Source: http://www.google.com/patents/US7609176?dq=4200770
Timestamp: 2014-12-21 06:15:35
Document Index: 503763584

Matched Legal Cases: ['art 101', 'art 102', 'art 104', 'art 1080', 'art 1081', 'art 1080', 'art 1080']

Patent US7609176 - Traffic information prediction apparatus - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inAdvanced Patent SearchPatentsA traffic information prediction system, including a traffic information prediction apparatus, of the present invention comprises travel status measuring means for measuring a travel status of a vehicle and accumulating it as travel record information, and traffic information predicting means for predicting...http://www.google.com/patents/US7609176?utm_source=gb-gplus-sharePatent US7609176 - Traffic information prediction apparatusAdvanced Patent SearchPublication numberUS7609176 B2Publication typeGrantApplication numberUS 11/053,902Publication dateOct 27, 2009Filing dateFeb 10, 2005Priority dateFeb 27, 2004Fee statusLapsedAlso published asCN1661645A, CN100357989C, US20050206534Publication number053902, 11053902, US 7609176 B2, US 7609176B2, US-B2-7609176, US7609176 B2, US7609176B2InventorsKenichiro Yamane, Yoshinori Endo, Kimiyoshi Machii, Junsuke FujiwaraOriginal AssigneeHitachi, Ltd.Export CitationBiBTeX, EndNote, RefManPatent Citations (6), Non-Patent Citations (1), Referenced by (17), Classifications (16), Legal Events (4) External Links: USPTO, USPTO Assignment, EspacenetTraffic information prediction apparatusUS 7609176 B2Abstract A traffic information prediction system, including a traffic information prediction apparatus, of the present invention comprises travel status measuring means for measuring a travel status of a vehicle and accumulating it as travel record information, and traffic information predicting means for predicting traffic information on a route on the basis of the travel record information and statistical traffic information to predict an arrival time to any place on a route containing the destination. The traffic information predicting means compares a traveling trace based on the statistical traffic information and the traveling trace based on the travel record information to calculate the degree of progress of the travel record based on the statistical traffic information, and correct the traveling trace based on the statistical traffic information on the basis of the degree of progress.
FIELD OF INVENTION The present invention relates to a traffic information prediction system, more specifically, a prediction system which includes in-situ traffic information prediction apparatus (abbreviated as a traffic information prediction apparatus) for providing most-likely predicted arrival time by predicting a travel time (necessary time) to a destination.
SUMMARY OF THE INVENTION In order to attain the above objects, a traffic information prediction apparatus according to the present invention has a construction which has statistical traffic information created in advance on the basis of various kinds of traffic information such as VICS, probe car data, etc., and can accurately estimate a travel time to a destination by using measurement information obtained under running of a vehicle and the statistical traffic information, and also supply a highly reliable predicted arrival time. Specifically, it comprises running state measuring means for measuring a running state of a vehicle and accumulating the running state of the vehicle thus measured as running record information, and traffic information predicting means for predicting traffic information on a route on the basis of the running record information and the statistical traffic information to predict an arrival time at any place on the route containing the destination. The traffic information predicting means compares a travel locus based on the statistical traffic information and a traveling trace based on the running record information to determine the degree of the running recording progress to the statistical traffic information and correct the traveling trace based on the statistical traffic information on the basis of the degree of progress, thereby predicting the traffic information.
BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a diagram showing an example of the construction of a traffic information prediction apparatus.
DETAILED DISCRIPTION OF THE INVENTION Next, a traffic information prediction apparatus according to the present invention will be described in detail. FIG. 1 is a diagram showing the total system construction of the traffic information prediction apparatus of the present invention.
The traffic information prediction apparatus 10 regarding the present invention comprises a map data base. (abbreviated as �DB�) 100, a route searching part 101, a route information managing part 102, an information transmitting/receiving unit 103, a user identifying part 104, a running state measuring unit 105, a running record DB 106, a statistical traffic information DB 407, a traffic information processing unit 108 and a prediction result outputting unit 109. Furthermore, the traffic information processing unit 108 comprises a traffic information predicting part 1080 and a statistical DB correcting part 1081. The traffic information prediction apparatus 10 is an in-vehicle mounted terminal or portable terminal which is equipped with a car navigation device or a device having the same function as the car navigation such as a laptop personal computer, a PDA, a cellular phone terminal or the like, and it calculates a route by using the map DB 100 and the statistical traffic information DB 107 which are equipped in advance, predicts a travel time to a destination by using the statistical traffic information DB 107 and the running record DB 106 which has been accumulated until then with respect to the route, and outputs the predicted arrival time or the like. If necessary, the traffic information prediction apparatus 10 may access an outside traffic information center 11 to obtain real-time traffic information and predict the arrival time.
The traffic information predicting part 1080 has a function of predicting a route to a destination and a traffic condition around the vehicle by using the running record DB 106 and the statistical traffic information DB 107. For example, the prediction processing when the route to the destination is set will be described with reference to FIG. 6 to FIG. 9. A case of FIG. 6 is considered as a simple example of a road network. A to E of FIG. 6 denote end points (called nodes) of links, and 20 to 23 denote links. The respective data such as the link length of each link and a statistical travel speed calculated from a statistical travel time every time zone are assumed to be set as shown in FIG. 7. The statistical travel time at 10:00 in FIG. 7 means a statistical travel speed at a time which is above 10:00 and less than 10:05. In this embodiment, a travel time from the node A to the node E is first predicted by using the statistical traffic information shown in FIG. 7 to calculate a predicted arrival time. When the vehicle departures from the node A at the time of 10:00:00, it is predicted that it needs 72 seconds (average speed is 30 km/h)3 to pass through the link 20. The time has not yet reached 10:05 at the time point 10:01:12 at which the vehicle will arrive at the start point (node B) of the next link 21, and thus 25 km/h at 10:00 is selected as the predicted travel velocity of the link 21. Therefore, the travel time necessary to pass through the link 21 is equal to 144 seconds, and the total travel time from the node A is equal to 216 seconds (10:03:36). Likewise, the travel time necessary to pass through the link 22 is calculated as 82 seconds (total 298 seconds, 10:04:58). The travel time necessary to pass through the final link 23 is equal to 173 seconds (total 471 seconds, 10:07:51), and thus it is necessary to switch the velocity to the velocity of 10:05 on the way. That is, the velocity of 10:00 (25 km/h) is selected for the first 2 seconds after entering the link 23, and thus the travel distance during that time is substantially equal to 14 m. Thereafter, the velocity of 10:05 (15 km/h) is selected for the remaining distance 1186 m, and thus substantially 285 seconds is calculated as a needed time. Finally, the travel time necessary to pass through the link 23 is calculated as 287 seconds (total 585 seconds, 10:09:45). From the above result, the predicted arrival time at which the vehicle departing from the node A at 10:00:00 arrives at the node E is calculated as 10:09:45, and the traveling trace of the overall route is indicated by a graph 30 of FIG. 8. The predicted arrival time to arrive at a desired destination on the way to the destination can be calculated by using the statistical traffic information DB 107 according to the manner as described above. However, in the traffic information predicting part 1080, the above prediction calculation is carried out by further using past running record data (using the running record DB 106) in combination to correct the predicted arrival time. For example, it is assumed that the vehicle departing from the node A at 10:00:00 arrives at the node C at 10:05:00 with the node E set as the destination like the above-described example (the traveling trace corresponds to a graph 31)). At that time point (actually-recorded travel time Th′=300 seconds, 10:05:00), the predicted arrival time to the node C based on the statistical traffic information DB base 107 is equal to 10:03:36 (statistical travel time Th=216 seconds). Therefore, it is calculated that the vehicle arrives at the node C with a delay of one minute and twenty-four seconds (39%) with respect to the statistical travel time Th. This delay (in some cases, it is not delayed, but advanced) will be referred to as the �degree of progress.� The degree of progress is represented by the difference or ratio between the predicted arrival time (statistical travel time) and the actually-recorded travel time). Furthermore, the degree of progress is regarded as a result of the composite action between two factors that the actual traffic condition is more congested as compared with the estimation and that the driver drives his/her car more comfortably as compared with the statistical driver's driving characteristic, and the travel time based on the statistical traffic information DB 107 is corrected by the following method on the assumption that the degree of progress described above is continued until the destination. When the destination is far and a long travel time is needed, it may be considered that the prediction precision is deteriorated because the prediction must be carried out in the remote future. In this case, it may be adopted that the following prediction is carried out from the current time (or departure scheduled time) to a predetermined near future time (for example, until two hours elapse), and then the statistical travel time data described above is used in a move remote future with no prediction. In place of the predetermined near future time, a predetermined distance (for example, until 200 km) from the current place (departure place) may be used as a prediction target as described above.
In FIG. 9, reference numeral 40 denotes a statistical travel time transition about links to be predicted (in this embodiment, the links 22 and 23 of FIG. 8), and it is obtained by referring to the statistical traffic information DB 107. The variable �t� denotes a current time on a current day of prediction, and in the above embodiment, it corresponds to 10:05:00. Reference numeral 41 denotes an actually-recorded travel time transition about the links until the current time t of the prediction current day. In the traffic information prediction apparatus 10, it cannot be known unless it is obtained from the traffic information center 11 through communications, and there is no problem even if this information is not obtained. The information to be predicted at present is a travel time (predicted travel time) 42 in the near future subsequent to the current time t. In order to calculate a travel time Td′ (t+n) of the time (t+n) corresponding to a future time of n period at the current time t, the statistical travel time Td(t+n) at the time (t+n) and the degree of progress which corresponds to the ratio of the actually-recorded travel time Th′ in the past running record and the statistical travel time Th are applied to the following equation.
Here, γ denotes a coefficient, and it is normally set to 1. However, when the prediction value and the past statistical value are not matched with each other, for example, when the degree of progress (Th′/Th) is larger than a normal range, the coefficient γ may be set to be smaller than 1 in accordance with the degree of progress, or conversely the coefficient γ may be set to be larger than 1 in accordance with the degree of progress when the prediction value and the past statistical value are not matched with each other, for example, when the degree of progress is smaller than the normal range, thereby correcting the value of (γ�Th′/Th1) so that the value is near to 1 (for example, the value of (γ�Th′/Th1) is not corrected so as to step over �1� like if γ�Th′/Th1 is equal to 1.2, the value is set to 1.1, and if γ�Th′/Th1 is equal to 0.8, the value is set to 0.9). Or, when a link and a time as prediction targets are considerably far and in the future (for example, a place which is far by 150 km or more or a time after two or more hours elapse) as compared with the current place and the present time, the prediction precision may be lowered, and thus the value of γ�Th′/Th1 is corrected so that it approaches 1 in accordance with the distance or the arrival time. Or, the statistical data is used (γ�Th′/Th1 is set to 1) without any prediction target because high prediction precision is unexpected. That is, attention is dynamically paid so that the predicted travel time to be determined is not a unique value. Or, the actually-recorded travel time and the statistical traffic jam condition and the number of traffic lanes in the prediction target link are considered, and when during the actually-recording time the driver is under a free running state where he/she can freely pass surrounding cars, however, the prediction target link is under a non-free running state (when traffic jam occurs or the number of traffic lanes is equal to 1), the value of (γ�Th′/Th1) is corrected so as to approach 1 in accordance with the degree of freedom of running and then the prediction is made, or the statistical data may be used without any prediction target. Conversely, when the non-free running state is set at the actually-recording state, however, the prediction target link is under the free running state, the value of (γ�Th′Th1) may be corrected in accordance with the degree of freedom of the running so that it is far from 1 (for example, the value of (γ�Th′/Th1) does not exceed 1 like if γ�Th′/Th1 is equal to 1.2, it is corrected to 1.3, and if γ�Th′/Th1 is equal to 0.8, it is corrected to 0.7), and the statistical data are used without prediction, or the prediction may be carried out by using a driver's past average degree of progress under the freely running state. Furthermore, the unique value as described above is not used as the degree of progress at the actually-recording time, but different values may be calculated for the freely running state and the non-freely running state respectively, and the prediction may be carried out by using the degree of progress corresponding to each state of the prediction target link. In this case, when the same running state at the prediction time does not exist under the actually-recording state, the value of (γ�Th′/Th1) may be corrected.
During running, it is judged whether prediction should be carried out or not as in the case of S52 (S63). If the prediction is carried out (YES in S63), a near future travel time predicted value of each link is calculated (S64) as in the case of S53, the traveling trace based on the predicted data and the predicted arrival times to the destination and the check points, etc., are calculated (S65), and the predicted arrival times to the destination and the check points are output to the display device 140 or the speaker 148 (S66). FIG. 15 shows an example of the output of a prediction result to the display device when the time (8:09) at which the current position of the vehicle passes over the A cross-point corresponds to the timing of the prediction. In FIG. 15, the predicted arrival time 87 to the A cross-point is corrected to 8:09, and further a display of �+3� which denotes a delay of 3 minutes from the initial predicted arrival time of 8:06 is added as the degree of progress. Conversely, when the arrival time at the A cross-point is advanced further by 3 minutes than the initial predicted arrival time, �−3 minutes� is displayed. The predicted arrival times to a B cross-point 88 and a destination 83 which are obtained by calculating the traveling trace based on the travel time prediction of a subsequent link to the current position 82 on the route and the predicted data are corrected by using the degree of progress which is calculated from the statistical data Th and the actually recorded data Th′ on the travel time from the departure place to the A cross-point, and a display of the degree of progress is also added. The display of the type 86 of the data corresponding to the original data for the calculation of the predicted arrival times is changed from �statistics� to �prediction�. As an output to the speaker 148, for example, a voice of �vehicle is behind schedule and will arrive after a delay of 9 minutes� may be output to the speaker 148. If a destination ahead of the B cross-point is out of the prediction target because the destination is far, the traveling trace based on any one of the statistical data and the predicted data is calculated for each link, and it may be clarified which one of these data is used for the calculation in each link as shown in FIG. 16. In FIG. 16, reference numerals 90 and 91 denote that links are calculated on the basis of the predicted data and the statistical data, respectively, and also denote that the type 86 of the data corresponding to the original data for the calculation of the predicted arrival time uses both of �prediction/statistics�.
Patent CitationsCited PatentFiling datePublication dateApplicantTitleUS6314368 *Jun 7, 1995Nov 6, 2001Hailemichael GurmuVehicle guidance system and method thereforUS6563433 *Jun 7, 2001May 13, 2003Exy Concept Co., Ltd.Liquefied raw garbage collection method and systemUS6922629 *Aug 9, 2002Jul 26, 2005Aisin Aw Co., Ltd.Traffic information retrieval method, traffic information retrieval system, mobile communication device, and network navigation centerJP2002251698A Title not availableJP2004020288A Title not availableJPH1089977A Title not available* Cited by examinerNon-Patent CitationsReference1Japanese office action dated Feb. 27, 2009.Referenced byCiting PatentFiling datePublication dateApplicantTitleUS7907590May 18, 2006Mar 15, 2011Lg Electronics Inc.Providing information relating to traffic congestion tendency and using the sameUS7940741May 18, 2006May 10, 2011Lg Electronics Inc.Providing traffic information relating to a prediction of speed on a link and using the sameUS7940742May 18, 2006May 10, 2011Lg Electronics Inc.Method and device for providing traffic information including a prediction of travel time to traverse a link and using the sameUS8009659 *Jan 18, 2007Aug 30, 2011Lg Electronics Inc.Providing congestion and travel information to usersUS8050853 *May 18, 2006Nov 1, 2011Lg Electronics Inc.Providing traffic information including sub-links of linksUS8086393May 18, 2006Dec 27, 2011Lg Electronics Inc.Providing road information including vertex data for a link and using the sameUS8190362 *Jan 14, 2010May 29, 2012Inrix, Inc.Displaying road traffic condition information and user controlsUS8275540Nov 21, 2011Sep 25, 2012Inrix, Inc.Dynamic time series prediction of traffic conditionsUS8332131May 17, 2006Dec 11, 2012Lg Electronics Inc.Method and apparatus for providing transportation status information and using itUS8392100 *Aug 6, 2009Mar 5, 2013Clarion Co., Ltd.Method and apparatus for determining traffic dataUS8615354Aug 13, 2010Dec 24, 2013Inrix, Inc.Displaying road traffic condition information and user controlsUS8682571Jun 20, 2013Mar 25, 2014Inrix, Inc.Detecting anomalous road traffic conditionsUS8700296Aug 16, 2011Apr 15, 2014Inrix, Inc.Dynamic prediction of road traffic conditionsUS8711850Jun 14, 2006Apr 29, 2014Lg Electronics Inc.Format for providing traffic information and a method and apparatus for using the formatUS20100036594 *Aug 6, 2009Feb 11, 2010Clarion Co., Ltd.Method and apparatus for determining traffic dataUS20100185382 *Jan 14, 2010Jul 22, 2010Inrix, Inc.Displaying road traffic condition information and user controlsUS20120078507 *Sep 27, 2010Mar 29, 2012Toyota Motor Engineering & Manufacturing North America, Inc.Systems and Methods for Estimating Local Traffic Flow* Cited by examinerClassifications U.S. Classification340/994, 340/989, 340/988, 701/414, 701/423International ClassificationG08G1/09, G08G1/0969, G09B29/10, G08G1/123, G08G1/01, G08G1/00, G08G1/13, G09B29/00, G01C21/00Cooperative ClassificationG08G1/0104European ClassificationG08G1/01BLegal EventsDateCodeEventDescriptionDec 17, 2013FPExpired due to failure to pay maintenance feeEffective date: 20131027Oct 27, 2013LAPSLapse for failure to pay maintenance feesJun 7, 2013REMIMaintenance fee reminder mailedJun 8, 2005ASAssignmentOwner name: HITACHI, LTD., JAPANFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:YAMANE, KENICHIRO;ENDO, YOSHINORI;MACHII, KIMIYOSHI;AND OTHERS;REEL/FRAME:016670/0956;SIGNING DATES FROM 20050203 TO 20050204RotateOriginal ImageGoogle Home - Sitemap - USPTO Bulk Downloads - Privacy Policy - Terms of Service - About Google Patents - Send FeedbackData provided by IFI CLAIMS Patent Services©2012 Google