Method for predicting traffic space mean speed and traffic flow rate, and method and apparatus for controlling isolated traffic light signaling system through predicted traffic flow rate

A method for predicting a traffic flow rate at a point on a road to control a traffic light signaling system measures a traffic density on the road to predict a traffic flow rate by utilizing the fact that a velocity of a vehicle on the road is restricted by an interval between successive vehicles, since the traffic density is locally increased when the vehicle interval is not uniform and therefore the spatial mean speed is lowered. This method offers higher accuracy by utilizing a correction coefficient obtained from an actual vehicle distribution, for instance, a coefficient derived from entropy. An apparatus for controlling a traffic light signaling system installed on a point of a road by utilizing this predicting method, thereby smoothing a traffic condition, includes video cameras for picking up images of a traffic condition at an upper stream of an intersection, an A/D converter for converting an analog video output signal into a digital video signal, two sets of image memories for storing digital image data about two scenes imaged by the video cameras at a proper time interval, an image processing unit for extracting moving objects from the images, a data process/control unit for calculating a total number of vehicles within a predetermined area and each space headway, whereby a vehicle distribution pattern is recognized an a correction coefficient is calculated, and an input/output unit for interfacing with the traffic light signaling system installed on the road.

BACKGROUND OF THE INVENTION 
1. Field of the Invention 
The present invention relates to a method for predicting a traffic space 
mean speed and a traffic flow rate from a traffic density on a road, and 
further to a method and an apparatus for controlling a traffic light 
signaling system located at an intersection based upon the predicted 
traffic flow rate. 
2. Description of the Related Art 
Conventionally, to either maintain a smooth traffic condition, or construct 
a proper traffic system, traveling conditions of vehicles are measured to 
predict a traffic flow rate of the vehicles traveled on a road. Velocities 
of the vehicles traveling on the road are restricted by intervals among 
the successively traveling vehicles. As a consequence, an average velocity 
of a group of traveling vehicles may be predicted from a traffic density 
of the traveling vehicle group. 
The conventional traffic flow rate predicting method is established under 
condition that the following relationship is satisfied. 
That is, assuming now that a traffic flow rate is "q", a space mean speed 
is "v", and a traffic density is "k", a basic equation (1) can be 
satisfied: 
EQU q=kv (1) 
It should be understood that a traffic space mean speed implies an 
arithmetic average value for velocities of vehicles located within a 
predetermined section on a road at a certain time instant, whereas a 
traffic density implies a quantity of vehicles present on a road in a unit 
length thereof at a certain time instant. 
A relationship between the space mean speed "v" and the traffic density "k" 
is represented as a k-v curve in FIG. 4. 
In FIG. 4, an abscissa indicates the traffic density "k" and an ordinate 
denotes the space mean speed "v". If the intervals among the successively 
traveling vehicles are narrow and the traffic density becomes high, then 
the vehicles could travel only in low speeds, resulting in a traffic jam. 
Eventually, the traffic density is brought into a jam density, so that a 
vehicle stream cannot be moved. Conversely, if the intervals among the 
successively traveling vehicles are wide and the traffic density becomes 
low, then a vehicle stream can be moved at high speeds. Eventually, each 
of these vehicles can freely travel at a velocity determined by the road 
conditions. 
A crosspoint "kj" between the k-v curve and the abscissa represents a jam 
density, whereas a crosspoint "vf" between the k-v curve and the ordinate 
represents a free speed. Both of a curve pattern and these crosspoints may 
be determined based upon the road conditions and the like. 
As a typical k-v relational expression fv(k), the following equation (2) is 
known. The equation (2) represents such a case that the traffic density 
"k" and the space mean speed "v" can satisfy a linear relationship. As 
explained above, when the space mean speed "v" is expressed by the traffic 
density "k", the traffic flow rate "q" becomes the function of only the 
traffic density "k", and therefore becomes a k-q curve as indicated in 
FIG. 5. This implies that the traffic flow rate may be predicted from the 
traffic density. 
EQU v=vf (1-k/kj) (2) 
In a conventional control method for isolated traffic signals which are not 
intervened from other signals, a time gap control method for predicting 
traffic conditions based on time headways has been widely utilized that 
when the time headway is below than the threshold value, the green time is 
prolonged, and when the time headway exceeds the threshold value, a 
decision can be made that the saturation flow has passed through, whereby 
the green time is ceased. 
A saturation flow implies such a traffic flow that vehicles travel while 
keeping a substantially minimum constant interval, and thus becomes a 
maximum flow rate of the vehicles at an incoming passage of a certain 
intersection. For instance, such a constant traffic flow corresponds to 
this saturation flow that if a vehicle stream is stopped at a traffic 
light, after turning-ON of the green light is commenced and approximately 
three vehicles located from the top position have passed, the subsequent 
vehicles are advanced. 
FIG. 5 represents a relationship between a traffic flow rate and a traffic 
density in the conventional traffic flow rate predicting method. FIG. 5 
indicates such a condition that a measurement is carried out for a unit 
time under constant traveling flow where no influence caused by the 
signaling control is given. 
In FIG. 5, under a light traffic condition from traffic density of 0 to 
traffic density of "kc" at which the maximum traffic flow rate appears, 
when a total number of vehicles present within the section increases, the 
traffic flow rate also increases. However, when the traffic density 
exceeds "kc" and is brought into a heavy traffic condition, a smoothness 
of the vehicle traveling (average speed) is lowered and eventually, when 
the traffic density becomes "kj", no vehicle can travel. Accordingly, 
other than "kc", there are two traffic density conditions with respect to 
a certain traffic flow rate. 
In the short time measurement of the road traffic flow where the influence 
caused by the traffic signal control is given, there is observed a large 
number of different vehicle distribution patterns even in the same traffic 
density. When too many vehicle groups are formed, the short time traffic 
flow rate approaches 0 irrespective of to the traffic density. As a 
consequence, the short-time traffic flow rate of the road traffic is 
present within an area surrounded by the curve and the abscissa shown in 
FIG. 5. This has been apparently proved by the actual traffic flow 
measurements obtained by the Applicant's experiments. 
As described above, in accordance with the conventional traffic flow rate 
predicting method, there is such a problem that although the traffic flow 
rate obtained from the traffic density should be present on the "k-q" 
curve of FIG. 5, a plurality of actual short-time traffic flow rates would 
be present in an area surrounded by the X axis and the curve, which 
improperly reflects the actual traffic flow rate. 
Also, in the conventional isolated traffic signal control method, there is 
another problem that since a certain time is required to directly measure 
the traffic flow rate, this measuring time may cause a delay control. 
Further, in the above-explained conventional isolated traffic signal 
control method, since fluctuation in the time headway becomes large, 
depending on the different combinations of the preceding and succeeding 
vehicles, it is rather difficult to set the threshold values of the time 
headway. If a small threshold value is set, then a saturation flow would 
not pass through the cross-section thoroughly. Conversely, if a very large 
threshold value is set, then even when the saturation flow is ended, the 
green light signal would be continuously outputted vainly. 
Then, in the above-explained conventional isolated traffic signal control 
method, the initial green time is previously set to a preselected constant 
green time, and the fixed initial green time is outputted even when no 
vehicle is located within the fixed initial green time. As a result, there 
is another problem that waste time happens to occur. 
Moreover, in accordance with the conventional isolated traffic signal 
control method, since the input information used in the traffic signal 
control corresponds to a condition amount derived from the local data 
(quantity of passing vehicle and sensing pulse width), it is practically 
difficult to entirely grasp complex traffic flows. 
JP-A-1-281598 issued to Soga et al describes that a recognition apparatus 
for recognizing the license plate of the vehicle traveling on the road is 
commonly utilized as the traffic-flow measurement apparatus by operating 
the switching unit. In this conventional recognition apparatus of Soga et 
al, when the traffic flow is measured, the viewing angle of the ITV camera 
used to pick up the image of the license plate is selected to be a large 
viewing angle so as to pick up image of the road. After the road image is 
inputted, the vehicle images are independently extracted one by one by way 
of the image processing techniques, thereby calculating the velocities, 
sorts, and quantity of passing vehicles. Although this conventional 
apparatus does not clearly disclose the concrete processing method for 
calculating the velocities and the like, since this apparatus utilizes 
such a processing technique for recognizing the numeral data indicated on 
the license plate, it seems that a very complex arithmetic calculation has 
been employed. 
Marcy discloses a monitoring system in U.S. Pat. No. 4,390,951 which 
measures both of the mean overall speed of vehicles passing over the 
surveyed road section and the combined length of vehicles simultaneously 
present on the surveyed road, obtains an encumbrance parameter by diving 
the combined length by the mean overall speed to be recognized as a degree 
of loading of the road, and then controls the traffic lights corresponding 
to the traffic flow rate predicted from this encumbrance parameter. The 
monitoring system of Marcy must actually measure the velocities and the 
lengths of the respective vehicles passing the entrance and the exit of a 
predetermined road area, namely must measure a large number of elements, 
resulting in a complex monitoring system. 
JP-A-3-273,400 by Naito discloses a method for measuring traveling 
conditions of traffic by employing a CCD camera by monitoring one typical 
vehicle selected from the traffic in order to predict the traffic 
conditions. This measuring system is to avoid such a difficulty in 
processing the image data for tracking a preselected vehicle without 
confusion for image recognition purposes, and is therefore to grasp the 
traveling conditions of a single vehicle in such a manner that a large 
quantity of measurement sampling areas are provided on the road monitored 
by the CCD camera, and the passages of the vehicles through these sampling 
areas are sequentially detected. Accordingly, this measuring system 
requires the mechanism to actually measure the velocities of the vehicles. 
SUMMARY OF THE INVENTION 
The present invention is to solve the above-described conventional 
problems, and has an object to provide a method for predicting a traffic 
flow rate properly corresponding to the actual traffic flow rate, and as 
another object to provide an isolated traffic signal control method for 
controlling a traffic light signaling system based upon the predicted 
traffic flow rate. 
Also, to achieve the above-mentioned objects, in accordance with another 
aspect of the present invention, the traffic light signaling system is 
controlled based upon such a traffic flow rate predicted from a spatial 
vehicle distribution pattern which has been produced by measuring traffic 
flow conditions on a road space at a certain instant. 
Furthermore, to achieve the above-explained objects, in accordance with a 
further aspect of the present invention, a trend of vehicles located in an 
upper stream from an intersection is imaged by video cameras in a bird's 
eye manner, the resultant image data are processed by an image processing 
apparatus to obtain a spatial distribution pattern of vehicles present in 
the measurement section, and a traffic flow rate for several seconds is 
predicted from this spatial vehicle distribution pattern, whereby a 
control signal is transmitted to the traffic light signaling system. 
According to the present invention, the mean speed and the traffic flow 
rate of the road traffic flow within the traffic measurement section, 
which are varied from time to time as in urban areas, can be predicted in 
high precision by employing the spatial information without any time 
delays. That is, the space mean speed indicative of the k-v relational 
expression is set to the upper limit value at this traffic density, and 
this upper limit value is multiplied by the correction coefficient ranging 
from 0 to 1 in response to the group formation states of the vehicles, 
whereby both of the space mean speed and the traffic flow rate can be 
predicted in high precision. 
Also, according to the present invention, the jammed or saturated traffic 
flow may be readily predicted based upon the spatial vehicle distribution. 
The green times for the traffic lights can be distributed under optimum 
condition. In addition, since the initial green time which was 
conventionally constant, may be varied in accordance with the traffic 
flows, an excessive initial green time may be eliminated. 
Moreover, in accordance with the present invention, the spatial vehicle 
distribution pattern can be obtained by employing the video cameras and 
the simple image processing apparatus. Based upon this distribution 
pattern, the proper control signal without any waste time may be 
transmitted to the traffic light signaling system.

DESCRIPTION OF THE PREFERRED EMBODIMENTS 
Referring now to the drawings, various embodiments of the present invention 
will be described. 
FIRST EMBODIMENT 
A method for predicting space mean speed and a traffic flow rate according 
to a first embodiment of the present invention will now be explained. In 
accordance with this first embodiment, a correction coefficient is 
constructed based upon a parameter referred to "entropy" in order to 
predict a traffic flow rate. 
FIG. 1 schematically shows section information of a vehicle distribution. 
In FIG. 1, reference numerals 1 indicate vehicles. Assuming now that "n" 
vehicles are present within a section L (meters) with a certain vehicle 
distribution at a certain time instant, and intervals among the "n" 
vehicles (space headway) are Di (meters) where symbol "i" is equal to 1, 
2, 3, - - - , n, spatial vehicle entropy during the traffic measurement 
may be calculated by the following equation (3): 
##EQU1## 
Based on this equation (3), it is possible to express such a difference in 
traffic flow conditions by numerical values when the same number of 
vehicles are distributed in difference within the same traffic measuring 
region. 
In other words, when all of the vehicles are arranged at an equi-interval 
(Di=L/n), the entropy of spatial vehicles becomes maximum based on the 
above-described equation (3), and this maximum entropy will be referred to 
"Hmax". Also, when the respective vehicles' intervals become minimum and 
"n" vehicles constitute a single vehicle group while no further vehicle is 
present within the traffic measuring region, the resultant entropy becomes 
minimum and this minimum entropy will be referred to "Hmin". Then, these 
entropy values Hmax and Hmin may be expressed by the below-mentioned 
equations (4) and (5), respectively: 
##EQU2## 
Now, a calculation is made by dividing a difference between the entropy 
condition H and the minimum condition Hmin as a numerator by a difference 
between the maximum condition Hmax and the minimum condition Hmin as a 
denominator. Thus, the calculated coefficient (H-Hmin)/(Hmax-Hmin) ranges 
from 0 to 1. In case of the normal traffic flow, since the entropy 
condition H becomes Hmax, this coefficient becomes 1. In case of the 
minimum space headway, all of the vehicles on the road become a single 
group and thus the entropy condition H becomes Hmin, so that the resultant 
coefficient become 0 and the vehicle speed becomes 0. As a consequence, 
this coefficient indicates a degree of smoothness of the traffic flow at 
the same density, and may be used as such a correction coefficient that 
the traffic density is coincident with the actual traffic flow. 
Accordingly, as represented in equation (6), this coefficient is 
multiplied by the (k-v) relational formula fv(k), thereby predicting a 
space mean speed: 
EQU Vse=fv(k).cndot.(H-Hmin)/(Hmax-Hmin) (6) 
where symbol "Vse" denotes a predicted value for the space mean speed. It 
should be noted that a relative coefficient between the actually measured 
value in the straight lanes of the crossroads and the space mean speed 
predicted by the equation (6) could reach 0.971. Using the equation (1), 
the predicted space mean speed Vse is multiplied by the traffic density k, 
thereby predicting a traffic flow rate, as shown in equation (7): 
EQU Qse=Vse.cndot.k (7) 
where symbol Qse indicates a predicted value of the traffic flow rate. 
In accordance with the traffic flow rate predicting method of the first 
embodiment of the present invention, the traffic density is first 
calculated, the correction coefficient is calculated based on the vehicle 
distribution's entropy representative of the vehicle distribution pattern 
by utilizing the equation (6), and then the traffic density is corrected 
by way of the equation (7) in order to predict the actual traffic 
flowrate. Thus, the traffic flow rate predicting method predicts the 
traffic flow-rate only by obtaining the vehicle distribution at a certain 
time instant within a measurement section. 
As previously described, according to the first embodiment, both of the 
mean velocities and the traffic flow rates within the traffic section of 
the road, which are varied time to time, can be predicted at high 
precision. In particular, the above-described isolated traffic signal 
control method with use of entropy is optimized as a method for 
instantaneously predicting a traffic flow rate from a vehicle distribution 
condition of a traffic section with a length of approximately 70 meters. 
SECOND EMBODIMENT 
Then, an isolated traffic signal control method according to a second 
embodiment of the present invention will now be explained with reference 
to an algorithm shown in FIG. 2. First, a total number "n" of vehicles 
located within a traffic measurement section is obtained (step 11). A 
judgement is made as to whether the vehicles are present or not in the 
measurement area (steps 12 and 13). If the vehicles are present, then 
initial green time Te-min is calculated by equation (8) (step 14), and 
thus green signal is transmitted during the initial green time (steps 15, 
16, 17): 
EQU Te-min=max (n.cndot.ts, L/V) (8) 
where symbol "ts" denotes mean time headway in saturated traffic flow, and 
symbol "V" indicates mean speed in saturated traffic flow. 
A time period required to let the last vehicle of "n" queuing vehicles pass 
through the cross-section is calculated from n.times.ts. Also, a time 
period when the vehicle located at the last end of the traffic measurement 
section runs through the cross-section is calculated from L/V. As a 
consequence, the larger value in the above time periods is set as the 
initial green time based only on the information about the quantity of 
vehicles. When there are only a small number of vehicles in the traffic 
measurement section, a comparison of n.cndot.ts and L/V is preferably 
introduced into the procedures in order to prevent the initial green time 
from being so short that all the approaching vehicles cannot pass. As 
described above, based on the equation (8), it is set the minimum time 
period required for either the queuing vehicle or the approaching vehicles 
which are present when the green time is commence to pass through. 
Accordingly, it is possible to prevent an increase of waste time caused by 
the unnecessarily lengthy initial green time. 
Once the initial green time is finished, the entropy and the density are 
iteratively calculated until a predetermined maximum limit green time Tmax 
(step 18) based on the equations (3) and (4) used in the first embodiment 
(step 19). And also, a predicted traffic flow rate Qse is subsequently 
obtained from the equations (6) and (7) (step 20). Then, a comparison is 
made between the predicted traffic flow rate Qse and a threshold value Qc 
(step 21). If the predicted traffic flow rate is smaller than the 
threshold value, then the green traffic light is alternated by other 
traffic lights. Conversely, if the predicted traffic flow rate is greater 
than the threshold value, then the green time is extended (steps 22, 23). 
This process operation is continued until the maximum green time limit 
Tmax (step 18). When the time exceeds the maximum green time limit Tmax, 
the green light signal process is ended. 
As previously explained in detail, in accordance with the second 
embodiment, there is such a merit that the optimum green times of the 
traffic signal controller can be properly distributed based upon the 
predicted value of the jammed traffic flow derived from the spatial 
vehicle distribution. Also, there is another advantage that the initial 
green time which was originally constant, can be varied in accordance with 
the traffic flows based on the equation (8). 
THIRD EMBODIMENT 
In FIG. 3, there is shown an apparatus for predicting a traffic flow rate 
and for controlling traffic lights, according to a third embodiment of the 
present invention. 
In FIG. 3, reference numerals 31 and 32 denote video cameras respectively 
furnished at roads intersecting each other for imaging a trend of 
vehicle's groups at an upper stream of an intersection in a bird's eye 
viewing form. Reference numeral 33 indicates the main body of the 
apparatus, reference numeral 34 shows a video signal selecting unit, 
reference numeral 35 represents an A/D converting unit. Further, reference 
numeral 36 indicates a first image memory for input image 1, reference 
numeral 37 shows a second image memory for input image 2, reference 
numeral 38 denotes an image processing unit, reference numeral 39 
represents a data process and control unit, reference numeral 40 denotes 
an input/output unit, and reference numeral 41 denotes a traffic light 
signaling system. 
Operation of the above-explained third embodiment will now be described. In 
the third embodiment, the video information obtained by imaging a trend of 
a vehicle group at an upper stream of the intersection with employment of 
the video camera 31 or 32, is transmitted to the main body 33 of the 
apparatus for predicting traffic flow rate and controlling traffic lights. 
In the main body 33, conditions of step signals indicating green, red and 
yellow lights of the traffic light and its complemental traffic light 
located at the crossroad are acquired via the input/output unit 40 for 
judgement purposes. Then, the video signal selecting unit 34 selects 
either the video signal from the video camera 31, or the video signal from 
the video camera 32, and the A/D converting unit 35 converts the selected 
video signal into digital video data. Subsequently, the digital video data 
about two images (namely, input image 1 and input image 2) which have been 
picked up in a predetermined interval, are stored into the first image 
memory 36 and the second image memory 37. The image processing unit 38 
reads out the digital video data from these image memories 36 and 37, and 
subtracts one of these digital video data for two images from the other 
(frame subtraction). As a result of this frame subtraction, only a moving 
object located in the traffic measurement region can be extracted (symbols 
painted on the crossroads and others are erased). A major merit of this 
frame subtraction may withstand an instantaneous variation in brightness, 
so that the traffic flow measurement by utilizing such a frame subtraction 
is suitable for imaging such an outdoor place where brightness is widely 
changed. Although this frame subtraction method cannot extract a stopping 
object as a demerit, since the traffic flow rates of such conditions that 
no vehicle is present, and the vehicles are stopped within the entire 
traffic measurement region are equal to 0, there is no problem in the 
traffic flow measurement. The image-processed video data by the image 
processing unit 38 is written into the first image memory 36. 
Next, the image-processed digital video data is furnished to the data 
process/control unit 39 so as to measure the positions of all the moving 
objects within the traffic flow measurement section measured from the 
intersection, which have been extracted by way of the frame subtraction, 
thereby obtaining a total number of these vehicles and also each of space 
headway. Furthermore, based on the equations (3), (4) and (5) employed in 
the first embodiment, the traffic density and the spatial vehicle 
distribution pattern are calculated by this data process/control unit 39, 
and a predicted traffic flow rate is obtained from the equations (6) and 
(7) in the data process/control unit 39. Then, the traffic lights 
signaling system 41 is controlled by using the isolated traffic signal 
control method according to the second embodiment. 
As described above, in accordance with the third embodiment, the spatial 
vehicle distribution pattern can be obtained by using the video cameras 
and the simple image processing apparatus, and therefore, the proper 
control signal without any waste time can be transmitted to the traffic 
light signaling system based upon the spatial vehicle distribution 
pattern. 
One video camera employed for measuring a scene on a road where the green 
light is displayed, picks up images of an incoming traffic flow, and then 
the image processing apparatus judges whether the green time should be 
extended, or ceased in response to the image data. When the green light 
signaling is changed to the opposite road, the other video camera starts 
to pick up images of another incoming traffic flow. A similar control will 
be continued while the video cameras are switched. 
As apparent from the above-explained embodiments, in accordance with the 
present invention, both of the mean speeds and the traffic flow rates of 
the vehicles traveled in the road section, which are varied time to time, 
can be predicted in high precision without any delay. 
Moreover, according to the present invention, the interruption of jammed 
traffic flows can be detected at high precision from the spatial vehicle 
distribution. The optimum green times can be distributed to the traffic 
light signaling, so that a traffic jam occurring near an intersection can 
be effectively solved. Additionally, the initial green time which was 
conventionally constant, may be varied in accordance with the traffic 
flow. 
Also, according to the present invention, the spatial vehicle distribution 
can be obtained by employing the video cameras and the simple image 
processing apparatus, and the control signal for indicating whether or not 
the present traffic light representation is extended in response to the 
spatial vehicle distribution, is directly transmitted to the traffic 
lights signaling system, so that the traffic lights can be properly 
controlled without any waste time.