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An Improved Methodology to Generate a Digital Road Network Using Location Positioning Data of Probe Vehicles
Proceedings of the 12th IFAC Symposium on Transportation Systems Redondo Beach, CA, USA, September 2-4, 2009
An Improved Methodology to Generate a Digital Road Network Using Location Positioning Data of Probe Vehicles Hideaki Ito*, Yoshiki Suga** Toru Higashi*, Yasuo Asakura*** * Institute of Systems Science Research Shinmachi-I.S.Bldg., Komusubidana-cho 428, Shinmachidori-Shijo Nakagyo-ku, Kyoto, 604-8223, Japan ** Institute of Urban Transport Planning Co.,Ltd. 1-1-11, Tsurigane-cho, Chuo-ku, Osaka, 540-0035, Japan *** Professor, Graduate School of Science & Technology, Kobe University 1-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan Abstract: In recent years, traffic survey methods using probe vehicles have become popular in Japan. Enormous probe vehicle data are stored in database day after day. These probe data can be utilized not only for observing individual travel behavior in a road network but also for measuring LOS (Level of Services) indexes such as travel time, loss time caused by traffic congestion, and so on. The authors have already developed a methodology of generating a digital road network by using location-positioning data of probe vehicles. This paper aims to improve the network generation methodology so that more accurate road network map can be generated. Using both hypothetical and actual data, numerical tests are examined to validate the improved methods and to estimate the data size for generating a road network with required accuracy. 1. INTRODUCTION The location positioning data were obtained from commercial vehicles with GPS (Global Positioning Systems) recorder, which were operated by a courier in Kyoto, Japan. The longitude and latitude of a vehicle movement were observed every one second for several months. First of all, let’s us plot simply all positioning data stored in database using GIS (Geographic Information Systems) as shown in Figure 1. Each figure shows a different part of a road network in Kyoto. Both figures seem to represent the configuration of actual road networks. This implies that a road network would rise to the surface when we superpose the trajectories of moving vehicles. Thus, we have developed a methodology to generate a digital road network based on enormous location positioning data of probe vehicles. Particularly, this paper aims at the further accuracy improvement of the methodology that we developed. 2. METHODOLOGICAL ADVANTAGES
Fig. 1. Probe data view on GIS
Generating digital road network data using probe vehicle data has several advantages compared with present process of digitization. When a digital road map is prepared, a road network is generally digitized using digitizer tablet from an aerial photograph, a survey map and so on. However, those conventional methodologies including hands-on process need labor consuming work and large amount of cost. Furthermore equivalent process is required to up-date the map. We intend to propose a method that uses only location positioning data obtained through probe vehicles with GPS. 978-3-902661-50-0/09/$20.00 © 2009 IFAC
Survey area: Kyoto in Japan
This method scarcely needs hands-on process as well. These features show that the methodology could be adapted to everywhere in the world including developing countries where enough stock of map data may not exist. The generated map is expected to have valuable traffic information in addition to the geographic configuration of a road network; averaged travel time of each link, traffic regulation at each intersection and so on. The proposed methodology has the immediacy and robustness for change of
292
10.3182/20090902-3-US-2007.0027
12th IFAC CTS (CTS 2009) Redondo Beach, CA, USA, September 2-4, 2009
road environment because road network data would be updated day-by-day. When probe vehicle data are used in conventional traffic data processing, typical failures may occur in map matching process for calculation of LOS indexes. They are frequently caused by lacks or mistakes of traffic regulation. By using actual trajectories of probe vehicles, traffic regulations concerned with a link and a node such as one-way, left or right turn prohibition could be easily estimated.
(A) Previous methodology
3. OUTLINE OF THE PREVIOUS METHODOLOGY
Probe Data
Probe Data
Data reading: Node & Link generating
Data reading: Node & Link generating
Data cleaning
Data cleaning
iteration
Fundamental idea of generating digital road network data shown in the previous study (Ito et al. 2006) is very simple. The left side in Figure 2 shows the previous methodology. In the first process (data reading) the node and the link are generated. Each point corresponding to a position obtained by a probe vehicle is defined as a node. A node has the attributes such as time of day, longitude, and latitude. A link is generated by connecting two adjacent nodes of the same vehicle in time. The attributes of time and travel speed and vector are given to the link. This procedure is iterated for all trajectories of a probe vehicle.
(B) New methodology
Extraction of road geometric
Aggregation of link
Aggregation of link (each processing of road geometric type)
Add link attribute
Add link attribute
a road network
a road network
Fig. 2. Process Flow Chart (previous and new methodology) Step1 Link group extraction (Blue line)
However, errors are inevitable for location positioning data due to the characteristics of GPS. The accuracy of GPS deteriorates in downtown areas especially, which have higher buildings that sometimes interfere with the GPS signal. Therefore data cleaning processing that is the second process is necessary and not unimportant such as eliminating links with extremely higher or lower travel speed.
Step2 The estimated road centerline
The data cleaning process has three processing steps. First step, the inaccurate links with abnormal speed and travel time are removed. This step is necessary to take away obvious location positioning errors in the GPS data. When the vehicle has stopped in the signal and the railroad crossing, coordinates of the probe vehicle data may have small differences by the GPS errors. Those data should have the same coordinates. In the second step, those data are aggregated to one representative point. Last, the contiguous straight-line vectors are aggregated to one vector. By the last step specially, the volume of probe data can be decreased.
Step3 The estimated road centerline is extended
The third process is link aggregation processing. The probe vehicle data that uses this step still has some minor errors though the critical errors, which are a combination of noise, bias and blunders have eliminated in the previous process. Therefore the GPS trajectories taken from the same route will not be exactly the same, as shown in Figure 3.
Step4 Moving and adsorbing link group to the estimated road centerline
Result of adsorbing to the estimated centerline 4 5
6
4
66 6 6 5
5
3
4
43
Indicator of a link (frequency-of-appearance value) by add link attribute process
Fig. 3. Actual running tracks (above) and running tracks by probe vehicle car data (below)
Fig. 4. Link aggregation methodologies image (previous) 293
12th IFAC CTS (CTS 2009) Redondo Beach, CA, USA, September 2-4, 2009
Previously we proposed the methodology for aggregating some links into a single link. Figure 4 shows an image of this link aggregation procedure. The link aggregation algorithm has four processing steps.
Before processing
After processing
The first step of link aggregation is to extract a group of links. Contiguous links of which distance is less than a threshold are extracted from all links, and the link of nearly the same direction vector is further extracted from those extracted links. This procedure is executed at each link. The second step is estimation of the centerline of a road section. The geometric average of each of origin / destination points is calculated within a group of links, and new origin and destination nodes and a new link are generated. This calculation is repeated until this process covers all links within a group. After the straight line that extends this link is the estimated centerline of the road section (Step 3).
Before
The last step of this aggregation process is to adsorb each link of the link group to the estimated centerline. There may remain some links without being aggregated due to GPS errors as result of link aggregation processing. Therefore, a new attribute frequency-of-appearance value is added to the estimated centerline. A new attribute shows the number of the adsorbed links, when adsorbing a link. After the aggregation processing finishes in all links, the link of big attribute value is judged to be an effective link. If the high link of frequency-of-appearance is chosen, an effective link group (road map) will be displayed. 4. METHODOLOGICAL PROBLEMS The previous methodology had several problems from the viewpoint of road map reproducibility as yet. Figure 5 shows the failure result of the link aggregation by methodological problems. In the previous methodology the links extracted by nearly the same direction vector are simply aggregated. However, because the amount of the vector’s change is too large, the links necessary for aggregating cannot be extracted suitably. The other problem is a link that does not actually exist is generated due to GPS data error. Most of these problems are caused by GPS errors that the probe vehicle data has. To judge the accuracy of estimated link, attribute frequency-of-appearance value was added. However the link aggregation process had caused huge high computational overhead for calculating the link frequency of appearance value that gave to the chopped estimated road centerline. Therefore a new efficient methodology would be presented in the next section as a root-and-branch solution for these problems.
After
Missing
Missing
Fig. 5. Failure result of the link aggregation 5.1 Extraction of road geometric In this process, all positional data are labeled into three parts; the straight-line part, the curve part, and the intersection part. In the straight-line part, difference of direction vector of a contiguous links is assumed to be not too large. Therefore the variation point where the direction vector difference is large shows the existence of the curve start. On the other hand, in the terminal of the curve the direction vector difference becomes small. The curve part is extracted judging the starting point and the terminal of the curve by the point where the threshold is exceeded, as shown in Figure 6 and 7. The judgment procedure of the curve and the intersection is the same method, and only the argument value is different. The points of the remainder are labeled as a straight-line part. Figure 8 shows the judgment result of the straight-line part, the curve part, and the intersection part. In each part the different link aggregation algorithm needs to be applied.
Pf: Point forward 10m from Pia
5. IMPROVEMENT OF METHODOLOGY To improve the accuracy of road map reproducibility in a curve section, a discrimination of road part processing is built into a new methodology. The link aggregation algorithm has been improved about each three parts extracted by road geometric type. The flow of entire new data processing is shown in Figure 2(B). The data reading process and the data cleaning process are the same as previous methodology.
After
Pia: Base point
Abs(Vector Pia:Pf minus Vector Pb:Pia) > 20 degree = Pia is curve start
Pb: Point behind 10m from Pia
Fig. 6. Starting point judgment of curve and intersection
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12th IFAC CTS (CTS 2009) Redondo Beach, CA, USA, September 2-4, 2009
Step1 Selection of target link (red line)
Pf1: Point forward 10m from Pib Pib: Base point Pf2: Point forward 10m from Pib
Step2 Link group extraction included in target link buffer
Abs(Vector Pib:Pf1 minus Vector Pib:Pf2) < 20 degree = Pib is curve terminal
Fig. 7. Terminal point judgment of curve and intersection Blue line: straight-line part Red line: intersection part Green line:curve part
Step3 The estimated centerline (yellow line) is defined from the mean value of the starting points and the terminal points of the each links that cut in the borderline
Step4 The estimated centerline (green line) is extended to the edge of the link buffer
Step5 Moving and adsorbing to the estimated centerline
Fig. 8. Judgment result of the straight-line part, the curve part, and the intersection part 5.2 Link aggregation algorithm of straight- line part The link aggregation algorithm of a straight-line part was also improved in terms of computational speed and the road map reproducibility accuracy. The improved link aggregation algorithm has five processing steps. Figure 9 shows the link aggregation algorithm image. The first step of link aggregation is to extract a group of links. One link is chosen from all links at random. The target link buffer where with the threshold in surroundings of the chosen link is made. Next, the links included in the target link buffer are extracted. Contiguous links of which distance from chosen link in previous step is less than a threshold are extracted from all links as candidates, and candidate links is counted. Until the number of candidates links doesn't change the threshold is enlarged at constant intervals. Then links that have nearly same direction vector are selected from those candidates. This process is executed by all extracted links. In this paper the threshold distance is set as 10 meters that is the mean value of error distance of GPS data. The threshold value for direction vector is assumed to be within thirty degrees. However, this threshold value should be changed according to the kind and the area of the data used.
Fig. 9. Link aggregation algorithm image (straight-line part) The next step is estimation of the centerline from the selected links by previous step. First the selected links are cut in the borderline of the target link buffer. Then the estimated centerline is defined from the mean value of the starting points and the terminal points of the each links that cut in the borderline. The starting point position of the centerline is computed from the average value of the starting point coordinates of the links that are composed of the group. Similarly, the terminal point position is computed from the links. This procedure is repeated until convergence. The accuracy of the centerline is improved by this processing. In the fourth step, the estimated centerline is extended to the edge of the link buffer. The last step of this aggregation procedure is to adsorb each link of the link group to the estimated centerline. The difference between previous algorithm and new algorithm is the treatment of an adsorption parameter. Although the adsorption parameter of previous algorithm is the number of a link, the parameter of new algorithm is the length of a link. This parameter can be used as an index that shows the likelihood of the centerline. Moreover, because the centerline
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12th IFAC CTS (CTS 2009) Redondo Beach, CA, USA, September 2-4, 2009
is not subdivided in detail like an old algorithm, this procedure can compute at high speed. Figure 10 shows the output of the link aggregation procedure. The road centerline is estimated in high accuracy by the link aggregation processing according to the direction that can run.
straight-line part (result of the link aggregation)
Blue line: straight-line part Red line: intersection part Green line:curve part
curve part (each point is a node) * The same color points show tracks of the same trajectory
approximates by the spline curve
Fig. 11. Link aggregation algorithm image (curve part) Before
After
intersection part (the links of the intersection part remain)
approximates by the straight line
Fig. 10. Result of the link aggregation (straight-line part) straight-line part (result of the link aggregation)
5.3 Link aggregation algorithm of curve part After processing of a straight-line part is completed, the link of the curve part that remains on a map is aggregated. The algorithm of a curve part is simple.
Fig. 12. Link aggregation algorithm image (intersection part)
First, the trajectory data to which it runs at the same section (same starting point and terminal point) is extracted. Then, to improving approximate accuracy, data with shortest distance and data with longest distance are excluded from the extracted trajectory dataset. Last, the centerline of the curve part is estimated by approximating between the starting point and terminal points by the circular arc or the spline curve or the straight line, etc. The approximation curve should be selected by the kind of the data used. Because the area of data that we used in this paper is urban area, we adopted the spline curve. Figure 11 shows the link aggregation algorithm image of curve part.
5.5 Result of the link aggregation
5.4 Link aggregation algorithm of intersection part The last processing of link aggregation algorithm is the aggregation in remains link in the intersection portion. The links that was judged to the intersection by the extraction of road geometric is assumed to have long length and large curvature by GPS errors (left chart of Figure 12).
Also the accuracy of GPS data used in this paper is about 10 meters. Therefore, in the intersection the reproduction of the curve in the approximation such as spline curve is difficult. So we adopted simply approximation by the straight line (right chart of Figure 12).
Figure 13 shows the output of the link aggregation procedure. In the road map before improvement (old algorithm), the curve and crossing with low frequency of appearance is deleted. However, in the road map after improvement (new algorithm), the curve and the crossing part are created correctly. 6. A ROAD MAP COMPARISON Figure 14 shows the created road map was overlapped with the actual road map in Kyoto. It is found that the road map made from figure corresponds to an about actual road map. However all the road networks are not covered, because the
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12th IFAC CTS (CTS 2009) Redondo Beach, CA, USA, September 2-4, 2009
volume of GPS data used was few (number of links: 49,309, total length of links: 415.4km, total running times: 18.1 hours) and there was a lot of data where it ran repeating the same road. To cover all the road networks the huge amount of GPS data where it runs repeating large range is necessary. Blue line: straight-line part Red line: intersection part Green line:curve part
Fig. 14. Overlapping of created road map and actual map Old algorithm
New algorithm
One-way
Both directions One-way
Both directions
Fig. 13. Result of the link aggregation The road map that is generated by this methodology has both different directions links as double link. For a one-way road only one link that it can run is created and when it is possible to run in both direction two links are created respectively. This direction link can be used as traffic regulations information. Figure 15 shows the result of the traffic regulations information. When sufficient volumes of GPS data are available, a road map with both different directions can be identified. 7. CONCLUSION In this paper, we have described the improvement of creation method of high-precision road maps from positioning data obtained from probe vehicles. The fundamental methodology of generating a digital road network that we developed had several problems from the viewpoint of road map reproducibility, particularly in a curve section. Therefore, in curve section we have adopted another algorithm. In addition, the improvement of the algorithm in the straight part was the very effective in the accuracy improvement and the most effective in order to reduce the amount of data. If the method shown in this paper is used, a map will be able to be created to low cost rather than the conventional method. In this paper only few GPS data was used. In the future the accuracy of the map making will be verified by using a huge GPS data. We will investigate the method of estimating the data size for generating a road network with required.
Fig. 15. Result of the traffic regulations REFERENCES Ito, H., Suga, Y., Higashi, T. and Asakura, Y. (2006). Generating a road map based on location positioning data of probe vehicles, IFAC Symposium on Control in Transportation Systems, Vol.11, part 1. Asakura, Y., Hato, E., Daito, T. and Tanabe, J. (2000). A. Monitoring Travel Behaviour Using PHS Based Location Data. Journal of Infrastructure Planning and Management (in Japanese), No.653/IV-48, pp.95-104. Morris, S., Morris, A., Barnard, K. (2004). Digital trail libraries. Joint Conference on Digital Libraries, pp.6371. Ogle, J., R. Guensler, W. Bachman, M. Koutsak, and J. Wolf (2002). Accuracy of GPS for Determining Driver Performance Parameters. Transportation Research Record, No.1818, pp.12-24. Ohumori N., Muromachi Y., Harada N. and Ohta K. (2002). Analysis of Day-to-Day Variations of Travel Time Using GPS and GIS. Proceedings of the Third International Conference on Traffic and Transportation Studies (ICTTS), Vol.2, pp.1306-1313. Suh, YC, Konish, Y., Shibasaki, R. (2002). Assessing the improvement of positioning accuracy using a GPS and pseudolites signal in urban area. The Student Forum of the Geoinformation Forum Japan, 4, pp.36-41.
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An Improved Methodology to Generate a Digital Road Network Using Location Positioning Data of Probe Vehicles Hideaki Ito*, Yoshiki Suga** Toru Higashi*, Yasuo Asakura*** * Institute of Systems Science Research Shinmachi-I.S.Bldg., Komusubidana-cho 428, Shinmachidori-Shijo Nakagyo-ku, Kyoto, 604-8223, Japan ** Institute of Urban Transport Planning Co.,Ltd. 1-1-11, Tsurigane-cho, Chuo-ku, Osaka, 540-0035, Japan *** Professor, Graduate School of Science & Technology, Kobe University 1-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan Abstract: In recent years, traffic survey methods using probe vehicles have become popular in Japan. Enormous probe vehicle data are stored in database day after day. These probe data can be utilized not only for observing individual travel behavior in a road network but also for measuring LOS (Level of Services) indexes such as travel time, loss time caused by traffic congestion, and so on. The authors have already developed a methodology of generating a digital road network by using location-positioning data of probe vehicles. This paper aims to improve the network generation methodology so that more accurate road network map can be generated. Using both hypothetical and actual data, numerical tests are examined to validate the improved methods and to estimate the data size for generating a road network with required accuracy. 1. INTRODUCTION The location positioning data were obtained from commercial vehicles with GPS (Global Positioning Systems) recorder, which were operated by a courier in Kyoto, Japan. The longitude and latitude of a vehicle movement were observed every one second for several months. First of all, let’s us plot simply all positioning data stored in database using GIS (Geographic Information Systems) as shown in Figure 1. Each figure shows a different part of a road network in Kyoto. Both figures seem to represent the configuration of actual road networks. This implies that a road network would rise to the surface when we superpose the trajectories of moving vehicles. Thus, we have developed a methodology to generate a digital road network based on enormous location positioning data of probe vehicles. Particularly, this paper aims at the further accuracy improvement of the methodology that we developed. 2. METHODOLOGICAL ADVANTAGES
Fig. 1. Probe data view on GIS
Generating digital road network data using probe vehicle data has several advantages compared with present process of digitization. When a digital road map is prepared, a road network is generally digitized using digitizer tablet from an aerial photograph, a survey map and so on. However, those conventional methodologies including hands-on process need labor consuming work and large amount of cost. Furthermore equivalent process is required to up-date the map. We intend to propose a method that uses only location positioning data obtained through probe vehicles with GPS. 978-3-902661-50-0/09/$20.00 © 2009 IFAC
Survey area: Kyoto in Japan
This method scarcely needs hands-on process as well. These features show that the methodology could be adapted to everywhere in the world including developing countries where enough stock of map data may not exist. The generated map is expected to have valuable traffic information in addition to the geographic configuration of a road network; averaged travel time of each link, traffic regulation at each intersection and so on. The proposed methodology has the immediacy and robustness for change of
292
10.3182/20090902-3-US-2007.0027
12th IFAC CTS (CTS 2009) Redondo Beach, CA, USA, September 2-4, 2009
road environment because road network data would be updated day-by-day. When probe vehicle data are used in conventional traffic data processing, typical failures may occur in map matching process for calculation of LOS indexes. They are frequently caused by lacks or mistakes of traffic regulation. By using actual trajectories of probe vehicles, traffic regulations concerned with a link and a node such as one-way, left or right turn prohibition could be easily estimated.
(A) Previous methodology
3. OUTLINE OF THE PREVIOUS METHODOLOGY
Probe Data
Probe Data
Data reading: Node & Link generating
Data reading: Node & Link generating
Data cleaning
Data cleaning
iteration
Fundamental idea of generating digital road network data shown in the previous study (Ito et al. 2006) is very simple. The left side in Figure 2 shows the previous methodology. In the first process (data reading) the node and the link are generated. Each point corresponding to a position obtained by a probe vehicle is defined as a node. A node has the attributes such as time of day, longitude, and latitude. A link is generated by connecting two adjacent nodes of the same vehicle in time. The attributes of time and travel speed and vector are given to the link. This procedure is iterated for all trajectories of a probe vehicle.
(B) New methodology
Extraction of road geometric
Aggregation of link
Aggregation of link (each processing of road geometric type)
Add link attribute
Add link attribute
a road network
a road network
Fig. 2. Process Flow Chart (previous and new methodology) Step1 Link group extraction (Blue line)
However, errors are inevitable for location positioning data due to the characteristics of GPS. The accuracy of GPS deteriorates in downtown areas especially, which have higher buildings that sometimes interfere with the GPS signal. Therefore data cleaning processing that is the second process is necessary and not unimportant such as eliminating links with extremely higher or lower travel speed.
Step2 The estimated road centerline
The data cleaning process has three processing steps. First step, the inaccurate links with abnormal speed and travel time are removed. This step is necessary to take away obvious location positioning errors in the GPS data. When the vehicle has stopped in the signal and the railroad crossing, coordinates of the probe vehicle data may have small differences by the GPS errors. Those data should have the same coordinates. In the second step, those data are aggregated to one representative point. Last, the contiguous straight-line vectors are aggregated to one vector. By the last step specially, the volume of probe data can be decreased.
Step3 The estimated road centerline is extended
The third process is link aggregation processing. The probe vehicle data that uses this step still has some minor errors though the critical errors, which are a combination of noise, bias and blunders have eliminated in the previous process. Therefore the GPS trajectories taken from the same route will not be exactly the same, as shown in Figure 3.
Step4 Moving and adsorbing link group to the estimated road centerline
Result of adsorbing to the estimated centerline 4 5
6
4
66 6 6 5
5
3
4
43
Indicator of a link (frequency-of-appearance value) by add link attribute process
Fig. 3. Actual running tracks (above) and running tracks by probe vehicle car data (below)
Fig. 4. Link aggregation methodologies image (previous) 293
12th IFAC CTS (CTS 2009) Redondo Beach, CA, USA, September 2-4, 2009
Previously we proposed the methodology for aggregating some links into a single link. Figure 4 shows an image of this link aggregation procedure. The link aggregation algorithm has four processing steps.
Before processing
After processing
The first step of link aggregation is to extract a group of links. Contiguous links of which distance is less than a threshold are extracted from all links, and the link of nearly the same direction vector is further extracted from those extracted links. This procedure is executed at each link. The second step is estimation of the centerline of a road section. The geometric average of each of origin / destination points is calculated within a group of links, and new origin and destination nodes and a new link are generated. This calculation is repeated until this process covers all links within a group. After the straight line that extends this link is the estimated centerline of the road section (Step 3).
Before
The last step of this aggregation process is to adsorb each link of the link group to the estimated centerline. There may remain some links without being aggregated due to GPS errors as result of link aggregation processing. Therefore, a new attribute frequency-of-appearance value is added to the estimated centerline. A new attribute shows the number of the adsorbed links, when adsorbing a link. After the aggregation processing finishes in all links, the link of big attribute value is judged to be an effective link. If the high link of frequency-of-appearance is chosen, an effective link group (road map) will be displayed. 4. METHODOLOGICAL PROBLEMS The previous methodology had several problems from the viewpoint of road map reproducibility as yet. Figure 5 shows the failure result of the link aggregation by methodological problems. In the previous methodology the links extracted by nearly the same direction vector are simply aggregated. However, because the amount of the vector’s change is too large, the links necessary for aggregating cannot be extracted suitably. The other problem is a link that does not actually exist is generated due to GPS data error. Most of these problems are caused by GPS errors that the probe vehicle data has. To judge the accuracy of estimated link, attribute frequency-of-appearance value was added. However the link aggregation process had caused huge high computational overhead for calculating the link frequency of appearance value that gave to the chopped estimated road centerline. Therefore a new efficient methodology would be presented in the next section as a root-and-branch solution for these problems.
After
Missing
Missing
Fig. 5. Failure result of the link aggregation 5.1 Extraction of road geometric In this process, all positional data are labeled into three parts; the straight-line part, the curve part, and the intersection part. In the straight-line part, difference of direction vector of a contiguous links is assumed to be not too large. Therefore the variation point where the direction vector difference is large shows the existence of the curve start. On the other hand, in the terminal of the curve the direction vector difference becomes small. The curve part is extracted judging the starting point and the terminal of the curve by the point where the threshold is exceeded, as shown in Figure 6 and 7. The judgment procedure of the curve and the intersection is the same method, and only the argument value is different. The points of the remainder are labeled as a straight-line part. Figure 8 shows the judgment result of the straight-line part, the curve part, and the intersection part. In each part the different link aggregation algorithm needs to be applied.
Pf: Point forward 10m from Pia
5. IMPROVEMENT OF METHODOLOGY To improve the accuracy of road map reproducibility in a curve section, a discrimination of road part processing is built into a new methodology. The link aggregation algorithm has been improved about each three parts extracted by road geometric type. The flow of entire new data processing is shown in Figure 2(B). The data reading process and the data cleaning process are the same as previous methodology.
After
Pia: Base point
Abs(Vector Pia:Pf minus Vector Pb:Pia) > 20 degree = Pia is curve start
Pb: Point behind 10m from Pia
Fig. 6. Starting point judgment of curve and intersection
294
12th IFAC CTS (CTS 2009) Redondo Beach, CA, USA, September 2-4, 2009
Step1 Selection of target link (red line)
Pf1: Point forward 10m from Pib Pib: Base point Pf2: Point forward 10m from Pib
Step2 Link group extraction included in target link buffer
Abs(Vector Pib:Pf1 minus Vector Pib:Pf2) < 20 degree = Pib is curve terminal
Fig. 7. Terminal point judgment of curve and intersection Blue line: straight-line part Red line: intersection part Green line:curve part
Step3 The estimated centerline (yellow line) is defined from the mean value of the starting points and the terminal points of the each links that cut in the borderline
Step4 The estimated centerline (green line) is extended to the edge of the link buffer
Step5 Moving and adsorbing to the estimated centerline
Fig. 8. Judgment result of the straight-line part, the curve part, and the intersection part 5.2 Link aggregation algorithm of straight- line part The link aggregation algorithm of a straight-line part was also improved in terms of computational speed and the road map reproducibility accuracy. The improved link aggregation algorithm has five processing steps. Figure 9 shows the link aggregation algorithm image. The first step of link aggregation is to extract a group of links. One link is chosen from all links at random. The target link buffer where with the threshold in surroundings of the chosen link is made. Next, the links included in the target link buffer are extracted. Contiguous links of which distance from chosen link in previous step is less than a threshold are extracted from all links as candidates, and candidate links is counted. Until the number of candidates links doesn't change the threshold is enlarged at constant intervals. Then links that have nearly same direction vector are selected from those candidates. This process is executed by all extracted links. In this paper the threshold distance is set as 10 meters that is the mean value of error distance of GPS data. The threshold value for direction vector is assumed to be within thirty degrees. However, this threshold value should be changed according to the kind and the area of the data used.
Fig. 9. Link aggregation algorithm image (straight-line part) The next step is estimation of the centerline from the selected links by previous step. First the selected links are cut in the borderline of the target link buffer. Then the estimated centerline is defined from the mean value of the starting points and the terminal points of the each links that cut in the borderline. The starting point position of the centerline is computed from the average value of the starting point coordinates of the links that are composed of the group. Similarly, the terminal point position is computed from the links. This procedure is repeated until convergence. The accuracy of the centerline is improved by this processing. In the fourth step, the estimated centerline is extended to the edge of the link buffer. The last step of this aggregation procedure is to adsorb each link of the link group to the estimated centerline. The difference between previous algorithm and new algorithm is the treatment of an adsorption parameter. Although the adsorption parameter of previous algorithm is the number of a link, the parameter of new algorithm is the length of a link. This parameter can be used as an index that shows the likelihood of the centerline. Moreover, because the centerline
295
12th IFAC CTS (CTS 2009) Redondo Beach, CA, USA, September 2-4, 2009
is not subdivided in detail like an old algorithm, this procedure can compute at high speed. Figure 10 shows the output of the link aggregation procedure. The road centerline is estimated in high accuracy by the link aggregation processing according to the direction that can run.
straight-line part (result of the link aggregation)
Blue line: straight-line part Red line: intersection part Green line:curve part
curve part (each point is a node) * The same color points show tracks of the same trajectory
approximates by the spline curve
Fig. 11. Link aggregation algorithm image (curve part) Before
After
intersection part (the links of the intersection part remain)
approximates by the straight line
Fig. 10. Result of the link aggregation (straight-line part) straight-line part (result of the link aggregation)
5.3 Link aggregation algorithm of curve part After processing of a straight-line part is completed, the link of the curve part that remains on a map is aggregated. The algorithm of a curve part is simple.
Fig. 12. Link aggregation algorithm image (intersection part)
First, the trajectory data to which it runs at the same section (same starting point and terminal point) is extracted. Then, to improving approximate accuracy, data with shortest distance and data with longest distance are excluded from the extracted trajectory dataset. Last, the centerline of the curve part is estimated by approximating between the starting point and terminal points by the circular arc or the spline curve or the straight line, etc. The approximation curve should be selected by the kind of the data used. Because the area of data that we used in this paper is urban area, we adopted the spline curve. Figure 11 shows the link aggregation algorithm image of curve part.
5.5 Result of the link aggregation
5.4 Link aggregation algorithm of intersection part The last processing of link aggregation algorithm is the aggregation in remains link in the intersection portion. The links that was judged to the intersection by the extraction of road geometric is assumed to have long length and large curvature by GPS errors (left chart of Figure 12).
Also the accuracy of GPS data used in this paper is about 10 meters. Therefore, in the intersection the reproduction of the curve in the approximation such as spline curve is difficult. So we adopted simply approximation by the straight line (right chart of Figure 12).
Figure 13 shows the output of the link aggregation procedure. In the road map before improvement (old algorithm), the curve and crossing with low frequency of appearance is deleted. However, in the road map after improvement (new algorithm), the curve and the crossing part are created correctly. 6. A ROAD MAP COMPARISON Figure 14 shows the created road map was overlapped with the actual road map in Kyoto. It is found that the road map made from figure corresponds to an about actual road map. However all the road networks are not covered, because the
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volume of GPS data used was few (number of links: 49,309, total length of links: 415.4km, total running times: 18.1 hours) and there was a lot of data where it ran repeating the same road. To cover all the road networks the huge amount of GPS data where it runs repeating large range is necessary. Blue line: straight-line part Red line: intersection part Green line:curve part
Fig. 14. Overlapping of created road map and actual map Old algorithm
New algorithm
One-way
Both directions One-way
Both directions
Fig. 13. Result of the link aggregation The road map that is generated by this methodology has both different directions links as double link. For a one-way road only one link that it can run is created and when it is possible to run in both direction two links are created respectively. This direction link can be used as traffic regulations information. Figure 15 shows the result of the traffic regulations information. When sufficient volumes of GPS data are available, a road map with both different directions can be identified. 7. CONCLUSION In this paper, we have described the improvement of creation method of high-precision road maps from positioning data obtained from probe vehicles. The fundamental methodology of generating a digital road network that we developed had several problems from the viewpoint of road map reproducibility, particularly in a curve section. Therefore, in curve section we have adopted another algorithm. In addition, the improvement of the algorithm in the straight part was the very effective in the accuracy improvement and the most effective in order to reduce the amount of data. If the method shown in this paper is used, a map will be able to be created to low cost rather than the conventional method. In this paper only few GPS data was used. In the future the accuracy of the map making will be verified by using a huge GPS data. We will investigate the method of estimating the data size for generating a road network with required.
Fig. 15. Result of the traffic regulations REFERENCES Ito, H., Suga, Y., Higashi, T. and Asakura, Y. (2006). Generating a road map based on location positioning data of probe vehicles, IFAC Symposium on Control in Transportation Systems, Vol.11, part 1. Asakura, Y., Hato, E., Daito, T. and Tanabe, J. (2000). A. Monitoring Travel Behaviour Using PHS Based Location Data. Journal of Infrastructure Planning and Management (in Japanese), No.653/IV-48, pp.95-104. Morris, S., Morris, A., Barnard, K. (2004). Digital trail libraries. Joint Conference on Digital Libraries, pp.6371. Ogle, J., R. Guensler, W. Bachman, M. Koutsak, and J. Wolf (2002). Accuracy of GPS for Determining Driver Performance Parameters. Transportation Research Record, No.1818, pp.12-24. Ohumori N., Muromachi Y., Harada N. and Ohta K. (2002). Analysis of Day-to-Day Variations of Travel Time Using GPS and GIS. Proceedings of the Third International Conference on Traffic and Transportation Studies (ICTTS), Vol.2, pp.1306-1313. Suh, YC, Konish, Y., Shibasaki, R. (2002). Assessing the improvement of positioning accuracy using a GPS and pseudolites signal in urban area. The Student Forum of the Geoinformation Forum Japan, 4, pp.36-41.
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