Patent Publication Number: US-2016245648-A1

Title: Computer product, unevenness analysis method, and unevenness analyzer

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This is a continuation application of International Application PCT/JP2014/079894 filed on Nov. 11, 2014 which claims priority from a Japanese Patent Application No. 2013-235489 filed on Nov. 13, 2013, the contents of which are incorporated herein by reference. 
    
    
     FIELD 
     The embodiments discussed herein are related to a computer product, an unevenness analysis method, an unevenness analyzer. 
     BACKGROUND 
     Road surfaces are degraded by the load of vehicles such as automobiles and motorcycles, the forces of nature and aging whereby, unevenness may occur. For example, obstacles such cracks or depressions in road surfaces or cracks resulting from an earthquake cause unevenness in a road surface. Unevenness in a road surface affects the safety of vehicles traveling on the road surface and therefore, is desirably detected and remediated at an early stage. 
     According to a related technique, for example, an accelerometer is equipped on a vehicle, vibration of the vehicle during travel is measured as acceleration, and road surface unevenness is analyzed from the measured acceleration. For example, according to another technique, a first acceleration in an upward and downward direction at a spring and a second acceleration in an upward and downward direction at a spring are detected and corrected, and based on the corrected first and second acceleration, an index representing the flatness of a road surface is obtained. For an example of a related technique, refer to Japanese Laid-Open Patent Publication No. 2005-315675. 
     Nonetheless, with the conventional techniques, a problem arises in that detection of road surface unevenness is difficult. For example, even when the state of the road surface unevenness is the same, if the traveling state of the vehicle differs, the measured value obtained by an accelerometer equipped on the vehicle differs. More specifically, for example, when a vehicle is accelerating or decelerating, vertical movement is larger than when the vehicle is traveling at a constant speed and the measured acceleration value of the vehicle tends to be larger. Therefore, if the same measuring threshold is used to detect road surface unevenness without taking into consideration the traveling state of the vehicle, the accuracy of the unevenness detection may decrease. 
     SUMMARY 
     According to an aspect of an embodiment, a non-transitory, computer-readable recording medium stores therein an unevenness analysis program that causes a computer to perform based on an analysis parameter, analysis of motion data of a mobile object and analysis of unevenness of a road surface traveled by the mobile object. The unevenness analysis program causes the computer to execute a process including identifying based on a motion status of the mobile object indicated by the motion data, first motion data that is one of motion data for a predetermined period from a stopped state of the mobile object and motion data for a predetermined distance from the stopped state of the mobile object; and performing even when the motion data of the mobile object indicates movement at a same speed, and with respect to second motion data that belongs to the identified first data, comparison with third motion data that does not belong to the identified first motion data, and executing detection of unevenness of the road surface by a reduced sensitivity. 
     The object and advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the claims. 
     It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are not restrictive of the invention. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a diagram depicting an example of an unevenness analysis method according to a first embodiment, for road surfaces; 
         FIG. 2  is a diagram depicting an example of system configuration of a system  200 ; 
         FIG. 3  is a block diagram of an example of hardware configuration of an unevenness analyzer  201 ; 
         FIG. 4  is a block diagram of an example of hardware configuration of a travel data measuring device  202 ; 
         FIG. 5  is a diagram depicting one example of travel data  500 ; 
         FIG. 6  is a diagram depicting one example of the contents of analysis parameters  600 ; 
         FIG. 7  is a block diagram of an example of functional configuration of the unevenness analyzer  201 ; 
         FIG. 8  is a flowchart of an example of a procedure of a road surface unevenness analysis process by the unevenness analyzer  201 ; 
         FIG. 9  is a flowchart of an example of a procedure of a vertical acceleration correction process by the unevenness analyzer  201 ; 
         FIG. 10  is a flowchart of an example of a procedure of a brake section identifying process by the unevenness analyzer  201 ; 
         FIG. 11  is a flowchart of an example of a procedure of an accelerator section identifying process by the unevenness analyzer  201 ; 
         FIG. 12  is a diagram depicting an example of travel data  1200  in the brake section identifying process by the unevenness analyzer  201 ; and 
         FIG. 13  is a diagram depicting an example of travel data  1300  in the accelerator section identifying process by the unevenness analyzer  201 . 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Embodiments of an unevenness analysis program, an unevenness analysis method, an unevenness analyzer, and a recording medium according to the present invention will be described in detail with reference to the accompanying drawings. 
       FIG. 1  is a diagram depicting an example of the unevenness analysis method according to a first embodiment, for road surfaces. In  FIG. 1 , an unevenness analyzer  100  is a computer that based on an analysis parameter, analyzes motion data of a mobile object  110  and analyzes the unevenness of a road surface traveled by the mobile object  110 . 
     Here, the mobile object  110  is an object capable of powered motion on a road surface by, for example, an internal combustion engine and human power. More specifically, for example, the mobile object  110  is a vehicle such as an automobile, a motorcycle, and a bicycle that uses wheels to move on a road surface, or a snowmobile that uses rails to move on the surface of snow. Further, a road surface is the surface of a road. A road surface further includes snow surfaces and ice surfaces. 
     Road surface unevenness is an unlevel portion on a road surface. For example, in an uneven road surface, depressions and cracks occurring from degradation of the road surface consequent to the passage of time and vehicular load are present. Further, an uneven road surface has cracks caused by natural forces such as earthquakes, debris such as rocks put on the road by natural forces or human actions, and artificially created objects. Artificially created unevenness, for example, includes crosswalks painted on road surfaces and the like. 
     Motion data of the mobile object  110  is data that indicates the motion status of the mobile object  110 . The motion status of the mobile object  110  represents changes in the moving state of the mobile object  110 . The moving state, for example, may be a stopped state, an accelerating state, a decelerating state, a constant speed state, and the like. The stopped state is when the mobile object  110  is stopped, i.e., the speed of the mobile object  110  is 0. The accelerating state is when the velocity of the mobile object  110  increasing. The decelerating state is when the velocity of the mobile object  110  is decreasing. The constant speed state is when the speed of the mobile object  110  is substantially constant. 
     The motion data of the mobile object  110  includes, for example, information such as measurement position, measurement time, a measured acceleration value obtained periodically or on an irregular basis by an accelerometer equipped on the mobile object  110 . Further, acceleration of the mobile object  110 , for example, may be acceleration in a longitudinal direction of the mobile object  110 , acceleration in a lateral direction of the mobile object  110 , and acceleration in a vertical direction of the mobile object  110 . Further, the accelerometer may be a vibration sensor or some other similar sensor that senses movement. 
     Acceleration in the respective directions, for example, is measured by sensors configured to measure acceleration in the respective directions. Further, for example, the unevenness analyzer  100  may measure the longitudinal, lateral, and vertical acceleration of the mobile object  110  by performing vector analysis of the measured values obtained by sensors configured to measure acceleration in oblique directions of the mobile object  110 . 
     An analysis parameter is a parameter for analyzing road surface unevenness from motion data of the mobile object  110 . The analysis parameter includes a measuring threshold of the accelerometer. The measuring threshold of the accelerometer is a threshold used by the unevenness analyzer  100  to detect road surface unevenness. The unevenness analyzer  100 , for example, compares vertical acceleration of the mobile object  110  and the measuring threshold of the accelerometer, and when the absolute value of vertical acceleration is greater than the measuring threshold of the accelerometer, determines that the road surface is uneven. 
     In the description hereinafter, description will be given taking a vehicle such as an automobile, a motorcycle, a bicycle, and the like as one example of the mobile object  110 . Further, in the description hereinafter, the mobile object  110  will be indicated as “vehicle  110 ”, and the motion data of the mobile object  110  will be indicated as “travel data of the vehicle  110 ”. 
     Here, when the vehicle  110  is traveling in an urban area, there are sections where the speed of the vehicle  110  has to be reduced or the vehicle  110  has to be stopped consequent to other vehicles  110  or traffic signals. Therefore, during travel, the travel status of the vehicle  110  transitions through various states such as the stopped state, the accelerating state, the decelerating state, and the constant speed state. 
     On the other hand, even when the state of the road surface unevenness is the same, if the travel status of the vehicle  110  differs, the measured value obtained by the accelerometer equipped on the vehicle  110  may differ. Therefore, if road surface unevenness is detected using the same measuring threshold without taking the travel status of the vehicle  110  into consideration, the accuracy of unevenness detection may decrease. 
     For instance, when the vehicle  110  is accelerating or decelerating, vertical movement becomes larger than when the vehicle  110  is traveling at a constant speed and therefore, the measured value of vertical acceleration of the vehicle  110  tends to be larger. More specifically, for example, when the vehicle  110  is traveling 30 km/h on a road and is accelerating having transitioned from the stopped state to the accelerating state, the vertical acceleration tends to be greater than the acceleration when the vehicle  110  is traveling at a constant speed of 30 km/h on the same road. Therefore, for example, if the vehicle  110  is assumed to be traveling at a constant speed of 30 km/h and the measuring threshold of the accelerometer is defined, road surface unevenness may be errantly detected when the vehicle  110  is accelerating from the stopped state and traveling at 30 km/h on a flat road. 
     Thus, in the first embodiment, the unevenness analyzer  100  executes unevenness detection by reducing the sensitivity of road surface unevenness detection when the traveling vehicle  110  is accelerating from a stopped state or decelerating state to a stopped state to be lower than that for other states. As a result, the unevenness analyzer  100  can analyze road surface unevenness with high accuracy by taking into consideration the effects of increasing acceleration with respect to the travel status of the vehicle  110 . Hereinafter, an example of an unevenness analysis process of the unevenness analyzer  100  will be described. 
     (1) The unevenness analyzer  100  obtains travel data of the vehicle  110 . The travel data of the vehicle  110 , for example, is information that includes the acceleration of the vehicle  110  measured at a constant period or at a constant distance by the accelerometer equipped on the vehicle  110 . In the example depicted in  FIG. 1 , the unevenness analyzer  100  obtains travel data that includes the acceleration of the vehicle  110  measured at measuring points P 1  to Pn. The accelerometer may be provided in the unevenness analyzer  100  or may be provided on the vehicle  110 . 
     (2) Based on the travel status of the vehicle  110  indicated by the obtained travel data, the unevenness analyzer  100  identifies travel data for a predetermined distance or travel data for a predetermined period from a stopped state of the vehicle  110 . 
     Here, travel data for a predetermined period (or within a predetermined distance) from a stopped state of the vehicle  110 , for example, is travel data measured during a period (or, a distance) of a section where the vehicle  110  is accelerating and the travel status of the vehicle  110  transitions from a stopped state to an accelerating state, and transitions from the accelerating state to a constant speed state. Alternatively, the travel data is travel data measured during a period (or distance) of a section where the vehicle  110  is decelerating and the travel status of the vehicle  110  transitions from the decelerating state to a stopped state. 
     Further, travel data for a predetermined period (or, a predetermined distance) from a stopped state of the vehicle  110  may be travel data measured during a predetermined period (or predetermined distance) from the stopped state when the travel status of the vehicle  110  transitions from a stopped state to an accelerating state. Alternatively, the travel data may be travel data measured during a predetermined period (or predetermined distance) until a stopped state when the travel status of the vehicle  110  transitions from a decelerating state to the stopped state. The predetermined period (or predetermined distance) in this case can be set arbitrarily and, for example, a value of several seconds (or, several meters) is set. 
     In the example depicted in  FIG. 1 , the travel status of the vehicle  110  changes between a stopped state, an accelerating state, a constant speed state, a decelerating state, and a stopped state. More specifically, for example, the travel status is a stopped state at point P 1 , an accelerating state from point P 1  to point P 3 , a constant speed state from point P 3  to point P(n−1), a decelerating state from point P(n−1) to point Pn, and a stopped state at point Pn. In this case, the unevenness analyzer  100  identifies travel data that includes acceleration from point P 1  to point P 3 , and from point P(n−1) to point Pn. 
     (3) The unevenness analyzer  100  makes comparison concerning the identified travel data and travel data not belonging to the identified travel data, and executes detection of road surface unevenness by a reduced sensitivity, even when the travel data of the vehicle  110  indicates movement at the same speed. Here, detection of road surface unevenness is a process of comparing vertical acceleration of the vehicle  110  and the measuring threshold of the accelerometer, and determining that unevenness is present in a road surface when the absolute value of the vertical acceleration is greater than the measuring threshold of the accelerometer. 
     Further, a lowering of the sensitivity of road surface unevenness detection is making a condition for the unevenness analyzer  100  to determine that unevenness of a road surface stricter. For example, concerning travel data belonging to the identified travel data, the unevenness analyzer  100  may increase the measuring threshold of the accelerometer and compare the increased measuring threshold and the vertical acceleration to thereby, execute detection of road surface unevenness. 
     Further, the unevenness analyzer  100  may set travel data belonging to the identified travel data to be excluded from road surface unevenness detection. The unevenness analyzer  100  may make the absolute value of the vertical acceleration of the identified travel data smaller and compare the absolute value of the vertical acceleration for which the absolute value has been made smaller and the measuring threshold of the accelerometer to thereby, execute detection of road surface unevenness. 
     As described, according to the unevenness analyzer  100  of the first embodiment, unevenness detection can be executed by a sensitivity that has been set to be lower than for other travel data and that is based on travel data for a predetermined distance or travel data for a predetermined period from a stopped state of the vehicle  110 . 
     For example, according to the unevenness analyzer  100 , when the vehicle  110  is in an accelerating state from a stopped state, or a decelerating state to a stopped state, unevenness detection can be executed with the sensitivity of road surface unevenness detection being set lower than for other states. As a result, the unevenness analyzer  100  can analyze road surface unevenness with a high accuracy by taking into consideration the effects of the travel status of the vehicle  110  on the detection of road surface unevenness. 
     An example of system configuration of a system  200  according to a second embodiment will be described. Portions identical to those described in the first embodiment are given the same reference numerals used in the first embodiment and description thereof is omitted hereinafter. 
       FIG. 2  is a diagram depicting an example of system configuration of the system  200 . In  FIG. 2 , the system  200  includes an unevenness analyzer  201 , a travel data measuring device  202  (2 devices in the example depicted in  FIG. 2 ), and a vehicle  203  (2 vehicles in the example depicted in  FIG. 2 ). In the system  200 , the unevenness analyzer  201  and the travel data measuring devices  202  are connected through a wired or a wireless network  220 . The network  220 , for example, is a local area network (LAN), a wide area network (WAN), the Internet, and the like. 
     The unevenness analyzer  201  is a computer that analyzes unevenness of a road surface traveled by the vehicles  203 . More specifically, for example, the unevenness analyzer  201  is a server, a personal computer (PC), and the like. 
     The travel data measuring device  202  is a computer that measures travel data of the vehicle  203 . More specifically, for example, the travel data measuring device  202  may be a communications device such as a smartphone, a mobile telephone, a tablet PC, and the like, and further may be a vehicle-equipped device such as a car navigation device equipped on the vehicle  203 . 
     The vehicle  203  is an automobile, a motorcycle, a bicycle, and the like. Travel data of the vehicle  203  will be described in detail with reference to  FIG. 5 . The unevenness analyzer  201  and the travel data measuring devices  202  correspond to the unevenness analyzer  100  depicted in  FIG. 1  and the vehicles  203  correspond to the mobile object  110  (the vehicle  110 ) depicted in  FIG. 1 . 
       FIG. 3  is a block diagram of an example of hardware configuration of the unevenness analyzer  201 . In  FIG. 3 , the unevenness analyzer  201  has a central processing unit (CPU)  301 , memory  302 , an interface (I/F)  03 , a disk drive  304 , and a disk  305 , respectively connected by a bus  300 . 
     Here, the CPU  301  governs overall control of the unevenness analyzer  201 . The memory  302 , for example, includes read-only memory (ROM), random access memory (RAM), and flash ROM. More specifically, for example, the flash ROM and the ROM store various types of programs, and the RAM is used as a work area of the CPU  301 . Programs stored in the memory  302  are loaded onto the CPU  301 , whereby the CPU  301  executes encoded processes. 
     The I/F  303  is connected to the network  220  through a communications line and is connected to other computers (for example, the travel data measuring device  202  depicted in  FIG. 2 ) via the network  220 . The I/F  303  administers an internal interface with the network  220  and controls the input and output of data from other computers. The I/F  303 , for example, may be a modem, a LAN adapter, and the like. 
     The disk drive  304  is a control device that under the control of the CPU  301 , controls the reading and writing of data with respect to the disk  305 . The disk drive  304 , for example, may be a magnetic disk drive, an optical disk drive, and the like. The disk  305  is a medium that stores data written thereto under the control of the disk drive  304 . For example, when the disk drive  304  is a magnetic disk drive, the disk  305  may be a magnetic disk. Further, the unevenness analyzer  201  may have a solid state drive (SSD) in place of the disk drive  304 . When the disk drive  304  is a SSD, in place of the disk  305 , semiconductor memory can be used. Further, the unevenness analyzer  201  may have a SSD in addition to the disk drive  304 . In addition to the configuration above, the unevenness analyzer  201  may further have, for example, a keyboard, a mouse, a display, and the like. 
       FIG. 4  is a block diagram of an example of hardware configuration of the travel data measuring device  202 . In  FIG. 4 , the travel data measuring device  202  has a CPU  401 , memory  402 , a disk drive  403 , a disk  404 , a display  405 , an input device  406 , an I/F  407 , a timer  408 , a global positioning system (GPS) unit  409 , and an accelerometer  410 . The respective components are connected by a bus  400 . 
     Here, the CPU  401  governs overall control of the travel data measuring device  202 . The memory  402 , for example, includes ROM, RAM, and flash ROM. More specifically, for example, the flash ROM and ROM store various types of programs; and the RAM is used as a work area of the CPU  401 . Programs stored in the memory  402  are loaded onto the CPU  401  whereby, the CPU  401  executes encoded processes. 
     The disk drive  403  is a control device that under the control of the CPU  401 , controls the reading and writing of data with respect to the disk  404 . The disk drive  403  may be, for example, a magnetic disk drive, an optical disk drive, and the like. The disk  404  is a medium that stores data written thereto under the control of the disk drive  403 . For example, when the disk drive  403  is a magnetic disk drive, the disk  404  may be a magnetic disk. Further, the travel data measuring device  202  may have a SSD in place of the disk drive  403 . When the disk drive  403  is a SSD, in place of the disk  404 , semiconductor memory can be used. Further, the travel data measuring device  202  may have a SSD in addition to the disk drive  403 . 
     The display  405  displays data such as documents, images, and functional information in addition to a cursor, icons, and toolboxes. The display  405 , for example, may be a CRT, a TFT liquid display, a plasma display, and the like. The input device  406  has keys for imputing text, numerals, instructions, and the like; and performs data input. The input device  406  may be a touch panel input pad, a numeric pad, and the like. 
     The I/F  407  is connected to the network  220  through a communications line and is connected to other devices (for example, the unevenness analyzer  201  depicted in  FIG. 2 ) via the network  220 . The I/F  407  administers an internal interface with the network  220 , and controls the input and output of data from external devices. 
     The GPS unit  409  receives radio waves (GPS signals) from GPS satellites, and outputs position information indicating the position of the travel data measuring device  202  (the vehicle  203 ). The position information of the travel data measuring device  202  (the vehicle  203 ), for example, is information specifying one point on earth by latitude, longitude, altitude, etc. 
     The accelerometer  410  outputs tri-axial (longitudinal, lateral, and vertical) acceleration of the travel data measuring device  202 . The above configuration of the travel data measuring device  202 , for example, may omit the timer  408 , the GPS unit  409 , and the accelerometer  410 . In this case, the travel data measuring device  202 , for example, may obtain from a sensor equipped on the vehicle  203 , the acceleration of the vehicle  203 , the time, position, etc. 
       FIG. 5  is a diagram depicting one example of the travel data  500 . In  FIG. 5 , the travel data  500  has fields for dates, times, latitudes, longitudes, speeds, GPS error, longitudinal acceleration, lateral acceleration, and vertical acceleration. The travel data  500  stores travel data information (for example, travel data information  500 - 1  to  500 - 7 ) as records consequent to information being set into the fields for respective time points during travel of the vehicle  203 . In the example depicted in  FIG. 5 , although the travel data information is measured at 0.5-second intervals, the travel data information can be measured at constant distance intervals. 
     Here, the date and the time are information that indicates the date and time that the information of the record was obtained. The date and time are measured by the timer  408  of the travel data measuring device  202 . The longitude and the latitude are information indicating the position of the vehicle  203  and are measured from GPS radio waves received by the GPS unit  409  of the travel data measuring device  202 . 
     The speed is information that indicates the speed of the vehicle  203  at the time indicated in the record. The unit of the speed is km/h. Here, the travel data measuring device  202  need not directly measure the speed. For example, the travel data measuring device  202  can calculate the speed from the time, the longitude, and the latitude. The travel data measuring device  202  calculates the distance traveled by the vehicle  203 , from the longitude and latitude of the travel data information  500 - 1  and the longitude and latitude of the travel data information  500 - 2 . Further, the travel data measuring device  202  divides the calculated distance by the difference of the time of the travel data information  500 - 2  and the time of the travel data information  500 - 1  and thereby, calculates the speed. 
     The GPS error is error indicating the extent to which the latitude and longitude position information by the GPS signal may deviate. The longitudinal acceleration is information indicating longitudinal acceleration of the vehicle  203  at the time of the record. The lateral acceleration is information indicating lateral acceleration of the vehicle  203  at the time of the record. The vertical acceleration is information indicating vertical acceleration of the vehicle  203  at the time of the record. The unit of the longitudinal, lateral, and vertical acceleration, for example, is m/s 2 . 
     Longitudinal acceleration takes a negative value when the mobile object accelerating since a backward force is applied to the accelerometer  410 ; and takes a positive value when the mobile object is decelerating. Vertical acceleration takes a positive value when the mobile object is moving upward and takes negative value when the mobile object is moving downward. Further, lateral acceleration takes a positive value when the mobile is moving rightward and takes a negative value when the mobile object is moving leftward. Depending on the installation orientation of the travel data measuring device  202 , the positive and negative values of acceleration may be reversed. 
     The travel data  500  depicted in  FIG. 5  corresponds to the travel data of the vehicle  110  depicted in  FIG. 1 . The travel data  500 , for example, is stored to the disk  404  depicted in  FIG. 4 . 
       FIG. 6  is a diagram depicting one example of the contents of the analysis parameter  600 . The analysis parameter  600  has values of non-brake longitudinal acceleration Pb-a, non-accelerator longitudinal acceleration Pa-a, a 0-20 km/h_correction coefficient Ps-a, a 21-40 km/h_correction coefficient Ps-b, a 41-50 km/h_correction coefficient Ps-c, a 81+km/h_correction coefficient Ps-d, a brake correction coefficient Pb-b, an accelerator correction coefficient Pa-b, and a road surface unevenness detection threshold. The analysis parameter  600 , for example, is stored in the memory  302  or the disk  305  depicted in  FIG. 3 . 
     Here, the non-accelerator longitudinal acceleration Pa-a is a first threshold used for determining whether a measured section is an accelerator section. A measured section is a section that has multiple measuring points. The unevenness analyzer  201  identifies the travel status of the vehicle  203  for each measured section. 
     Travel status of the vehicle  203  is a traveling state of the vehicle  203  during the measured section. Traveling states, for example, include a stopped section, an accelerator section, a brake section, a constant speed section, and the like. The travel status of the vehicle  203  corresponds to the motion status of the mobile object  110  of the first embodiment. A stopped section is a section where the vehicle  203  is stopped, i.e., a section where the speed is 0. An accelerator section is a section where the vehicle  203  enters an accelerating state by the accelerator. A brake section is a section where the vehicle  203  enters a decelerating state by the brake. A constant speed section is a section where the vehicle  203  is traveling at a substantially constant speed. 
     The non-brake longitudinal acceleration Pb-a is a second threshold used for determining whether the measured section is a brake section. 
     The 0-20 km/h_correction coefficient Ps-a is a correction coefficient for vertical acceleration in a measured section where the vehicle  203  is in a constant speed state of 0-20 km/h. The 21-40 km/h_correction coefficient Ps-b, the 41-50 km/h_correction coefficient Ps-c, and the 81+km/h_correction coefficient Ps-d are similar correction coefficients. Between 51-80 km/h_correction is not performed and therefore, no corresponding correction coefficient exists. 
     The brake correction coefficient Pb-b is a correction coefficient for vertical acceleration in a brake section. The accelerator correction coefficient Pa-b is a correction coefficient for vertical acceleration in an accelerator section. The road surface unevenness detection threshold is a threshold for determining road surface unevenness. The unevenness analyzer  201  detects road surface unevenness by comparing the road surface unevenness detection threshold and vertical acceleration. For example, the unevenness analyzer  201  determines that unevenness is present in a road surface, when the vertical acceleration is greater than the road surface unevenness detection threshold. The road surface unevenness detection threshold corresponds to the measuring threshold of the accelerometer of the first embodiment. 
       FIG. 7  is a block diagram of an example of functional configuration of the unevenness analyzer  201 . In  FIG. 7 , the unevenness analyzer  201  is configured to include a receiving unit  701 , an identifying unit  702 , an executing unit  703 , and a display unit  704 . More specifically, for example, these functions are implemented by executing on the CPU  301 , a program stored in a storage apparatus such as the memory  302  and the disk  305  depicted in  FIG. 3 , or by the I/F  303 . Process results of the functional units, for example, are stored to a storage apparatus such as the memory  302  and the disk  305  depicted in  FIG. 3 . 
     The receiving unit  701  has a function of receiving the travel data  500  from the travel data measuring device  202 . The receiving unit  701  receives the travel data  500  when the unevenness analyzer  201  executes detection of road surface unevenness after the travel data measuring device  202  finishes obtaining the travel data  500  for the road surface. Further, when the unevenness analyzer  201  and the travel data measuring device  202  are connected by the network  220  which is wired, the receiving unit  701  may receive the travel data  500  from the travel data measuring device  202  in real-time. Thus, the receiving unit  701  can obtain the travel data  500  for detecting road surface unevenness. 
     The identifying unit  702  has a function of separating the travel data  500  received by the receiving unit  701  into measured sections, and for each measured section, identifying the travel status of the vehicle  203 . The identifying unit  702  by identifying the measured section to be one of a stopped section, a brake section, an accelerator section, and a constant speed section, identifies the travel status of the vehicle  203 . 
     The identifying unit  702  determines whether the vehicle  203  is in an accelerating state, based on a temporal change in the longitudinal acceleration included in the travel data  500  for a first measured section. The identifying unit  702 , when determining the vehicle  203  to be in an accelerating state, identifies the first measured section to be an accelerator section. The identifying unit  702  determines whether the vehicle  203  is in a stopped state, based on a temporal change in the position included in the travel data  500  for a second measured section measured before the travel data  500  for the first measured section. The identifying unit  702 , when determining the vehicle  203  to be in a stopped state, identifies the second measured section to be a stopped section. 
     The identifying unit  702  identifies whether the vehicle  203  is in a decelerating state, based on a temporal change in the longitudinal acceleration included in the travel data  500  for the first measured section. The identifying unit  702 , when determining the vehicle  203  to be in a decelerating state, identifies the first measured section to be a brake section. The identifying unit  702  determines whether the vehicle  203  is in a stopped state, based on a temporal change in the position included in the travel data  500  for a second measured section measured after the travel data  500  for the first measured section. The identifying unit  702 , when determining the vehicle  203  to be in a stopped state, identifies the second measured section to be a stopped section. The identifying unit  702  identifies sections other than brake sections, accelerator sections, and stopped sections to be a constant speed section. 
     The identifying unit  702  can determine that the vehicle  203  is in a stopped state, when there is no change in the positions included in the travel data  500  for the measured section. Further, when each longitudinal acceleration included in the travel data  500  for a measured section is the non-accelerator longitudinal acceleration Pa-a or less, the identifying unit  702  can determine that the vehicle  203  is in an accelerating state. When each longitudinal acceleration included in the travel data  500  for a measured section is the non-brake longitudinal acceleration Pb-a or greater, the identifying unit  702  can determine that the vehicle  203  is in a decelerating state. 
     The executing unit  703  has a function of detecting road surface unevenness by a sensitivity that corresponds to the travel status of the vehicle  203  identified by the identifying unit  702 . 
     When the measured section has been identified to be a brake section, the executing unit  703  multiplies the vertical acceleration included in the travel data  500  and the brake correction coefficient Pb-b to reduce the absolute value of the vertical acceleration included in travel data  500 . Thereafter, the executing unit  703  compares the reduced absolute value of the vertical acceleration included in the travel data  500  and the road surface unevenness detection threshold to thereby, detect road surface unevenness. The executing unit  703 , for example, determines that road surface unevenness is present at a point indicated by the longitude and latitude, when the reduced absolute value of the vertical acceleration included in the travel data  500  is greater than the road surface unevenness detection threshold. 
     When the measured section has been identified to be a brake section, the executing unit  703  may increase the road surface unevenness detection threshold, compare the increased road surface unevenness detection threshold and the vertical acceleration included in the travel data  500 , and detect road surface unevenness. The executing unit  703  determines that road surface unevenness is present at a point indicated by the longitude and latitude, when the vertical acceleration included in the travel data  500  is greater than the increased road surface unevenness detection threshold. Further, when the measured section has been identified to be a brake section, the executing unit  703  may exclude the travel data  500  from the road surface unevenness detection. 
     When the measured section has been identified to be an accelerator section, the executing unit  703  multiplies the vertical acceleration included in the travel data  500  and the accelerator correction coefficient Pa-b to reduce the absolute value of the vertical acceleration included in the travel data  500 . Thereafter, the executing unit  703  compares the reduced absolute value of the vertical acceleration included in the travel data  500  and the road surface unevenness detection threshold to thereby, detect road surface unevenness. The executing unit  703 , for example, determines that road surface unevenness is present at a point indicated by the longitude and latitude, when the reduced absolute value of the vertical acceleration included in the travel data  500  is greater than the road surface unevenness detection threshold. 
     Further, when the measured section has been identified to be an accelerator section, the executing unit  703  can increase the road surface unevenness detection threshold, compare the increased road surface unevenness detection threshold and the vertical acceleration included in the travel data  500 , and detect road surface unevenness. The executing unit  703  determines that road surface unevenness is present at a point indicated by the longitude and latitude, when the vertical acceleration included in the travel data  500  is greater than the increased road surface unevenness detection threshold. Further, when the measured section has been identified to be an acceleration section, the executing unit  703  can exclude the travel data  500  from the road surface unevenness detection. 
     Here, even when the state of the road surface unevenness is the same, if the speed of the vehicle  203  differs, the value measured by the accelerometer  410  equipped on the vehicle  203  may differ. Therefore, if road surface unevenness is detected using the same measuring threshold without taking the travel status of the vehicle  203  into consideration, the accuracy of unevenness detection may decrease. 
     For example, since the lower the speed of the vehicle  203  is, the smaller the movement is, the measured value of vertical acceleration of the vehicle  203  tends to be smaller. More specifically, for example, the vertical acceleration of the vehicle  203  traveling at 60 km/h on a road surface having a depression tends to be greater than the vertical acceleration of the vehicle  203  when the vehicle  203  travels on the same road surface at 30 km/h. 
     For example, the measuring threshold of the accelerometer  410  is assumed to be defined under the assumption that the vehicle  203  travels at a constant speed of 60 km/h. In this case, when the vehicle  203  travels at a constant speed of 30 km/h on a road surface having a depression, the vertical acceleration becomes small compared to a case of travel at 60 km/h and as a result, the road surface unevenness may not be detected. 
     Thus, by a sensitivity that corresponds to the speed of the vehicle to execute unevenness detection with respect to a road surface traveled by the vehicle, the executing unit  703  can mitigate the effects of the travel status of the vehicle  203  on the detection of road surface unevenness and analyze road surface unevenness accurately. 
     When the measured section has been identified to be a constant speed section, the executing unit  703  multiplies the vertical acceleration included in the travel data  500  and a correction coefficient (Ps-a to Ps-d) that corresponds to the speed of the vehicle  203  to reduce or increase the absolute value of the vertical acceleration included in the travel data  50 . Thereafter, the executing unit  703  compares the reduced or increased absolute value of the vertical acceleration included in the travel data  500  and the road surface unevenness detection threshold to thereby, detect road surface unevenness. The executing unit  703 , for example, determines that road surface unevenness is present at a point indicated by the longitude and latitude, when the reduced or increased absolute value of the vertical acceleration included in the travel data  500  is greater than the road surface unevenness detection threshold. 
     When the speed of the vehicle  203  is 50 km/h or less, the executing unit  703  increases the absolute value of the vertical acceleration included in the travel data  500  and when the speed of the vehicle  203  is 81 km/h or greater, the executing unit  703  reduces the absolute value of the vertical acceleration included in the travel data  500 . 
     Further, when the measured section has been identified to be a constant speed section, the executing unit  703  can correct the road surface unevenness detection threshold according to the speed of the vehicle  203 , compare the corrected road surface unevenness detection threshold and the vertical acceleration included in the travel data  500 , and detect road surface unevenness. The executing unit  703  determines that road surface unevenness is present at a point indicated by the longitude and latitude, when the vertical acceleration included in the travel data  500  is greater than the corrected road surface unevenness detection threshold. 
     When the speed of the vehicle  203  is 50 km/h or less, the executing unit  703  reduces the road surface unevenness detection threshold and when the speed of the vehicle  203  is 81 km/h or greater, the executing unit  703  increases the road surface unevenness detection threshold. 
     When the measured section has been identified to be a stopped section and the subsequent measured section has been identified to be an accelerator section, the executing unit  703  detects road surface unevenness for the stopped section similarly in the case of an accelerator section. Further, when the measured section has been identified to be a stopped section and the previous measured section has been identified to be a brake section, the executing unit  703  detects road surface unevenness for the stopped section similarly in the case of a brake section. 
     The display unit  704  has function of displaying locations of road surface unevenness detected by the executing unit  703 . More specifically, for example, the display unit  704  executes display to a display, output of an alarm, print out to a printer, and transmission to an external terminal. 
       FIG. 8  is a flowchart of an example of a procedure of the road surface unevenness analysis process by the unevenness analyzer  201 . In the flowchart depicted in  FIG. 8 , the receiving unit  701  receives the travel data  500  from the travel data measuring device  202  (step S 801 ). The identifying unit  702  corrects the vertical acceleration included in the received travel data  500  (step S 802 ). Correction of the vertical acceleration is explained in detail with reference to  FIGS. 9, 10, and 11 . 
     The executing unit  703  compares the corrected vertical acceleration and the road surface unevenness detection threshold, and detects road surface unevenness (step S 803 ). The executing unit  703  determines that road surface unevenness is present at a point indicated by the longitude and latitude, when the corrected vertical acceleration is greater than the road surface unevenness detection threshold. The display unit  704  displays locations of detected road surface unevenness (step S 804 ), ending the series of operations according to the flowchart. By executing the flowchart, the unevenness analyzer  201  detects road surface unevenness and displays locations of detected road surface unevenness. 
       FIG. 9  is a flowchart of an example of a procedure of a vertical acceleration correction process by the unevenness analyzer  201 . In the flowchart depicted in  FIG. 9 , the identifying unit  702  calculates the brake acceleration determining product Pb-c (step S 901 ). More specifically, for example, Pb-c is calculated by equation (1) using the non-brake longitudinal acceleration Pb-a, where n is a measuring point count of measuring points in a measured section. 
         Pb - c=Pb - a×n   (1)
 
     The identifying unit  702  calculates the accelerator acceleration determining product Pa-c (step S 902 ). More specifically, for example, Pa-c is calculated by equation (2) using the non-accelerator longitudinal acceleration Pa-a, where n is the measuring point count of the measured section. 
         Pa - c=Pa - a×n   (2)
 
     The identifying unit  702  obtains the first section as the measured section (step S 903 ). The identifying unit  702  sums the longitudinal acceleration in the obtained measured section and sets the resulting sum as Σ (step S 904 ). The identifying unit  702  determines whether Σ is greater than Pb-c, as a rough determination of whether the obtained measured section is a brake section (step S 905 ). If Σ is greater than Pb-c (step S 905 : YES), the identifying unit  702  determines whether each longitudinal acceleration in the obtained measured section is the non-brake longitudinal acceleration Pb-a or greater, as a determination of whether the obtained measured section is a brake section (step S 906 ). If each longitudinal acceleration in the obtained measured section is the non-brake longitudinal acceleration Pb-a or greater (step S 906 : YES), the obtained measured section is a brake section and therefore, the unevenness analyzer  201  transitions to operations in a flowchart that is depicted in  FIG. 10  and depicts an example of a brake section identifying process. If each longitudinal acceleration in the obtained measured section is not the non-brake longitudinal acceleration Pb-a or greater (step S 906 : NO), the obtained measured section is not a brake section and therefore, the unevenness analyzer  201  transitions to step S 907  for identification of an accelerator section. 
     If Σ is not greater than Pb-c (step S 905 : NO), the identifying unit  702  determines whether Σ is less than Pa-c, as a rough determination of whether the obtained measured section is an accelerator section (step S 907 ). If Σ is less than Pa-c (step S 907 : YES), the identifying unit  702  determines whether each longitudinal acceleration in the obtained measured section is the non-accelerator longitudinal acceleration Pa-a or less, as a determination of whether the obtained measured section is an accelerator section (step S 908 ). If each longitudinal acceleration in the obtained measured section is the non-accelerator longitudinal acceleration Pa-a or less (step S 908 : YES), the obtained measured section is an accelerator section and therefore, the unevenness analyzer  201  transitions to operations in a flowchart that is depicted in  FIG. 11  and depicts an example of an accelerator section identifying process. If each longitudinal acceleration in the obtained measured section is not the non-accelerator longitudinal acceleration Pa-a or less (step S 908 : NO), the obtained measured section is not an accelerator section and therefore, the unevenness analyzer  201  transitions to step S 909 . 
     If Σ is not less than Pa-c (step S 907 : NO), the identifying unit  702  calculates an average speed in the obtained measured section (step S 909 ). For example, the identifying unit  702  sums the speeds in the obtained measured section and divides by the measuring point count n of the measured section to calculate the average speed. The executing unit  703  corrects the vertical acceleration of each measuring point in the measured section according to the average speed (step S 910 ). More specifically, for example, when the average speed in the obtained measured section is 0 to 20 km/h, the executing unit  703  multiples the vertical acceleration at each measuring point in the measured section by the 0-20 km/h_correction coefficient Ps-a and corrects the vertical acceleration; and performs similar operations in cases where the average speed in the obtained measured section is 21 to 40 km/h, 41 to 50 km/h, or 81+km/h. When the average speed in the obtained measured section is 51 to 80 km/h, the executing unit  703  does not correct the vertical acceleration. 
     The identifying unit  702  determines whether processing has been completed for all sections (step S 911 ). If processing has not been completed for all sections (step S 911 : NO), the identifying unit  702  obtains the next section as the measured section (step S 912 ), returns to step S 904 , and performs processing with respect to the obtained measured section. If processing has been completed for all sections (step S 911 : YES), the identifying unit  702  ends the vertical acceleration correction process, ending the series of operations in the flowchart. By executing the flowchart, the unevenness analyzer  201  identifies the measured section and when the measured section is not an accelerator section or a brake section, the unevenness analyzer  201  corrects the vertical acceleration according to the speed the vehicle  203 . 
       FIG. 10  is a flowchart of an example of a procedure of the brake section identifying process by the unevenness analyzer  201 . In the flowchart depicted in  FIG. 10 , since the section has been identified to be a brake section, the executing unit  703  multiplies the vertical acceleration at each measuring point in the section by the brake correction coefficient Pb-b and corrects the vertical acceleration (step S 1001 ). The identifying unit  702  obtains the next section as the measured section (step S 1002 ). 
     The identifying unit  702  identifies the measured section to be a brake section, a stopped section, or neither (step S 1003 ). The identifying unit  702  identifies the obtained measured section to be a brake section, when each longitudinal acceleration in the obtained measured section is the non-brake longitudinal acceleration Pb-a or greater. Further, the identifying unit  702  identifies the obtained measured section to be a stopped section, when the latitude and longitude in the obtained measured section is continuously one half the measuring point count n of the obtained measured section or greater. The identifying unit  702  identifies the obtained measured section to be “neither” when the obtained measured section is identified to not be a brake section or a stopped section. 
     If the obtained measured section is identified to be a brake section (step S 1003 : brake section), the unevenness analyzer  201  returns to step S 1001 , and the identifying unit  702  corrects the vertical acceleration of the section. If the obtained measured section is identified to be a stopped section (step S 1003 : stopped section), the executing unit  703  multiplies the vertical acceleration at each measuring point in the stopped section by the brake correction coefficient Pb-b and corrects the vertical acceleration (step S 1004 ). Thereafter, the identifying unit  702  returns to step S 911  depicted in  FIG. 9 . 
     If the obtained measured section is identified to be “neither” (step S 1003 : neither), the identifying unit  702  returns to step S 907  depicted in  FIG. 9  and performs identification for an accelerator section, ending the series of operations in the flowchart. By executing the flowchart, the unevenness analyzer  201  performs identification for a brake section and a stopped section, and when the measured section is a brake section or a stopped section, the unevenness analyzer  201  corrects the vertical acceleration by the brake correction coefficient Pb-b. 
       FIG. 11  is a flowchart of an example of a procedure of the accelerator section identifying process by the unevenness analyzer  201 . In the flowchart depicted in  FIG. 11 , since the section has been identified to be an accelerator section, the executing unit  703  multiplies the vertical acceleration at each measuring point in the section by the accelerator correction coefficient Pa-b and corrects the vertical acceleration (step S 1101 ). The identifying unit  702  obtains the previous section as the measured section (step S 1102 ). 
     The identifying unit  702  determines whether the obtained measured section is a stopped section (step S 1103 ). The identifying unit  702  identifies the obtained measured section to be a stopped section when the latitude and longitude in the obtained measured section is continuously one half the measuring point count n of the obtained measured section or greater. 
     If the obtained measured section is identified to be a stopped section (step S 1103 : YES), the executing unit  703  multiplies the vertical acceleration at each measuring point in the stopped section by the accelerator correction coefficient Pa-b and corrects the vertical acceleration (step S 1104 ). Thereafter, the identifying unit  702  returns to step S 911  depicted in  FIG. 9 . 
     If the obtained measured section is identified to not be a stopped section (step S 1103 : NO), the identifying unit  702  returns to step S 911  depicted in  FIG. 9 , ending the series of operations in the flowchart. By executing the flowchart, the unevenness analyzer  201  performs identification for a stopped section, and when the measured section is an accelerator section or a stopped section, the unevenness analyzer  201  corrects the vertical acceleration by the accelerator correction coefficient Pa-b. 
       FIG. 12  is a diagram depicting an example of travel data  1200  in the brake section identifying process by the unevenness analyzer  201 . One example of brake section identification by the unevenness analyzer  201  will be described using the travel data  1200 . In the present example, processes related to an accelerator section will be omitted. 
     In the present example, the measuring point count n of the measured section is assumed to be 4 and the values indicated in  FIG. 6  will be used as the analysis parameter  600 . The travel data  1200  depicted in  FIG. 12  is a collection of the fields for brake section identification in the travel data  500  depicted  FIG. 5 . In  FIG. 12 , the travel data  1200  has fields for point names, longitudinal acceleration, vertical acceleration, latitude, and longitude, and stores travel data information (for example, travel data information  1200 - 1  to  1200 - 20 ) as records consequent to information being set into the respective fields. 
     Here, a point name is an identifier of a measuring point. k1-1 to k1-4, k2-1 to k2-4, k3-1 to k3-4, k4-1 to k4-4, k5-1 to k5-4 respectively correspond to one measured section. The travel data  1200  includes five measured sections. The longitudinal and the vertical acceleration; and the latitude and longitude respectively correspond to the same information as the longitudinal and the vertical acceleration; and the latitude and longitude in the travel data  500  depicted in  FIG. 5 . 
     The identifying unit  702  calculates the brake acceleration determining product Pb-c for the travel data  1200  depicted in  FIG. 12 , where Pb-a=1.1, n=4, and therefore, calculates: 
         Pb - c= 1.1×4=4.4
 
     The identifying unit  702  obtains the first section k1-1 to k1-4 as measured section#1. The identifying unit  702  calculates the sum Σ of the longitudinal acceleration in measured section#1. From the travel data information  1200 - 1  to  1200 - 4  in  FIG. 12 , Σ is: 
       Σ=0.3+0.2+1.2+1.0=2.7
 
     The identifying unit  702  compares the calculated Σ and Pb-c. Since Σ&gt;Pb-c is not true, the identifying unit  702  identifies measured section#1 to not be a brake section. 
     The identifying unit  702  obtains the next section k2-1 to k2-4 as measured section#2. The identifying unit  702  calculates the sum Σ of the longitudinal acceleration in measured section#2. From the travel data information  1200 - 5  to  1200 - 8  in  FIG. 12 , Σ is: 
       Σ=1.3+1.2+0.9+1.3=4.7
 
     The identifying unit  702  compares the calculated Σ and Pb-c. Since Σ&gt;Pb-c is true, the identifying unit  702  determines that measured section#2 may be a brake section. The identifying unit  702  determines whether each longitudinal acceleration in measured section#2 is the non-brake longitudinal acceleration Pb-a or greater. Since the longitudinal acceleration 0.9 in travel data information  1200 - 7  is not the Pb-a or greater, the identifying unit  702  identifies measured section#2 to not be a brake section. 
     The identifying unit  702  obtains the next section k3-1 to k3-4 as measured section#3. The identifying unit  702  calculates the sum Σ of the longitudinal acceleration in measured section#3. From the travel data information  1200 - 9  to  1200 - 12  depicted in  FIG. 12 , Σ is: 
       Σ=1.4+1.6+2.1+1.2=6.3
 
     The identifying unit  702  compares the calculated Σ and Pb-c. Since Σ&gt;Pb-c is true, the identifying unit  702  determines that measured section#3 may be a brake section. The identifying unit  702  determines whether each longitudinal acceleration in measured section#3 is the non-brake longitudinal acceleration Pb-a or greater. Since each longitudinal acceleration in measured section#3 is Pb-a or greater, the identifying unit  702  identifies measured section#3 to be a brake section. 
     The executing unit  703  multiplies each vertical acceleration included in the travel data information  1200 - 9  to  1200 - 12  by the brake correction coefficient Pb-b 0.3 and respectively corrects each to 0.66, 1.59, 0.96, and 1.38. 
     The identifying unit  702  obtains the next section k4-1 to k4-4 as measured section#4. The identifying unit  702  determines whether measured section#4 is a stopped section. In the travel data information  1200 - 13  to  1200 - 16  depicted in  FIG. 12 , since the latitude and longitude included in two or more successive records are not the same, the identifying unit  702  identifies measured section#4 to not be a stopped section. 
     The identifying unit  702  calculates the sum Σ of the longitudinal acceleration in measured section#4. From the travel data information  1200 - 13  to  1200 - 16  depicted in  FIG. 12 , Σ is: 
       Σ=1.3+1.1+1.1+1.1=4.6
 
     The identifying unit  702  compares the calculated Σ and Pb-c. Since Σ&gt;Pb-c is true, the identifying unit  702  determines that measured section#4 may be a brake section. The identifying unit  702  determines whether each longitudinal acceleration in measured section#4 is the non-brake longitudinal acceleration Pb-a or greater. Since each longitudinal acceleration in measured section#4 is Pb-a or greater, the identifying unit  702  identifies measured section#4 to be a brake section. 
     The executing unit  703  corrects the vertical acceleration included in the travel data information  1200 - 13  to  1200 - 16  by the brake correction coefficient Pb-b 0.3 and respectively corrects each to 0.33, 0.33, 0.33, and 0.33. 
     The identifying unit  702  obtains the next section k5-1 to k5-4 as measured section#5. The identifying unit  702  determines whether measured section#5 is a stopped section. In the travel data information  1200 - 18  to  1200 - 20  depicted in  FIG. 12 , since the latitude and longitude included in two or more successive records are the same, the identifying unit  702  identifies measured section#5 to be a stopped section. 
     The identifying unit  702  multiplies each vertical acceleration included in the travel data information  1200 - 17  to  1200 - 20  by the brake correction coefficient Pb-b 0.3 and respectively corrects each to 0.96, 0.63, 0.69, and 0.57. 
     Up to this point, the unevenness analyzer  201  completes processing of one continuous brake section. The unevenness analyzer  201  compares the corrected vertical acceleration and the road surface unevenness detection threshold and thereby, executes road surface unevenness detection. 
       FIG. 13  is a diagram depicting an example of travel data  1300  in the accelerator section identifying process by the unevenness analyzer  201 . One example of accelerator section identification by the unevenness analyzer  201  will be described using the travel data  1300 . In the present example, processing related to a brake section will be omitted. 
     In the present example, the measuring point count n of the measured section is assumed to be 4 and the values indicated in  FIG. 6  will be used as the analysis parameter  600 . The travel data  1300  depicted in  FIG. 13  has the same fields as the travel data  1200  depicted in  FIG. 12 . 
     The identifying unit  702  calculates the accelerator acceleration determining product Pa-c for the travel data  1300  depicted in  FIG. 13 , where Pa-a=−0.8, n=4, and therefore, calculates: 
         Pa - c=− 0.8×4=−3.2
 
     The identifying unit  702  obtains the first section k1-1 to k1-4 as measured section#1. The identifying unit  702  calculates the sum Σ of the longitudinal acceleration in measured section#1. From the travel data information  1300 - 1  to  1300 - 4  depicted in  FIG. 13 , Σ is: 
       Σ=0.3+0.2+0.6+0.3=1.4
 
     The identifying unit  702  compares the calculated Σ and Pa-c. Since Σ&lt;Pa-c is not true, the identifying unit  702  identifies measured section#1 to not be an accelerator section. 
     The identifying unit  702  obtains the next section k2-1 to k2-4 as measured section#2. The identifying unit  702  calculates the sum Σ of the longitudinal acceleration in measured section#. From the travel data information  1300 - 5  to  1300 - 8  depicted in  FIG. 13 , Σ is: 
       Σ=0.4+0.9+0.9−0.8=1.4
 
     The identifying unit  702  compares the calculated Σ and Pa-c. Since Z&lt;Pa-c is not true, the identifying unit  702  identifies measured section#2 to not be an accelerator section. 
     The identifying unit  702  obtains the next section k3-1 to k3-4 as measured section#3. The identifying unit  702  calculates the sum Σ of the longitudinal acceleration in measured section#3. From the travel data information  1300 - 9  to  1300 - 12  depicted in  FIG. 13 , Σ is: 
       Σ=−0.9−1.1−1.2−1.2=−4.4
 
     The identifying unit  702  compares the calculated Σ and Pa-c. Since Σ&lt;Pa-c is true, the identifying unit  702  determines that measured section#3 may be an accelerator section. The identifying unit  702  determines whether each longitudinal acceleration in measured section#3 is the non-accelerator longitudinal acceleration Pa-a or less. Since each longitudinal acceleration in measured section#3 is Pa-a or less, the identifying unit  702  identifies measured section#3 to be an accelerator section. 
     The executing unit  703  multiplies each vertical acceleration included in the travel data information  1300 - 9  to  1300 - 12  by the accelerator correction coefficient Pa-b 0.2 and respectively corrects each to 0.44, 1.06, 0.64, and 0.92. 
     The identifying unit  702  again obtains the previous section k2-1 to k2-4 as measured section#2. The identifying unit  702  determines whether measured section#2 is a stopped section. In the travel data information  1300 - 6  to  1300 - 7  depicted in  FIG. 13 , since the latitude and longitude in two or more successive records is the same, the identifying unit  702  identifies measured section#2 to be a stopped section. 
     Up to this point, the unevenness analyzer  201  completes processing of one continuous accelerator section. Subsequently, the unevenness analyzer  201  sequentially processes the measured sections from measured section#4. Since measured section#4 is identified to be an accelerator section similarly to measured section#3, the unevenness analyzer  201  corrects the vertical acceleration included in the travel data for measured section#4. 
     Since measured section#3 (previous section) is identified to not be a stopped section, the unevenness analyzer  20  proceeds to processing for measured section#5 (next section). Since the unevenness analyzer  201  identifies measured section#5 to not be a brake section or an accelerator section, the unevenness analyzer  201  corrects according to the speed, the vertical acceleration included in the travel data for measured section#5. 
     As described, the unevenness analyzer  201  according to the second embodiment identifies travel data indicating acceleration from a stopped state and travel data indicating deceleration to a stopped state. With respect to the identified travel data, the unevenness analyzer  201  sets the sensitivity of the unevenness detection for a road surface traveled by the vehicle  203  to be lower than the sensitivity for other travel data and executes road surface unevenness detection. As a result, the unevenness analyzer  201  can reduce the effects of the accelerating state and decelerating state of the vehicle  203  on the detection of road surface unevenness and perform analysis of road surface unevenness with high accuracy. 
     The unevenness analyzer  201  increases the measuring threshold of the accelerometer  410  and compares the increased measuring threshold and the measured value of the accelerometer  410  indicated by the identified travel data to thereby, execute road surface unevenness detection. Further, the unevenness analyzer  201  excludes the identified travel data from detection of road surface unevenness. Further, the unevenness analyzer  201  reduces the absolute value of the value measured by the accelerometer  410  indicated in the identified travel data and compares the reduced absolute value and the measuring threshold of the accelerometer  410  to thereby, execute road surface unevenness detection. 
     As a result, the unevenness analyzer  201  can accurately analyze road surface unevenness for identified travel data for which the value measured by the accelerometer  410  is larger than for other travel data. Further, when the measuring threshold of the accelerometer  410  is increased, in travel data other than the identified travel data, comparison is made with the measuring threshold of the accelerometer  410  before being the increase and therefore, the unevenness analyzer  201  stores the increased measuring threshold of the accelerometer  410  and the original measuring threshold of the accelerometer  410  before the increase. Thus, the volume of storage used by the unevenness analyzer  201  increases. On the other hand, when the absolute value of the measured value of the accelerometer  410  indicated by the identified travel data is reduced, the unevenness analyzer  201  does not store the original absolute value of the measured value of the accelerometer  410  before the reduction. Therefore, volume of storage used by the unevenness analyzer  201  does not change. The measuring threshold of the accelerometer  410  is a value that differs according to the measured vehicle  203  and therefore, reducing the absolute value of the measured value of the accelerometer  410  is effective when the unevenness analyzer  201  performs road surface unevenness analysis with respect to a large number of the vehicles  203 . 
     The unevenness analyzer  201 , with respect to travel data that does not belong to identified travel data, executes road surface unevenness detection by a sensitivity that corresponds to the speed of the vehicle  203  indicated by the travel data. As a result, the unevenness analyzer  201  can reduce the effects of the speed of the vehicle  203  on road surface unevenness detection and perform analysis of road surface unevenness with high accuracy. 
     The unevenness analyzer  201  corrects the measuring threshold of the accelerometer  410  according to the speed of the vehicle and compares the corrected measuring threshold and the measured value of the accelerometer  410  indicated by the travel data that does not belong the identified travel data and thereby, executes road surface unevenness detection. Further, the unevenness analyzer  201  corrects according to the speed of the vehicle, the measured value of the accelerometer  410  indicated by the travel data that does not belong the identified travel data and compares the corrected measured value and the measuring threshold of the accelerometer  410  and thereby, executes road surface unevenness detection. 
     As a result, the unevenness analyzer  201  can accurately analyze road surface unevenness for travel data measured at different speeds. Further, when the measured value of the accelerometer  410  indicated by the travel data that does not belong the identified travel data is corrected according to the speed of the vehicle, the volume of storage used by the unevenness analyzer  201  does not change. 
     The unevenness analyzer  201  determines whether the vehicle  203  is in an accelerating state, based on a temporal change in the longitudinal acceleration of the vehicle  203  indicated by a first travel data group of the vehicle  203 . When determining that the vehicle  203  is in an accelerating state, the unevenness analyzer  201  determines whether the vehicle  203  is in a stopped state, based on a temporal change in the position of the vehicle  203  indicated in a second travel data group of the vehicle  203 , measured before the first travel data group. When determining that the vehicle is in a stopped state, the unevenness analyzer  201  identifies the first travel data group and the second travel data group as travel data indicating acceleration from a stopped state. 
     The unevenness analyzer  201  determines whether the vehicle  203  is in a decelerating state, based on a temporal change in the longitudinal acceleration of the vehicle  203  indicated by the first travel data group of the vehicle  203 . When determining that the vehicle  203  is in a decelerating state, the unevenness analyzer  201  determines whether the vehicle  203  is in a stopped state, based on a temporal change in the position of the vehicle  203  indicted by a second travel data group of the vehicle  203 , measured after the first travel data group. When determining that the vehicle  203  is in a stopped state, the unevenness analyzer  201  identifies the first travel data group and the second travel data group as travel data indicating deceleration to a stopped state. 
     As a result, the unevenness analyzer  201  can identify travel data the indicates acceleration from a stopped state and travel data that indicates deceleration to a stopped state, in such travel data the measured value of the accelerometer  410  is larger than for other travel data. 
     The unevenness analyzer  201  determines whether the total acceleration of the vehicle  203  indicated by the first travel data group of the vehicle  203  is less than the product of the first threshold and the travel data count of the first travel data group. The unevenness analyzer  201  determines that the vehicle  203  is in an accelerating state when the above total is less than the above product, and acceleration of the vehicle  203  indicated by the first travel data group of the vehicle  203  is the first threshold or less. 
     Further, the unevenness analyzer  201  determines whether the total acceleration of the vehicle  203  indicated by the first travel data group of the vehicle  203  is greater than the product of the second threshold and the travel data count of the first travel data group. The unevenness analyzer  201 , determines that vehicle is in a decelerating state when the above total is greater than the above product, and the acceleration of the vehicle  203  indicated by the first travel data group of the vehicle  203  is the second threshold or greater. 
     As a result, when determining that the vehicle  203  is not in an accelerating state by comparison of the total acceleration, the unevenness analyzer  201  need not perform comparison of the acceleration of the first travel data group and therefore, can quickly determine that the vehicle  203  is not in an accelerating state. Similarly, the unevenness analyzer  201  can quickly determine that the vehicle  203  is not in a decelerating state. Since the time that the vehicle  203  travels at a constant speed is greater than the time when the vehicle  203  is in an accelerating state or decelerating state, instances when the vehicle  203  is not in an accelerating state and instances when the vehicle  203  is not in a decelerating state are frequent. By quickly determining instances when the vehicle  203  is not in an accelerating state and instances when the vehicle  203  is not in a decelerating state, the unevenness analyzer  201  can quickly execute road surface unevenness detection. 
     The unevenness analysis program for road surfaces described in the present embodiments can be implemented by executing a prepared program on a computer such as personal computer or work station. The unevenness analysis program for road surfaces is recorded on a computer-readable recording medium such as a hard disk, a flexible disk, CD-ROM, MO, DVD and the like, and is executed by being read from the recording medium by a computer. Further, the unevenness analysis program for road surfaces may be distributed via a network such as the Internet. 
     According to one aspect of the invention, an effect is achieved in that the detection accuracy of road surface unevenness can be improved. 
     All examples and conditional language provided herein are intended for pedagogical purposes of aiding the reader in understanding the invention and the concepts contributed by the inventor to further the art, and are not to be construed as limitations to such specifically recited examples and conditions, nor does the organization of such examples in the specification relate to a showing of the superiority and inferiority of the invention. Although one or more embodiments of the present invention have been described in detail, it should be understood that the various changes, substitutions, and alterations could be made hereto without departing from the spirit and scope of the invention.