JPH07104998B2 - Vehicle travel time measurement method - Google Patents

Description

DETAILED DESCRIPTION 3.1 Objective [Industrial field of application] The present invention relates to a method for measuring travel time in a certain section of a vehicle, which is used for improving a road traffic control system.

While the sophistication of road traffic control systems aims to provide highly accurate traffic information and signal control as one of its goals,
In order to achieve that goal, it is indispensable to accurately predict and measure the number of vehicles (traffic density) and the travel time of vehicles on the road section in the route or region.

Among them, the measuring methods can be divided into a) using communication between vehicles on the road and b) using only a vehicle detector on the road. The present invention relates to a method for highly accurately measuring travel time of a vehicle in a certain section by using the vehicle.

[Prior art and its drawbacks] The measurement of traffic density and travel time in a road section involves various technical problems because it is not the measurement of traffic flow at a point but the measurement of traffic flow in a section.

Conventional methods for putting the above-mentioned measurement into practical use only by using a road sensor are classified into the following four types (1) to (4).

(1) Method of using correlation between point traffic volume, occupancy rate, density and traffic density This is as shown in FIG. Measure the velocity at the point, and assume that the state at this point is uniform in section L.
Traffic volume / traffic density correlation chart, occupancy rate / traffic density correlation chart or speed / traffic density correlation chart or (occupancy rate / traffic volume) / traffic density correlation chart shown in Figure 12 (a), (b) and (c) The traffic density K of the section L is calculated from the traffic density K, and the number N of vehicles existing in the section L = KL is calculated from the traffic density K, and the number N of vehicles existing in the section L and the outflow per unit time at the exit of the section L This is a method of obtaining the travel time T = N / Q of the section from the traffic volume Q.

Among the above, according to the past measured data, the one using the correlation between (occupancy rate / traffic volume) and traffic density has the highest accuracy, but there is an error of ± 30% or more. .

The method (1) has the following drawbacks.

It is assumed that the traffic volume, occupancy rate, and speed measured at the representative points are uniform in each section, but when traffic conditions become congested, there are cases where no correlation can be obtained. Therefore, it is necessary to set the density of the detectors to 100 m intervals, which is an obstacle to practical use.

Since the occupancy rate and speed are saturated after entering the traffic jam situation, the traffic density cannot be measured with high accuracy.

The correlation diagram with the traffic density depends on the road structure and other driving causes, and is not uniquely determined, and the error is large.

(2) The principle of method measurement by the optical space traffic flow measuring device was developed at the Mechanical Engineering Research Center in 1976. First
As shown in Fig. 13, the detection lines L ₁ , L ₁ ′; L ₁ , L on the road surface
₂ ′; L ₂ , L ₃ ′, etc. are monitored by one lens 1, and an image plane 2 formed by arranging photoelectric elements on one silicon wafer
A road image is formed on the top of the road, and vehicles passing on each detection line are detected. Between adjacent lines (L ₁ →
Since the passage speed can be known from the time it takes to pass L ₁ ′), (L ₂ → L ₂ ′), and the spatial velocity distribution can be measured, the spatial average velocity is calculated and the travel time of the section is calculated. The formula for calculating the section travel time from the spatial average speed is as follows based on the distribution in Fig. 14.

Average space velocity V = ΣViKi / K = Σqi / K = Q / J This method has the following drawbacks.

Due to the use of optical means, there is a limit due to the field of view of the camera, and with installation at a height of 5 m, the 100 m section is the measurement limit, and it is not suitable for measurement of curved road sections.

Due to optical means, against bad weather such as snowfall and rainfall
S / N is bad, and it may not be possible to measure in the twilight state at dusk. Also, maintenance such as polishing the lens is required once every few months.

It is the instantaneous spatial velocity distribution that is measured, and the number of existing vehicles cannot be measured.

(3) Method by vehicle number reading (image processing) As shown in FIG. 15, high speed shutter cameras 3 and 4 are installed at the entrance A and the exit B of the travel time measuring section L,
The number plate of the passing vehicle 8 is photographed at high speed, the resulting image is image-processed by the vehicle number reading devices 5 and 6, the vehicle number is decoded and converted into data, and the vehicle number matching device 7 is used for the vehicles at two points A and B. This is a method in which the numbers are collated, the coincident ones are extracted, and the travel time between two points is obtained from the arrival time difference between the points A and B.

This method has the following drawbacks.

Since the optical means is used, when the traffic is congested, the images of the vehicle may overlap between the front vehicle and the rear vehicle, and the number plate may not be read.

Due to optical means, against bad weather such as snowfall and rainfall
S / N is bad and lighting is required at night. In addition, it may not be possible to measure due to dusk at dusk, and once every several months.
It is necessary to perform maintenance such as polishing and polishing the lens.

(4) Method of using matrix length sensor This is to install normal ultrasonic vehicle detectors or loop vehicle detectors D _{1 to} D ₄ as illustrated in FIG. 16 and occupy each sensor. The speed is determined from the rate, the traffic congestion matrix length L is determined from each result as shown in Table 1, and assuming the average vehicle occupation length l, the number of vehicles existing in the traffic congestion matrix length N = L This is the method of finding / l.

At the beginning of the matrix length, the traffic flow Q per unit time is calculated, and the travel time T = N / Q of the matrix length is calculated. In this way, the travel time can be obtained by measuring the matrix length.

This method has the following drawbacks.

Since the matrix length sensors are installed at discrete intervals of 300 m, 500 m, 700 m, and 1000 m, the judgment error is large as shown in FIG.

Assuming the average occupancy length 1 of one vehicle, the number of existing vehicles N = L / l ... a) existing in the matrix length is calculated. However, since 1 differs in the actual traffic flow depending on the vehicle type mixture ratio, The error increases.

That is, as is clear from the formula a), this system also has L
Since there is also an error, it is not possible to expect a travel time measurement error of ± 30% or less.

[Problems to be Solved] In view of the above points, the present invention has an object to improve the accuracy of the travel time measuring method by the line length sensor using the ultrasonic type vehicle sensor of the above (4). , The matrix length judgment in FIG. 17 is performed by continuous measurement, not by discrete digital judgment, and the number N of vehicles existing in the matrix length is N.
Is automatically measured with high accuracy and divided by the amount of traffic Q per unit time to obtain the travel time T in the congestion queue length.
= This is an attempt to automatically measure N / Q with high accuracy.

3.2 Structure [Problem-solving means] The number of vehicles that exist in a section (traffic density) and the section travel time best represent the traffic conditions in a road section. In the conventional traffic control system, since there is no effective means for directly measuring these various quantities, as described above, it is calculated from the traffic volume / occupancy rate at the representative point.

On the other hand, as an investigation method for measuring the number of existing road sections, there is an input output method. This is the number of vehicles passing through the entrance at the time when the marker (marked vehicle) is run and the passing vehicle is counted at the entrance and the exit until the marker passes from the entrance to the exit of the road section and the marker passes through the exit. This is a method in which the initial value of the number of existing sections is used, and thereafter, the number of vehicles entering the section is added and the number of vehicles leaving the section is subtracted to measure the number of existing sections in real time.

The present invention (a) obtains vehicle shape data of each vehicle by detecting each vehicle passing through an entrance and an exit of a road section using an ultrasonic type vehicle detector, and (b) obtains vehicle shape data of each vehicle. By automatically matching the marker with the specific vehicle shape data determined in advance as the above marker, (c) the vehicle shape data obtained from the entrance and the vehicle shape data obtained from the exit of the selected marker under predetermined conditions Check below and if they match,
It is determined that the marker has passed the section, and (d)
With the number of vehicles that entered the section between the time when the marker entered the entrance and the time when the section passed, as the initial value of the number of existing sections, the input / output method is executed and real-time automatic measurement of the number of existing sections is performed. Based on the vehicle detection signal from the vehicle, the number of passing vehicles per unit time, that is, the traffic volume for distribution, is measured, and (f) the vehicle travel time in the same section is calculated by dividing the number of vehicles existing in the section by the distribution traffic volume. It was done.

[Embodiment of the Present Invention] Next, an embodiment of the present invention will be described with reference to FIGS. 1 to 10.

As shown in FIG. 1, a vehicle travel time measuring device for realizing the method according to the present invention includes ultrasonic type vehicle shape detecting devices 10A and 10B installed at a plurality of points A and B on a road and a central road traffic control. The processing device 30 is installed in the room and is connected to the vehicle shape detection devices 10A and 10B by transmission lines 20A and 20B.

Similarly, each of the ultrasonic type vehicle shape detecting devices 10A and 10B has an ultrasonic type vehicle detector 11 having an ultrasonic wave transmitter / receiver H provided at an appropriate height from the road R surface through which the vehicle V passes, and the same. A vehicle shape data creation unit 12 that creates vehicle shape data having vehicle height information about a vehicle that has passed through the point from the detection information obtained from the vehicle detector, and processing of the created vehicle shape data by the processing device 30. Therefore, the vehicle height pattern conversion unit 13 that converts the vehicle height pattern having a representative value that characterizes the vehicle type, and the converted vehicle height pattern is parallel-serial converted to the transmission lines 20A, 20B.
It is composed of a data transmission unit 14 for sending to the.

The ultrasonic vehicle detector 11 and the vehicle shape data creation unit 12 sequentially measure the time from when the ultrasonic wave is transmitted from the ultrasonic handset H to when the ultrasonic wave is reflected from the road surface or the vehicle and reaches the ultrasonic wave handset H. The vehicle detection time is memorized and converted into a fixed length, and all the detected vehicles are converted into a vehicle shape with a fixed vehicle length (normalized vehicle shape), and the vehicle height of the vehicle is calculated from the stored data. Calculation formula h = Hh (1-T / Th) (m) where, h: vehicle height Hh: ultrasonic transmitter mounting height Th: arrival time of reflected wave from road surface T: arrival time of reflected wave from vehicle Then, the information that the calculated vehicle height changes momentarily as the vehicle progresses is stored in a predetermined number of blocks (for example, 8
Block) and creates it as standardized vehicle shape data having vehicle height information of the vehicle to be sensed.

That is, the vehicle shape data creation unit 12 uses the ultrasonic vehicle detector 11
The vehicle height shape of the passing vehicle is detected by using the vehicle detection signal output by the above, and standardized vehicle shape data as illustrated in FIG. 2A is created.

The vehicle shape data creating unit for creating such standardized vehicle shape data is one of the ultrasonic type vehicle type identifying devices disclosed by the applicant in Japanese Patent Application No. 55-38586 and Japanese Patent Application No. 55-110879. Departments are available.

The vehicle height pattern conversion unit 13 converts the standardized vehicle shape data output from the vehicle shape data creation unit 12 into a vehicle height pattern having a representative value that characterizes the vehicle type. The representative value is an average value of the vehicle height data obtained by the vehicle shape data creation unit 12 when the vehicle height values of three or more blocks out of the eight blocks of the standardized vehicle shape data have a continuous error of 10 cm or less. It is called the representative value, and this is the vehicle height value that characterizes the vehicle type.

For example, the standardized vehicle shape data shown in FIG. 2 (a) is converted into the vehicle height pattern shown in FIG. 2 (b), and the representative value is set to 1.35 m as a vehicle height value characterizing the vehicle type. There are various vehicle height patterns depending on the shape unique to the vehicle and the shape of the cargo. FIG. 3 shows an example of such a vehicle height pattern.

Each vehicle shape detection device 10A, 10B processes the converted vehicle height pattern from the data transmission unit 14 via the telephone lines 20A, 20B and a processing device 30.
Send to.

The processing device uses each vehicle shape detection device 10 via the transmission lines 20A and 20B.
A, the data receiving unit 31 that receives the data sent from 10B, serial-parallel conversion and output, the vehicle height pattern that is the received data, the specific vehicle height pattern of the vehicle designated as the marker, as described below. A storage unit 32 that stores the representative value detected from the extracted vehicle height pattern, the number N of existing vehicles in the road section L, and the amount of traffic Q in each area in each area.
, The detected vehicle height pattern and the specific vehicle height pattern are matched and selected as a marker, a marker selection registration unit 33 for registering this, and a representative value is obtained from the vehicle height pattern of the registered marker, Representative value time series data creating unit 34 for creating representative value time series data, and section L.
The collation verification unit 35 that collates the representative values in the representative value time-series data corresponding to the entrance A and the exit B of FIG. The input / output method execution unit 36 that executes the input / output method (Input Output method), the distribution traffic volume measurement unit 37 that measures the number of passing vehicles Q per unit time at the exit, and the existing number N are divided by the distribution traffic volume Q. And a travel time calculation unit 38 for calculating travel time.

When the processing device 30 receives the vehicle height patterns sent from the vehicle shape detection devices 10A and 10B, the processing device 30 sequentially stores the vehicle height patterns in the first area 32a of the storage unit 32 and corresponds to the entrance A and the exit B of the road section L, respectively. Time series data of the vehicle height pattern is created (steps p ₁₁ and _{12 in} FIG. 4). The marker selection registration unit 33 stores each vehicle height pattern of the time-series data of the vehicle height pattern in the storage unit.
The specific vehicle height pattern stored in the second area 32b of 32 is collated by the vehicle height pattern collating means to which the pattern recognition technique is applied, and only the pattern which coincides with this is extracted as a marker (p. _{21 in} FIG. 4). , ₂₂ ).

The specific vehicle height pattern has a simple shape with high accuracy in comparison among various vehicle height patterns that are actually various as illustrated in FIG. 3, such as a box-shaped refrigeration vehicle and a freight car with a hood. For example, the one shown in FIG. 3 is selected and defined. In this way, the marker selection registration unit 33 extracts only the pattern of FIG. 3 from the vehicle height pattern time series data. This is a filtering action for the traffic flow that extracts only vehicles of a specific pattern, such as a refrigerating vehicle, from vehicles of various shapes passing through the entrance A and the exit B of the section L, so to speak.

Next, the representative value time series data creation unit 34, as described above,
From the vehicle height patterns of the markers extracted from the data from the entrance and the exit of the road section, representative values of the vehicle height of the vehicle are sequentially obtained (Fig. 4, p ₃₁ , ₃₂ ), and as shown in Fig. 6, create a representative value time-series data at the inlet and outlet sections L, stores it in the third area 32c of the storage unit 32 (p _4).

The matching verification unit 35 sequentially matches the representative value of the vehicle height in the representative value time series data of the point A with the representative value in the representative value time series data of the point B under a predetermined condition, and the agreement of the representative values is confirmed. In this case, the markers at the two points A and B are tested as the same marker, and it is determined that the marker has passed from the point A to the point B (FIG. 4, p ₅ ). There are the following two verification methods for this marker.

The first method is as shown in FIG.
The minimum travel time Hmin (section distance L /
A gate for the maximum speed x) and the maximum travel time Hmax (L / minimum speed y) is provided, and the absolute value of the difference between the representative value hi of the marker Vm and the representative value hi is less than a predetermined value hi '(that is, | hi to hi ′ ｜ <
△, △, for example, when 5% of hi) is detected, h
The marker Vm 'of i'is determined to be the same vehicle as Vm.

The second method is the absolute difference between the minimum travel time Hmin of the vehicle from the point A passing time to the certain marker Vm and the maximum number of vehicles Nmax that can be present in the section L during the traffic jam and the representative value hi of the marker Vm. When a hi 'whose value is less than a predetermined value is detected, that hi'
Marker Vm 'are those determined to be identical to the vehicle and Vm (Fig. 5 p ₅₁ ~p _53).

The maximum number of vehicles Nmax is Nmax = L / l _0, where L is the section and the average value of the length of one vehicle occupied is l ₀ .

When no matching representative value is found for the one marker within the above conditions, the next marker hi + 1 is similarly verified (p ₅₄ ).

The input / output method execution unit 36 is an automated input / output method (Input Output method), which is a survey method for measuring the number of vehicles existing in a road section. In the input / output method, a marker is run and the number of vehicles passing through the entrance at the time when the marker arrives at the exit is counted by counting the passing vehicles at the entrance and exit until the marker passes from the entrance to the exit of the road section. Set the initial value of the number of vehicles, and after that, add the number of vehicles entering the section,
This is a method of performing real-time measurement of the number of vehicles existing in a section by subtracting the number of vehicles that exit.

The processing device 30 receives one vehicle height pattern data of the vehicle to be detected from the vehicle shape detection devices 10A and 10B at any time. The input / output method execution unit 36 performs addition operation based on vehicle height pattern data input from the entrance side vehicle shape detection device 10A, and subtraction operation based on vehicle height pattern data input from the exit side vehicle shape detection device 10B. The counter is reset by the marker selection signal from the marker selection registration unit 33, and the subtraction is started based on the data input from the exit side detection device 10B by the input of the coincidence signal from the collation verification unit 35. Is configured.

Thus, when the marker is selected for the vehicle passing through the entrance A, the input / output method execution unit 36 is reset and the number of vehicles entering the section L based on the vehicle height pattern input from the entrance side vehicle shape detection device 10A. Is measured, and only the number of vehicles that have entered the section is added up and counted until the verification unit 35 detects that the marker has passed the exit B. As shown in FIG. 8, the time point t ₁ at which the verification unit 35 outputs the coincidence signal.
The count value of is set as the initial value N _{1 of the} number of existing sections (Fig. 4
p _6), thereafter, the number entering the zone on the basis of the vehicle height pattern input from the inlet-side vehicle shape detecting apparatus 10A is added, advancing from the interval based on the vehicle height pattern input from the exit-side vehicle shape detecting apparatus 10B The number of existing units is subtracted, and the number of existing units in the section L is N.
There are measured in real time, the measured value is taken into the fourth area 32d of the memory unit 32 (FIG. 4 p _7).

If the input / output method based on the initial value N ₁ based on the initial marker selection and passage detection is continued forever, the measured value of the number of vehicles in the section will be between the actual value due to a sensing error of the ultrasonic vehicle sensor. There is a risk of error. In order to prevent this, the processing device constantly checks the vehicle height pattern data sent from the entrance side vehicle shape detection device 10A with the specific vehicle height pattern to search for a marker, as illustrated in FIG. If at the time of t ₂ is found next marker, to clear the measured value of up to it, set the initial value N ₂ of section existence number by using the new markers again, the entry number by subsequent Similarly, input and output method By adding and subtracting the number of vehicles that have advanced, as shown by the dotted line in FIG. 9, the number of sections that exist every moment is obtained.

On the other hand, the distribution traffic volume measuring unit 37 always inputs the vehicle detection signal from the exit B, and sequentially uses the vehicle detection signal and the clock signal to sequentially determine the number of passing vehicles per unit time, that is, the distribution traffic volume Q. , This is the fifth area of the storage unit 32
Store in 32e.

The travel time calculation unit 38 performs a calculation to divide the number N of existing sections obtained by the input / output method execution unit 36 by the divided traffic volume Q obtained by the divided traffic volume measurement unit 37 to pass the section L. calculating the travel time T required (Fig. 4 p _8). For example, if the number of vehicles currently existing in the section L is 100 and the distribution traffic is 10 per 30 seconds, the vehicle at the end of the vehicle queue in the section (100th vehicle from the exit) is The travel time T required to pass the section is: T = 100 units / (10 units / 30 seconds) = 300 seconds = 5 minutes.

As shown in the variation pattern of travel time in the road section in Fig. 10, it is the process from congestion start to complete congestion to congestion elimination that requires direct measurement with high accuracy regarding travel time. If it is possible to detect that the marker passes through the entrance A and the exit B of the road section L, the travel time can be directly measured as the difference between the passing times. Further, even if the markers cannot be detected densely, the number of vehicles existing in the section can be measured in real time, so the travel time during a traffic jam can be obtained as (number of vehicles present / volume of traffic per unit time). Therefore, it is possible to continuously measure the section travel time for the start of traffic jam-complete traffic jam-clearance of traffic jam.

Thus, in the present invention, the travel time measurement on the congested route is automated with high accuracy by using the automatic detection of markers and the automatic counting of the number of sections existing.

Consistent explanation of operation In the vehicle shape detection devices 10A and 10B, the ultrasonic vehicle detector 11 and the vehicle shape data creation unit 12 detect the envelope shape of the vehicle passing through the points A and B, and then, as shown in FIG. The standardized vehicle shape data as shown in a) is obtained, the vehicle height pattern conversion unit 13 converts the standardized vehicle shape data into a vehicle height pattern as shown in FIG. 1B, and the data transmission unit 14 transmits the vehicle height pattern to the processing device 30. Processors are points A and B
Among various height patterns of passing vehicles transmitted by
For example, only the predetermined pattern shown in FIG. 3 is extracted, and this is selected and registered as a marker. Then, a representative value characterizing the vehicle type is sequentially obtained from the vehicle height pattern of each marker. In this way, the representative value time-series data of the marker as shown in FIG. 6 is obtained from the vehicle shape data detected at the measurement start point A and the measurement end point B, respectively.

The verification unit 35 determines whether or not the absolute value of the difference between the representative values of the vehicle heights is within 5% of the representative value on the entrance side, and if the representative values are in agreement, the marker is detected. It is determined to pass the section. When the passage of the marker is detected,
The initial value of the number of vehicles existing in the section L is obtained, and the execution unit of the input / output method obtains the number N of existing vehicles every moment thereafter.

When the number N of existing sections is obtained, the travel time calculation unit 38 calculates N / Q using the distribution traffic volume Q obtained by the distribution traffic volume measurement unit to obtain the travel time T of the section.

In this way, for example, as shown in FIG. 9, the continuous number of existing vehicles (matrix length) and travel time are automatically measured with high accuracy.

Next, the technical problems of the present invention will be listed.

Frequency of Marker Detection The first object of the present invention is to measure the matrix length by a matrix length sensor currently in practical use in traffic control → estimate the number of existing vehicles → estimate the travel time by ± 30% or more. There is an error in improving accuracy. For that purpose, the input / output method is automated, the number N of vehicles existing in the queue length is automatically measured with high accuracy, and this is divided by the amount of traffic Q per unit time to obtain the travel time T = N / We were able to improve Q to an accuracy of ± 10% or less.

The marker detection frequency for automating the input / output method may be about one per 1000 passing vehicles. Marker detection is
It is necessary to set the initial value No of the number N of existing sections and to correct the accumulated error due to detection error of the sensor.
To measure the number of existing vehicles with an error of a few percent, even one out of every 1000 passing vehicles can be put to practical use.

Sensor installation interval and vehicle type mixture ratio L (m) is the sensor installation interval, and 1 (m) is the average vehicle occupancy length (= average vehicle length + average vehicle distance) during complete traffic congestion. When the vehicle type mixing rate of the pattern is set to a (%), when the traffic is completely congested, (L / l)
There are vehicles with the same pattern of × (a / 100) (vehicles).
If the number of vehicles is around 10 (units), each can be identified by its representative value. That is, the vehicle can be individually identified by the pattern recognition technique. When 1 = 5 (m) and a = 2 (%), L <2500 (m), and there is no problem in the normal sensor installation interval of L <500 m.

According to the above method, it is possible to improve the accuracy of measuring the queue length (the number of vehicles existing) of a congested route and the travel time using the queue length detector in the current traffic control. If the ultrasonic vehicle type discriminator is installed at the same interval as the current matrix length sensor in Fig. 16, the marker detection frequency will increase,
The accuracy of travel time measurement can be expected to be ± 5% or less, and the number of vehicles present can be expected to be highly accurate.

[Effects of the Invention] The present invention enables highly accurate measurement (± 10% or less) of traffic conditions on a congested route, which is impossible by the conventional method, from the start of traffic to complete traffic congestion. In addition, because it uses an ultrasonic type vehicle sensor that is not affected by adverse environmental conditions such as snow, rain, nighttime, etc., it is a method that is far superior to that by optical means and can be put to practical use in traffic control. Can be provided.

[Brief description of drawings]

FIG. 1 is a block diagram showing a configuration of an embodiment of the present invention, FIG. 2 is a copy diagram showing an example of standardized vehicle shape data and vehicle height pattern conversion, and FIG. 3 is an example of types of vehicle height patterns. FIG. 4 is a flow chart for explaining the operation of the processing device, FIG. _{5 is} a detailed view of step p ₅ of FIG. 4, and FIG. 6 is an example of representative value time series data (when there is no traffic). A graph, FIG. 7 is a schematic diagram for explaining one of the principles of the verification test, FIG. 8 is a graph for explaining the setting of the initial value of the number of existing vehicles, and FIG. 9 is a graph showing the relationship between travel time and the number of existing vehicles. Figure 10 is a graph showing the fluctuation of travel time depending on the time of day. Fig. 11 is a correlation diagram of point traffic volume / occupancy rate / speed and section traffic density, Fig. 12 is a correlation diagram of point information and traffic density, Fig. 13 is a schematic diagram of optical spatial traffic flow measuring device, and Fig. 14 Is a spatial velocity distribution diagram, FIG. 15 is a schematic diagram for explaining a travel time measuring device by reading the vehicle number, FIG. 16 is a diagram showing an installation example of a queue length sensor, and FIG. 17 is a device using the queue length sensor It is a graph explaining the error of.

Claims (1)

[Claims] (A) At the entrance and exit of a section of a road, ultrasonic waves are sent from the ultrasonic type vehicle detector toward the road at a constant cycle, and the time until the reflected wave arrives after sending is sequentially measured. The vehicle height is calculated from each measured value, one vehicle shape data is created for the vehicle from the momentary change of the vehicle height accompanying the progress of the vehicle, and the vehicle shape data of each vehicle is transmitted to the center. , (B) In the center, a) The vehicle shape data transmitted from the entrance and the exit is collated with the vehicle shape data of the vehicle to be used as a marker that is stored in advance, and only the vehicle shape data when it matches is used as the marker. While extracting, b) a representative value of the vehicle height is obtained from the shape data of the marker, and representative value time series data is created for the marker passing through each of the entrance and the exit. The cost of each marker that has passed through the entrance The table values are sequentially compared with the representative values of the markers that have passed through the exit, and if they almost match, it is determined that the marker has passed through the section, and d) the section has passed since the point when the marker passed through the entrance. The number of vehicles that have passed through the entrance up to the point of judgment is used as the initial value for the number of existing sections, and thereafter the number of vehicles that pass through the entrance is added and the number of vehicles that pass through the exit is subtracted to determine the number of sections that exist in the section. In addition to the real-time measurement, e) the number of exits passing per unit time is measured at any time to obtain the traffic volume for fencing, and f) the number of vehicles existing in the section is divided by the traffic volume for the section at that time, and the end of the long line of the section A method of automatically measuring the travel time required for a vehicle to pass through the section.