WO2017033215A1 - 渋滞予防システム、渋滞予防方法、及び、記録媒体 - Google Patents
渋滞予防システム、渋滞予防方法、及び、記録媒体 Download PDFInfo
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- WO2017033215A1 WO2017033215A1 PCT/JP2015/004307 JP2015004307W WO2017033215A1 WO 2017033215 A1 WO2017033215 A1 WO 2017033215A1 JP 2015004307 W JP2015004307 W JP 2015004307W WO 2017033215 A1 WO2017033215 A1 WO 2017033215A1
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
- G08G1/0141—Measuring and analyzing of parameters relative to traffic conditions for specific applications for traffic information dissemination
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/0116—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from roadside infrastructure, e.g. beacons
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0133—Traffic data processing for classifying traffic situation
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
- G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
- G08G1/081—Plural intersections under common control
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/095—Traffic lights
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
- G08G1/096716—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information does not generate an automatic action on the vehicle control
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
- G08G1/096725—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information generates an automatic action on the vehicle control
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
- G08G1/096775—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission where the origin of the information is a central station
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
Definitions
- the present invention relates to a traffic jam prevention system, a traffic jam prevention method, and a recording medium.
- Patent Document 1 An example of a technique for preventing such traffic congestion is disclosed in Patent Document 1, for example.
- the travel support device described in Patent Document 1 determines the traffic flow state from the acceleration of the vehicle and the inter-vehicle distance from another vehicle, and switches the vehicle travel control according to the determination result, so that the traffic flow is Prevent transitions to traffic conditions.
- Patent Document 2 discloses another technique for preventing traffic congestion.
- the traffic control system described in Patent Document 2 detects or predicts traffic in the vicinity of a road section where traffic volume is locally concentrated and traffic congestion is likely to occur, such as a lane junction, and traffic flow in the road section is detected. Controls the running of the vehicle so that it does not transition to a congested state.
- Patent Document 3 discloses a technique for controlling the degree of congestion by calculating the degree of congestion based on road use reservation information and setting a usage fee according to the degree of congestion.
- Patent Document 4 discloses a technique for determining whether or not a road section is congested based on vehicle position information, and setting a usage charge for the road section according to the determination result.
- Non-Patent Literature 1 and Non-Patent Literature 2 disclose heterogeneous mixed learning techniques for generating a prediction model for each group having the same pattern and regularity of data.
- An object of the present invention is to solve the above-described problems and provide a traffic jam prevention system, a traffic jam prevention method, and a recording medium that can more reliably prevent traffic jam at a target point at a target time.
- the first traffic jam prevention system calculates a target traffic state that is a traffic state that should be achieved at a point different from the target point in order to prevent a traffic jam at a target time at the target point, Display control means for controlling the display means so that the target traffic state at the point is displayed on the display means.
- a target traffic state that is a traffic state to be achieved at a point different from the target point in order to prevent traffic jam at a target point at the target point is calculated, and the target at the point is calculated.
- the display means is controlled so that the traffic state is displayed on the display means.
- the first recording medium readable by the computer calculates a target traffic state that is a traffic state that should be achieved at a point different from the target point in order to prevent congestion at the target point at the target point.
- the program which controls the said display means and performs a process is stored so that the said target traffic state in the said point may be displayed on a display means.
- the second traffic jam prevention system calculates a target traffic state that is a traffic state to be achieved at a point different from the target point in order to prevent a traffic jam at the target time at the target point, Log generating means for generating and outputting a log indicating the target traffic state at the point and the measured value of the traffic state at the point.
- a target traffic state that is a traffic state that should be achieved at a point different from the target point in order to prevent traffic jam at a target time at the target point is calculated.
- a log indicating the target traffic condition and the measured value of the traffic condition at the relevant point is generated and output.
- the second recording medium readable by the computer calculates a target traffic state that is a traffic state to be achieved at a point different from the target point in order to prevent congestion at the target time at the target point. And storing a program for generating and outputting a log indicating the target traffic state at the point and the measured value of the traffic state at the point and executing the process.
- the effect of the present invention is to more reliably prevent congestion at the target time at the target point.
- FIG. 1 is a diagram showing a configuration of a traffic management system in the first embodiment of the present invention.
- the traffic management system includes a traffic jam prevention system 10, a traffic jam prediction device 20, a traffic state control device 30, and a display device 40.
- the traffic jam prevention system 10, the traffic jam prediction device 20, and the traffic state control device 30 are connected to each other via a network or the like.
- the display device 40 is connected to the traffic jam prevention system 10 via a network or the like.
- the traffic state control device 30 is disposed at each of a plurality of points (positions and sections), which are monitoring and control targets of the traffic state on the road network, and the traffic state control means (hereinafter referred to as the traffic state control device 30). (Also referred to as control means) to control the traffic state at the point.
- a traffic state control device 30 is arranged at each of the points X1, X2,.
- the traffic state for example, vehicle speed, the number of passing vehicles per unit time, vehicle density, and the like are used as the traffic state.
- the traffic state is also referred to as a traffic flow.
- the number of passing vehicles per unit time in the traffic state is also described as the traffic volume.
- the traffic jam prediction device 20 predicts the occurrence of traffic jam at each point based on the traffic conditions at one or more points.
- the traffic congestion prevention system 10 calculates a traffic state (target traffic state) to be achieved at each point in order to prevent occurrence of traffic jam at a specific point (target point) at a specific point (target point).
- the traffic state at each point is controlled via 30.
- FIG. 2 is a diagram showing a configuration of the traffic jam prevention system 10 according to the first embodiment of the present invention.
- the traffic jam prevention system 10 includes a reception unit 11, a prevention processing unit 12, a control processing unit 13, a traffic state DB (database) 14, a transmission unit 15, a display control unit 16, an instruction receiving unit 17, and log generation.
- the receiving unit 11 collects measured values of traffic conditions at each point.
- the receiving unit 11 collects traffic state measurement values at predetermined collection intervals, for example, by sensors arranged at each point.
- the receiving unit 11 may collect traffic state measurement values based on probe data transmitted from the in-vehicle terminal. Further, the reception unit 11 may collect traffic state measurement values from other devices such as the traffic jam prediction device 20.
- FIG. 5 is a diagram showing an example of a traffic state in the first embodiment of the present invention.
- the vehicle density at each point for each collection time is collected as the traffic state.
- the traffic state DB 14 stores the traffic state measurement values collected by the receiving unit 11 at each point.
- the receiving unit 11 collects a traffic state prediction model (also referred to as a prediction formula) used for traffic jam prediction from the traffic jam prediction device 20 or the like.
- a traffic state prediction model also referred to as a prediction formula
- the prediction model is a model for predicting the traffic state at a specific time (predicted time) at a specific point (predicted point) from the traffic state at each point.
- the prediction model is generated, for example, using machine learning technology based on the time series of traffic conditions at each point.
- a prediction model is produced
- the prediction model may be a general time series model such as a linear regression model, an autoregressive model, or an autoregressive moving average model.
- a prediction model such as Equation 1 is used to predict the vehicle density in the traffic state.
- ⁇ ij is a parameter (weight coefficient) to be multiplied by ⁇ i (t).
- FIG. 6 is a diagram showing an example of a prediction model in the first embodiment of the present invention.
- T 0.5h (hour)
- T 1h
- T 1.5 hours later
- prediction model not only a linear function like Formula 1 but the arbitrary function whose traffic state is an explanatory variable may be used.
- a plurality of prediction models may be generated for the same prediction point. In this case, these prediction models may be used for each predicted time situation such as weather, day of the week, or time zone.
- the prediction model DB 122 stores the prediction model collected by the receiving unit 11.
- the receiving unit 11 collects traffic jam prediction information from the traffic jam prediction device 20 or the like.
- the receiving unit 11 collects traffic jam prediction information at a predetermined time interval, for example.
- FIG. 7 is a diagram showing an example of traffic jam prediction information in the first exemplary embodiment of the present invention.
- the traffic jam prediction information includes the prediction time (prediction execution time), the presence or absence of traffic jam, the time when traffic jam is predicted (traffic jam prediction time), and the occurrence of traffic jam.
- the point (congestion prediction point) is shown.
- the prediction information DB 121 stores traffic jam prediction information collected by the receiving unit 11.
- the receiving unit 11 collects a fundamental diagram of each point used for traffic jam prediction from the traffic jam prediction device 20 or the like.
- FIG. 8 is a diagram showing an example of a fundamental diagram in the first embodiment of the present invention.
- the fundamental diagram is an expression method used for analyzing traffic conditions, and is a diagram (graph) showing the relationship between the vehicle density, the vehicle speed, and the number of passing vehicles at each point on the road network. If the vehicle density is specified, the vehicle speed and the number of passing vehicles at that point can be estimated from the fundamental diagram.
- the fundamental diagram indicates that when the vehicle density exceeds a certain value (density threshold), the vehicle speed and the number of passing vehicles decrease, and the traffic flow transitions from a free flow to a traffic jam state. In the embodiment of the present invention, this density threshold is used as an index for preventing traffic jam.
- the diagram storage unit 125 stores the fundamental diagram for each point collected by the reception unit 11.
- the prevention processing unit 12 includes a prediction information DB 121, a prediction model DB 122, a control means DB 123, a traffic state calculation unit 124 (also referred to as a calculation unit), and a diagram storage unit 125.
- the control means DB 123 stores control means information.
- FIG. 9 is a diagram illustrating an example of control means information in the first exemplary embodiment of the present invention.
- the control means information in FIG. 9 includes cost information (cost information) related to a change in traffic state by the traffic state control device 30 arranged at each point, and a controllable amount (maximum value, minimum value) of the traffic state. Is set.
- cost information cost information
- maximum value, minimum value a controllable amount of the traffic state.
- a cost coefficient representing the cost associated with the change per unit of the traffic state is set as the cost information.
- a cost function as described later may be set as the cost information.
- Control means information is used to calculate the target traffic state at each point.
- the controllable amount and cost information in the control means information are set (converted) by the control processing unit 13 based on control characteristics and cost information of a control model described later.
- the traffic state calculation unit 124 calculates the target traffic state of each point to prevent the traffic jam at the traffic jam prediction point based on the traffic state and the prediction model of each point.
- the control processing unit 13 includes a control model DB 131 and a state control unit 132.
- the control model DB 131 stores a control model that represents the characteristics of the control means of the traffic state control device 30 arranged at each point.
- FIG. 10 is a diagram illustrating an example of a control model in the first embodiment of the present invention.
- the control model of FIG. 10 relates to the type of control means included in the traffic state control device 30 arranged at each point, the relationship between the control content by the control means and the traffic state (control characteristics), and the change of the control content.
- Cost information cost information
- cost it is possible to use an arbitrary cost that represents a demerit that may occur to the road management or the user in accordance with the change in the control content, such as the cost, power, and necessary personnel accompanying the change in the control content.
- the control model may be set by a road management operator based on the specifications of the traffic state control device 30 at each point, or based on information collected from the traffic state control device 30. May be set.
- the state control unit 132 generates control information for realizing the target traffic state at each point based on the control model stored in the control model DB 131 so that the traffic state at each point becomes the target traffic state.
- the traffic state control device 30 is controlled (so as to approach the target traffic state).
- the transmission unit 15 transmits the control information generated by the control processing unit 13 to the traffic state control device 30 at each point.
- the display control unit 16 controls the display device 40 such that the target traffic state at each point is displayed on the display device 40.
- the instruction receiving unit 17 receives from the display device 40 an instruction for correcting the target traffic state at each point.
- the log generation unit 18 generates a state log 181 indicating a target traffic state and a time series of measured values of the traffic state.
- the log storage unit 19 stores the state log 181 generated by the log generation unit 18.
- the traffic jam prevention system 10 may be a computer that includes a CPU (Central Processing Unit) and a storage medium that stores a program, and that operates by control based on the program.
- a CPU Central Processing Unit
- a storage medium that stores a program, and that operates by control based on the program.
- FIG. 3 is a block diagram showing a configuration of the traffic jam prevention system 10 realized by a computer according to the first embodiment of the present invention.
- the traffic jam prevention system 10 includes a CPU 101, a storage device 102 (storage medium) such as a hard disk and a memory, an input / output device 103 such as a keyboard and a display, and a communication device 104 that communicates with other devices.
- the CPU 101 executes a program for realizing the reception unit 11, the traffic state calculation unit 124, the state control unit 132, the transmission unit 15, the display control unit 16, the instruction reception unit 17, and the log generation unit 18.
- the storage device 102 stores data of the prediction information DB 121, the prediction model DB 122, the control unit DB 123, the diagram storage unit 125, the control model DB 131, the traffic state DB 14, and the log storage unit 19.
- the communication device 104 receives traffic jam prediction information, a prediction model, a traffic state at each point, and a fundamental diagram from another device or the like. Moreover, the communication device 104 transmits control information to the traffic state control device 30 at each point.
- the input / output device 103 may accept input of traffic jam prediction information, a prediction model, a traffic state at each point, and a fundamental diagram from an operator or the like. Further, the input / output device 103 may output control information to be set in the traffic state control device 30 at each point to an operator or the like.
- each component of the traffic jam prevention system 10 may be realized by a logic circuit.
- a plurality of components may be realized by one logic circuit, or may be realized by a plurality of independent logic circuits.
- each component of the traffic jam prevention system 10 may be distributed in a plurality of physical devices connected by wire or wirelessly.
- the traffic jam prevention system 10 may be realized by distributed processing by a plurality of computers.
- a traffic jam prevention service by the traffic jam prevention system 10 may be provided to the operator in the SaaS (Software as Service) format.
- the traffic state control device 30 is arranged at points X1, X2,... Of the road network as shown in FIG. 1, and the traffic state shown in FIG. Assume that the distance between points is, for example, 10 kilometers.
- the prediction model DB 122, the prediction information DB 121, and the diagram storage unit 125 store the prediction model of FIG. 6, the traffic jam prediction information of FIG. 7, and the fundamental diagram of FIG. 8, respectively.
- the control means DB 123 and the control model DB 131 store control means information and control models as shown in FIGS.
- FIG. 4 is a flowchart showing the traffic jam prevention process in the first embodiment of the present invention.
- the traffic state calculation unit 124 of the prevention processing unit 12 acquires a traffic jam prediction point and a traffic jam prediction time from the traffic jam prediction information (step S11).
- the traffic state calculation unit 124 determines the traffic congestion prediction point “X6” and the traffic congestion prediction time “2015/08/01 12:00” from the traffic jam prediction information at the current time “2015/08/01 10:00” in FIG. (After 2 hours).
- the traffic state calculation unit 124 acquires a prediction model for predicting the traffic state at the traffic jam prediction time for each point from the prediction model DB 122 (step S12).
- the traffic state calculation unit 124 uses the acquired prediction model to calculate the target traffic state at each point at the current time in order to prevent the traffic jam at the traffic jam predicted point at the traffic jam predicted time (step S13).
- the traffic state calculation unit 124 obtains a target traffic state at each point by obtaining a solution to an optimization problem expressed by an objective function of Formula 2, Formula 3 and Formula 4 of a constraint equation. calculate.
- Equation 2 ⁇ * i (t) indicates the target traffic state (vehicle density) at the current time t at the point xi.
- c i indicates a cost coefficient of the traffic state control device 30 arranged at the point xi. This objective function is such that the sum of values obtained by multiplying the difference between the measured value of the traffic state at the current time t and the target traffic state to prevent traffic congestion (the control amount of the traffic state) by the cost coefficient is minimized. It indicates that the target traffic state ⁇ * i (t) at the time is determined.
- Equation 2 an expression other than Equation 2 may be used as the objective function.
- an expression relating to the difference between the traffic state at the previous time t ⁇ 1 and the traffic state at the current time t may be used as the objective function so that the traffic state at each point does not vibrate.
- other objective functions that are acceptable or preferable for road management such as a combination of such a difference in traffic state and Equation 2, may be used.
- an arbitrary cost function using the control amount as an input may be defined for each point.
- different cost coefficients and cost functions may be defined according to conditions such as the location and the time zone. Different cost functions may be defined according to the sign of the control amount.
- Equation 3 ⁇ TH j represents a density threshold value at the point xj.
- This constraint equation indicates that the target traffic state is limited so that the predicted value of the traffic state (vehicle density) at the traffic jam occurrence prediction time t + T is equal to or less than the density threshold at all points.
- This constraint equation is set in order to avoid the occurrence of traffic jams at other points as a result of trying to prevent traffic jams at traffic jam prediction points.
- one or more arbitrary points including a traffic jam prediction point may be defined.
- Formula 3 uses a prediction model that predicts the vehicle density at the predicted time from the vehicle density at the current time. For example, from other traffic conditions such as the number of passing vehicles, or a combination of a plurality of traffic conditions A prediction model for predicting the vehicle density at the prediction time may be used.
- a prediction model for predicting the vehicle density at the prediction time may be used.
- other constraint conditions that are permitted or preferable in road management such as a traffic volume storage constraint in which the total traffic volume (number of vehicles) on the road network is stored even after control. May be added.
- d MAX i indicates the maximum value of the traffic state controllable amount by the traffic state control device 30 arranged at the point xi.
- This constraint equation indicates that the target traffic state is limited so that the control amount is less than or equal to the maximum controllable amount at all points.
- a similar constraint equation may be used for the minimum value.
- different values may be defined depending on conditions such as the location and the time zone. Further, different controllable amounts may be defined depending on whether the control amount is positive or negative.
- FIG. 11 is a diagram illustrating an example of a calculation result of the target traffic state in the first embodiment of the present invention.
- the traffic state calculation unit 124 the prediction model acquired in step S12, the traffic state at the current time “2015/08/01 10:00” in FIG. 5, the density threshold value in FIG. 8, and the control means information in FIG. Is used to calculate the target traffic state at each point as shown in FIG.
- the display control unit 16 generates a display screen 161 for displaying the target traffic state and the like at each point, and displays the display screen 161 on the display device 40 (step S14).
- FIG. 12, FIG. 13, and FIG. 14 are diagrams showing examples of the display screen 161 in the first embodiment of the present invention.
- the traffic state display area 162 displays the traffic jam prediction time and the traffic jam prediction point related to the traffic to be prevented, the measured value of the traffic state at each point at the current time, and the target traffic state. Yes.
- one hour after the target traffic state is set in the traffic state display area 163 at each point at the predicted traffic jam time, the traffic jam predicted point, and the current time, A predicted value of the traffic state after 2 hours and a predicted result of occurrence of traffic congestion are displayed.
- predicted values are calculated by, for example, the target traffic state and the prediction model in the traffic state calculation unit 124.
- the traffic state calculation unit 124 also predicts the occurrence of a traffic jam using the predicted value of the traffic state and the density threshold value.
- the display screen 161 in FIG. 14 indicates that no traffic jam occurs at the traffic jam predicted point (point “X6”) at the traffic jam predicted time (after 2 hours).
- the display control unit 16 causes the display device 40 to display a display screen 161 as shown in FIGS. 12 to 14 in accordance with the operation of the switching buttons 165a to 165e by the operator.
- the display screen 161 may display the control content by the control means as generated in step S16 described later together with the target traffic state of each point.
- the operator refers to the display screen 161 displayed on the display device 40 and can confirm the effect of preventing traffic jam by controlling the traffic state at each point.
- the operator refers to these display screens 161 and corrects (changes) the target traffic state when there is a problem with the value of the target traffic state or the effect of preventing traffic jams.
- the operator overwrites the target traffic state on the display screen 161 and operates the correction button 164 to correct the target traffic state.
- the instruction receiving unit 17 receives the correction of the target traffic state from the display device 40 (step S15).
- step S15 for example, when the target traffic state is corrected within a predetermined time after the display screen 161 is displayed (step S15 / Y), the display control unit 16 performs step for the corrected target traffic state.
- the process from S14 is performed.
- indication reception part 17 may receive the correction
- the instruction receiving unit 17 may receive correction of parameters such as the format of the objective function and the cost (cost coefficient or cost function) used in the objective function.
- the instruction receiving unit 17 may receive correction of parameters such as a constraint expression format, a density threshold used in the constraint expression, and a controllable amount (maximum value, minimum value) of the traffic state.
- indication reception part 17 may receive the change of a prediction model.
- step S13 When these changes related to the objective function and the constraint equation are performed, the processing from step S13 is performed.
- step S15 for example, when the target traffic state is not corrected within a certain time after the display screen 161 is displayed or when no correction is instructed (step S15 / N), the state control unit 132 of the control processing unit 13 is displayed. Generates control information for each point (step S16).
- the state control part 132 determines the content of the control information corresponding to the target traffic state of each point based on the control characteristic of a control model.
- the type of target traffic state for example, vehicle density
- the type of traffic state represented by the control model for example, vehicle speed
- the state control unit 132 determines other types of traffic states (for example, the number of passing vehicles).
- the type of the control information may be determined by converting the type using the measured value or the estimated value.
- FIG. 15 is a diagram showing an example of control information in the first embodiment of the present invention.
- the control information indicates the control contents to be executed by the control means of the traffic state control device 30 at each point together with the target traffic state.
- the state control unit 132 generates control information as shown in FIG. 15 using the control model shown in FIG. 10 for the target traffic state shown in FIG.
- the transmission unit 15 transmits the control information generated by the control processing unit 13 to the traffic state control device 30 at each point, and instructs execution of traffic state control (step S17).
- the traffic state control device 30 at each point executes traffic control by the control means according to the control content of the control information.
- the traffic state control device 30 at the point X1 50 kilometers before the traffic jam prediction point controls the gates of the toll gate so that the number of gates that can pass is “2”.
- the traffic state control device 30 at the point X2 40 kilometers before the predicted traffic congestion point controls the driving support light so that the lighting pattern becomes “lighting pattern A”.
- the traffic state control device 30 at the point X3 30 kilometers before the predicted traffic congestion point displays the “presentation information A” on a guide board or the like.
- the log generation unit 18 generates a state log 181 indicating the target traffic state of each point and the traffic state after execution of control (step S18).
- the log generation unit 18 stores the generated state log 181 in the log storage unit 19.
- FIG. 16 is a diagram illustrating an example of the status log 181 according to the first embodiment of this invention.
- the traffic congestion measurement value collected at a predetermined collection interval together with the traffic jam prediction time and the traffic jam prediction point, the control execution time, the control content of each point, the target traffic state, and the traffic congestion to be prevented. Is recorded.
- the log generation unit 18 generates a status log 181 as shown in FIG.
- the log generation unit 18 displays the generated state log 181 on the display device 40 in response to an instruction from an operator or the like.
- the operator can check whether the control according to the target traffic state is performed by the control means at each point with reference to the state log 181 displayed on the display device 40.
- the instruction receiving unit 17 may receive the correction of the target traffic state, the correction related to the objective function and the constraint equation for calculating the target traffic state, and the change of the prediction model, similarly to step S15 described above.
- steps S12 to S18 are repeatedly executed at a predetermined control interval until the traffic congestion prediction time.
- the target traffic state of the point is calculated and the traffic state is controlled.
- FIG. 17 is a diagram showing an example of traffic control in the first embodiment of the present invention.
- instruction receiving unit 17 is not limited to step S15, and may receive correction of the target traffic state or the like at any timing during which the processing of steps S12 to S18 is repeatedly executed.
- the traffic state is controlled in response to the occurrence of a traffic jam at a traffic jam forecast point at a traffic jam forecast time.
- the present invention is not limited to this, and the target traffic state is calculated at a predetermined control interval so that no congestion occurs after a predetermined time at all points to be monitored and controlled, or at any one or more arbitrary points.
- the traffic state control may be repeated.
- the traffic state is controlled for one traffic jam (one combination of the traffic jam prediction time and the traffic jam prediction point)
- the traffic state may be controlled for a plurality of traffic jams (a plurality of combinations of traffic jam prediction times and traffic jam prediction points).
- the constraint equation of Formula 3 is generated for the plurality of traffic jams.
- the display screen 161 and the status log 181 also indicate the traffic jam prediction time and the traffic jam prediction point related to the plurality of traffic jams.
- the distance between the points to be monitored and controlled is 10 kilometers.
- the distance between points can be any distance such as tens of meters, hundreds of meters, kilometers, tens of kilometers, etc. Good.
- the traffic state calculation unit 124 calculates the target traffic state by obtaining a solution to the optimization problem expressed by the objective function of Formula 2, Formula 3 and Formula 4 of the constraint equation. did. That is, the target traffic state to be achieved at a point different from the target point was calculated in order to prevent traffic congestion at the target point.
- the point different from the target point all the points to be monitored and controlled (point i) are used in Equation 3, but as the point different from the target point, the traffic between the current time and the target time You may select and use the point where a state has a high correlation with an object point.
- the target traffic state may be calculated and used for points existing in these ranges.
- a correlation is expressed as a weighting factor ⁇ ij of the traffic state at each point used as an explanatory variable in the prediction model corresponding to the target point and the target time.
- a very small weighting factor ⁇ ij can be obtained for a point having no correlation.
- the target traffic state obtained for these points becomes almost the same value as the current traffic state, and the selection of the points having high correlation as described above can be realized.
- the target traffic state of a point having a weighting coefficient ⁇ ij of a certain value or less is deleted from the optimization variable, and the current traffic state value is used as a fixed value.
- the optimization problem may be solved.
- the traffic state at a point on the near side of the predicted traffic congestion point in the traveling direction is controlled.
- the present invention is not limited to this, and the target traffic state is calculated by the traffic state calculation unit 124, such as controlling the traffic state of the point ahead of the traffic jam prediction point in addition to the point ahead of the traffic jam prediction point in the traveling direction.
- the traffic state at each point on the road network may be controlled.
- control means of the traffic state control device 30 at each point can be used as the control means of the traffic state control device 30 at each point.
- Charge control In charge control, the amount of traffic flowing into a particular point (section) is controlled by increasing or decreasing the usage fee for a specific point (section).
- control characteristics of the control model for example, the relationship between the charge at each point (section) and the traffic volume is described.
- a plurality of control characteristics are set according to calendar attributes such as days of the week and time zones. Based on such control characteristics, the state control unit 132 determines a fee corresponding to the target traffic state and sets it in the control information.
- Control with a running light type driving assistance light a lighting pattern of lights such as LED (Light Emitting Diode) lights arranged on the road side is operated to control the running speed of the vehicle. That is, the lighting is turned on so as to run parallel to the traveling vehicle, and the relative speed with the traveling vehicle is increased or decreased, thereby affecting the driver's sense of speed and controlling the traveling speed. For example, when the light appears to be slow for the driver, an illusion that the traveling speed increases can be brought about, and speed suppression can be promoted. Conversely, if the light appears fast to the driver, speed recovery can be encouraged.
- the speed can be controlled with a fine time granularity as long as the running light type driving assistance light is arranged.
- the probability distribution of the influence on the speed given by the lighting pattern, the expected value, the average value, etc. are set as the control characteristics for the vehicle entering the section at a certain speed. Based on such control characteristics, the state control unit 132 determines a lighting pattern corresponding to the target traffic state and sets it in the control information.
- Tollgate gate control In the tollgate gate control, the amount of traffic flowing from the tollgate is controlled by changing the total number of tollgate gates and the number of ETC (Electronic Toll Collection system) gates.
- ETC Electronic Toll Collection system
- the traffic volume passing through the toll gate is proportional to the number of ETC gates. That is, the number of passing through the toll gate per unit time increases as the number of ETC gates increases, and decreases as the number of ETC gates decreases.
- the total number of gates at the toll gate also directly affects the number of toll gates passing through.
- control characteristics of the control model for example, the total number of toll gates or the relationship between the number of ETC gates and the traffic volume is described.
- control characteristics may be set for each condition such as the traffic volume of the toll booth and the ratio or estimated amount of the vehicle on which the ETC terminal is mounted.
- changing the total number of toll gates and the number of ETC gates involves cost factors such as requiring direct labor by humans or difficult to change until the traffic volume is interrupted. It may be reflected in the cost.
- the state control unit 132 determines the total number of gates and the number of ETC gates corresponding to the target traffic state, and sets them in the control information.
- ETC gate opening / closing timing control the traffic volume passing through the toll gate is controlled by changing the operation settings (opening / closing timing and opening / closing speed) of the gate bar.
- the driver of the vehicle passing through the ETC gate controls the traveling speed while checking the opening / closing state of the gate. For this reason, the vehicle speed in the vicinity of the gate changes depending on the time (timing) from when the vehicle enters the toll gate and it is determined to open the gate bar until it starts to raise the bar and the opening / closing speed of the bar. Since the operation of the gate bar is performed by a machine, the setting can be changed with fine time granularity.
- control characteristics of the control model for example, entry speed to the ETC gate and gate bar operation time (gate timing and opening speed) and vehicle interval (interval associated with traffic volume) are described.
- the traffic volume passing through the toll gate for the operation setting of the gate bar may be described as the control characteristic.
- the state control unit 132 determines the operation setting of the gate bar corresponding to the target traffic state and sets it in the control information.
- Guidance control to SA Service Area
- PA Parking Area
- the traffic volume is increased by encouraging the vehicle driver and passengers to use SA and PA.
- the vehicle is evacuated from the road, so that the number of vehicles existing on the road can be reduced.
- guidance means to SA / PA information presentation to a guide board arranged on a road, a terminal mounted on a vehicle, information presentation to a mobile terminal possessed by a driver or a passenger, etc. can be considered.
- Information to be presented includes information that prompts guidance directly, such as clearly indicating the purpose of preventing traffic jams, and information that prompts guidance indirectly related to campaigns and incentives at stores in SA / PA. It is done.
- control characteristics of the control model for example, the relationship between the presented information and the proportion of vehicles using SA / PA according to the content is described. Based on such control characteristics, the state control unit 132 determines presentation information corresponding to the target traffic state and sets it in the control information.
- Control by the information presented on the information board In the control by the information presented on the information board, the traffic volume is controlled by information displayed on the information board arranged on the road. The amount of information that can be displayed on the information board is limited, but can be displayed in characters. For this reason, various drivers can be guided by the information displayed on the information board. Examples of the contents displayed on the information board include contents that prompt speed increase and suppression because traffic congestion is expected, contents that prompt the user to refrain from changing lanes, and the like. In this case, as the control characteristics of the control model, for example, the relationship between the display content and the proportion of the vehicle according to the content is described. Based on such control characteristics, the state control unit 132 determines the display content corresponding to the target traffic state and sets it in the control information.
- a traffic state is controlled by using a patrol vehicle, a maintenance vehicle or the like owned by a road management company as a pacemaker vehicle. Since these vehicles are vehicles under the control of the road management company, the speed and driving behavior as instructed can be realized. For example, the behavior of the following vehicle can be controlled to some extent by instructing the pacemaker vehicle to drive strictly following the speed limit or to run at a speed slightly below the speed limit. Moreover, the traffic state can be controlled without increasing the cost by combining the original purpose of the pacemaker vehicle and the control of the traffic state, such as patrol and moving to a maintenance place.
- the relationship between the driving pattern of the pacemaker vehicle and the influence of the driving pattern on other nearby vehicles is described as the control characteristic of the control model.
- the state control unit 132 determines a driving pattern corresponding to the target traffic state and sets it in the control information.
- the traffic state is controlled by urging the autonomous traveling vehicle to switch traveling patterns such as vehicle speed and vehicle interval.
- the autonomous vehicle may be, for example, a vehicle capable of completely automatic driving without human operation.
- the autonomous traveling vehicle may be a vehicle capable of partially driving automatically, for example, equipped with a front vehicle tracking traveling function (cruise control) or the like.
- the traveling pattern can be controlled by changing the setting of forward tracking, that is, the tracking distance, the reaction speed, and the like.
- Such driving pattern control can be used in many vehicles due to the widespread use of vehicles equipped with automatic driving technology, and a great effect can be expected.
- the influence on the subsequent vehicles of the vehicle group is large, so the traffic volume that can be controlled by the traveling pattern can be increased.
- the control characteristics of the control model for example, the relationship between the travel pattern and the influence of the travel pattern on the traffic volume in the vicinity is described.
- the control characteristic may be set in association with the traffic volume in the vicinity. Based on such control characteristics, the state control unit 132 determines a travel pattern corresponding to the target traffic state and a vehicle to which the travel pattern is applied, and sets it in the control information.
- control means such as traffic signal control may be used in addition to the above-described control means.
- the traffic state calculation unit 124 calculates the target traffic state using an objective function that takes into account the cost of the control means at each point.
- the present invention is not limited to this, and when there are a plurality of control means at each point, the state control unit 132 may select one or more combinations of the control means at each point. In this case, for example, the state control unit 132 can obtain only a small control amount, but preferentially selects a control unit that produces an effect in a short time.
- the state control unit 132 selects a control means such as toll control that can obtain a large control amount, although it takes time for the difference between the traffic state obtained by the means and the target traffic state to take effect. Also good.
- the state control unit 132 may select the control means based on the control model and restrictions of each control means so that the probability that the target traffic state can be achieved is high and the cost for changing the traffic state is low. .
- FIG. 18 is a block diagram showing a characteristic configuration of the embodiment of the present invention.
- the congestion prevention system 10 includes a traffic state calculation unit 124 (calculation unit) and a display control unit 16.
- the traffic state calculation unit 124 calculates a target traffic state that is a traffic state to be achieved at a point different from the target point in order to prevent congestion at the target time at the target point.
- the display control unit 16 controls the display means so that the target traffic state at each point is displayed on the display means.
- FIG. 19 is a block diagram showing another characteristic configuration of the embodiment of the present invention.
- the traffic jam prevention system 10 includes a traffic state calculation unit 124 (calculation unit) and a log generation unit 18.
- the traffic state calculation unit 124 calculates a target traffic state that is a traffic state to be achieved at a point different from the target point in order to prevent congestion at the target time at the target point.
- the log generation unit 18 generates and outputs a log indicating the target traffic state at each point and the measured value of the traffic state at the point.
- the traffic state calculation unit 124 calculates a target traffic state that is a traffic state to be achieved at a point different from the target point in order to prevent congestion at the target time at the target point.
- the system can be adjusted so that an operator or the like can more reliably prevent traffic jams.
- the reason is that the display control unit 16 controls the target traffic state at each point to be displayed on the display means, or the log generation unit 18 displays the target traffic state at each point and the measured value of the traffic state. This is because a log to be generated is generated and output.
- the operator can change the value of the target traffic state, the objective function for calculating the target traffic state, the format and parameters of the constraint equation, and the prediction model so that a more appropriate target traffic state is set.
- the second embodiment of the present invention in using a prediction model that predicts the traffic state at the predicted time from the predicted traffic state at the current time at each point and the predicted traffic state at the future time, Different from the first embodiment of the present invention.
- Equation 5 a prediction model such as Equation 5 is used.
- t 0 is the time of the starting point
- ⁇ ij, ⁇ k, ij , ⁇ l, ij are parameters multiplied by ⁇ i (t), ⁇ i (t 0 + t k ), and ⁇ ′ i (t 0 + t l ), respectively.
- the predicted values of the traffic state at the past time, the traffic state at the current time, and the traffic state at the future time are used as explanatory variables, respectively. ing.
- the predicted value ⁇ ′ i (t 0 + t 1 ) is calculated by a prediction model that predicts the traffic state after t 1 hours based on the traffic state at time t 0 .
- the predicted value ⁇ ′ i (t 0 + t 1 ) may be collected by the receiving unit 11 from another device such as the traffic jam prediction device 20 and stored in the traffic state DB 14.
- the traffic state calculation unit 124 obtains the solution of the optimization problem expressed by the objective function of Equation 6, Equation 7 and Equation 8, and the target traffic state of each point. Is calculated.
- a cost coefficient for each point is used as the cost.
- different cost coefficients and cost functions are used depending on conditions such as the time zone as in the first embodiment. May be defined. Further, it is considered that the expected control amount at the future time includes a larger error due to the prediction error as the time difference between the current time and the future time is larger. Therefore, in the target function of Formula 6, a cost corresponding to the future time may be used, such as increasing the cost as the time difference between the current time and the future time is larger.
- the traffic state calculation unit 124 predicts the traffic state at the target time at the target point from the traffic state at the current time at each point and the predicted value of the traffic state after the current time and before the target time. This is because the target traffic state is calculated using the prediction model.
- Traffic jam prevention system 101 CPU 102 storage device 103 input / output device 104 communication device 11 receiving unit 12 prevention processing unit 121 prediction information DB 122 Prediction model DB 123 Control means DB 124 Traffic State Calculation Unit 125 Diagram Storage Unit 13 Control Processing Unit 131 Control Model DB 132 State Control Unit 14 Traffic State DB DESCRIPTION OF SYMBOLS 15 Transmission part 16 Display control part 161 Display screen 162 Traffic state display area 163 Traffic state display area 164 Correction button 165 Switching button 17 Instruction reception part 18 Log generation part 181 State log 19 Log storage part 20 Congestion prediction apparatus 30 Traffic state control apparatus 40 Display device
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Abstract
Description
はじめに、本発明の第1の実施の形態の構成を説明する。
料金制御では、特定の地点(区間)の利用料金を上げる、または、下げることにより、当該地点(区間)に流入する交通量を制御する。この場合、制御モデルの制御特性として、例えば、各地点(区間)の料金と交通量との関係が記述される。また、曜日等のカレンダ属性や時間帯に応じた複数の制御特性が設定される。状態制御部132は、このような制御特性をもとに、目標交通状態に対応する料金を決定し、制御情報に設定する。
走光型運転支援灯による制御では、路側に配置されたLED(Light Emitting Diode)照明等の照明の点灯パターンを操作し、車両の走行速度を制御する。すなわち、走行車両と並走するように照明を点灯させ、走行車両との相対速度を上げる、または、下げることにより、運転者の速度感覚に影響を与え、走行速度を制御する。例えば、運転者にとって光が遅く見える場合、走行速度が上昇するような錯覚をもたらし、速度抑制を促すことができる。逆に、運転者にとって光が速く見える場合、速度回復を促すこともできる。走光型運転支援灯が配置された区間であれば、細かい時間粒度で、速度を制御できる。この場合、制御特性として、例えば、ある速度で区間に進入する車両に対して、点灯パターンが与える速度上の影響の確率分布や、その期待値、平均値等が設定される。状態制御部132は、このような制御特性をもとに、目標交通状態に対応する点灯パターンを決定し、制御情報に設定する。
料金所ゲートの制御では、料金所のゲート総数やETC(Electronic Toll Collection system)ゲート数を変更することで、料金所から流入する交通量を制御する。料金所を通過する車両にETC端末を搭載する車両が一定割合で存在する場合、料金所を通過する交通量は、ETCゲート数と比例する。すなわち、単位時間あたりの料金所の通過台数は、ETCゲート数が増加すると上昇し、ETCゲート数が減少すると下降する。また、料金所のゲート総数も、より直接的に、料金所の通過台数に影響を与える。この場合、制御モデルの制御特性として、例えば、料金所のゲート総数やETCゲート数と交通量との関係が記述される。また、制御特性は、料金所の交通量と、そのうちのETC端末を搭載する車両の割合や推定量等との条件毎に設定されていてもよい。また、料金所のゲート総数やETCゲート数の変更には、人による直接労働が必要、あるいは、交通量が途切れるまで変更が難しい等のコスト要因が存在するため、これらのコスト要因が制御モデルのコストに反映されてもよい。状態制御部132は、このような制御特性をもとに、目標交通状態に対応するゲート総数やETCゲート数を決定し、制御情報に設定する。
ETCゲートの開閉タイミングの制御では、ゲートバーの操作設定(開閉タイミングや開閉速度)を変更することで、料金所を通過する交通量を制御する。ETCゲートを通過する車両の運転者は、ゲートの開閉状況を確認しながら走行速度を制御する。このため、車両が料金所に進入し、ゲートバーを開くと判断されてからバーを上げ始めるまでの時間(タイミング)やバーの開閉速度によって、ゲート近傍での車両速度が変化する。ゲートバーの操作は機械により行われるため、細かい時間粒度で設定を変更できる。この場合、制御モデルの制御特性として、例えば、ETCゲートへの進入速度とゲートバーの操作設定(開閉タイミングや開閉速度)に対する料金所通過時間や車両間隔(交通量に関連づけられた間隔)が記述される。また、制御特性として、ゲートバーの操作設定に対する、料金所を通過する交通量が記述されていてもよい。状態制御部132は、このような制御特性をもとに、目標交通状態に対応するゲートバーの操作設定を決定し、制御情報に設定する。
SA/PAへの誘導制御では、車両の運転者や同乗者に対して、SAやPAの利用を促すことにより、交通量を制御する。車両にSAやPAを利用させる場合、その車両を道路上から退避させることになるため、道路上に存在する車両数を削減できる。SA/PAへの誘導手段としては、道路に配置された案内板や、車両に搭載された端末への情報提示、運転者や同乗者が所持するモバイル端末への情報提示等が考えられる。提示する情報の内容としては、渋滞予防のためという目的を明示する等、直接的に誘導を促す情報や、SA/PA内の店舗によるキャンペーンやインセンティブに係る、間接的に誘導を促す情報が考えられる。この場合、制御モデルの制御特性として、例えば、提示した情報とその内容に従ってSA/PAを利用した車両の割合との関係が記述される。状態制御部132は、このような制御特性をもとに、目標交通状態に対応する提示情報を決定し、制御情報に設定する。
情報板への提示情報による制御では、道路に配置されている情報板に表示する情報により、交通量を制御する。情報板に表示可能な情報は、その量は限定されているものの、文字により表示可能である。このため、情報板に表示する情報により、多様な運転者の誘導が可能である。情報板への表示内容としては、渋滞が予想されているために速度向上や抑制を促す内容や、車線変更を控えるように促す内容等が考えられる。この場合、制御モデルの制御特性として、例えば、表示内容とその内容に従った車両の割合との関係が記述される。状態制御部132は、このような制御特性をもとに、目標交通状態に対応する表示内容を決定し、制御情報に設定する。
ペースメーカーによる制御では、道路管理会社等が保有するパトロール車両やメンテナンス車両等をペースメーカー車両として用いることにより、交通状態を制御する。これらの車両は道路管理会社の制御下にある車両であるため、指示通りの速度や、運転挙動を実現できる。例えば、ペースメーカー車両に、制限速度を厳密に守るような運転や、制限速度をやや下回る速度での走行を指示することにより、後続する車両の挙動をある程度制御できる。また、パトロールやメンテナンス場所への移動等、ペースメーカー車両の本来の目的と交通状態の制御を兼ねることで、コストを増やすことなく、交通状態を制御できる。この場合、制御モデルの制御特性として、例えば、ペースメーカー車両の運転パターンとその運転パターンが近傍の他の車両に与える影響との関係が記述される。状態制御部132は、このような制御特性をもとに、目標交通状態に対応する運転パターンを決定し、制御情報に設定する。
自律走行車による制御では、自律走行車に対して、車両速度や車両間隔等の走行パターンの切り替えを促すことにより、交通状態を制御する。ここで、自律走行車は、例えば、人の操作なく完全に自動運転が可能な車両でもよい。また、自律走行車は、例えば、前方車両追尾走行機能(クルーズコントロール)等を搭載した、部分的に自動運転が可能な車両でもよい。クルーズコントロールを搭載した車両の場合、前方追尾の設定、すなわち追尾距離や反応速度等を変更することにより、走行パターンを制御できる。また、前方に車両がいない場合であっても、ある程度の走行パターンが可能な車両も存在する。このような走行パターンの制御は、自動運転技術の搭載車の普及により、多くの車両で利用可能となっており、大きな効果が見込める。さらに、クルーズコントロール機能を持つ車両による車両群、すなわち、隊列走行が構成できる場合、車両群の後続車両に与える影響が大きいため、走行パターンにより制御可能な交通量を増やすことができる。この場合、制御モデルの制御特性として、例えば、走行パターンと、その走行パターンが近傍の交通量に与える影響と、の関係が記述される。制御特性は、近傍の交通量に関連づけられて設定されていてもよい。状態制御部132は、このような制御特性をもとに、目標交通状態に対応する走行パターンや当該走行パターンを適用する車両を決定し、制御情報に設定する。
次に、本発明の第2の実施の形態について説明する。
101 CPU
102 記憶デバイス
103 入出力デバイス
104 通信デバイス
11 受信部
12 予防処理部
121 予測情報DB
122 予測モデルDB
123 制御手段DB
124 交通状態算出部
125 ダイアグラム格納部
13 制御処理部
131 制御モデルDB
132 状態制御部
14 交通状態DB
15 送信部
16 表示制御部
161 表示画面
162 交通状態表示領域
163 交通状態表示領域
164 修正ボタン
165 切替ボタン
17 指示受付部
18 ログ生成部
181 状態ログ
19 ログ格納部
20 渋滞予測装置
30 交通状態制御装置
40 表示装置
Claims (16)
- 対象地点における対象時刻の渋滞を防ぐために前記対象地点と異なる地点において達成すべき交通状態である目標交通状態を算出する、算出手段と、
前記地点における前記目標交通状態が表示手段に表示されるように、当該表示手段を制御する、表示制御手段と、
を備える、渋滞予防システム。 - 前記算出手段は、前記地点における現在の交通状態から前記対象地点における前記対象時刻の交通状態を予測する予測モデルを用いて、前記目標交通状態を算出する、
請求項1に記載の渋滞予防システム。 - 前記表示手段は、前記地点における現在の交通状態が前記目標交通状態である場合の、前記対象地点の交通状態の予測値を表示する、
請求項2に記載の渋滞予防システム。 - さらに、前記目標交通状態、前記予測モデル、及び、前記目標交通状態を算出するための式やパラメータの内の少なくとも一つの修正指示を受け付ける、指示受付手段を含む、
請求項2または3に記載の渋滞予防システム。 - さらに、前記地点における交通状態が、当該地点に対して算出された前記目標交通状態となるように、当該地点に配置された交通状態制御手段を制御する、状態制御手段、
を備える、請求項1乃至4のいずれかに記載の渋滞予防システム。 - 前記算出手段は、前記地点において前記目標交通状態を達成するためのコストが最小になるように、前記地点における前記目標交通状態を算出する、
請求項1乃至5のいずれかに記載の渋滞予防システム。 - 前記算出手段は、前記地点における現在時刻の交通状態、及び、現在時刻より後、かつ、前記対象時刻より前の交通状態の予測値から前記対象地点における前記対象時刻の交通状態を予測する予測モデルを用いて、前記目標交通状態を算出する、
請求項1乃至6のいずれかに記載の渋滞予防システム。 - 対象地点における対象時刻の渋滞を防ぐために前記対象地点と異なる地点において達成すべき交通状態である目標交通状態を算出し、
前記地点における前記目標交通状態が表示手段に表示されるように、当該表示手段を制御する、
渋滞予防方法。 - コンピュータに、
対象地点における対象時刻の渋滞を防ぐために前記対象地点と異なる地点において達成すべき交通状態である目標交通状態を算出し、
前記地点における前記目標交通状態が表示手段に表示されるように、当該表示手段を制御する、
処理を実行させるプログラムを格納する、コンピュータが読み取り可能な記録媒体。 - 対象地点における対象時刻の渋滞を防ぐために前記対象地点と異なる地点において達成すべき交通状態である目標交通状態を算出する、算出手段と、
前記地点における前記目標交通状態と当該地点の交通状態の測定値とを示すログを生成し、出力する、ログ生成手段と、
を備える、渋滞予防システム。 - 前記ログ生成手段は、前記地点における前記目標交通状態と当該地点の当該目標交通状態を用いた制御後の交通状態の測定値とを示すログを生成する、
請求項10に記載の渋滞予防システム。 - 前記算出手段は、前記地点における現在の交通状態から前記対象地点における前記対象時刻の交通状態を予測する予測モデルを用いて、前記目標交通状態を算出する、
請求項10、または、11に記載の渋滞予防システム。 - さらに、前記目標交通状態、前記予測モデル、及び、前記目標交通状態を算出するための式やパラメータの内の少なくとも一つの修正指示を受け付ける、指示受付手段を含む、
請求項12に記載の渋滞予防システム。 - さらに、前記地点における交通状態が、当該地点に対して算出された前記目標交通状態となるように、当該地点に配置された交通状態制御手段を制御する、状態制御手段、
を備える、請求項10乃至13のいずれかに記載の渋滞予防システム。 - 対象地点における対象時刻の渋滞を防ぐために前記対象地点と異なる地点において達成すべき交通状態である目標交通状態を算出し、
前記地点における前記目標交通状態と当該地点の交通状態の測定値とを示すログを生成し、出力する、
渋滞予防方法。 - コンピュータに、
対象地点における対象時刻の渋滞を防ぐために前記対象地点と異なる地点において達成すべき交通状態である目標交通状態を算出し、
前記地点における前記目標交通状態と当該地点の交通状態の測定値とを示すログを生成し、出力する、
処理を実行させるプログラムを格納する、コンピュータが読み取り可能な記録媒体。
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