JPS62295200A - Road traffic information monitor - Google Patents

Abstract

(57) [Summary] This bulletin contains application data before electronic filing, so abstract data is not recorded.

Description

[Detailed Description of the Invention] 3. Detailed Description of the Invention Industrial Field of Application The present invention provides effective use of road facilities by providing accurate information to road users and appropriate traffic flow control. This invention relates to a road traffic information monitoring device.

Conventional technology Conventionally, this type of road traffic information monitoring device has been equipped with sensors installed around the road and a processing device for grasping traffic flow phenomena. The current state of traffic flow was monitored by processing based on point-based criteria.

Problems to be Solved by the Invention However, since the above-mentioned conventional road traffic information monitoring devices process data obtained from sensors according to uniform standards, it is difficult to incorporate temporal and spatial changing characteristics of traffic flow. Therefore, there was a problem in that accurate and immediate traffic flow monitoring was not possible.

The present invention is intended to solve these conventional problems.
The object of the present invention is to provide an excellent traffic flow information monitoring device capable of accurate and immediate traffic flow monitoring.

Means for Solving the Problems In order to solve the above problems, the present invention uses data collection means to detect the number of vehicles, occupancy rate, and speed, and uses prediction means to calculate the actual approximate curve of traffic density and traffic volume. The system is configured to detect traffic density and traffic volume as changes in distance over time, and predict traffic congestion when there is a large positive or negative change in the values.

Operation The present invention has the above-mentioned structure, and since temporal and spatial change characteristics of traffic flow can be incorporated from the data obtained from the data collection means, accurate and immediate traffic flow monitoring can be performed. becomes.

Embodiment FIG. 1 is a block diagram of a road traffic information monitoring device according to an embodiment of the present invention.

In FIG. 1, 1 is a data collection means for detecting road traffic information, 2 is a phenomenon grasping means for detecting the current traffic condition from the signal of the data collection means 1, and 3 is a prediction of future traffic conditions from the signal of the phenomenon grasping means 2. It is a prediction means for predicting.

4 is a vehicle sensor that detects the number of passing vehicles, occupancy rate, speed, vehicle type, etc. at a certain point, and 5 is an image sensor that performs image processing and authentication from signals from television cameras to detect sudden events such as traffic accidents. It is something to detect. Reference numeral 6 is an AVI sensor that reads the license plate of the vehicle and detects the travel time of the vehicle by comparing the number plates at multiple locations. Reference numeral 7 denotes a man-machine through which a person in charge inputs information on the scene of a traffic accident or the like.

8 performs traffic flow judgment and sudden event detection from signals such as number of vehicles, occupancy rate, speed, etc., sudden event signals, and travel time signals.
It is a microscopic means of understanding that outputs traffic flow mode signals.

Reference numeral 9 denotes a micro-comprehension means for calculating section state quantities from signals such as the number of vehicles, occupancy rate, speed, etc., travel time signals, and traffic flow signals.

Reference numeral 10 denotes a normal time prediction means for predicting the normal time from the traffic flow mode signal and the section state quantity signal, and 11 denotes a post-emergency event prediction means for making a prediction after the occurrence of an unexpected event from the traffic flow mode signal and the section state quantity signal. be.

Next, the operation of the above embodiment will be explained.

In FIG. 1, when the data collection means 1 collects data, the micro grasping means 8 and the macro grasping means 9 process the data at a cycle according to the purpose, and the past and present spatial characteristics and temporal characteristics are By taking this into account, we can grasp the current state of traffic flow, detect sudden accidents, and calculate the state quantities in the target section. Furthermore, the means for predicting those results 3
It is used to predict traffic flow.

In this way, according to the above embodiment, the data is collected by the data collection means 1, and is used by the micro grasping means 8, the macro grasping means 9, and the prediction means 3, taking into account the change characteristics, to accurately determine the traffic flow. It is possible to understand and predict the current situation, and as a result, provide accurate information to road users and efficiently operate road resources.

The operation of the present invention will be explained in more detail.

First, the relationship between the traffic volume and speed at one point in the traffic flow, in which congestion is determined based on the characteristics of the traffic flow, is generally expressed as shown in FIG. It is known that this characteristic can also be seen in the relationship between traffic volume and occupancy rate and between traffic volume and density. As can be seen from this figure, the data distribution shows a specific trend when driving freely, but when traffic is congested, the data distribution shows a discrete distribution and no specific trend is observed. However, looking at this Figure 2 and looking at the second period, it is impossible to accurately distinguish between free running and congested traffic. As a result, in the past, a method was used in which a threshold value was set for a single parameter such as only the speed or only the occupancy rate. However, if we observe the approximate curve in Figure 2 and the actual values and distances over time, it becomes possible to distinguish between free running and traffic congestion. Figure 3 shows this.

As can be seen from Figure 3, there is a relatively stable distribution in the early stages, and at a certain moment, there are 11 strong positive triggers followed by strong negative triggers, and then large fluctuations continue. In this fluctuation pattern, when a strong positive trigger is seen, it is when a traffic jam occurs.

Note that the approximate curve in FIG. 2 is based on the least squares method. Taking advantage of this characteristic, it is possible to judge whether the traffic flow is free running or congested.

Second, using the results of the previous traffic jam determination, it is possible to select a collection cycle tailored to the purpose. In monitoring traffic flow during traffic congestion, when calculating state quantities that represent stable overall trends in a specific section, the collection period of data to be processed is set to several minutes, and it is necessary to immediately detect unexpected events. If it is necessary to make a judgment, the data collection cycle is set in seconds. Accurate monitoring becomes possible by selecting the collection period in this manner.

Thirdly, by extracting and using the characteristics of changes in traffic data during a specific time period at the sensor installation point, it is possible to know the scale, time of occurrence, and impact of traffic flow fluctuations such as congestion, and to understand the current state of traffic flow. It can be used for prediction.

Fourth, by comparing the change characteristics of the traffic data extracted for each sensor installation point with the characteristics of the point that is thought to have an impact, the propagation of traffic flow can be understood and used to predict traffic jams and detect sudden events. do.

In the above points, not only conventional vehicle detectors that collect point data such as traffic flow, occupancy rate, and speed, but also I
In the case of a sensor that collects spatial data using a TV camera, the same effect can be achieved by extracting and utilizing change characteristics such as brightness distribution by performing image processing.

This embodiment has the following advantages.

(1) Accurate determination of congestion can be made by using the changing characteristics of traffic data.

(2) Using the results of (1), a processing cycle tailored to the purpose is adopted, allowing accurate understanding of traffic flow phenomena.

(3) By using the time-series change characteristics of each point, it is possible to accurately understand and predict the time, scale, and impact of traffic flow fluctuations such as congestion.

(4) By using the spatial change characteristics of multiple points, it is possible to understand the propagation of traffic flow, and accurately understand and predict traffic flow fluctuations.

(5) As a result of the above, it is possible to reduce the workload for traffic control operators and to make efficient use of road resources.

(6) Also, by receiving appropriate information for road users, they can make effective decisions about road use.

Effects of the Invention As is clear from the above embodiments, the present invention detects the number of vehicles, occupancy rate, and speed by a data collection means, and calculates the actual traffic density and traffic volume with respect to the approximate curve of traffic density and traffic volume by a prediction means. By detecting the value of distance as a change over time, when the value fluctuates sharply in the positive or negative direction,
Since it is configured to predict traffic congestion, it is possible to incorporate temporal and spatial change characteristics of traffic flow from the data obtained from the data collection means, resulting in accurate and immediate monitoring of traffic flow. has.

[Brief explanation of drawings]

Fig. 1 is a block diagram of a road traffic information monitoring device according to an embodiment of the present invention, Fig. 2 is a characteristic diagram of traffic density and traffic volume according to the same embodiment, and Fig. 3 is an actual diagram of the approximate curve of Fig. 2. FIG. 2 is a characteristic diagram showing the values of traffic density and traffic volume as changes in distance over time. 1... Data collection means, 2... Phenomena understanding means, 3.
Forecast (1 means, 4...Vehicle detector, 5...Image sensor, 6...A■■, 7...Man-machine, 8...
Micro grasping means, 9... Macro grasping means, 10.
... Normal time prediction means, 11... Prediction means after the occurrence of an unexpected event. Name of agent: Patent attorney Toshio Nakao and one other person Figure 2 Density - Figure 3

Claims (1)

[Claims] A data collection means for detecting the number of vehicles, occupancy rate, and speed using a vehicle sensor; a phenomenon grasping means for inputting signals from the data collecting means and outputting a traffic flow mode and section status; and a phenomenon grasping means. By inputting the traffic signal and detecting the change in distance over time, the actual values of traffic density and traffic volume are compared to the approximate curve of traffic density and traffic volume.This method is used to predict traffic congestion when there is a significant change in the positive or negative direction. A road traffic information monitoring device equipped with