JP2013105379A - Server-side method for solving traffic jam to support travel - Google Patents

Abstract

PROBLEM TO BE SOLVED: To reduce an operation load during execution of travel support for preventing a vehicle from being caught in a traffic jam, by reducing an arithmetic load for traffic jam prediction and allowing traffic jams to be suppressed or solved.SOLUTION: A server-side method for solving traffic jams to support travels includes: a step of setting virtual nodes on the basis of map information; a step of calculating a power spectrum from frequency analysis of an acceleration of a representative vehicle representing vehicles 2 existing between virtual nodes; a step of calculating a maximum value of variations in inclination of simple linear regression of the power spectrum as an inclination maximum value; a step of estimating an inter-vehicle distance distribution from a distance between the representative vehicle and a preceding vehicle to calculate a minimum value of covariance; a step of estimating a vehicle group distribution ahead of the representative vehicle from correlation between the minimum value of covariance and the inclination maximum value; a step of predicting a traffic jam in real time on the basis of the vehicle group distribution; and a step of providing information of real-time traffic jam prediction to the vehicles 2 existing between virtual nodes.

Description

  The present invention relates to a server side congestion elimination travel support method.

  Conventionally, for example, a system is known that acquires acceleration from a plurality of vehicles, detects inter-vehicle distances between each vehicle and a preceding vehicle, and calculates traffic congestion prediction using these accelerations and inter-vehicle distances (for example, Patent Document 1).

Japanese Patent Application No. 2011-175858

  By the way, according to the system according to the above prior art, it is possible to predict the possibility that the vehicle will be involved in a traffic jam and to reduce the operation load at the time of running support for avoiding the vehicle being caught in the traffic jam. It is desired.

  The present invention has been made in view of the above circumstances, while reducing the calculation load of traffic jam prediction, enabling suppression or elimination of traffic jam, and at the time of execution of driving support for avoiding the vehicle being caught in traffic jam It is an object of the present invention to provide a server side congestion elimination travel support method capable of reducing the operation load.

  In order to solve the above problems and achieve the object, a server side congestion elimination traveling support method according to claim 1 of the present invention sets a calculation target section based on map information stored in advance, and the calculation target A step of setting a virtual link that connects a plurality of virtual nodes and adjacent virtual nodes based on a predetermined condition within a section (for example, step S12 in the embodiment), and at least existing between the adjacent virtual nodes Step of setting one representative vehicle representing the vehicle existing between the virtual nodes based on one or more vehicles (for example, vehicle 2 in the embodiment) (for example, step S14 in the embodiment) ), A step of acquiring the acceleration of the representative vehicle (for example, step S16 in the embodiment), and a frequency analysis based on the frequency analysis of the acquired acceleration. A step of calculating a warped spectrum (for example, step S17 in the embodiment), a single regression line of the calculated power spectrum is calculated, and a maximum value of a change amount of the slope of the single regression line in a predetermined frequency range is calculated. The step of calculating as the slope maximum value (for example, step S18 in the embodiment), the step of acquiring the inter-vehicle distance between the representative vehicle and the preceding vehicle (for example, step S19 in the embodiment), and the acquired A step of estimating the inter-vehicle distance distribution using the distribution estimation method from the inter-vehicle distance (for example, step S20 in the embodiment), and a step of calculating the minimum covariance from the estimated inter-vehicle distance distribution (for example, implementation) The vehicle group distribution ahead of the representative vehicle is estimated from the correlation between step S21) and the minimum covariance value and the maximum slope value. Step (for example, step S22 in the embodiment), step for performing real-time traffic jam prediction based on the estimated vehicle group distribution (for example, step S24 in the embodiment), and the vehicle existing between the virtual nodes Providing the real-time traffic jam prediction information (for example, step S26 in the embodiment).

  Furthermore, in the server side traffic jam elimination travel support method according to claim 2 of the present invention, the step of setting the virtual node and the virtual link includes the number of vehicles existing in the calculation target section, and the calculation target section. The virtual node is set based on at least one of a distance, a road type of the calculation target section, and a road shape of the calculation target section.

According to the server side congestion elimination travel support method according to claim 1 of the present invention, even when there are a plurality of vehicles in the calculation target section, the acceleration of the representative vehicle between the virtual nodes and the representative vehicle and the preceding vehicle The calculation of traffic congestion can be reduced compared to the case of calculating traffic congestion prediction for a plurality of vehicles without setting a virtual node. The traffic jam can be predicted easily.
The information on real-time traffic jam prediction obtained for this representative vehicle can be quickly provided to a plurality of vehicles in the calculation target section, and the occurrence of traffic jams can be quickly and efficiently linked with the plurality of vehicles. Accurate suppression or quick resolution can be possible.

  Furthermore, according to the server-side congestion elimination traveling support method according to claim 2 of the present invention, it is possible to appropriately and easily set a virtual node capable of appropriate congestion prediction and reduction of calculation load.

It is a block diagram of the information provision system which implement | achieves the server side congestion elimination driving | running assistance method which concerns on embodiment of this invention. It is a figure which shows the example of the acceleration spectrum which concerns on embodiment of this invention. It is a figure which shows the example of probability density distribution which concerns on embodiment of this invention. It is a figure which shows the example of distribution of the covariance value which concerns on embodiment of this invention. It is a figure which shows the example of the correlation map of covariance minimum value and inclination maximum value which concerns on embodiment of this invention. It is a figure which shows the example of the relationship between the traffic density and traffic volume which concerns on embodiment of this invention. It is a figure which shows the example of the correlation map with the logarithm of the covariance minimum value about the inter-vehicle distance distribution which concerns on embodiment of this invention, and the logarithm of inclination maximum value about an acceleration spectrum. It is a flowchart which shows operation | movement of the vehicle which concerns on embodiment of this invention. It is a flowchart which shows the operation | movement of the server apparatus which concerns on embodiment of this invention, ie, the server side congestion elimination driving | running assistance method. It is a figure which shows an example of the correspondence of the acceleration spectrum of the representative vehicle between each virtual node of the calculation object area which concerns on embodiment of this invention, and the presence or absence of a traffic jam sign.

DESCRIPTION OF EMBODIMENTS Hereinafter, an embodiment of a server-side congestion elimination traveling support method of the present invention will be described with reference to the accompanying drawings.
The information providing system 1 that realizes the server-side congestion elimination traveling support method according to the present embodiment can communicate with a plurality of vehicles 2 that are information providing targets and the plurality of vehicles 2 as shown in FIG. The server apparatus 3 is provided.

  The vehicle 2 includes, for example, a vehicle communication device 11, various sensors 12, a switch 13, various actuators 14, a display device 15, a speaker 16, and a vehicle processing device 17.

  The vehicle communication device 11 is communicable with the communication device 31 of the server device 3, and various types of information can be obtained by direct wireless communication connection or communication connection via a predetermined communication network to the communication device 31 of the server device 3. Send and receive.

For example, the predetermined communication network includes a base station for wireless communication, and a public communication network such as the Internet for connecting the base station for wireless communication and the server device 3 in a wired manner. Information transmitted by radio communication from the vehicle communication device 11 configured by a communication terminal or the like is received by the base station, and transferred from the base station to the server device 3 by wired communication.
In addition, the form of communication between the vehicle 2 and the server apparatus 3 is not limited to the said form, For example, other forms of communication, such as communication via a communication satellite, may be employ | adopted.

  The various sensors 12 are, for example, a vehicle speed sensor that detects the speed of the vehicle 2, a yaw rate sensor that detects the yaw rate of the vehicle 2, and the like, and signals of detection results relating to the running state of the vehicle 2 are sent to the vehicle processing device 17. Output.

The switch 13 outputs various signals related to, for example, traveling control of the vehicle 2 to the vehicle processing device 17.
The various signals output from the switch 13 are, for example, a signal relating to the operation state (for example, operation position) of the brake pedal or accelerator pedal by the driver and the vehicle 2 automatically according to the driver's input operation. Various signals related to automatic traveling control for controlling the traveling state (for example, a signal for instructing control start and control stop, and a signal for instructing increase or decrease of the target inter-vehicle distance with respect to the target vehicle speed or the preceding vehicle).

  The various actuators 14 are, for example, a throttle actuator that controls the driving force of the vehicle 2, a brake actuator that controls the braking of the vehicle 2, a steering actuator that controls the turning of the vehicle 2, and the vehicle processing device 17. Drive control is performed by a control signal output from.

The display 15 is, for example, various displays including a display screen such as a liquid crystal display screen, a head-up display that performs display by projection on the front window, and various lamps. Display or lighting or extinguishing is performed in accordance with the control signal output from 17.
The speaker 16 outputs an alarm sound or a sound according to a control signal output from the vehicle processing device 17.
The display 15 and the speaker 16 may be included in various in-vehicle devices such as a navigation device.

  The vehicle processing device 17 includes, for example, a current position detection unit 21, a vehicle communication control unit 22, a travel control unit 23, and a notification control unit 24.

The current position detection unit 21 uses the positioning signal received by the antenna 21a that receives a positioning signal such as a GPS (Global Positioning System) signal for measuring the position of the vehicle 2 using an artificial satellite, for example, to detect the current position of the vehicle 2. Detect position.
The current position detection unit 21 may further detect the current position of the vehicle 2 by using a calculation process of autonomous navigation based on the speed and yaw rate of the vehicle 2 output from the various sensors 12 together.

The vehicle communication control unit 22 controls transmission / reception of various types of information by the vehicle communication device 11.
For example, the vehicle communication control unit 22 receives information on the speed of the vehicle 2 detected by the vehicle speed sensors of the various sensors 12 and information on the current position of the vehicle 2 detected by the current position detection unit 21 from the vehicle communication device 11. 3 to the communication device 31.
Further, for example, the vehicle communication control unit 22 acquires the real-time traffic jam prediction information and the travel guide information that are transmitted from the communication device 31 of the server device 3 and received by the vehicle communication device 11 to obtain the travel control unit 23 and the notification. Output to the control unit 24.

  The travel control unit 23 is created in the server device 3 and is output from the vehicle communication control unit 22, the real-time traffic jam prediction information and the travel guide information, the various signals output from the switch 13, and the various sensors 12. Based on the detection result signal relating to the traveling state of the vehicle 2, the driving of the vehicle 2 is controlled by, for example, driving and controlling a throttle actuator, a brake actuator, and a steering actuator.

  For example, the travel control unit 23 starts or stops the execution of the automatic travel control according to a signal output from the switch 13 and sets or changes the target vehicle speed and the target inter-vehicle distance in the automatic travel control.

Further, for example, the travel control unit 23 has a high possibility that a traffic jam will occur ahead of the traveling direction of the vehicle 2 in the real-time traffic jam prediction information created in the server device 3 (or that a traffic jam has already occurred). Etc.) is indicated so that the vehicle 2 avoids the traffic jam, and the subsequent vehicle of the vehicle 2 is less likely to cause the traffic jam, or the traffic jam around the vehicle 2 is resolved. Thus, the required target vehicle speed and target inter-vehicle distance are set, or the traveling state of the vehicle 2 is changed.
Then, automatic travel control that maintains these target vehicle speed and target inter-vehicle distance (for example, constant speed travel control that matches the actual vehicle speed to the target vehicle speed, and actual inter-vehicle distance with respect to other vehicles (for example, preceding vehicles)) The inter-vehicle distance control (for example, follow-up traveling control) is performed so as to match the target inter-vehicle distance.

  Further, for example, when the travel guide information is generated in the server device 3 and output from the vehicle communication control unit 22, the travel control unit 23 causes the vehicle 2 to avoid a traffic jam according to the travel guide information. In addition, the target vehicle speed and the target inter-vehicle distance can be set so that the subsequent vehicle of the vehicle 2 is less likely to cause traffic jams or the traffic jams around the vehicle 2 are eliminated. 2. Change the running state of 2.

  The notification control unit 24 controls various notification operations by controlling the display 15 and the speaker 16 based on real-time traffic jam prediction information and travel guide information created in the server device 3.

  For example, the notification control unit 24 has a high possibility that a traffic jam will occur ahead of the traveling direction of the vehicle 2 in the real-time traffic jam prediction information created by the server device 3 (or that a traffic jam has already occurred). Is displayed, for example, by controlling display on the display screen of the display 15 or lighting or extinguishing of the lamp body, and controlling output of alarm sound or sound from the speaker 16. Information on these traffic jams is notified.

  Further, for example, the notification control unit 24 avoids the traffic jam according to the real-time traffic jam prediction information or the travel guide information created in the server device 3, and further the subsequent vehicle of the vehicle 2 is jammed. Instructing the necessary driving operation (for example, increasing the inter-vehicle distance to the preceding vehicle, suppressing the acceleration operation, etc.) in such a manner that the traffic congestion around the vehicle 2 is resolved. .

  The server device 3 includes, for example, a communication device 31, a communication control unit 32, an information storage unit 33, a map data storage unit 34, a virtual node setting unit 35, and a traffic jam sign calculation unit 36. Yes.

  The communication device 31 can communicate with the vehicle communication device 11 of the vehicle 2, and transmits and receives various types of information by direct wireless communication connection to the vehicle communication device 11 of the vehicle 2 or communication connection via a predetermined communication network. To do.

  For example, the predetermined communication network includes a base station for wireless communication and a public communication network such as the Internet for connecting the base station for wireless communication and the server device 3 by wire, and is transmitted from the communication device 31 by wired communication. Real-time traffic jam prediction information and travel guide information are received by the base station and transferred from the base station to the vehicle 2 by wireless communication.

The communication control unit 32 controls transmission / reception of various types of information by the communication device 31.
For example, the communication control unit 32 transmits real-time traffic jam prediction information and travel guide information created by the traffic jam prediction unit 50 described later from the communication device 31 to the vehicle communication device 11 of the vehicle 2.
Further, for example, the communication control unit 32 acquires information on the speed of the vehicle 2 and information on the current position transmitted from the vehicle communication device 11 of the vehicle 2 and received by the communication device 31, and outputs the information to the information storage unit 33. To do.

  The information storage unit 33 includes information on the speed and current position of the plurality of vehicles 2 received by the communication device 31, and information on real-time traffic jam prediction for the representative vehicle between the virtual nodes created by the traffic jam prediction unit 50. And driving guide information and the like are stored.

The map data storage unit 34 stores map data.
The map data includes, for example, road coordinate data indicating position coordinates on the road required for map matching processing based on the current position of the vehicle 2, and road data (such as route search and route guidance) ( For example, a node that is a coordinate point composed of latitude and longitude at a predetermined position on a road such as an intersection and a branch point, a link that is a line connecting each node, a road shape, a road type, and the like.

The virtual node setting unit 35 sets a predetermined calculation target section based on the map data stored in the map data storage unit 34, and connects between the virtual node and the adjacent virtual node based on a predetermined condition in the calculation target section. Set the virtual link to be used.
Note that the predetermined calculation target section is, for example, a road section in which there is a possibility that traffic congestion may occur statistically.

The predetermined condition is, for example, at least one of the number of vehicles 2 existing in the calculation target section, the distance of the calculation target section, the road type of the calculation target section, and the road shape of the calculation target section. Is the condition for.
For example, the predetermined condition for the number of vehicles 2 existing in the calculation target section is to restrict the number of vehicles 2 that are equal to or larger than a predetermined number between adjacent virtual nodes.
For example, the predetermined condition for the distance of the calculation target section is to restrict the distance between adjacent virtual nodes to about several hundred meters or less.
For example, the predetermined condition for the road type of the calculation target section is to make the distance between adjacent virtual nodes on the expressway longer than the distance between adjacent virtual nodes on a general road.
For example, the predetermined condition for the road shape of the calculation target section is to set virtual nodes before and after the road shape where traffic congestion is statistically likely to occur.

  The traffic jam sign calculation unit 36 includes, for example, an acceleration calculation unit 41, a frequency analysis unit 42, a single regression line calculation unit 43, a slope maximum value calculation unit 44, a preceding vehicle detection unit 45, and an inter-vehicle distance calculation unit 46. The inter-vehicle distance distribution estimation unit 47, the covariance minimum value calculation unit 48, the interphase calculation unit 49, and the traffic jam prediction unit 50 are configured.

The acceleration calculation unit 41 is, for example, a vehicle that exists between the virtual nodes based on at least one vehicle 2 that exists between the virtual nodes for each adjacent virtual node set by the virtual node setting unit 35. By setting one representative vehicle representing 2, a plurality of vehicles 2 are quantized.
For example, the acceleration calculation unit 41 sets the vehicle 2 that has entered first between the virtual nodes as a representative vehicle, or averages the state quantities (for example, speed and position) of each of the plurality of vehicles 2 existing between the virtual nodes. A virtual vehicle having an average state quantity obtained by converting into a representative vehicle is used as a representative vehicle.

  And the acceleration calculation part 41 is based on the information of the speed of each vehicle 2 memorize | stored in the information storage part 33, or the information of the present position, for example from each time change of a speed, or a time-dependent change of a present position. The acceleration of the vehicle 2 is detected, and the acceleration of the representative vehicle is calculated based on the acceleration of each vehicle 2.

The frequency analysis unit 42 performs frequency analysis on the acceleration of the representative vehicle calculated by the acceleration calculation unit 41, and calculates a power spectrum corresponding to the frequency.
For example, the frequency analysis is performed on the acceleration of the representative vehicle detected by the acceleration calculation unit 41 in two different appropriate driving states, so that the frequency as a power spectrum as shown in FIGS. Acceleration spectra 51 and 53 corresponding to are calculated.

The single regression line calculation unit 43 calculates a single regression line in the power spectrum calculated by the frequency analysis unit 42.
For example, simple regression lines 52 and 54 are calculated for the acceleration spectra 51 and 53 shown in FIGS.

  The slope maximum value calculation unit 44 calculates, as the slope maximum value, the maximum value of the change amount of the slope of the single regression line in a predetermined frequency range with respect to the single regression line calculated by the single regression line calculation unit 43.

  For example, the slope maximum value calculation unit 44 performs a predetermined frequency range Y (for example, a frequency range corresponding to a time range from several seconds to several minutes) with respect to the single regression lines 52 and 54 shown in FIGS. And slopes α1 and α2 (= Y / X) are calculated based on the change X of the spectral value at 0 to 0.5 Hz or the like.

The preceding vehicle detection unit 45 detects, for example, a preceding vehicle that exists ahead of the representative vehicle in the traveling direction based on the information on the current positions of the plurality of vehicles 2 stored in the information storage unit 33.
The inter-vehicle distance calculation unit 46 calculates the inter-vehicle distance for each preceding vehicle of the representative vehicle detected by the preceding vehicle detection unit 45.

  The inter-vehicle distance distribution estimating unit 47 calculates the inter-vehicle distance of the representative vehicle for each preceding vehicle calculated by the inter-vehicle distance calculating unit 46 and the number of detected preceding vehicles (for example, the plurality of information on the current position acquired by the communication control unit 32). The inter-vehicle distance distribution is estimated on the basis of the number of vehicles 2 that are the preceding vehicles of the representative vehicle.

For example, the inter-vehicle distance distribution estimator 47 may change the vehicle group in front of the representative vehicle from the information on the inter-vehicle distance and the number of vehicles (that is, a set of a plurality of vehicles 2 having a relatively dense inter-vehicle distance). A Gaussian distribution (probability density distribution) is applied to each vehicle group using a distribution estimation method such as minute Bayes.
For example, when two vehicle groups are detected, the two vehicle groups can be regarded as a distribution obtained by linearly combining two Gaussian distributions. For example, as shown in FIG. 3, a probability function P1 ( A probability function P (X) representing the entire distribution is obtained as the sum (superposition) of X) and P2 (X).

  Here, when the Gaussian distribution (probability function) is represented by N (x | μ, Σ), the superposition of a plurality of Gaussian distributions exemplified in FIG. 3 is described as shown in the following formula (1). The

In the above formula (1), for example, for an arbitrary natural number k, an expected value (average value) μ _k represents a position having the highest density. The covariance value (matrix) Σ _k represents the distortion of the distribution, that is, how the density decreases when moving away from the expected value μ _k . A mixing coefficient (mixing ratio) π _k (0 ≦ π _k ≦ 1) of the Gaussian distribution represents a ratio of how much each Gaussian distribution contributes, and is a so-called probability.

The covariance minimum value calculation unit 48 performs a calculation process using variational Bayes or the like in order to obtain a parameter (covariance) that maximizes the likelihood function obtained from the above-described probability function P (X), for example.
For example, for the probability function P (X) obtained as a superposition of a plurality of Gaussian distributions as exemplified in FIG. 3, the covariance minimum value calculating unit 48 calculates the covariance value Σ for each Gaussian distribution. _k is calculated. Then, the minimum value of the plurality of covariance values Σ _k obtained for each Gaussian distribution is calculated.

For example Figure 4 a graph of the distribution of the covariance value sigma _k as shown in (A) 56, to the variable of the covariance values sigma _k [delta] (e.g., the covariance value sigma _k itself, etc.), the variable [delta] = 0 A sharp graph indicates that there is no fluctuation in the vehicle group, that is, the vehicle distance is in a substantially constant traveling state.
On the other hand, the distribution of the covariance value sigma _k as shown in FIG. 4 (B) is a graph 57 and a value of the positive area δ2 with a peak in the negative region value δ1 of variable δ according to the covariance value sigma _k The graph 58 is composed of two graphs 58 having peaks. Each of the graphs 57 and 58 has a predetermined fluctuation range with respect to the variable δ related to the covariance value Σ _k , and there is a fluctuation of the vehicle group, in other words, a set of vehicles 2 having different inter-vehicle distances from the representative vehicle. Suggests that there is more than one.
For example, in FIG. 4A, the minimum value (covariance minimum value) of the covariance value Σ _k is substantially zero. For example, in FIG. 4B, the minimum value of the covariance value Σ _k is two values δ1. , Δ2 is the smaller value δ1.

The correlation calculation unit 49 creates a correlation map between the gradient maximum value calculated by the gradient maximum value calculation unit 44 and the covariance minimum value calculated by the covariance minimum value calculation unit 48.
For example, in the image (concept) diagram of the correlation map between the maximum slope value and the minimum covariance value shown in FIG. 5, the horizontal (X) axis is the minimum covariance value X, and the vertical (Y) axis is the maximum slope value Y. The correlation of variables (X, Y) is mapped.

For example, in the correlation map shown in FIG. 5, two regions 59 and 60 are shown, and a boundary region 61 where the two regions 59 and 60 overlap is present. The region 59 corresponds to a state where the covariance minimum value is relatively small and the variation of the vehicle group is small, in other words, a state where the inter-vehicle distance is relatively constant. Conversely, the region 60 corresponds to a state where the covariance minimum value is relatively large and the variation of the vehicle group is large, in other words, a state where there are a plurality of sets of vehicles having different inter-vehicle distances.
The boundary region 61 is a region where a change in the vehicle group changes from a small state to a large state, and traffic congestion can be predicted by quantitatively finding the state of the vehicle group corresponding to the boundary region 61.

For example, in the diagram showing the relationship between the traffic density and the traffic volume as shown in FIG. 6, the horizontal (X) axis of the graph represents the traffic density that means the number of other vehicles 2 existing within a predetermined distance from an appropriate representative vehicle. The reciprocal of this traffic density corresponds to the inter-vehicle distance. The vertical (Y) axis is a traffic volume that means the number of vehicles passing through a predetermined position.
For example, the diagram showing the relationship between the traffic density and the traffic volume as shown in FIG. 6 can be regarded as representing a traffic flow that means the flow of the vehicle 2.

The traffic flow illustrated in FIG. 6 can be roughly divided into four states (regions).
The first state is a free flow state in which the possibility of traffic congestion is low, and here, a certain amount of acceleration and inter-vehicle distance can be secured.
The second state is a mixed flow state in which the braking state and the acceleration state of the vehicle 2 are mixed. The state of this mixed flow is the state before the transition to the congestion flow, and the driver's freedom of driving decreases, and the probability of transition to the congestion flow due to the increase in traffic density (reduction of the inter-vehicle distance). It is in a high state.
The third state is a traffic flow state indicating a traffic jam.
The fourth state is a critical region that is a transition state that exists during the transition from the free flow state to the mixed flow state. This critical region is a state where the traffic volume and the traffic density are higher than those of the free flow, and the state transitions to a mixed flow due to a decrease in traffic volume and an increase in traffic density (a reduction in the inter-vehicle distance). The critical region is sometimes called metastable flow or metastable flow.

For example, the region 59 shown in FIG. 5 includes the free flow and critical region shown in FIG. 6, for example, and the region 60 shown in FIG. 5 includes, for example, the mixed flow and the congestion flow shown in FIG. Will be included.
Therefore, for example, the boundary region shown in FIG. 5 is a boundary state including both the critical region shown in FIG. 6 and the mixed flow state, for example, and is the boundary of the critical region shown in FIG.
By quantitatively grasping the critical region including the boundary of this critical region, it is possible to prevent the occurrence of traffic congestion by suppressing the transition to the mixed flow state.

The critical region quantification will be described below with reference to FIGS. 7A and 7B showing a correlation map between the logarithm of the covariance minimum value for the inter-vehicle distance distribution and the logarithm of the slope maximum value for the acceleration spectrum, for example. explain.
FIG. 7A is a simplified drawing of the traffic flow map shown in FIG. 6, and FIG. 7B shows a correlation map between the logarithm of the minimum covariance and the logarithm of the slope maximum.
The logarithm of the covariance minimum value and the logarithm of the slope maximum value shown in FIG. 7B are the slope maximum value calculated by the slope maximum value calculation unit 44 and the covariance minimum calculated by the covariance minimum value calculation unit 48. It is calculated as a logarithmic value with respect to the value and depicts the parameterization of the phase transition state in the critical region.

For example, in FIG. 7B, the region 62 includes the critical region shown in FIG. 7A, and the region 63 includes the mixed flow state shown in FIG. The critical line 64 means a critical point where there is a high possibility that traffic congestion will occur if the critical line 64 is shifted to the mixed flow state beyond this. The boundary region 65 between the regions 62 and 63 corresponds to the boundary of the critical region immediately before the critical line 64.
Note that the correlation map illustrated in FIG. 7B is stored in a memory (not shown) in the traffic jam sign calculation unit 36.

  The traffic jam prediction unit 50 determines whether or not the boundary state of the critical region exists in the correlation map created by the correlation calculation unit 49, and creates real-time traffic jam prediction information according to the determination result. Further, when there is a boundary state of the critical region in the correlation map, the travel guide information is created by referring to the map data stored in the map data storage unit 34 in order to prevent the transition to the traffic jam. To do. Further, when creating the real-time traffic jam prediction information and the travel guide information, the control target section (for example, from the start position of the calculation target section, between the virtual nodes associated with the representative vehicle having a traffic jam sign higher than a predetermined threshold value). Set the section to reach. Then, the information storage unit 33 stores real-time traffic jam prediction information and travel guide information.

  Real-time traffic jam prediction information is information related to the possibility of traffic jams or whether or not traffic jams have already occurred, for example, corresponding to the case where the boundary state of the critical region exists in the correlation map, This indicates that the possibility of traffic jams (traffic sign) is higher than a predetermined threshold, and the possibility of traffic jams (traffic predictor) corresponding to the case where there is no critical region boundary state in the correlation map ) Is lower than a predetermined threshold.

The traffic jam sign degree is a parameter corresponding to the slope maximum value calculated by the slope maximum value calculation unit 44, for example, and increases when the possibility of traffic jam ahead in the traveling direction of the representative vehicle is high, and the possibility is low. If you get smaller.
In addition, an arbitrary value can be set as the predetermined threshold value for determining the magnitude of the traffic jam sign degree, but “−45 degrees”, which is generally known as (1 / f) fluctuation characteristics, is set as the predetermined threshold value. It can be.

For example, when the slope α is small with respect to the single regression line calculated by the single regression line calculation unit 43, this corresponds to the case where the shock wave (vibration, fluctuation) received from the preceding vehicle is small, and the reaction delay with respect to the preceding vehicle is small. This corresponds to a case where the distance between the vehicles becomes long and it is difficult to form a vehicle group, that is, there is little possibility of traffic jams. In this case, the traffic jam sign is a small value.
On the contrary, when the inclination α is large, it corresponds to the case where the shock wave (vibration, fluctuation) received from the preceding vehicle is large, the reaction delay with respect to the preceding vehicle is large, and the vehicle group tends to become dense, that is, there is a possibility that the traffic congestion will occur. Corresponds to large case. In this case, the sign of congestion is a large value.
Here, the shock wave (vibration, fluctuation) means that each vehicle 2 repeats the acceleration and deceleration operations to propagate this operation (front-rear movement) to the rear vehicle 2 as a kind of vibration. To do.

Therefore, the traffic jam prediction unit 50 responds to the magnitude of the slope α of the single regression line calculated by the single regression line calculation unit 43, more specifically, the slope maximum value calculated by the slope maximum value calculation unit 44. Calculate the traffic sign.
For example, the traffic jam prediction unit 50 obtains a function (for example, y = ax + b) indicating the relationship between the slope maximum value (x) and the traffic jam sign degree (y) in advance and is calculated by the slope maximum value calculation unit 44. The traffic jam sign degree (y) with respect to the slope maximum value (x) is calculated.
The traffic jam prediction unit 50 creates a relationship between the maximum value of the slope and the corresponding traffic sign value in advance and stores it in a memory as a table, and the traffic jam sign level for the calculated slope maximum value is stored in the table. It can also be obtained by reference.

For example, when information on real-time traffic jam prediction indicating that the possibility of traffic jams (prediction of traffic jam) is higher than a predetermined threshold is transmitted from the server device 3 to the vehicle 2 by the communication control unit 32, the vehicle 2 Various notification operations are performed to indicate that there is a high possibility that traffic congestion will occur in the display 15 and the speaker 16.
For example, the display 15 switches the display of a two-color signal (blue and red, etc.) indicating whether or not a traffic jam has occurred, turns on or blinks a single color lamp indicating the occurrence of a traffic jam, or indicates an alarm indicating the occurrence of a traffic jam. A message is output.
For example, the speaker 16 outputs an alarm sound or an alarm sound indicating that a traffic jam has occurred.

Further, the travel guide information is used for travel control of the vehicle 2 in order to prevent the transition to the mixed flow illustrated in FIG. 7, or from the display 15 or the speaker 16 of the vehicle 2. This is information notified to the driver.
For example, the travel guide information includes information on the target vehicle speed and target inter-vehicle distance in the automatic travel control required for avoiding traffic jams and eliminating traffic jams in the vehicle 2, increasing the inter-vehicle distance with respect to the preceding vehicle, and suppressing acceleration operations. Information on a predetermined driving operation, information on route search and route guidance for the vehicle 2, and the like.
When the travel guide information is transmitted from the server device 3 to the vehicle 2 by the communication control unit 32, the content of the travel guide information is notified to the driver from the display 15 or the speaker 16, or the content of the travel guide information is displayed. As it is realized, it is used for automatic travel control.

  Real-time traffic jam prediction information and travel guide information for each representative vehicle stored in the information storage unit 33 is associated with each representative vehicle by the communication control unit 32 within the control target section set by the traffic jam prediction unit 50. It is transmitted to the vehicle 2 existing between the virtual nodes.

Note that the communication control unit 32 may provide each vehicle 2 with each information when transmitting the real-time traffic jam prediction information and the travel guide information to the vehicles 2 existing between the virtual nodes in the control target section. Alternatively, each piece of information may be provided only to a certain vehicle 2 with respect to a group (vehicle group) of the vehicles 2 within a certain range that are considered to affect the formation of traffic congestion.
For example, the communication control unit 32 targets only a vehicle group within a predetermined distance range (for example, about several hundred meters in which the effect is achieved by adopting the traffic jam elimination action) that can be regarded as forming one traffic jam, Targeting only a predetermined number of vehicles 2 within the predetermined distance range (for example, about 10% to 30%), or among vehicles 2 having a traffic jam sign degree larger than a predetermined threshold, the traffic jam sign degree is high. Each item is provided with priority from the top.

  The information providing system 1 according to the present embodiment has the above-described configuration. Next, the operation of the information providing system 1 will be described.

First, the operation of the vehicle 2 will be described below.
For example, in step S01 shown in FIG. 8, the speed of the vehicle 2 is detected by the vehicle speed sensors of the various sensors 12, and the current position of the vehicle 2 is detected by the current position detector 21.
Next, in step S <b> 02, information on the speed and current position of the vehicle 2 is transmitted to the server device 3.

Next, in step S03, it is determined whether or not real-time traffic jam prediction information and travel guide information are received from the server device 3.
If the determination result is “NO”, the determination process of step S03 is repeatedly executed.
On the other hand, if this determination is “YES”, the flow proceeds to step S 04.
In step S04, notification control and travel control are executed in accordance with real-time traffic jam prediction information and travel guide information, and the process proceeds to the end.

Below, operation | movement of the server apparatus 3, ie, the server side congestion elimination driving | running | working assistance method is demonstrated.
First, for example, in step S11 shown in FIG. 9, a predetermined calculation target section is set based on the map data stored in the map data storage unit.
Next, in step S12, a virtual link that connects between a virtual node and adjacent virtual nodes is set based on a predetermined condition within the calculation target section.

Next, in step S <b> 13, speed and current position information is received from the plurality of vehicles 2.
Next, in step S14, one representative vehicle that represents the vehicle 2 existing between the virtual nodes is set based on at least one vehicle 2 existing between the virtual nodes.
Next, in step S15, it is determined whether the representative vehicle has entered between the virtual nodes.
Note that this determination processing corresponds to, for example, determining whether or not there is a vehicle 2 that has entered first between the virtual nodes when the vehicle 2 that has first entered between the virtual nodes is a representative vehicle. For example, when a representative vehicle is a virtual vehicle having an average state quantity obtained by averaging the state quantities (for example, speed and position) of each of the plurality of vehicles 2 existing between the virtual nodes, This corresponds to determining whether or not there is a newly entered vehicle 2 between the virtual nodes.
If this determination is “NO”, the flow returns to step S 13 described above.
On the other hand, if this determination is “YES”, the flow proceeds to step S16.

Next, in step S <b> 16, based on the speed information or the current position information of each vehicle 2 stored in the information storage unit 33, the representative vehicle is determined based on the change in speed with time or the change in current position with time. 2 acceleration is calculated.
Next, in step S17, frequency analysis is performed on the acceleration of the representative vehicle, and a power spectrum corresponding to the frequency is calculated.

Next, in step S18, a single regression line is calculated in the power spectrum, and the maximum value of the amount of change in the slope of the single regression line in the predetermined frequency range is calculated as the maximum value of the slope.
Next, in step S19, a preceding vehicle existing ahead in the traveling direction of the representative vehicle is detected, and an inter-vehicle distance of the representative vehicle with respect to each preceding vehicle is calculated.
Next, in step S20, the inter-vehicle distance distribution is estimated based on the inter-vehicle distance of the representative vehicle with respect to each preceding vehicle and the number of detected preceding vehicles.

Next, in step S21, the minimum value of covariance is calculated from the inter-vehicle distance distribution.
Next, in step S22, the vehicle group distribution ahead of the representative vehicle in the traveling direction is estimated from the correlation between the minimum covariance value and the maximum slope value.
Next, in step S23, it is determined whether or not the boundary state of the critical region exists in the correlation map between the minimum covariance value and the maximum slope value of the acceleration spectrum.
If this determination is “NO”, the flow returns to step S 13 described above.
On the other hand, if this determination is “YES”, the flow proceeds to step S24.

Next, in step S24, real-time traffic jam prediction information indicating that the possibility of traffic jams (prediction of traffic jam) is higher than a predetermined threshold is created, and the control target section (for example, the start position of the calculation target section) is created. To an interval between the virtual nodes associated with the representative vehicle having a traffic jam sign level higher than a predetermined threshold.
Next, in step S25, travel guide information for urging avoidance of traffic jams and elimination of traffic jams in the vehicle 2 existing between the virtual nodes in the control target section is created.
Next, in step S26, real-time traffic jam prediction information and travel guide information are transmitted to the vehicles 2 existing between the virtual nodes in the control target section, and the process proceeds to the end.

  For example, as shown in FIGS. 10A and 10B, the Nth virtual node by an arbitrary natural number N sequentially set forward in the traveling direction of each vehicle 2 based on a predetermined condition within the calculation target section TA. Between the N + 1th virtual node and the N + 2th virtual node, as the vehicle 2 enters between the virtual nodes, the first representative vehicle A, the second A representative vehicle B, a third representative vehicle C,... Are set.

  First, for example, as shown in FIG. 10 (A), when the first vehicle 2 enters between the Nth virtual nodes on the front side (rear side in the traveling direction) in the traveling direction of each vehicle 2 within the calculation target section TA. The first first representative vehicle A is set between the Nth virtual nodes, and the acceleration spectrum SA is calculated for the first representative vehicle A. Then, a traffic jam sign degree is calculated based on the acceleration spectrum SA, the presence / absence of a traffic jam sign is determined according to whether the traffic jam sign level is higher than a predetermined threshold value, and the determination result is stored.

Next, when the first vehicle 2 enters between the N + 1 virtual nodes adjacent to the Nth virtual node, the first first representative vehicle A is set between the N + 1 virtual nodes and the Nth virtual node is set. In the meantime, the second representative vehicle B is set based on the vehicle 2 existing between the Nth virtual nodes.
Then, the acceleration spectra SA and SB are calculated for the representative vehicles A and B, the presence / absence of a traffic jam sign is determined based on the acceleration spectra SA and SB, and the determination result is stored.

Next, for example, as shown in FIG. 10B, when the first vehicle 2 enters between the N + 2 virtual nodes adjacent between the (N + 1) th virtual nodes, the first first representative vehicle between the (N + 2) virtual nodes. A is set, and the second and third representative vehicles B and C are set between the (N + 1) th virtual nodes and between the Nth virtual nodes.
Then, the acceleration spectra SA, SB, SC are calculated for each representative vehicle A, B, C, the presence / absence of a traffic jam sign is determined based on each acceleration spectrum SA, SB, SC, and the determination result is stored. The

  Thereafter, as similar processing is repeated, for example, as shown in FIG. 10C, it is determined that there is a traffic jam sign between appropriate virtual nodes in the calculation target section TA (for example, between the N + 2 virtual nodes). Then, between the virtual nodes and between the virtual nodes existing on the rear side in the traveling direction (for example, between the (N + 2) th virtual node, between the (N + 1) th virtual node, and between the Nth virtual nodes) is set as the control target section. The Then, real-time traffic jam prediction information and travel guide information are transmitted to the vehicles 2 existing between the virtual nodes in the control target section.

As described above, according to the information providing system 1 according to the present embodiment, the acceleration of the representative vehicle between the virtual nodes and the representative vehicle and the preceding vehicle, even when there are a plurality of vehicles 2 in the calculation target section. Therefore, the calculation load can be reduced compared to, for example, a case where no virtual node is set, and real-time traffic congestion prediction can be easily performed.
Then, the real-time traffic jam prediction information obtained for the representative vehicle and the created travel guide information can be quickly and synchronously provided to the plurality of vehicles 2 in the calculation target section. It is possible to accurately and efficiently suppress or quickly eliminate the occurrence of traffic jams in a prompt and efficient manner.

  Further, the predetermined conditions for setting the virtual node in the calculation target section are the number of vehicles 2 existing in the calculation target section, the distance of the calculation target section, the road type of the calculation target section, and the road of the calculation target section. By setting a condition for at least one of the shapes, it is possible to appropriately and easily set a virtual node capable of appropriately predicting traffic congestion and reducing the calculation load.

  Furthermore, by using easily acquirable information such as the acceleration of the plurality of vehicles 2 and the inter-vehicle distance between each vehicle 2 and the preceding vehicle, calculation of traffic congestion is easily performed in real time without requiring special information. be able to.

  Furthermore, in addition to real-time traffic jam prediction information regarding the possibility of traffic jams or whether or not traffic jams have already occurred, for example, to support the suppression or elimination of traffic jams, or the avoidance of being caught in traffic jams By providing the vehicle 2 with the driving guide information, it is possible to support appropriate driving.

  In the above-described embodiment, the server device 3 is configured such that the inter-vehicle distance with respect to the preceding vehicle transmitted from the vehicle 2 is detected when the inter-vehicle distance with respect to the preceding vehicle is detected by, for example, a radar device mounted on the vehicle 2. May be obtained.

In the above-described embodiment, each vehicle 2 may have a function equivalent to that of the server device 3 described above.
In this case, each vehicle 2 transmits the traffic jam prediction information obtained from the function to the server device 3, and the server device 3 obtains the travel guide information based on the traffic jam prediction information transmitted from each vehicle 2. The information providing system 1 may be configured so as to be created and distributed to each vehicle 2.
In this case, for example, when an appropriate vehicle 2 out of a vehicle group constituted by the vehicles 2 existing between virtual nodes within a predetermined distance range shows a high congestion sign, Even if there is another vehicle 2 showing only a low traffic jam sign level, a plurality of vehicles 2 in this vehicle group including the vehicle 2 showing a low traffic jam sign level are simultaneously controlled for avoiding traffic jams. The server apparatus 3 manages so as to make a call.
In other words, the vehicles 2 in the vehicle group within a certain range of conditions are likely to change so as to show a traffic jam sign level equivalent to the vehicle 2 showing a high traffic jam sign rate in the near future. By applying the control, it is possible to effectively eliminate the congested vehicle group.

DESCRIPTION OF SYMBOLS 1 Information provision system 2 Vehicle 3 Server apparatus 32 Communication control part 33 Information storage part 34 Map data storage part 35 Virtual node setting part 36 Traffic jam sign calculation part 41 Acceleration calculation part 42 Frequency analysis part 43 Single regression line calculation part 44 Inclination maximum value Calculation unit 45 Leading vehicle detection unit 46 Inter-vehicle distance calculation unit 47 Inter-vehicle distance distribution estimation unit 48 Covariance minimum value calculation unit 49 Interphase calculation unit 50 Congestion prediction unit

Claims (2)

Setting a calculation target section based on map information stored in advance, and setting a virtual link connecting a plurality of virtual nodes and the adjacent virtual nodes based on a predetermined condition in the calculation target section;
Setting one representative vehicle representing the vehicle existing between the virtual nodes based on at least one vehicle existing between the adjacent virtual nodes;
Obtaining the acceleration of the representative vehicle;
Calculating a power spectrum corresponding to the frequency from the frequency analysis of the acquired acceleration;
Calculating a single regression line of the calculated power spectrum, and calculating a maximum value of a change amount of the slope of the single regression line in a predetermined frequency range as an inclination maximum value;
Obtaining an inter-vehicle distance between the representative vehicle and a preceding vehicle;
Estimating the inter-vehicle distance distribution using a distribution estimation method from the acquired inter-vehicle distance; and
Calculating a minimum value of covariance from the estimated inter-vehicle distance distribution;
Estimating the vehicle group distribution ahead of the representative vehicle from the correlation between the minimum value of the covariance and the slope maximum value;
Performing real-time traffic jam prediction based on the estimated vehicle group distribution;
Providing the real-time traffic jam prediction information for the vehicles existing between the virtual nodes;
The server side traffic congestion elimination travel support method characterized by including. The step of setting the virtual node and the virtual link includes the number of the vehicles existing in the calculation target section, the distance of the calculation target section, the road type of the calculation target section, and the road shape of the calculation target section. The server side congestion elimination travel support method according to claim 1, wherein the virtual node is set based on at least one of.

Images