Patent Publication Number: US-8532965-B2

Title: Method and system for traffic simulation of road network

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is based upon and claims the benefit of priority from prior Japanese Patent Application No. 2008-235139, filed Sep. 12, 2008, the entire contents of which are incorporated herein by reference. 
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates to a technique of simulating the condition of traffic in a road network. 
     2. Description of the Related Art 
     Road traffic control systems are designed, generally for controlling the traffic in accordance with the actual traffic of many vehicles running on the roads. In any road network, the roads and traffic facilities must be changed or new roads and new facilities must be built, in order to eliminate traffic congestion or to ensure a smooth traffic flow on a road. However, traffic congestion may occur on other roads or the traffic flow on other roads may become less smooth. 
     In view of this, the traffic control plan must be verified or quantitatively evaluated for its effect. The traffic simulation technique is therefore very important. Since traffic simulation evaluates the traffic control and predicts the traffic conditions on various roads, it can help to plan an effective traffic control system. 
     Traffic simulation methods are classified into two types, i.e., macrosimulation method and microsimulation method. In the macrosimulation method, the traffic of vehicles is regarded as a continuous fluid flow, as described in, for example, Easy Traffic Simulation, Japan Society of Traffic Engineering, Maruzen Co., Ltd., June 2006, ISBN 4-905990-31-9C3051. The reference describes a traffic simulation technique that utilizes a block density method to predict the traffic congestion on highways. 
     In the microsimulation method, the behavior of each vehicle on a specific road is first simulated, the results of simulation are then accumulated for the respective time periods, and the traffic flow of the vehicles is reproduced on a road model, as described in, for example, Jpn. Pat. Appln. KOKAI Publication No. 2004-258889. This reference discloses a traffic simulator that uses molecular dynamics, which is usually applied in the fields of physics and material studies. The traffic simulator describes the influence each vehicle imposes on any nearby vehicle, as a potential hazard, and reproduces and displays the behavior of the vehicle. 
     In the macrosimulation method, the calculation load on the computer used is smaller than in the microsimulation method. In the microsimulation method, the calculation load on the computer is large because a calculation must be performed to simulate, as pointed out above, the behavior of each vehicle. The macrosimulation method, in which the calculation load on the computer is small, is now used in most cases to design a road network. 
     To design a road network for a broad area, it is necessary to predict traffic congestion, which more influences the traffic condition than anything else. Traffic congestion results from, in many cases, the drivers&#39; lane changing at junctions or strange behavior of individual vehicles. The traffic simulator that performs the macrosimulation method defines the roads existing in each road-network section as links, and processes the traffic data (average value) averaged for each link. Further, the traffic simulator uses not only the average data for each link, but also the data actually acquired by a plurality of vehicle sensors provided along the roads, reproducing the traffic condition and predicting a traffic condition. The traffic simulator then displays the reproduced traffic condition and the predicted traffic condition on a display screen. 
     However, the traffic simulator performing the macrosimulation method cannot simulate the behavior of each vehicle or process the various aspects of behavior, to achieve microscopic reproduction of traffic congestion. Consequently, with any traffic simulator that performs the macrosimulation method it is not always easy to reproduce or predict traffic congestions. 
     BRIEF SUMMARY OF THE INVENTION 
     An object of this invention is to provide a system that can microscopically reproduce or predict the behavior of each vehicle running on a road, and can display the traffic condition, including congestion, in various modes on a display screen. 
     According to an aspect of this invention, there is provided a system in which a traffic simulator performs the microsimulation method, thereby reproducing or predicting a traffic condition on a road, and which has the function of microscopically displaying the simulation result in various modes on a display screen. 
     A system according to the aspect of the invention, which is designed to perform traffic simulation of a road network, comprises: 
     a traffic simulator configured to perform traffic simulation by a microsimulation method, to predict a traffic condition on an object road of the road network, by using road parameters defining the road network and model parameters used as initial-value parameters; and 
     a display controller configured to control a display unit, displaying a dynamic image showing a traffic condition of vehicles running on the road network, on the screen of the display unit, as a result of the traffic simulation, which has been output from the traffic simulator, and changing the image displayed on the screen, in terms of pattern, in accordance with a display instruction. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
       The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the invention, and together with the general description given above and the detailed description of the embodiments given below, serve to explain the principles of the invention. 
         FIG. 1  is a block diagram explaining the configuration of a system according to an embodiment of this invention; 
         FIG. 2  is a block diagram explaining the function of a road-network generation unit according to the embodiment; 
         FIGS. 3A and 3B  are diagrams explaining a road network according to the embodiment; 
         FIG. 4  is a block diagram explaining the function of an event generation unit according to the embodiment; 
         FIG. 5  is a block diagram explaining an input/output control unit according to the embodiment; 
         FIG. 6  is a diagram showing an exemplary result displayed on a screen, in the embodiment of the invention; 
         FIG. 7  is a diagram showing another exemplary result displayed on a screen, in the embodiment of the invention; 
         FIG. 8  is a flowchart explaining the operation of the system according to the embodiment; 
         FIG. 9  is a diagram showing an image of a road network, generated by the embodiment; 
         FIG. 10  is a diagram showing a method of displaying images on the display screen in the embodiment; 
         FIGS. 11A to 11G  are diagrams explaining an exemplary method of displaying images in the embodiment; 
         FIGS. 12A to 12C  are diagrams explaining another exemplary method of displaying images in the embodiment; and 
         FIG. 13  is a block diagram explaining the configuration of a system according to another embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     Embodiments of the present invention will be described with reference to the accompanying drawings. 
     [Configuration of the System] 
     As shown in  FIG. 1 , a system  10  according to an embodiment of the invention has an input/output (I/O) unit  1 , a traffic control system (TCS)  2 , a network  3 , a traffic simulator  4 , and an external storage unit  5 . The I/O unit  1  has an input unit and a display unit  6 . The input unit is, for example, a keyboard or a mouse  9 . The display unit  6  is an output unit and has a display screen. The external storage unit  5  includes, for example, a hard disk drive, and stores various programs and data, which the traffic simulator uses to perform its function. 
     The traffic control system  2  is a computer system owned by a road management company that manages ordinary roads and toll roads. The system  2  performs data communication with the traffic simulator  4  via the network  3 . The traffic control system  2  controls road facilities such as traffic lights, toll receipt systems installed at toll gates, and the like, and various devices such as vehicle sensors (described later) installed along roads. 
     The traffic simulator  4  comprises a computer system and has, as major components, a central processing unit (CPU)  7  and an internal storage unit  8 . The CPU  7  performs the functions of a road-network generation unit  11 , reproduction process unit  12 , event generation unit  13 , prediction unit  14  and I/O control unit  15 . As will be described later, the traffic simulator  4  is configured mainly to simulate the traffic condition of vehicles running on the road network (i.e., traffic flows and traffic congestion) and to output the simulation results to the display unit  6 , thereby to display the simulation results on the screen of the display unit  6 . 
     The road-network generation unit  11  uses, for example, the software called “road editor,” generating road parameters (character data) representing the road network that the traffic simulator  4  will simulate. The road network includes the lanes of each road, new roads, branches and junctions. As seen from  FIGS. 3A and 3B , the road parameters are redefined by road segments (RS), number of lanes, nodes (N) and links (L), etc. 
     Any road segments RSn (n being a serial number) is one of the parts (shape elements) into which the road in question (hereinafter referred to as “object road”) is divided in accordance with their shapes. The road exemplified in  FIG. 3A  is divided into six road segments RS 1  to RS 6 . Each road segment is identified with a node (N) and a link (L). The node (N) is the end (link junction) of the road segment. The position of the node (N) is designated by a node number N 1 , N 2 , N 3 , N 4  or N 5  in the example of  FIG. 3A . The link (L) indicates that part of the road that connects two adjacent nodes. The position of the link (L) is designated by the link number L 1 , L 2 , L 3 , L 4 , L 5  or L 6  in the example of  FIG. 3A . The road-network generation unit  11  connects node numbers, link numbers and lane numbers, one to another, generating road parameters. The road parameters thus generated are stored, as road data  18 , in the internal storage unit  8 . 
     The reproduction process unit  12  reproduces the actual traffic condition (traffic flow and congestion) on the object road or a traffic condition similar to the actual traffic condition, from the road parameters (i.e., road data  18 ) generated by the road-network generation unit  11  and pertaining to the road network. At this point, the reproduction process unit  12  calculates the number of vehicles running on the road and the average speed of the vehicles, from the traffic amount and traffic density, both acquired from the traffic control system  2  through the network  3 . Note that the traffic control system  2  has calculated the traffic amount and traffic density from the data acquired by the vehicle sensors (described later) installed along the object road. 
     The reproduction process unit  12  uses the number of vehicles and the average speed, performing simulation in which each vehicle is made to run at the average speed for a predetermined time. The reproduction process unit  12  acquires model parameters (initial-value parameters) by the simulation and stores the model parameters in the internal storage unit  8 . 
     The event generation unit  13  performs the function of designating a specific position (i.e., object road) on the road network and a specified vehicle running on the object road, thereby generating, in the course of simulation, event data representing an event that hinders the road traffic, such as an engine trouble or a traffic accident. 
     The prediction unit  14  executes a simulation engine (software) designed for use in the microsimulation method described above, thus predicting the traffic condition (i.e., traffic flow and traffic congestion) on the object road, on the basis of the road parameters and the model parameters that are stored in the internal storage unit  8 . More specifically, the prediction unit  14  uses the road parameters and the model parameters, performing simulation in which many vehicle models are made to run at a variable speed. 
     The prediction unit  14  first performs the simulation, sequentially calculating the positions of all vehicle models. The prediction unit  14  then writes the positions thus calculated into the external storage unit  5  in time sequence, thus predicting the traffic condition. More precisely, the prediction unit  14  designates a specific point on the road and a specific vehicle on the road, causing the event generation unit  13  to generate event data representing, for example, the engine trouble. From the event data, the prediction unit  14  calculates the data about all vehicles that have evaded traffic congestion, at predetermined time intervals (e.g., intervals of 1 second). Further, the prediction unit  14  calculates the data about vehicles that have been caught in traffic congestion. The data items thus acquired, each representing the number of a vehicle, the time of data acquisition, the position of the vehicle, are written in the external storage unit  5  in time sequence. The traffic condition is thereby predicted. 
     The I/O control unit  15  receives the result of prediction from the external storage unit  5 , which the prediction unit  14  has acquired. The I/O control unit  15  then supplies the result of prediction to the display unit  6 . The display unit  6  displays the prediction result on its screen, in such a pattern as will be described later. That is, the I/O control unit  15  receives, from the external storage unit  5 , the data about the traffic condition predicted for the period from the present to a preset future time. The data received is supplied to the display unit  6 , which displays the data on its screen. 
     [Operation of the System] 
     How the system  10  according to this embodiment operates will be explained, mainly with reference to the flowchart of  FIG. 8 . 
     In the traffic simulator  4 , the road-network generation unit  11  generates road parameters that represent the road network on which to perform the traffic simulation (Step S 1 ). As shown in  FIG. 2 , the road-network generation unit  11  has four functions. More specifically, the unit  11  has a road-data setting unit  21 , a traffic-volume data setting unit  22 , an average speed setting unit  23 , and a toll-gate traffic-volume data setting unit  24 . 
     In accordance with the instruction coming from the above-mentioned road editor, the road-data setting unit  21  generates road data  18  when the input device of the I/O unit  1  is operated (Step S 2 ). As seen from  FIGS. 3A and 3B , the road data  18  is composed of road parameters, each including node (N), link (L) and number of lanes, etc. Thus, the road data  18  defines the road network including the object road. 
       FIG. 3A  is a diagram for explaining the road segments RS 1  to RS 6  that are defined by the nodes (N) and the links (L). As  FIG. 3A  shows, a road network is assumed, which has a main road  100 , a branch road  110  and a junction road  120 . Along the main road  100 , vehicle sensors  30  are provided at regular intervals in order to detect the vehicles running on the main road  100 . The traffic control system  2  receives the result of detection, from the vehicle sensors  30 . From the results of detection, the system  2  calculates the traffic volume and the traffic density. The traffic simulator  4  acquires the data representing the traffic volume and traffic density, from the traffic control system  2  through the network  3 . 
       FIG. 3B  is a diagram explaining the concept of nodes N 1  to N 5  and links L 1  to L 6  that define the road segments RS 1  to RS 6 . As described above, the nodes N 1  to N 5  are the ends (link junctions) of the respective road segments. The links L 1  to L 6  are those parts of roads, each connecting two adjacent nodes. The road-data setting unit  21  generates, as road data  18 , the position (coordinates) of each node, the connection of each link, the number of vehicles at each link, the inclination angle of each link (if the link is a slope), and the character road data representing the object road connected to a toll gate located at a node, if any, which is not connected to any other link. The road data  18  thus generated is stored in the internal storage unit  8 . 
     The traffic-volume data setting unit  22 , the average speed setting unit  23 , and a toll-gate traffic-volume data setting unit  24  at the toll gate input traffic volume data  19 A measured beforehand for the road, the average speed data  20  and the traffic volume data  19 B measured at the toll gate and sets them in the internal storage unit  8 . The traffic volume data  19 A contains the data representing the number of vehicles running on the object road (more precisely, the number of vehicles per unit time). The traffic volume data  19 B measured at the toll gate contains the data representing the number of vehicles per unit time, measured at the toll gate. The average speed data  20  represents the average speed of the vehicles running on the object road. 
     To be more specific, the traffic-volume data setting unit  22  is connected by the network  3  to the traffic control system  2  in accordance with an input from the input/output unit  1 . Then, the unit  22  acquires the traffic-volume data items about the respective links from the traffic control system  2  in real time, and sets these traffic-volume data items in the internal storage unit  8 . Alternatively, the traffic-volume data setting unit  22  may be configured to acquire vehicle passage data for each link, via the network  3  from the vehicle sensors  30  provided along the object road in accordance with an input from the input/output unit  1 , and then to set the vehicle passage data in the internal storage unit  8 . 
     Like the traffic-volume data setting unit  22 , the average speed setting unit  23  is connected by the network  3  to the traffic control system  2  in accordance with an input from the input/output unit  1 . Then, the average speed setting unit  23  acquires the average-speed data items about the respective links from the traffic control system  2  in real time, and sets these average-speed data items in the internal storage unit  8 . Alternatively, the average speed setting unit  23  may be configured to acquire average speed data vehicle passage data for each link, from the vehicle sensors  30  provided along the object road and to set the vehicle passage data in the internal storage unit  8 . 
     The toll-gate traffic-volume data setting unit  24  is connected by the network  3  to a system (not shown) installed at the toll gate to the toll road, in accordance with an input from the input/output unit  1 . The setting unit  24  acquires the vehicle number data representing how many vehicles have passed through the toll gate within a predetermined time. From the vehicle number data, the setting unit  24  calculates the traffic volume at the toll gate. The data representing the traffic volume thus calculated is stored in the internal storage unit  8 . 
     The road-network generation unit  11  thus acquires the road data (road parameters) defining the road network of the object road, the traffic volume data  19 A about the links, the average speed data  20  about the links, and the traffic volume data  19 B about the toll gate. The unit  11  then sets these data items  19 A,  20  and  19 B in the internal storage unit  8 . The data items  19 A,  20  and  19 B (not the road data  18 ) will be called “traffic-related data,” which has been obtained relatively recently in the traffic simulation. 
     Next, the reproduction process unit  12  uses the road data  18  and the traffic-related data, reproducing a road traffic condition (i.e., traffic flow and traffic congestion) that is similar to the actual traffic condition on the object road. The reproduction process unit  12  then acquires the model parameter of each vehicle running on the object road, or the model parameters of the traffic simulation (i.e., initial-value parameters), and sets the model parameters in the internal storage unit  8  (Step S 3 ). 
     More specifically, the reproduction process unit  12  calculates the number of the vehicles running on the object road and the average speed of these vehicles, from the traffic volume and traffic density the unit  12  has acquired via the network  3  from the traffic control system  2  or the vehicle sensors  30 . 
     Next, the reproduction process unit  12  uses the number of vehicles and the average speed of the vehicles, performing simulation in which each vehicle model is made to run at the average speed for a prescribed time. In the simulation, the reproduction process unit  12  performs optimization computation, utilizing, as functions, the vehicle parameters such as acceleration and braking, thereby calculating the model parameters (i.e., initial-value parameters). Further, the reproduction process unit  12  reproduces the traffic condition at regular intervals or at the same time on a specific day of every week, in the same way as described above, thereby calculating the model parameters. The unit  12  may adjust the model parameters in order to render the traffic condition similar to the actual traffic condition on the object road. 
     As described above, the reproduction process unit  12  stores the model parameters (i.e., initial-value data for simulation) in the internal storage unit  8 . The prediction unit  14  uses the model parameters in the traffic simulation to perform by the microsimulation method. 
     The prediction unit  14  predicts the traffic condition on the object road, from the road parameters (i.e., road data  18 ) and the model parameters stored in the internal storage unit  8  (Step S 5 ). To be more specific, the prediction unit  14  uses the road parameters and the model parameters, performing traffic simulation in which many vehicle models are made to run at a predetermined speed. The number of the vehicle models used in the simulation is a number equivalent to the actual traffic volume on the object road, for example 100 vehicles. 
     While the many vehicles are running, the prediction unit  14  acquires data items at every predetermined time interval, such as the link number (including the car model), lane number, distance and position, which all pertain to each vehicle model, and stores these data items sequentially in the external storage unit  5  (Step S 6 ). At this point, the prediction unit  14  designates a specific point on the road and a specified vehicle on the road. The event generation unit  13  generates event data representing, for example, the engine trouble (Step S 4 ). From the event data, the prediction unit  14  acquires the position data about all vehicle models that have evaded traffic congestion, at predetermined time intervals (e.g., intervals of 1 second). The data items acquired, each of which represents the number of a vehicle, the time of data acquisition, the position of the vehicle, are written in the external storage unit  5  in time sequence. The traffic condition is thereby predicted. 
     The event generation unit  13  generates event data in response to an input coming from the input unit and designating the specific point on the road and the specified vehicle on the road. The event data thus generated represents a trouble with any vehicle (such as the engine trouble), any traffic accident on the road, the toll gate closure due to traffic congestion, and the limitation to the number of vehicles allowed to pass through the toll gate. As shown in  FIG. 4 , the event generation unit  13  has a trouble-vehicle setting unit  31  and a traffic-volume limit setting unit  32 . 
     The trouble-vehicle setting unit  31  generates the above-mentioned event data in accordance with the instruction coming from the input unit and stores the event data in the internal storage unit  8 , after the reproduction process unit  12  has performed the traffic simulation on the object road identified with the road parameters. If the input unit designates a specific point on the road, the trouble-vehicle setting unit  31  will set an event mark to the specific point. 
     The traffic-volume limit setting unit  32  generates event data showing a limited traffic at the toll gate when a trouble develops in the specified vehicle. The unit  32  then stores the event data in the internal storage unit  8 . To be more specific, in response to the instruction that comes from the input unit, the traffic-volume limit setting unit  32  designates the number of the link at which the trouble has occurred and the toll gate connected to a link adjacent to that link, upon lapse of a predetermined time after the trouble. Then, the traffic-volume limit setting unit  32  closes the toll gate for a predetermined time or limits the number of vehicles allowed to pass through the toll gate. If a trouble occurs in the specified vehicle, the event generation unit  13  will sets the number of the link and a traffic limit mark to the toll gate connected to the link adjacent to that link. 
     The prediction unit  14  predicts a traffic congestion that may occur when the event data is generated (that is, when a traffic accident occurs). At this point, the unit  14  predicts the traffic condition, by writing the results of calculation (i.e., the number of the vehicle, the time and the vehicle position data) into the external storage unit  5  in time sequence, as has been described above. 
     The unit  14  can therefore predict when the traffic congestion involving all vehicles running on the link will be eliminated in the future by executing a traffic simulation wherein the event data is generated in the state where the vehicles are assumed to run at the average speed calculated based on the traffic volume and traffic density at each link. 
     The I/O control unit  15  acquires the result of prediction generated by the prediction unit  14  from, for example the external storage unit  5 . The prediction result, thus acquired, is displayed on the screen of the display unit  6  (Step S 8 ). On the basis of the prediction result (i.e., prediction data), the I/O control unit  15  may cause the display unit  6  to display the network of the object road as is illustrated in  FIG. 7 . The exemplary network of  FIG. 7  is composed of links  101  and links  102 . At the links  101 , traffic congestion is occurring. At the links  102 , normal traffic flows are achieved. On the screen of the display unit  6 , the links  101  are displayed, for example, in red, while the links  102  are displayed, for example, in yellow. 
     [Display Control in the Traffic Simulator] 
     How the I/O control unit  15  controls the display in the traffic simulator  4  according to this embodiment will be explained below in detail. 
     In this embodiment, the I/O control unit  15  has the function of controlling the display of the predicted (simulated) traffic condition (traffic flow and traffic congestion) on the network of the object road, in accordance with the display operation made at the I/O unit  1  (Steps S 7  and S 8 ). As  FIG. 5  shows, the I/O control unit  15  has an output unit  41  and a display controller  42 . The output unit  41  is configured to output the result of prediction. The output unit  41  is configured to control the displaying of the result of prediction. 
     The output unit  41  acquires the prediction data from the external storage unit  5 . That is, the unit  41  reads various data items such as the vehicle numbers, link numbers, lane numbers, travel distances from start points, time, and vehicle positions, and supplies these data items to the display unit  6  and a printer (not shown). 
     The display controller  42  controls the display unit  6  in accordance with the road parameters (road data  18 ) the road-network generation unit  11  has generated. So controlled, the display unit  6  displays a network image of the object road on its screen as illustrated in  FIG. 10 . Note that  FIG. 9  is a diagram that shows the image of the road network defined by road parameters of nodes and links. 
     The display controller  42  uses the various data items output from the output unit  41 , causing the display unit  6  to display, on its screen, the traffic condition predicted for the network of the object road, i.e., the images of all vehicles changed in position from time to time. That is, as shown in  FIG. 6 , the display controller  42  displays the behaviors (changes) the vehicles  60  take on the road, in a still-picture image or moving-picture image. The image of  FIG. 6  shows how the vehicles  60  are running on the main load  100 , branch road  110  and junction road  120  of the object road. As seen from  FIG. 6 , some of the vehicles  60  are caught in traffic congestion at the section  61  where the junction road  120  meets the main road  100 . Seeing the image thus displayed by the traffic simulator  4 , the person in charge of designing roads can plan to build a by-pass extending parallel to that section, in order to prevent such congestion as shown in  FIG. 6 . 
     The display controller  42  has the function of causing the display unit  6  to display such an image as shown in  FIG. 10 . As shown in  FIG. 10 , this image shows buttons  600  to  605 , a window  606 , a slider  607 , a window  608  and buttons  609  to  614 . The window  608  shows the time. How the display control unit  6  operates will be explained in detail, with reference to  FIG. 10  and  FIGS. 11A to 11G  and  FIG. 12A to 12C . 
     First, the display controller  42  causes the display unit  6  to display an animation (moving picture) that is the result of simulation (i.e., result of prediction) (see  FIG. 6 ), in accordance with the operation of the buttons  609  to  614  that are related to the playback of time-serial data. More precisely, the display controller  42  performs a playback process when the playback button  612  is pushed, a fast-feed process when the fast-feed button  613  is pushed, and a complete fast-feed process when the complete fast-feed button  614  is pushed. The fast-feed button  613  has the function of feeding the data, for example, at a speed twice the ordinary speed, at a speed four times the ordinary speed, or at a max speed eight times the ordinary speed when it is repeatedly pushed. 
     The “playback process” is a process of sequentially reproducing the time-serial data (i.e., vehicle position data) that is the result of prediction. The “fast-playback process” is a process of displaying time-serial data at a speed higher than the ordinary speed. 
     Further, the display controller  42  performs a rewind process when the rewind button  611  is pushed, a fast-rewind process when the fast-rewind button  612  is pushed, and a complete rewind process when the complete rewind button  609  is pushed. The fast-feed button  610  has the function of rewinding the data at a speed twice the ordinary speed, at a speed four times the ordinary speed when it is repeatedly pushed. The “rewind process” is a process of playing back the time-serial data at the ordinary speed in the reverse direction. 
     Moreover, the display controller  42  controls the display unit  6  when the slider  607  is operated, and causes the display unit  6  to display for a short time that part of the simulation result, which has been predicted for a specified time, in the form of an animation (moving picture). In this case, the time displayed in the window  608  changes as the slider  607  is moved. The slider  607  can be moved by operating the mouse  9 . 
     The display controller  42  performs a magnification process, a reduction process and a rotation process on a designed part of the image (i.e., simulation result), when the magnification button  600 , reduction button  601  and rotation button  604  are pushed. When operated, the button  602  sets the value by which to magnify the image every time the magnification button is pushed, and to reduce the image every time the reduction button  601  is pushed. When operated, the button  605  sets a rotation angle (degrees). The angle set by operating the button  605  is displayed in the window  606 . 
     The magnification process and the reduction process will be explained in detail, with reference to  FIG. 11A to 11G . 
     As shown in  FIG. 11A , the display controller  42  selects a region (broken-line frame) of the prediction-result image displayed on the screen of the display unit  6 . This region has been designated by operating the mouse  9 . When the magnification button  600  is pushed as sown in  FIG. 11B , the display controller  42  performs the magnification process, causing the display unit  6  to magnify the selected region, for example 20 times the original size, as shown in  FIG. 11C . The magnification button  600  may be further pushed while the magnified image is being displayed as shown in  FIG. 11C . Then, the display controller  42  controls the display unit  6 , which displays the image further magnified as shown in  FIG. 11G . The magnification button  600  may be pushed even further (see  FIG. 11 ). In this case, the display controller  42  causes the display unit  6  to display the image magnified as shown in  FIG. 11E , so that the traffic congestion may be recognized as occurring on the designated road on the road network. 
     On the other hand, the reduction button  601  may be pushed as illustrated in  FIGS. 11F ,  11 D and  11 B. If this is the case, the display controller  42  controls the display unit  6 , reducing the image from the size shown in  FIG. 11G , to the size shown in  FIG. 11C , and further to the size shown in  FIG. 11A . 
       FIGS. 12A to 12C  are diagrams explaining an exemplary rotation process. When the rotation button  604  is operated as shown in  FIG. 12B , the display controller  42  performs the rotation process, rotating an image shown in  FIG. 12A  clockwise by 90°, to such a position as shown in  FIG. 12C . Note that the display controller  42  has another function of performing a 3D rotation process to rotate a 3D image, by first determining an origin for the road image data and vehicle image data and then moving the apices of the 3D image around the origin thus determined. 
     Furthermore, the display controller  42  can cause the display unit  6  to display, on its screen, not only the data representing the above-mentioned prediction result, but also the traffic volume data, acquired from the vehicle sensors  30  in the past, the average speed data about the vehicles at each link, and similar data, all acquired from the vehicle sensors  30  in the past. 
     Configured as described above, the system according to this embodiment can perform the microsimulation method. The system can therefore achieve traffic simulation based on the road parameters and model parameters (i.e., initial-value parameters) that define a road network. The system can thus microscopically predict a traffic condition (i.e., traffic flow and traffic congestion) on any object road. In the system, the display unit  6  can display, on its screen, the result of simulation, i.e., the microscopically predicted behavior of each vehicle running on the object road. 
     In this case, the system according to this embodiment performs the ordinary reproduction process, the reproduction process on the time axis (including sliding process and fast-feed process), and the various display processes including a magnification process, a reduction process and a rotation process. Performing these processes, the system can display the result of simulation in various patterns on the screen of the display unit  6 . In other words, the system can display the traffic condition (including traffic congestion) on the object road in various patterns. The manager of the traffic control system  2  and the person in charge of designing roads can therefore easily understand the predicted traffic condition on the object road. 
     The system according to this embodiment can easily predict a traffic congestion on the object road, which may result from the trouble in a vehicle on the road or from a traffic accident on the road, and an unusual traffic condition on a toll road, which may result from the closing of a toll gate or the limitation to the number of vehicles allowed to pass through the toll gate. Hence, the system enables those concerned to make decisions to moderate or prevent the traffic congestion, within a shorter time than before. 
     Other Embodiment 
       FIG. 13  is a block diagram that shows the configuration of a system  10  according to another embodiment of this invention. 
     This system  10  has a configuration including a vehicle-mounted device  52 , a communications device  53 , and a data conversion unit  54 . The vehicle-mounted device  52  is mounted in a vehicle  51 . The communications device  53  is configured to perform communication with the vehicle-mounted device  52 . The system  10  is identical to the system of  FIG. 1  in any other structure aspect, and will not be described in detail. 
     The vehicle-mounted device  52  includes a wireless communications unit, an intra-vehicle sensor, a storage unit, and a controller. The wireless communications unit is configured to transmit the data about the vehicle  51  (hereinafter called “vehicle data”). The controller causes the wireless communications unit to transmit, by radio, the data stored in the storage unit to the communications device  53 . The data represents the model of the vehicle  51  and the data measured by the intra-vehicle sensor. The intra-vehicle sensor measures the time the vehicle  51  has run on each road segment and the average speed of the vehicle  51 , and outputs the data items representing the time and the average speed, respectively, to the controller. 
     The communications device  53  is installed, for example, on one side of the road. The device  53  collects the data items transmitted from the vehicle-mounted device  52  provided in each vehicle  15  running on the road and transmits these data items to the traffic simulator  4  via the network  3 . The communications device  53  is a dedicated short-range communications (DSRC) device and performs wireless communication that is either radio or optical communication. 
     The data conversion unit  54  is a component incorporated in the traffic simulator  4  and implemented by a computer system. The data conversion unit  54  receives the vehicle data from the communications device  53  and converts this data to traffic-related data, which will be used in the traffic simulation the traffic simulator  4  performs. The data conversion unit  54  supplies the traffic-related data to the road-network generation unit  11 . The road-network generation unit  11  performs the above-mentioned process on the traffic-related data. Alternatively, the unit  11  may receive the vehicle data from the data conversion unit  54  and may store this data in the internal storage unit  8 , without processing the data at all. 
     The system according to this embodiment can perform, in sequence, the processes related to the traffic simulation, thereby achieving the same advantages as the system of  FIG. 1 . Moreover, the system according to this embodiment can acquire, from each vehicle, the vehicle data that represents the behavior of the vehicle actually running on any road. Performing traffic simulation using the data about the vehicles actually running on the road, the traffic simulator  4  can predict traffic congestion on the road at high accuracy. 
     In addition, the traffic simulator  4  can use the data actually acquired from the vehicles, simulating the behavior of each vehicle. The system  10  can therefore help to verify traffic accidents on the basis of the data acquired immediately after the accidents. 
     Additional advantages and modifications will readily occur to those skilled in the art. Therefore, the invention in its broader aspects is not limited to the specific details and representative embodiments shown and described herein. Accordingly, various modifications may be made without departing from the spirit or scope of the general inventive concept as defined by the appended claims and their equivalents.