Map Matching for Fixed Sensor Data Based on Utility Theory
Map matching can provide useful traffic information by aligning the observed trajectories of vehicles with the road network on a digital map. It has an essential role in many advanced intelligent traffic systems (ITSs). Unfortunately, almost all current map-matching approaches were developed for GPS...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/5585131 |
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| _version_ | 1849473274157727744 |
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| author | Kangkang He Qi Cao Gang Ren Dawei Li Shuichao Zhang |
| author_facet | Kangkang He Qi Cao Gang Ren Dawei Li Shuichao Zhang |
| author_sort | Kangkang He |
| collection | DOAJ |
| description | Map matching can provide useful traffic information by aligning the observed trajectories of vehicles with the road network on a digital map. It has an essential role in many advanced intelligent traffic systems (ITSs). Unfortunately, almost all current map-matching approaches were developed for GPS trajectories generated by probe sensors mounted in a few vehicles and cannot deal with the trajectories of massive vehicle samples recorded by fixed sensors, such as camera detectors. In this paper, we propose a novel map-matching model termed Fixed-MM, which is designed specifically for fixed sensor data. Based on two key observations from real-world data, Fixed-MM considers (1) the utility of each path and (2) the travel time constraint to match the trajectories of fixed sensor data to a specific path. Meanwhile, with the laws derived from the distribution of GPS trajectories, a path generation algorithm was developed to search for candidates. The proposed Fixed-MM was examined with field-test data. The experimental results show that Fixed-MM outperforms two types of classical map-matching algorithms regarding accuracy and efficiency when fixed sensor data are used. The proposed Fixed-MM can identify 68.38% of the links correctly, even when the spatial gap between the sensor pair is increased to five kilometers. The average computation time spent by Fixed-MM on one point is only 0.067 s, and we argue that the proposed method can be used online for many real-time ITS applications. |
| format | Article |
| id | doaj-art-d35e30e82c304addb44910d3bced023c |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-d35e30e82c304addb44910d3bced023c2025-08-20T03:24:12ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/55851315585131Map Matching for Fixed Sensor Data Based on Utility TheoryKangkang He0Qi Cao1Gang Ren2Dawei Li3Shuichao Zhang4School of Transportation, Southeast University, Nanjing 211189, ChinaSchool of Transportation, Southeast University, Nanjing 211189, ChinaSchool of Transportation, Southeast University, Nanjing 211189, ChinaSchool of Transportation, Southeast University, Nanjing 211189, ChinaSchool of Civil and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, ChinaMap matching can provide useful traffic information by aligning the observed trajectories of vehicles with the road network on a digital map. It has an essential role in many advanced intelligent traffic systems (ITSs). Unfortunately, almost all current map-matching approaches were developed for GPS trajectories generated by probe sensors mounted in a few vehicles and cannot deal with the trajectories of massive vehicle samples recorded by fixed sensors, such as camera detectors. In this paper, we propose a novel map-matching model termed Fixed-MM, which is designed specifically for fixed sensor data. Based on two key observations from real-world data, Fixed-MM considers (1) the utility of each path and (2) the travel time constraint to match the trajectories of fixed sensor data to a specific path. Meanwhile, with the laws derived from the distribution of GPS trajectories, a path generation algorithm was developed to search for candidates. The proposed Fixed-MM was examined with field-test data. The experimental results show that Fixed-MM outperforms two types of classical map-matching algorithms regarding accuracy and efficiency when fixed sensor data are used. The proposed Fixed-MM can identify 68.38% of the links correctly, even when the spatial gap between the sensor pair is increased to five kilometers. The average computation time spent by Fixed-MM on one point is only 0.067 s, and we argue that the proposed method can be used online for many real-time ITS applications.http://dx.doi.org/10.1155/2021/5585131 |
| spellingShingle | Kangkang He Qi Cao Gang Ren Dawei Li Shuichao Zhang Map Matching for Fixed Sensor Data Based on Utility Theory Journal of Advanced Transportation |
| title | Map Matching for Fixed Sensor Data Based on Utility Theory |
| title_full | Map Matching for Fixed Sensor Data Based on Utility Theory |
| title_fullStr | Map Matching for Fixed Sensor Data Based on Utility Theory |
| title_full_unstemmed | Map Matching for Fixed Sensor Data Based on Utility Theory |
| title_short | Map Matching for Fixed Sensor Data Based on Utility Theory |
| title_sort | map matching for fixed sensor data based on utility theory |
| url | http://dx.doi.org/10.1155/2021/5585131 |
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