invention, since the travel time between the upstream point and the downstream point is calculated based on the result of the matching, the travel time of the vehicle between the upstream point and the downstream point can be accurately and efficiently calculated. It is possible to provide a traffic information management device capable of performing calculations and obtaining a highly reliable number of vehicles based on the calculation results.

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

When the loop type sensor is employed as the collection means, it becomes easy to collect the characteristic amount of the vehicle.

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

When the ultrasonic sensor is used as the collection means, the collection of the characteristic amounts becomes easy.

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

In this way, if a sensor for collecting the traveling sound is adopted as the collecting means, it becomes easy to collect the characteristic amount.

Preferably, each of the first and second collecting means includes a camera for obtaining an image of the vehicle and a single image from a group of vehicle width, height, length, color, brightness and pattern from the image. Alternatively, an image processing apparatus for extracting a plurality of selected feature amounts is included.

If a camera is employed as the collection means, the collection of the characteristic amounts becomes easy.

Preferably, each of the first and second collecting means includes an optical vehicle sensor.

When an optical vehicle sensor is used as the collection means, the collection of the characteristic amounts becomes easy.

[0027] Preferably, each of the first and second collecting means includes a microwave type vehicle sensor.

As described above, when the microwave type vehicle detector is used as the collection means, the collection of the characteristic amount becomes easy.

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

As described above, a plurality of types of feature values 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 the deviation of the number of vehicles from the predicted time of the passing time of the vehicles collected by the second collecting means.

As described above, if the difference in the number of vehicles is determined, the difference 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 the matching.

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

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

As described above, if only the feature quantity of a vehicle satisfying a specific criterion and the passing time of the vehicle are collected, the processing in the traffic information management device becomes easy.

[0037] Preferably, the traffic information management device further includes reliability calculating means for calculating the reliability of the travel time calculated by the calculating means.

When the reliability is calculated in this way, the evaluation of the travel time becomes easy. Preferably, the reliability calculation means calculates the reliability based on the travel time variance,
By using the Kalman filter, the accuracy of the number of existences estimation is improved.

By using the travel time variance and the Kalman filter as described above, it is possible to more accurately estimate the number of existing vehicles.

Preferably, the estimating means performs the estimation in consideration of the calculated reliability. If the estimation is performed in consideration of the reliability calculated as described above, the number of existing vehicles can be more accurately estimated.

According to another aspect of the present invention, a traffic information management device is provided at an upstream point of a road, and collects a traffic volume, a characteristic value of a passing vehicle, and a passing time of the vehicle.
Collection means, a second collection means installed at a downstream point of the road and collecting traffic volume, a feature quantity of a passing vehicle and a passing time of the vehicle, and a feature of the vehicle collected by the first collection means. The difference between the quantity and the feature amount of the vehicle collected by the second collection unit, the passing time of the vehicle collected by the first collection unit, and the passing time of the vehicle collected by the second collection unit. Matching the group of vehicle information collected by the first collection unit with the group of vehicle information collected by the second collection unit based on the reference time serving as the estimated travel time between these two points. Calculating means for calculating a travel time between the upstream point and the downstream point based on the result of the matching; and traffic volume collected by at least one of the first and second collecting means. Was And a estimation unit that estimates the number of existing vehicles between the upstream point and the downstream point from the line time.

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

According to another aspect of the present invention, a traffic information management method includes a first method for collecting, at an upstream point of a road, a traffic amount, a characteristic amount of a passing vehicle, and a passing time of the vehicle.
Collection steps and traffic volume,
A second collection step of collecting a feature amount of a passing vehicle and a passing time of the vehicle, an acquisition step of acquiring a reference time estimated to be a travel time between an upstream point and a downstream point, A prediction step of predicting a time at which the vehicle that has passed the upstream point will reach the downstream point, based on the reference time and the passing time of the vehicle collected in the first collection step; A first collection step based on a deviation of the characteristic amount from the characteristic amount collected in the first collection step and a deviation of the passing time of the vehicle collected in the second collection step from the predicted time; A matching step of matching the information of the group of vehicles collected by the method with the information of the group of vehicles collected by the second collecting step, and based on the result of the matching. Calculating a travel time between the upstream point and the downstream point, an amount of traffic collected by at least one of the first and second collection steps, and an upstream point and a downstream point from the calculated travel time. And a step of estimating the number of vehicles existing during the period.

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

According to another aspect of the present invention, a traffic information management method includes a first method for collecting a traffic volume, a characteristic value of a passing vehicle, and a passing time of the vehicle at an upstream point of a road.
Collection steps and traffic volume,
A second collection step of collecting a feature amount of the passing vehicle and a passing time of the vehicle; a feature amount of the vehicle collected by the first collection step; a feature amount of the vehicle collected by the second collection step; And the vehicle passing time collected in the first collecting step, the vehicle passing time collected in the second collecting step, and a reference time serving as an estimated travel time between these two points. A group of vehicle information collected by the first collection step;
A matching step of performing matching with the group of vehicle information collected in the second collection step, a calculation step of calculating a travel time between an upstream point and a downstream point based on a result of the matching, And an estimation step of estimating the number of vehicles existing between the upstream point and the downstream point based on the traffic volume collected by at least one of the two collection steps and the calculated travel time.

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

[0047]

DESCRIPTION OF THE PREFERRED EMBODIMENTS [First Embodiment] FIG. 1 is a block diagram showing a 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.
And a traffic information collecting device 200 installed on the road, a traffic information providing device 300 installed on the road, a CRT 400 for displaying video data and vehicle characteristics, and displaying information obtained from the traffic information providing device 300 Information display board 50 that performs
0 and a car 6 that obtains information from the traffic information providing device 300
00.

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

The traffic information processing apparatus measures traffic volume, speed and occupancy, collects OD information, measures the degree of congestion and the length of congestion, analyzes compression waves, detects sudden events, detects runaway vehicles, It performs specific vehicle tracking, earthquake detection, travel time measurement, existing vehicle number estimation, travel time prediction, inflow control processing, and traffic flow simulation processing.

The information monitoring device includes 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 constituted by ultrasonic vehicle sensors installed at at least two places (upstream and downstream) on a road, and collects vehicle characteristic amounts.

Here, the upstream point and the downstream point are as shown in FIGS. Here, a white triangle indicates an upstream point, a black triangle indicates a downstream point, and an arrow indicates a flow of a 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,
An upstream point and a downstream point may be set along the route, such as Tomei Expressway, National Highway No. 2, Koshu Kaido, Meiji Dori.

The traffic information providing device 300 includes the information display board 5
00 or the car 600.

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

Ultrasonic vehicle sensors 201a, 201b
Is provided on the road R, and detects that the vehicle V passing the road has passed the position, and detects the height (vehicle height) of the vehicle. The detection signals are sent to the primary processing units 203a and 203b.
Is input to The time at which the vehicle passed the upstream point or the downstream point and the vehicle height of the vehicle are output from the primary processing devices 203a and 203b, and sent to the traffic control center 100.

In this case, the ultrasonic vehicle detector 201 is used.
It is assumed that a is provided downstream of the road from the ultrasonic vehicle sensor 201b. Ultrasonic vehicle detector 201
The point where “a” is installed is called “downstream point”, and the point where the ultrasonic vehicle sensor 201b is installed is called “upstream point”.

In this embodiment, a group of vehicles including a fixed number (N) of vehicles (this vehicle is defined as a target vehicle) satisfying a certain standard at the upstream point is measured as one vehicle group. Subject to. At the downstream point, since the number of vehicles and the order of the vehicles constituting the vehicle group are considered to have changed, the characteristic amount of the vehicle group obtained at the upstream point and the characteristic amount of the vehicle obtained at the downstream point Is evaluated by a predetermined matching evaluation criterion as a whole,
The association of the vehicle group is performed.

FIG. 6 is a graph showing the vehicle height (characteristic amount) detected by the ultrasonic vehicle sensor 201b provided at the upstream point on the vertical axis, and the time at which the detection was performed on the horizontal axis. 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 frequency of appearance (here, the vehicle height is relatively high). . In the present embodiment, N = 10 vehicles (1) to (10) as vehicles of interest.
Is used. Detection of 10 vehicles is at time t
_It is assumed that the operation is performed for a period of time T from _u .

FIG. 7 is a graph showing the vehicle height (feature amount) detected by the ultrasonic vehicle sensor 201a provided at the downstream point on the vertical axis, and the time at which the detection was performed on the horizontal axis. is there. Detection herein shall be 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, the order of the vehicles of interest and the number of vehicles of interest in the vehicle group are changed at the downstream point compared with the upstream point (FIG. 6). See Figure 6 for more details.
7, the vehicles (2) and (5) are not detected in FIG. This is because a detection error of the sensor occurs, the vehicles (2) and (5) change lanes, proceed to another branch road, or stop.

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

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

[0065] For example, consider the individual vehicles, whether the vehicle has passed the upstream location at time t ₇ in FIG. 6 (7) corresponds to any of the vehicle detected by the downstream point, as follows Is determined.

[0066] First, the travel time between the point upstream and a point downstream obtained previously to obtain the reference time t _d by adding the time t _u that starts detecting the vehicle upstream point. Next, by adding (t ₇ −t _u ) to the reference time t _d , an expected time for the vehicle (7) (a time considered to pass the downstream point) is obtained.

The time t before and after the expected time for the 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 the travel time between the upstream point and the downstream point of the vehicle of interest vary on average.

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

First, time (t _d + (t ₇ -t _u )) ± t
_In the range of ₀ , a candidate vehicle corresponding to the vehicle of interest (here, the vehicle (7) in FIG. 6) is obtained. In this case, a vehicle having a characteristic amount relatively close to the characteristic amount of the target vehicle 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 way as when there are no candidate vehicles. Here, m ₀ is determined by the detection error of the characteristic amount and the number N of the vehicles of interest.

Then, for each of the candidate vehicles, a deviation x (a difference between characteristic amounts) x of the feature amount from the vehicle of interest is determined. Further, a deviation y of the time at which each candidate vehicle is detected at the downstream point from the expected time is obtained.

The vehicle with the smallest value of a | x | + b | y | is determined to be the vehicle closest to the target vehicle, and the processing is performed. Here, the variables a and b are predetermined constants.

One vehicle group (for example, FIG.
The minimum value of a | x | + b | y | is obtained for each of the target vehicles included in (10)) and for which a candidate vehicle exists, and the average value is calculated as the reference time (here, t _d ).

However, if the number of vehicles of interest without candidate vehicles exceeds a certain value n ₀ , it is determined that the vehicles cannot be associated at the reference time, and the evaluation value is infinite. Here, n ₀ is substantially determined by the probability of vehicle inflow and outflow between N and the upstream point and the downstream point.

For example, referring to FIG. 8, vehicles A and C located downstream of target vehicle A at the upstream point are candidate vehicles, and vehicles C located downstream of target vehicle C are referred to as candidate vehicles.
Is a candidate vehicle, and the vehicle of interest 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 vehicle E of interest at the upstream point, The number m of each candidate vehicle of E is 2, 0,
1, 3, and 1. The number of vehicles of interest without candidate vehicles is 1 (only vehicle of interest B). In addition, the number n of vehicles of interest with candidate vehicles is 4.

The reference time t _d is slightly shifted as shown by I to V in FIG. 9, and the evaluation value is calculated at each reference time. Among them, the one with the best evaluation value is determined to be the time when the vehicle has arrived.

FIGS. 10 to 12 show the traffic control center 100.
5 is a flowchart showing the processing performed in step (a).
Referring to the figure, in step S101, the system is initialized. In this initial setting, conditions such as a target vehicle, a feature amount, and a processing cycle are set. In addition, various constants and the like are set.

In step S103, the traffic control center 100 refers to the current time. Step S10
In 5, it is determined whether the present time is the timing to start the periodic processing based on the referenced time. Until the result is YES, the processing from step S103 is repeated.

If the answer is 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, this is to select the vehicles (1) to (10) shown in FIG.

In step S109, the passing time and the characteristic amount of each of the vehicles of interest (1) to (10) are detected and set. In step S111, the reference time is 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 target vehicle selected in step S113 is sequentially referred to. In step S115, the expected time of passage at the downstream point of each target vehicle is calculated. In step S117, the area around the expected time (± t ₀ ) is searched, and a candidate vehicle for the target vehicle is checked. In step S119, it is determined whether a candidate vehicle exists. If YES, step S1 is performed.
If NO, the process returns to step S113 to perform the process for the next target vehicle.

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

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

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

At step S131, the vehicle of interest associated with the upstream point and the downstream point (the vehicle of interest with the candidate vehicle)
With reference to the number n. Its whether the number is less than a predetermined value n ₀ in step S133 is determined, and infinite evaluation value in step S157 if YES. If NO, the total evaluation value is divided by the number n of the associated vehicles of interest in step S135. Then, the value obtained by dividing the evaluation value or the cumulative evaluation value determined to be infinity in step S137 by the number n of the associated vehicles of interest is recorded as the evaluation value at the reference time.

In step S139, it is determined whether the processing has been completed for all reference times to be investigated. If NO, the process returns to step S111, and matching at the next reference time is started. 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 the step S145, the process proceeds to a step S151 to refer to the time series data of the travel time measured up to the previous processing cycle. The change tendency of the travel time measured in step S153 is grasped, and the travel time in this cycle is estimated. The processing ends with the travel time estimated in step S155 as the travel time of the cycle.

In step S147, it is determined whether there is only one time zone in which 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 at which the evaluation value is minimum, and the process ends.

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

In the present embodiment, a reference time having the best evaluation value is selected in a time zone which may be obtained in this way, and a 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 time zone is sufficiently smaller than the other time zones in a steep state (a sharp valley is formed). Is a condition for adopting the reference time obtained by the matching.

On the other hand, as shown in FIG. 14, when there is no time zone where 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 inadequate, and the travel time in this cycle is determined by comprehensively evaluating travel time and the like measured in chronological order up to that time.

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

(1) Data relating to the comparison between each target vehicle and the corresponding candidate vehicle The deviation of the characteristic amount (x) The deviation from the expected time (y) The number of candidate vehicles (m units) (2) The target Data Considering Vehicle Rows Number of Vehicles of Interest with Corresponding Candidate Vehicles (n) Note that based on the above data, an evaluation value for each reference time is obtained by a function represented by the following equation (1). You may do so.

[0094]

(Equation 1)

In the equation (1), Σ is a process for obtaining the total of the target vehicle, 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 the numerical values between the variables.

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

(1) Data related to comparison between each target vehicle and the corresponding candidate vehicle Feature deviation (x) Deviation from expected time (y) (2) Data considering target vehicle train Corresponding candidate Number of vehicles of interest where vehicles exist (n) Time lag between vehicles of interest or deviation of number of vehicles (y
₂ ) Change in Rank of Target Vehicle (z) FIG. 15 is a diagram for explaining a change in rank of the target vehicle. In the figure, A to F indicate vehicles of interest, and ○ indicates vehicles that are not vehicles of interest. At the upstream point, notice vehicle A
It is assumed that the rankings of F to F are the first to sixth.

It is assumed that the vehicle of interest B is not detected at the downstream point, and that the vehicles of interest A, D, C, E, and F are detected in this order. That is, attention vehicles A, C, D, E, F
Are 1, 3, 2, 4, and 5, respectively. Thus, the order change Z of the target vehicle is A, C, D, E, F
Are 0, 0, 2, 1, and 1, respectively.

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

First, after the process of setting the passing time and the characteristic amount of the vehicle of interest in FIG. 16 (S209), the process of determining and setting the number of vehicles between the vehicles of interest (S210) is performed. This is specifically performed to determine the time lag (y ₂ ) between the vehicles of interest.

When it is determined in step S247 of FIG. 18 that there is a time zone in which the evaluation value suddenly decreases in only one place, 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 the evaluation value is determined to suddenly decrease.

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

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

[0107]

(Equation 3)

In the equations (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 the candidate vehicles. If NO, the process proceeds to step S259, and if YES, the process proceeds to step S259.
Proceed to 265.

In step S265, it is determined whether the processing has been completed for all reference times to be checked.
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 having the smallest evaluation value.

As described above, in the present embodiment, more detailed matching is performed in a time zone in which the evaluation value suddenly decreases, so that a more accurate determination of the travel time can be made.

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

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

In this embodiment, the passing time of the vehicle at the upstream point and the downstream point is obtained by the loop type vehicle sensor. In addition, the vehicle speed and the vehicle length are obtained as the feature amounts of the vehicle.

For example, the loop-type vehicle sensor 233a or 233b is connected to the first loop and the loop-type vehicle sensor 231.
If a or 231b is a second loop, when the vehicle passes an upstream point or a downstream point, the pulse shown in FIG. 20 is generated in the first or second loop.

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

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

Here, the diameter of the loop type vehicle sensor is r _l
, The length of the vehicle is l _v , the distance between the first and second loop type vehicle sensors is l _l, and the speed of the vehicle V is v, v = l _l / (t ₃ −t _{1) ) ... (7) l v =} {v (t 2 -t 1) -r l} / 2 ... relationship holds in (8). 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 obtained.

In the present embodiment, since a plurality of feature values are used for matching, the feature value shift (x)
It is necessary to use a vector as the value of. In addition, the following equation (9) may be used instead of equation (2), and equation (10) may be used instead of equation (4).

[0121]

(Equation 4)

In these expressions, x ^T indicates the transposition of the vector x. [Example of actual field data] 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 value for reference is shown below.

FIG. 22 is a graph showing the relationship between the detection result of the characteristic amount and the time at the upstream point (before the Shibuya ramp of Metropolitan Expressway No. 3), and FIG. 6 is a graph illustrating a relationship between a feature amount and time. There are a Shibuya on-ramp, a Shibuya off-ramp, and a Takagi-cho on-ramp between these upstream and downstream points. This data is obtained from the traffic lane during heavy traffic on weekdays, and there are many data for large trucks. The distance between the upstream point and the downstream point is 4
It is slightly less than km.

As can be seen from the figure, it can be seen at a glance that pattern matching of a long vehicle (large vehicle) is easy. 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 low.

Originally, on a highway in a city, a large vehicle has a small lane change except for an off-ramp going down to a branch or a general road, and has a low probability of overtaking another large vehicle. In addition, during heavy traffic, lane changes are considered to be particularly small, and therefore, if the location is the same, it is considered that the detection accuracy of travel time is improved by performing detection using a large vehicle.

In addition, before a junction of an expressway, before an off-ramp with a large outflow traffic, or before an on-ramp with a large inflow traffic, lane changes occur frequently and traffic is disturbed. On a holiday, the running probability of a large-sized vehicle is considerably low, which is not preferable for measurement. In this case, it is conceivable to increase the measurement unit. It may be possible to detect travel time.

In the measurement on the inter-city expressway, it is considered that the distance between the upstream point and the downstream point becomes longer (about several km to 10 km), and the configuration of the vehicle of interest is likely to change. However, on an interurban expressway, there is at most one inflow / outflow section, and the inflow / outflow probability is considered to be 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 if a logic taking into account the traveling characteristics of a large vehicle is adopted.

However, in the time period when traffic volume increases and traffic congestion starts in the traveling lane or the overtaking lane, the lane change of the vehicle generally increases, and the degree to which the large vehicle contributes to travel is generally high. It has some effect on time detection accuracy.
However, large vehicles are more likely to maintain their own lane than normal vehicles and tend to return to the original lane immediately after changing lanes. This is convenient for increasing the detection accuracy of travel time.

In addition, the amount of vehicles flowing into and out of an ordinary road is generally large between an upstream point and a downstream point (about 2 km). Therefore, the distribution of the running characteristics of how much a large car 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 a configuration 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 collecting device 200 is constituted by cameras 251a and 251b provided on a road.

In this embodiment, the camera 251
The passing time of the vehicle is obtained at the upstream point and the downstream point by a and 251b. In addition, vehicle length, vehicle width, vehicle height, vehicle speed, vehicle color, and other data are obtained as the vehicle feature amounts.

Based on the plurality of feature values or a single feature value among them, matching of vehicle groups is performed as in the first and second embodiments, and travel time is determined. Is done.

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

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

The traffic control center 100 recognizes frequency characteristics (frequency spectrum) from vehicle running sounds (engine sound, wind noise, tire sound, etc.), and performs matching using the frequency characteristics as characteristic amounts.

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

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

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

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

The vehicle passing time at the upstream point and the downstream point is obtained by the microwave type vehicle sensor.

Further, the vehicle speed and the vehicle height obtained by the Doppler effect can be obtained as the characteristic amounts of the vehicle. Also, similar to the case of using a loop type vehicle detector,
The vehicle length can also be obtained as a feature amount. That is, the output of the vehicle sensor was turned off when the output of the vehicle sensor was turned on by the head of the vehicle reaching the vehicle sensor and the rear portion of the vehicle passed the vehicle sensor. The time until the time is obtained, and the vehicle length is obtained from the time and the vehicle speed.

[Eighth Embodiment] In the above embodiment, the travel time is calculated by matching the information of a group of vehicles, and the number of vehicles present is estimated based on the 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 vehicles present may be estimated based on the calculated time (travel time) ( License plate matching).

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

[Modification of the above-described embodiment] In the above-described embodiment, matching with the passing vehicle at the downstream point is performed based on the passing vehicle at the upstream point. Alternatively, the matching with the past passing vehicle at the upstream point may be performed based on the passing vehicle at the downstream point. In this way, there is no need to wait for the vehicle to be detected at the downstream point, and real-time processing can be performed.

In the third and fourth embodiments, the vehicle length is detected as a feature value. However, in order to improve the performance of the matching process, the matching target is set to a vehicle length having a relatively low frequency of appearance. It is efficient if limited to vehicles that have them. Specifically, a vehicle whose vehicle length is equal to or more than a certain value (for example, a large truck) or a vehicle whose vehicle length is equal to or less than a certain value (for example, a light vehicle) may be set as the target vehicle.

When the device is started,
The range of the reference time to be evaluated is set to be sufficiently large, and the reference time to be evaluated is determined based on the travel time measured in the previous cycle and the maximum value of the travel time fluctuating in one cycle. The range may be narrowed.

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

Furthermore, the cycle for measuring the travel time may be changed in response to a change in traffic conditions such as traffic congestion or non-congestion.

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

The period for measuring the travel time is such that the number of vehicles flowing into the road to be measured or the number of vehicles flowing out of the target road between the upstream point and the downstream point is equal to the number of vehicles traveling on the target road. You may make it change by the ratio with respect to the number of vehicles.

Further, the cycle for measuring the travel time may be changed depending on the distance between the upstream point and the downstream point.

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

In the above-described road traffic control system, the information obtained from the traffic information collection device 200 is transmitted to the traffic control center 100, and the traffic control center 10
Although the measurement processing of the travel time and the like is performed at 0, information may be exchanged between the traffic information collection apparatuses 200, and the traffic information collection apparatus 200 may perform the measurement processing of the travel time. Further, the processing may be performed by a distributed processing apparatus separately installed on a road.

For transmission of information, a dedicated wired line, a public line, or a wireless line may be used.

When a plurality of types of feature amounts are used for matching, each of the feature amounts may be weighted.

In the present embodiment, the deviation from the expected time is used for matching, but a deviation of the number of vehicles from the expected time may be used instead.

Further, for example, both the ultrasonic sensor and the optical vehicle sensor can determine the vehicle height. Also,
Both the loop type sensor and the microwave type vehicle sensor can determine the vehicle length. Further, the camera (image input device) can determine the vehicle height and the vehicle length. As described above, since the same feature amount can be collected by different sensors, 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 the matching,
When the travel time is finally obtained, the reliability of the travel time is simultaneously obtained. For example, assuming that the finally obtained travel time 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. ).

[0159]

(Equation 5)

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

[Effects of Embodiment] The above-described embodiment has the following advantageous effects as compared with the prior art.

(1) Since the evaluation criteria for matching of the entire vehicle group are set based on the ambiguous feature amount of each vehicle in the vehicle group at the upstream point and the downstream point, when the inflow / outflow traffic and overtaking are large. However, it is possible to associate the vehicle groups. In addition, even if the coincidence of individual vehicles cannot be determined, a highly accurate measurement value can be obtained in terms of average travel time.

(2) Since the characteristic amount is not a rough numerical value such as the type of a large vehicle or a small vehicle but a numerical value represented by an analog numerical value, the measurement accuracy can be improved.

(3) As a criterion for evaluation, a shift of a feature amount and a shift from a reference time are mainly considered, so that highly accurate measurement is possible.

(4) In order to enhance the effect of matching,
Vehicles with a high frequency of appearance, such as the feature values of ordinary vehicles, are excluded from matching targets, so that highly accurate measurement is possible.

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

(6) When an image processing apparatus is used as in the fifth embodiment, a plurality of vague feature values can be extracted. By performing matching as an entire vehicle group, measurement accuracy can be further improved.

[Tenth Embodiment] A road traffic control system according to an embodiment described below provides 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 obtained 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 the travel time in this case is obtained as the variation.

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

Similarly, the number of vehicles (downstream traffic _{Qt, t + T (t)} ^{i + 1} ) that has passed downstream point i + 1 during the period from time _{t to t + T (t)} can be obtained.

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

FIG. 29 is a plan view for describing the configuration and operation of the road traffic control system according to the present embodiment. Referring to the figure, traffic information collecting devices 200a and 200b are provided at an upstream point and a downstream point of road R.

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

The traffic information collecting devices 200a and 200b
The characteristic amount of the vehicle passing through the point and the vehicle speed V _u ,
And V _d, the number passing the vehicle (traffic volume) Q _u, and Q _d is 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 _Qu and _Qd and the travel time T.

When the traffic information collecting devices 200a and 200b according to the first embodiment 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.
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 flowchart showing processing performed in the road traffic control system according to the present embodiment. Referring to the figure, in step S100, initialization is performed. In step S102, E (0), which is the initial value of the number E of vehicles, is obtained.

Since the initial values are not usually known, for example, from the traffic volumes Q _u , Q _d at the upstream point and the downstream point, the vehicle speeds V _u , V _d, and the distance L between the upstream point and the downstream point, for example. , Calculated by the following equation (12). The variance at that time is set to ∞ (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 for each time, the information of the initial value (a priori information) becomes completely meaningless after a certain period of time. Therefore, the initial value is not very important.

In step S104, the time is referred to and it is determined whether a predetermined measurement cycle has been reached. And YES
Wait until In step S106, the traffic volume Q _u in the upstream point and the downstream point, Q _d is measured traffic information collection device 200a, the 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 number of vehicles at that time is estimated by using a Kalman filter.

The method of estimating the number of vehicles existing 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 following relationship exists between the travel time T (t) (based on the time at which the vehicle arrives at the downstream point) and the number of vehicles E (t) between the upstream point and the downstream point at time t. 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)) = Q _d (t−T (t), t) (15) The travel time T and its reliability (accuracy or (Variation) can be obtained by filtering from the dynamic characteristic including the traffic volume measurement error based on the above equation (13) regarding the number of vehicles and the observation equation including the reliability based on the above equation (14) or (15). By the processing, the number of vehicles existing can be easily estimated.

In the present embodiment, the case where the number of lanes is one is assumed. However, in the case where the number of lanes is two, the total number of vehicles in each lane may be considered.

Here, for example, a Kalman filter can be used for the filtering processing. That is, when both the state equation and the observation equation include a temporally independent and uncorrelated error of a normal distribution, an optimal estimated value can be obtained by a recurrence formula.

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

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

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

Η (t) indicates the error (unknown) of the observation method at time t. At this time, assuming the characteristics of the error of ξ and η, the optimal 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 ) + {(T-1)} / {M (t) + {(t-1) + N (t)} = {(t) / N (t)} (t) = N (t) ｛M (t) + Σ (T-1)} / {M (t) + {(t-1) + N (t)} x (0) = {αN (0) + y (0) B} / {N (0) + B}} (0 ) = N (0) B / {N (0) + B} (17) Here, the characteristics of the error are 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)) Note that K (t) ) Is called a Kalman gain and is a weight for a predicted value from a time before an observed value.

From the above, the estimation formulas (13) to
The equations (15) can be applied as follows. Since either one of equations (14) and (15) may 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 number of existing vehicles is calculated as shown in equation (14). One must consider that it is shifted by T (t).

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

(1) Error in state equation: ξ (t) This error is a measurement error of the traffic volume at each point (for example, a 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 is 0, if the difference 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 information on measurement uncertainty at each point obtained by the survey.

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

For example, as a specific example of the 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.
This error may be considered.

As an example of the variation, when a plurality of travel times are obtained in substantially the same time zone, the variation may be converted into a variation in 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, so it is usual to set the error slightly larger.

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

When there is an inflow portion or an outflow portion between the upstream point and the downstream point, it is necessary to make an assumption such as uniform running.
The accuracy of estimating the number of vehicles present slightly decreases. However, when the traffic volume at the inflow section and the outflow section is smaller than that on the main line (between the upstream point and the downstream point), it becomes a quadratic error, and the overall error is considered to be small.

Referring to FIG. 31, a road traffic control system according to the present embodiment has a traffic information for measuring a traffic volume at an outflow portion and an inflow portion in addition to the system configuration in the tenth embodiment. Collection device 200c, 200d
Is provided. Traffic information collection device 200a, the traffic volume Q _o in the outflow section and the inflow section by 200b, feature quantity of Q _i and the vehicle is measured.

[0205] Further, the distance from the outlet section to a point downstream in the present embodiment as L _o, the distance from the inlet portion to the downstream point and L _i, and the distance from the upstream point to a downstream point in the L. Also, the travel time from the outflow to the downstream point is T _o, and the travel time from the inflow to the downstream point is T _i.
And the travel time from the upstream to the downstream is T.

T _i , T _o , and T are obtained by the methods of the first to ninth embodiments. In the present embodiment, the number of vehicles E is obtained by approximating the travel time T and the positional relationship between the upstream point, the downstream point, 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 the present embodiment instead 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 equations (19) and (20) are obtained by the following equations (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)} (L−L _i ) / V _u + L _i / V _d (22) In the present embodiment, it is possible to determine the number of vehicles existing in consideration of vehicles passing through the inflow portion and the outflow portion. it can.

FIG. 32 is a flowchart showing the processing performed by the road traffic control system according to the present embodiment. In the figure, steps S200 to S206 correspond to FIG.
0 corresponds to steps S100 to S106. Also, steps S208 and S2 in FIG.
10 corresponds to steps S108 and S11 in FIG.
Corresponds to 0.

In the present embodiment, step S20
After the measurement of the traffic volume in Step 6, the traffic volumes Q _i and Q _o at the inflow portion and the outflow portion (interchange) are measured in Step S207.

Further, after the processing in step S208,
In step S209, the travel time T from the inflow to the downstream is T
and _i, travel time T _o from the outflow portion to a point downstream is obtained by the process set forth in the embodiments of the first to seventh.

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

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

It should be noted that the embodiment disclosed this time is an example in all respects and is not restrictive. The scope of the present invention is defined by the terms of the claims, rather than the description above, and is intended to include any modifications within the scope and meaning equivalent to the terms of the claims.

[Brief description of the drawings]

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

FIG. 2 is a first diagram showing a specific example of an upstream point and a downstream point.

FIG. 3 is a second diagram showing a specific example of an upstream point and a downstream point.

FIG. 4 is a third diagram showing a specific example of an upstream point and a downstream point.

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

FIG. 6 is a diagram showing characteristic amounts of a vehicle group obtained at an upstream point.

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

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

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

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

FIG. 11 is a flowchart following FIG. 10;

FIG. 12 is a flowchart following FIG. 11;

FIG. 13 is a diagram illustrating 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 describing processing of the road traffic control system according to the second embodiment.

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

FIG. 17 is a flowchart following FIG. 16;

FIG. 18 is a flowchart following FIG. 17;

FIG. 19 is a diagram illustrating 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 by the system of FIG. 19;

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

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

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

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

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

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

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

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

FIG. 29 is a diagram illustrating a configuration of a road traffic control system according to a tenth embodiment.

FIG. 30 is a flowchart illustrating processing of the road traffic control system according to the tenth embodiment.

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

FIG. 32 is a flowchart illustrating processing of the road traffic control system according to the eleventh embodiment.

[Explanation of symbols]

Reference Signs List 100 Traffic control center 200 Traffic information collection device 201a, 201b Ultrasonic vehicle detector 231a, 231b, 233a, 233b Loop vehicle detector 251a, 251b Camera (image processing device) 271a, 271b Microphone

Claims (22)

[Claims] A first measuring means for measuring a travel time between an upstream point and a downstream point of a road; a second measuring means for measuring a traffic volume at at least one of the upstream point and the downstream point; A traffic information management device comprising: an estimating unit configured to estimate the number of vehicles existing between the upstream point and the downstream point from the traffic volume at at least one of the upstream point and the downstream point and the travel time. 2. The traffic information according to claim 1, further comprising a calculating means for calculating the reliability of the measured travel time, wherein the estimating means performs the estimation in consideration of the calculated reliability. Management device. 3. The traffic information management device according to claim 2, wherein the calculation means calculates reliability based on travel time variance, and improves the accuracy of estimating the number of vehicles by using a Kalman filter. 4. The travel time according to claim 1, wherein the first measuring means measures the travel time by license plate matching.
4. The traffic information management device according to any one of items 1 to 3. 5. The traffic information management device according to claim 1, wherein the first measurement unit measures the travel time based on uplink information. 6. A first collecting means installed at an upstream point of a road for collecting traffic volume, a characteristic value of a passing vehicle, and a passing time of the vehicle; A second collection unit that collects a feature amount of the vehicle and a passing time of the vehicle, an acquisition unit that acquires a reference time estimated to be a travel time between the upstream point and the downstream point, A prediction unit that predicts a time at which a vehicle that has passed through the upstream point will reach the downstream point based on the acquired reference time and the passing time of the vehicle collected by the first collection unit; The first of the feature amounts collected by the collection unit
Deviation from the feature amount collected by the collection means of
The information of the group of vehicles collected by the first collection unit and the collection by the second collection unit based on the deviation of the passing time of the vehicle collected by the collection unit from the predicted time. Matching means for performing matching with the information of the group of vehicles obtained; calculating means for calculating a travel time between the upstream point and the downstream point based on a result of the matching; A traffic information management device comprising: an estimation unit configured to estimate the number of vehicles existing between the upstream point and the downstream point from the traffic volume collected by at least one of the collection units and the calculated travel time. . 7. Each of the first and second collecting means includes a loop-type sensor, and identification means for identifying a vehicle length of the vehicle as a feature quantity based on an output of the sensor. The traffic information management device described in. 8. The traffic information management device according to claim 6, wherein each of said first and second collection means includes an ultrasonic sensor. 9. The system according to claim 6, wherein each of said first and second collecting means includes a sensor for collecting a running sound of the vehicle.
The traffic information management device described in. 10. Each of the first and second collecting means includes: a camera that obtains an image of a vehicle; and a vehicle width, a height, a vehicle length, a vehicle color, a brightness, and a pattern from the image. The traffic information management device according to claim 6, further comprising an image processing device that extracts a single or a plurality of selected feature amounts. 11. The traffic information management device according to claim 6, wherein each of said first and second collection means includes an optical vehicle sensor. 12. The traffic information management device according to claim 6, wherein each of said first and second collecting means includes a microwave type vehicle sensor. 13. The method according to claim 6, wherein the first and second collection units collect a plurality of types of feature amounts, and the matching unit performs matching by weighting the plurality of types of feature amounts. The traffic information management device according to any of the above. 14. The vehicle according to claim 6, wherein the matching unit determines a deviation of the passing time of the vehicles collected by the second collecting unit from the predicted time by determining a deviation of the number of vehicles. 14. The traffic information management device according to any one of to 13. 15. The traffic information management device according to claim 6, wherein the matching unit uses only a feature amount of a vehicle satisfying a specific criterion and a passing time of the vehicle for matching. 16. The traffic information management according to claim 6, wherein said first and second collection means collects only a feature amount of a vehicle satisfying a specific criterion and a passing time of the vehicle. apparatus. 17. The apparatus according to claim 17, further comprising a reliability calculating unit configured to calculate a reliability of the travel time calculated by the calculating unit.
The traffic information management device according to claim 6. 18. The traffic information management device according to claim 17, wherein said reliability calculation means calculates reliability based on travel time variance, and improves the accuracy of estimating the number of vehicles by using a Kalman filter. 19. The traffic information management device according to claim 18, wherein said estimating means performs estimation in consideration of said calculated reliability. 20. It is installed at an upstream point of a road,
A first collection unit that collects a characteristic amount of a passing vehicle and a passing time of the vehicle; and a second collection unit that is installed at a downstream point of a road and collects a traffic amount, a characteristic amount of the passing vehicle, and a passing time of the vehicle. A difference between the vehicle characteristic amount collected by the first collection unit and the vehicle characteristic amount collected by the second collection unit, and a difference between the vehicle characteristic amount collected by the second collection unit and the vehicle characteristic amount collected by the first collection unit. A group collected by the first collection unit based on a vehicle passage time, a vehicle passage time collected by the second collection unit, and a reference time serving as an estimated travel time between these two points. Vehicle information and matching means for matching the group of vehicle information collected by the second collection means; and a travel time between the upstream point and the downstream point based on a result of the matching. Calculating means for calculating the number of vehicles between the upstream point and the downstream point from the traffic amount collected by at least one of the first and second collecting means, and the calculated travel time. A traffic information management device comprising: an estimation unit for estimating the traffic information. 21. A first collection step of collecting traffic volume, a characteristic amount of a passing vehicle and a passing time of the vehicle at an upstream point of the road, and a traffic volume and characteristics of a passing vehicle at a downstream point of the road. A second collection step of collecting an amount and a passing time of the vehicle; an acquisition 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 prediction step of predicting a time at which a vehicle that has passed the upstream point will reach the downstream point based on the time and the passing time of the vehicle collected in the first collection step; The deviation of the obtained feature amount from the feature amount collected in the first collection step,
Information on a group of vehicles collected in the first collection step based on a deviation of the passing time of the vehicles collected in the second collection step from the predicted time, and the second collection A matching step of performing matching with information of a 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; An estimating step of estimating the number of vehicles between the upstream point and the downstream point based on the traffic volume collected by at least one of the second collecting step and the calculated travel time; Information management method. 22. A first collection step for collecting traffic volume, a characteristic amount 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 the passing vehicle at a downstream point of the road. A second collection step of collecting the quantity and the passing time of the vehicle; and a difference between the feature quantity of the vehicle collected by the first collection step and the feature quantity of the vehicle collected by the second collection step. And, based on the passing time of the vehicle collected in the first collecting step, the passing time of the vehicle collected in the second collecting step, and a reference time serving as an estimated travel time between these two points. A matching step for matching the group of vehicle information collected in the first collection step with the group of vehicle information collected in the second collection 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 amount collected by at least one of the first and second collecting steps; An estimating step of estimating the number of vehicles existing between the upstream point and the downstream point from the calculated travel time.