KR100313456B1 - Traffic information service system - Google Patents

Abstract

PURPOSE: A traffic information service system is provided to supply traffic information to a driver by collecting effectively data, reducing the amount of transmitting data, and filtering the data. CONSTITUTION: A traffic information service system is formed with a satellite(12), a signal transmission vehicle(14), a radio data station(16), and a traffic information center(18). The signal transmission vehicle(14) has a GPS(Global Positioning System) terminal, a radio transceiver, and a traffic information display unit. The signal transmission vehicle(14) is formed with a taxi or a bus. The traffic information display unit includes a video device for displaying a map or a text on a screen and an audio device for outputting sound. The radio data station(16) includes a wireless communication network and a wire communication network. The traffic information center(18) has a wire communication network and a CPU(Central Processing Unit).

Description

Automobile traffic information provision system

The present invention relates to a vehicle traffic information providing system for processing the traffic information used to select the driving road by the vehicle driver using the data obtained from the target road and providing the driver to the driver.

The rapid increase in the number of driver's licenses and the number of cars has led to traffic congestion and congestion on roads. For this reason, there is a strong demand for the construction of a system that allows motorists to select roads for driving by obtaining traffic information on a driving route for efficient road driving.

The conventional traffic information providing system collects the traffic information from the correspondents or expatriates and provides information to the regional block or road traffic information centers of the whole country by telephone or facsimile. The road traffic information center then processes this information into useful traffic information for the driver and provides the traffic information through radio broadcasting, TV broadcasting, telephone, PC communication, and variable information boards.

Background Art Conventionally, vehicle detectors have been mainly used for collecting information on vehicle traffic flows (traffic information) such as vehicle average speed and traffic volume. The vehicle detector generates a pulse in proportion to the time that the vehicle is present in the detection area, and counts the pulse for each measurement unit time to obtain a traffic condition. In addition, the information about the travel time between each road is obtained by the estimated operation using the traffic information of the points. The information by these vehicle detectors is limited point information on the road, and there is a problem that the traffic situation is delayed depending on the arrangement of the vehicle detectors.

Meanwhile, the means for providing traffic information mainly use radios, variable information boards or roadside communication systems. However, the method of providing information by radio has a problem that the broadcasting time or the number of broadcasting is very small and the information providing opportunity is not many. In addition, the information providing method by the variable information plate has a problem that the amount of information that can be provided in the case of the character information plate is limited and the figure information plate is difficult to understand for the driver who is not familiar with the road network for the first time. On the other hand, the roadside communication system that provides traffic information through car radio using a dedicated transmitter of AM broadcasting band installed on the side of the road is voice analog information, and the frequency of use is limited, so there is a limit to the amount of information, which completely meets various needs of drivers. There was a problem of lack of immediateness due to the inability to cope with a long information update cycle.

The present invention has been made to solve such a problem, and an object of the present invention is to provide a vehicle traffic information providing system that provides information of individual contents according to the needs of drivers of each road.

It is another object of the present invention to collect effective data even with a small vehicle, reduce the amount of transmitted data, filter out inappropriate data among the collected data, and infer data where no data can be obtained to make accurate traffic information. It is to provide a method for processing.

1 is a block diagram of a vehicle traffic information providing system according to the present invention

Figure 2 is a flow chart of the vehicle traffic information processing method according to the present invention

Figure 3 is a flow chart for processing traffic flow

4 is a flow chart for fusing data

* Explanation of symbols for main parts of the drawings

12: satellite 14: signaling vehicle

16: Wireless data base station 18: Traffic Information Center

32: Collect data on road conditions 34: Process traffic information

36: prediction of passage time 38: prediction of optimal route

40, 60: display 42: vehicle average speed estimation and prediction

44: share forecast 46: traffic forecast

54: calculation of data per link 56: statistical processing

58: data fusion

In order to achieve the above object, the present invention is a vehicle traffic information providing system for providing traffic information used to determine a driving route to a vehicle driver through a traffic information display device using data obtained from a vehicle driving on a road, And a number of signaling vehicles, wireless data base stations, and traffic information centers. The satellite provides position data to each signaling vehicle every unit time. Each signal transmitting vehicle has a GPS terminal and a radio transmitting and receiving device and sends position data provided from the satellite to the radio data base station through the radio transmitting and receiving device. The wireless data base station transmits the location data received from the signaling vehicle to the traffic information center. The traffic information center collects and processes the database and location data of the signal vehicle and the road divided into the connected unit section (PRU), and provides the driver with traffic information through the traffic information display device. A traffic information providing device is provided.

According to another aspect of the present invention, a method for providing traffic information used to determine driving directions to motorists by processing data obtained from a plurality of vehicles driving on a road, the signal for each target road (PRU) Obtain speed data from the location data and time data of the outgoing vehicles, estimate the average vehicle speed using the speed data on the road with the speed data, and calculate the average vehicle speed of the adjacent road and the data in the statistics database. Estimating the vehicle average speed using time series information of vehicle average speeds of non-existing roads and adjacent roads and estimated average speeds of adjacent roads and expressing the estimated or estimated vehicle average speeds to the driver. The method may further include estimating vehicle road occupancy, traffic volume, passing time, and optimum route using the estimated or estimated vehicle average speed.

Hereinafter, the present invention will be described in detail with reference to the accompanying drawings.

Referring to FIG. 1, the vehicle traffic information providing system 10 includes a satellite 12, a signal transmitting vehicle 14, a wireless data base station 16, and a traffic information center 18. As shown in FIG. The satellite 12 uses Global Positioning Satellite Technique (GPS). The signal transmitting vehicle 14 is a vehicle such as a taxi or a bus, and is equipped with a GPS terminal, a wireless transmitter, and a traffic information display device. As the signal transmitting vehicle 14 for acquiring the basic positional information, a vehicle such as an exemplary taxi, which is not unusual in driving mode and has a lot of movement, is preferable. The traffic information display apparatus has a video device that allows maps and text information to be displayed on a display so that drivers can view traffic information through vision, or an audio device that can hear traffic information through hearing. It can also have both video and audio devices.

The wireless data base station 16 has a wired communication network and a wireless communication network. The wireless data base station 16 preferably uses base stations of mobile communication that are used in the past. The traffic information center 18 has a wired communication network connected with the base station 16 and a central processing unit. The central processing unit is a database that receives the position data of the signal transmitting vehicle 14 transmitted from the signal transmitting vehicle 14 through the base station 16, converts and processes the collected information, and processes the processed traffic information into a database. (data base) has a device to store and store it. The structure of the database includes road-related data, name-related data, image-related data and area-related data, but the present invention is not limited thereto.

Referring to Figure 1 looks at the operation of the vehicle traffic information providing system 10. The GPS terminal of the signaling vehicle 14 requests the satellite 12 for position data every unit time. The satellite 12 then transmits the position data toward the signal transmitting vehicle 14. The GPS terminal of the signaling vehicle 14 receives the position data transmitted from the satellite. The signal transmitting vehicle 14 transmits its position data to the radio data base station 16 via the radio transceiver. The radio data base station 16 receives the position data transmitted from the signal transmitting vehicle 14 using the radio communication network. The wireless data base station 16 transmits the position data of the signal transmitting vehicle 14 to the traffic information center 18 using a wired communication network. The traffic information center 18 finally receives the position data of the signal transmitting vehicles 14. In addition, the traffic information center 18 collects the location data into a database and processes the data to produce traffic information to be provided to the driver. The traffic information processed at the traffic information center 18 is provided to each signaling vehicle 14 through the wireless data base station 16. The traffic information display device in the signaling vehicle 14 allows the driver to view the traffic information through the video device or the driver to hear the traffic information through the audio device. Drivers determine driving routes and driving behaviors based on traffic information. On the other hand, the processed traffic information is not the signal transmitting vehicle 14 but can be supplied to a general vehicle having a traffic information display device.

Referring to FIG. 2, the traffic information processing method in the traffic information center 18 includes collecting data 32 on road conditions including location data of the signal transmitting vehicle 14, and traffic information processing step ( 34), the passage time prediction step 36, the optimum route determination step 38 and the traffic information display step 40.

In order to process the traffic information, the traffic information center 18 first selects a target road and collects data on road conditions of each target road. The collected data is used to directly calculate the traffic flow of each road, ie vehicle average speed, vehicle share or traffic volume. Each vehicle's average speed, occupancy rate, or traffic volume is itself expressed as traffic information to drivers and used as a data for calculating the transit time. The predicted transit time is itself expressed to the driver as traffic information and used to determine the best route. The predicted best path is presented to the drivers. Drivers determine the route they want to go to via the traffic flow, transit time, optimal route and other traffic information.

1. Basic data collection stage for road conditions

Basic data collection step 32 of the road situation is divided into the step of selecting the target road, collecting data on the road situation and the step of detecting / modifying the exceptional vehicle.

First, the road selection step accurately establishes the cities of interest such as Seoul, metropolitan area, Daegu, Daejeon, and Gwangju and their city boundaries. Roads that are too small, such as alleys, are integrated, and roads that are too large, such as the Southern Ring Road, are divided into traffic lights and crossroads. Each road controlled by such integration, splitting, etc. is called a primary road unit (PRU). One PRU consists of one link and two nodes at both ends of the link. Links and links are connected by nodes. Nodes can be determined at intersections or traffic lights, but if the links are too long, they can be segmented according to traffic volume. That is, the PRU should be separated at three or more distances, and even if there are traffic lights on the branch road, it is preferable to divide the data at once and pool it by homogeneity test after data is obtained later. If there are N PRUs in H zones, there will be N (h) (h = 1 ~ H) PRUs in each zone. When traffic information centers collect location data of vehicles, they should be able to find the corresponding PRU without overlapping.

The data structure of each PRU collected in the traffic information center can be divided into major (city), sub-region (old or east), PRU status (including north-south, north-south direction), connected neighboring PRU status, and surrounding features (university, baseball field, nightlife, etc.) Bus lanes, etc. When data is accumulated, the average speed per day, time zone, and weather is input.

The data collection step for road conditions collects data on road conditions for all PRUs at a given time, for example 10 or 30 minutes. The time interval at which data on the road situation is collected is called a traffic situation reporting interval (TRI). A vehicle collecting data on road conditions selects a signaling vehicle 14 so that the appropriate number of vehicles in each zone (h = 1) is based on the main driving location. The selected signaling vehicle periodically selects its own location data. The CRI (Car location Reporting Interval), which is the signal transmission cycle of a vehicle, should be less than TRI, for example, TRI can be 10 minutes and CRI can be 1 minute, in which case each vehicle for 1 TRI As long as there is no problem in the data processing capacity of the central processing unit of the traffic information center 18, the shorter the CRI is, the better the TRI is to be determined by simulation. The vehicle is provided with various information such as vehicle identification number, time, and corresponding PRU along with vehicle position data obtained during 1 TRI.

In the detection / modification phase of the exceptional vehicle, when a value with a large deviation from the average data is observed, such data is excluded from the statistical inference process or adopts a weighted modified value. It is preferable to estimate the measurement error by mounting two signal output devices on 20 to 30% of the signal transmitting vehicle 14 in preparation for the large positional data of the signal transmitting vehicle 14.

2 Traffic Information Processing Stage

The traffic information processing step 34 is subdivided into a step 42 for estimating or predicting a vehicle speed, a step 44 for estimating a vehicle occupancy rate, a step 46 for estimating / predicting a traffic volume, and a correcting step using a true value.

3 shows a process of estimating or predicting a vehicle average speed by processing position data collected from the signal transmitting vehicles 14 in the traffic information center 18. As shown in FIG. The position data 50 transmitted from the signal transmitting vehicle 14 is first subjected to map matching 52. The map matching 52 accurately captures the position on the map of the signal transmitting vehicle 14. Calculation 54 of speed data for each link is made using the position matching data of map matching 52. The calculated speed data for each link is sent to a statistical process 56 and a data fusion process 58. In the data fusion process 58, the data is processed through the statistical processing 56 as necessary, and the statistically processed data is displayed as necessary. The data that has undergone the data fusion process 58 is displayed 60 to the drivers. The data fusion process 58 will be described in detail later with reference to FIG. 4.

A step 54 of calculating velocity data of a link having position data will be described. If the link length is L (unit: meter), the transmission period is T (unit: second), and the number of times of location transmission is n, when n = 0

to be. Here, n = 0 means that the signaling vehicle 14 has passed through the corresponding link without sending a single signal. if n = 1

to be. Here n = 1 means that the signaling vehicle 14 has only sent one signal in the link. Max (L ₁ , L ₂ ) is the length from one end of the link to the signal transmission position L ₁ and the length from the other end of the link to the signal transmission position L _{2 based} on the signal transmission position. _The longer of ₁ and L ₂ is shown. if n = 2

to be. n = 2 means that the signaling vehicle sent two signals in the link. L ₂ means the distance traveled by the signaling vehicle between two signals. if n = 3

to be. n = 3 means that three signals are sent within a link. L ₂ is the distance traveled while sending the first and second signals, and L ₃ is the distance traveled between the second and third signals. If n is greater than or equal to 3, generalize it and if more than 3 is for k, and if n = k (k = 3, 4, ...)

to be.

The statistical processing step 56 for the speed data for each link of the signal transmission vehicle thus obtained will be described as follows. This statistical processing step can be divided into the case where the speed data of the signal transmitting vehicles 14 are obtained on the target road and the case when the data is not.

After the speed data is obtained, the average speed data (hereinafter referred to as 'average speed data') is distinguished from average speed data (hereinafter referred to as 'average speed data'). Calculate. The estimated vehicle average speed on the target road is obtained from the average vehicle speed in the statistical database and the average speed data calculated above according to the following equation.

here Is the average vehicle speed data in the statistics database n _old Is The number of data used to find Is the average vehicle speed data obtained in real time n _current Is The number of data used to obtain. New revised vehicle average speed The next time you find the average vehicle speed Used as In other words, the average vehicle average speed information remains as a statistically representative representative value and is used to obtain the latest vehicle average speed information. Thus obtained Is the estimated vehicle average speed and is the latest information.

In another embodiment, the vehicle average speed may be calculated by giving different weights in consideration of variations depending on the model of the vehicle and the driver's driving tendency, rather than a simple arithmetic average.

For roads without estimated average speeds (ie roads with no up-to-date information) or future roads (PRUs), vehicle averages are determined using information on the average speed of adjacent links (PRUs) and time series information in the statistical database. To predict the velocity, spatio temporal kalman filtering is preferably used. The correlation structure plays an important role. When estimating the average vehicle speed, it is desirable to use sample data that is highly related to traffic conditions at specific time and location. For example, using BLUO THEORY, the best prediction is based on the linear combination of the average velocity data at the same day and the same time zone, the sample data obtained from the connected neighboring roads, and the data obtained immediately before the location. (optimal prediction) can be made. It is necessary to accurately estimate the variance-covariance matrix for optimal prediction. To increase the degree of freedom, pooling variance-covariance matrices of similar time zones can increase the accuracy of the estimation.

In the case of calculating the average vehicle speed of a road (PRU, link) that does not have the observed average speed data, and a future PRU, the method of calculating the vehicle average speed through the information of the adjacent link and time series analysis is as follows.

Suppose there are n links on the road. Link 1 is S ₁ , link 2 is S ₂ ,.. , Link n is represented by S _n . When the average velocity data obtained by calculating the position data of the signal transmission vehicle observed at an arbitrary time t in the link i (i = 1 to n) is expressed as Z _t (S _i ), such data is obtained from the time point 1 from the time point. If it is up to T, the observed average velocity data can be represented by the following matrix.

here Is a matrix of mean velocity data for n links observed at time 1, Is a matrix of mean velocity data for n links observed at time 2, Is a matrix of average velocity data for the n links observed at time point T.

Measurement of the matrices Assuming that the calculated average velocity data is obtained by adding white noise to a true value, the following modeling is possible for the average velocity data.

here Is a matrix of average velocity data computed from the measured position data Is a matrix of true values Is a matrix of error values. Statistically Wow Can assign a statistical distribution to realize the reality of uncertainty. That is, a matrix of true values at random t Let's say

True value at link i at random time t S _t (S _i ) Is assumed to be determined by p previous time points from an arbitrary time point t and an error value is added to it, S _t (S _i ) Can be expressed as the following formula.

here η _t (S) Is the error value and S _tk (k = 1 ~ p) are the true values for p previous time points. α _k (k = 1 to p) means an association between an arbitrary time point t and a previous time point k. Here are some definitions to develop the equation.

Assume that there is no velocity data observed at a particular time t of any particular link S ₀ . In order to predict the value of Z _t (S ₀ ) from previous average velocity data (Z ₁ , Z ₂ ,…, Z _t ), S _t (S ₀ ), which is the true value of Z _t (S ₀ ), is predicted. Use that value as the predicted value for Z _t (S ₀ ). In other words

From here The value of is obtained by using the correlation (correlation or covariance) between links at one point in time through the continuous process and the correlation between points in one area. But preferably If you get a value, at the next time point t + 1 The values are again Z ₁ , Z ₂ ,.. Not using Z _t When you get a value, use the values you got. This can save time to find the average vehicle speed.

The process of obtaining a value is to set an initial predictor as the first step. In other words Set the values. Normal Use (zero vector) The second step is to determine the initial covariance matrix. In other words

To decide. The third step is The values are updated according to the following equation.

Or use the following equation.

∑ _1/1 (s, r) = ∑ _1/0 (s, r)-[G ₁ (s)] ′ F ₁ ^-1 G ₁ (r)

The values used in the above formula can be calculated as follows.

to be.

The last stage 4 repeats the third stage t times Until you get.

On the other hand, we will look at how to obtain an estimate at a future point in time t + 1. The same modeling and definitions as defined above and the following equation can be used to derive an estimate.

here Is an estimate of the future, and α _k (k = 1 ~ p) is the association between future time t + 1 and some time i, (j = 1 to p) means the true value of the random point j.

When there is no average speed data observed at a specific time point of a specific link, a method of obtaining an average speed of a vehicle on a target road according to the present invention will be described.

Assume that all the average velocity data are known for the three links S ₁ , S ₂ , S ₃ and two time points t ₁ and t ₂ except for the average velocity data for S ₂ at t ₂ . The average velocity data is expressed as a matrix as follows.

and Assume that Z ₂ (S ₂ ) is estimated using a matrix.

S _t (s) = 0.5 × S _t-1 (s) + η _t (s)

In the above formula, the relation a between the time point t and the time point t-1 is 0.5. Assume the following values for all i and j:

σ _ξ ² = 1, σ _η ² = 1, C _s ^{i, j} = Cov [S ₀ (s _i ), S ₀ (s _j )] = 0.5

In addition, the following values are assumed for all time points t and all i and j.

C _η ^{i, j} = Cov [η _t (s _i ), η _t (s _j )] = 0.5

Now that we have modeled and made assumptions, we find Z ₂ (S ₂ ) for each step.

In one step Should be obtained. first Obtain

In the same way and Obtain In other words

In 2 steps Obtain In other words

finally

The average vehicle speed for the time point t ₂ at link S ₂ can be estimated to be 36.67 km / h.

As shown in FIG. 3, in the data fusion step 58, the traffic data for each link is fused with other traffic information and statistically processed traffic information such as vehicle average speed on the target road estimated or predicted in the statistical processing step. 4 illustrates this fusion step in detail. Referring to FIG. 4, first, a determination is made as to whether there is additional reliable driver information for the first (i = 1) link. Driver information is information provided by a driver operating a corresponding road (PRU) directly to the traffic information center using wired or wireless communication means. If there is driver information, the driver information is displayed. Then go to the next (i = i + 1) link. If there is no driver information, determine if there is current information about the link. Up-to-date information refers to the estimated vehicle average speed as data obtained from a signaling vehicle of a corresponding road (PRU) in real time. If there is no up-to-date information on the link (road, PRU), the estimated vehicle average speed is displayed through statistical processing. According to the process described above, the average vehicle speed predicted through statistical processing is the average vehicle speed estimated by performing statistical processing using neighboring links and past time series information when there is no latest information on signal transmitting vehicles. If there is the latest information, it is determined whether there is a difference from the past information. If there is no difference from past information, the latest information is displayed as it is. The historical information is a representative value of the average vehicle speed that can represent the past road situation of the target road. If there is a difference from the past information, new traffic information such as average vehicle speed is obtained according to the following equation.

Where V ₁ means the latest information and V ₂ means the past information. Σ ₁ is the latest variance and σ ₂ is the past variance. The calculated new traffic information is displayed 98 to the drivers. Then repeat the same process for the next link.

The average vehicle speed of the estimated or predicted target PRU may be expressed through a fusion process, and the vehicle average speed may be used to obtain traffic information of the next time point.

Referring back to FIG. 2, step 44 of predicting vehicle occupancy pre-investigates an inverse degree of speed and occupancy. Based on these results, a function (usually inversely proportional) is determined and the vehicle's road occupancy is calculated using this function and the estimated or estimated vehicle average speed. Since the average vehicle speed for each road (PRU) varies greatly over time, it is not possible to rely solely on the preliminary survey, but the results of the preliminary survey are reliable. However, due to the weather, construction section, and road characteristics, the occupancy rate and speed are not inversely proportional to each other, so it is advisable to conduct a separate preliminary investigation under such circumstances.

Referring to FIG. 2, the step 46 of estimating / predicting the traffic volume may be estimated using the signal transmitting vehicle or predicted using the predicted occupancy rate. The estimation using the signaling vehicle assumes that the number of signaling vehicles in each PRU is a Poisson distribution, and then estimates the parameters through a Bayes' method. On the other hand, the prediction using the predicted occupancy is to predict the traffic volume using the predicted road occupancy of the vehicle and the area of each PRU. Each PRU database contains the number of lanes.

Preferably, the traffic information processing step 34 includes a correction step with a true value. In this correction step, past information is used to infer future information. If past inference is not accurate, the past information is corrected.

3. Estimation of pass time

Referring to Fig. 2, the passage time estimation step 36 is a step of estimating the passage time of the vehicle between the start point and the end point. The passing time between the starting point and the ending point is predicted and corrected by time series or space-time analysis theory using the average vehicle speed, occupancy rate or traffic volume. The estimated passing time is expressed to the drivers. For example, suppose there are four PRUs in between to predict the transit time between Seoul National University and Jongno 2-ga at 3:00 pm, estimate the transit times for the four PRUs, and add them together to get the final transit time. To calculate. However, traffic may change after passing the first PRU at 3:00 pm. Therefore, since the transit time of the second PRU is different, the transit time of the second PRU is predicted in consideration of recent traffic conditions. This modification procedure is called DPA (Dynamic Prediction Algorithm). The DPA first establishes the start point, end point, and departure time. Second, reasonably select the road associated with the PRU between the starting point and the ending point. Assume that there are N PRUs between the start and end points. Third, if the transit departure time is not far from the current TRI, the next TRI can add a spatial model to the current TRI, and then use the time series model to predict the average vehicle speed for the N PRUs. If the transit departure time is far from the current TRI, the vehicle average speeds for the N PRUs of the next TRI are predicted. Therefore, this procedure is repeated until the passing time is reached.

At this time, it is necessary to predict the average vehicle speed in the future. The prediction of average vehicle speed in the future is to first determine the probability distribution of vehicle average speed such as normal, gamma, or chi-square, and then create a model of vehicle average speed (multi-variable time series) by considering appropriate explanatory factors. Next, this model is used to predict the average vehicle speed in the future. This prediction process first uses stem and leaf plots, box plots, normal probability plots or various QQ plots to determine the distribution and then provides the appropriate link function. Find the appropriate variance-covariance matrix to determine the time series model and validate the model with the modified data. Regression theory may be used in the model selection process. As a method of predicting the average vehicle speed at a certain point in time of a PRU, an algorithm for describing the statistical processing step may be used.

4. Optimal Path Prediction

Referring to FIG. 2, the optimum path finding step 38 is a step of finding the fastest pass path connecting two points using the estimated or estimated vehicle average speed for each road. The discovery of the best route first assigns numbers to all road intersections in the target road. Suppose it is from 1 to N. Second, the transit time D (i, j) of the road connecting two intersections is obtained by the following equation.

D (i, j) = V (i, j) × L (i, j)

Where V (i, j) is the estimated / predicted speed between i and j road intersection and L (i, j) is the distance between i and j road intersection. Create an N × N matrix by finding the transit times between all intersections. If there are no roads between i and j, D (i, j) sets an infinite or very large value. The following Minimum Spaning Tree (MST) algorithm calculates an optimal path between two arbitrary points. The MST procedure is a one-step connection between any one intersection and the intersection closest to that intersection. This straight line becomes the first MST. In Step 2, find the minimum distance to MST in Step 1 for each of the remaining N-2 points, minus the two points previously used. In step 3, the smallest distance to the MST of the N-2 points is connected to the MST. As a final step, repeat steps 2 and 3 until all points are included in the MST. Find and provide a route on the current MST for the two intersections presented by the driver. The estimated speeds of the roads constituting the path should be values of different periods (dynamic algorithm). In other words, when the transit time is very large, the estimated speeds for the roads in the latter part of the journey should be different, and as a result, the optimum route may be different. Therefore, the user's needs or improved optimal paths on the program are reported again.

According to the configuration of the present invention, traffic information of individual contents can be provided to the drivers according to the needs of the drivers of each road. In addition, the present invention collects effective data even with a small number of vehicles, reduces the amount of transmitted data, filters out unsuitable data from the collected data, infers data that cannot be obtained, makes accurate data, and provides accurate traffic information. can do.

Claims (5)

(Correct) In the car traffic information providing system that receives the location information from the GPS satellite and collects and processes the traffic information data transmitted wirelessly from the vehicle driving on the road and provides the vehicle driver with the traffic information for determining the driving route. A GPS terminal for transmitting and receiving its own location data transmitted from the satellite every unit time, and the traffic information center for each location data and data collection period (TRI) transmitted to the traffic information center for each vehicle signal generation period (CRI). A unit having a wireless data transmitting and receiving device for transmitting the preprocessing road traffic information data transmitted to and receiving the processed traffic information data transmitted from the traffic information center, and a traffic information display device for displaying the traffic information data to a vehicle driver. Traveling on a road unit (PRU) Call origination vehicle; A wireless data base station for relaying wireless data communication between the mobile vehicle and the traffic information center; A location data and a target road (PRU) having a database of roads divided into the signal transmitting vehicle and the unit road unit (PRU), and obtained for each vehicle signal generation period (CRI) from the signal generating vehicle traveling on a road; Calculate the average vehicle speed on the target roadway (PRU) from the time series information of the vehicle speed at), and calculate the average speed data from the position data, especially if there is position data of the vehicle passing through the target road for a preset time And estimating the vehicle average speed of the target road from the average speed data, and in the case of the target road without position data of the vehicle passing through the target road for a preset time, information on the vehicle speed of the adjacent road and the road without the data Estimates a vehicle average speed by processing time series information of A data processing device that predicts vehicle occupancy or transit time of the target roadway PRU from the released vehicle average speed and calculates an optimal route for a combination of intersections and possible destination roadway PRUs presented by the vehicle driver. Traffic information providing system comprising a traffic information center having a traffic information providing device. (Correct) The target road using the position data of the vehicle and the time series information on the vehicle speed on the target road, which are wirelessly transmitted every unit time from the signal generating vehicle traveling on the road, receiving its location information from the GPS satellite. CLAIMS 1. A method for providing traffic information of a PRU, the method comprising: acquiring position data for a predetermined time from a signal generating vehicle every unit time from a signal transmitting vehicle; The vehicle speed of the target road is calculated from the time data of the position data and the vehicle speed of each target road, the speed data of each vehicle passing through the target road is calculated and averaged, and the speed data of each vehicle is signaled. When the number of times of sending the position data of the vehicle is n, when the vehicle passes through the target road without sending the position data for a preset time, that is, when n = O, [Equation] Where L is the length of the link, T is the location data sending cycle, and when n = 1 [Equation] Where L ₁ is the length from one end of the link to the signal transmission position, and L ₂ is the length from the other end of the link to the signal transmission position, If n = k (k is 2 or more) [Equation] Provided that, L _k is a k-1 is the distance between the second position data sent out position and the K-th position data sent out position, L _k is targeted by the k-1-th position data sent time and the K-th position time of formula in the data sending time Computing the vehicle speed of the road, for each target road, if there is the position data of the vehicle passing through the target road for a predetermined time, the average speed data is calculated from the position data and the vehicle of the target road from the average speed data In the case of the target road without estimation of the average speed and the position data of the vehicle passing through the target road for a preset time, the vehicle is processed by processing information on the vehicle speed of the adjacent road and time series information of the road and the adjacent road without the data. Estimating an average speed; And displaying traffic information obtained by processing the vehicle average speed or the vehicle average speed. (Correct) The vehicle average speed of the target road calculated from the average speed data of each target road according to the following formula [Equation] Traffic information providing method comprising the operation. (Correction) The method according to claim 2 or 3, wherein the step of comparing the estimated vehicle average speed and the past information on the target road with the position data information and the difference between the estimated vehicle average speed and the past information is equal to or greater than a preset value; New estimated speed following formula [Equation] Where V ₁ is the latest information, that is, the estimated average speed of the vehicle, V ₂ is the historical information, σ ₁ is the latest variance, and σ ₂ is the past variance. The method of claim 2 or 3, further comprising estimating a transit time between a starting point and an arrival point using the vehicle average speed, wherein the transit time estimating step is performed after obtaining a transit time of a first PRU to which the vehicle is to proceed. A vehicle traffic information providing method for estimating the passing time of a subsequent PRU, taking into account the change in traffic conditions during the first PRU, and adding them together to predict the total passing time.