Trajectory-Based Road-Geometry and Crash-Risk Estimation with Smartphone-Assisted Sensor Networks
As mobile devices came into wide use, it became practical to collect travel data in personal logs. Many studies have been conducted to extract meaningful information from this trend. In this study, we present a system for monitoring road-geometry and crash-risk estimation, based on trajectories crea...
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| Format: | Article |
| Language: | English |
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Wiley
2014-03-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2014/943845 |
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| _version_ | 1849308334527610880 |
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| author | Dongwook Lee Ilmin Kim Minsoo Hahn |
| author_facet | Dongwook Lee Ilmin Kim Minsoo Hahn |
| author_sort | Dongwook Lee |
| collection | DOAJ |
| description | As mobile devices came into wide use, it became practical to collect travel data in personal logs. Many studies have been conducted to extract meaningful information from this trend. In this study, we present a system for monitoring road-geometry and crash-risk estimation, based on trajectories created using a smartphone-aided sensor network. The proposed system consists of a number of node vehicles with smartphone applications for GPS data collection and a map server which aggregates the collected GPS trajectories and estimates road conditions. In order to estimate road geometry and crash risk information, the trajectories were segmented and categorized into groups according to their headings. Based on the processed trajectories, the geometry of the road section was estimated using the principal curve method. The crash risk of the road section was estimated from the constructed road geometry and the density map of the trajectories. Our system was evaluated using bicycle trajectories collected from segregated bicycle tracks in Seoul, Korea. Constructed geometry and crash-risk information of the track was compared with real track geometry and crash data. As a result, the estimated road geometry showed over 74% similarity and the calculated crash risk (61%) matched the real crash data. |
| format | Article |
| id | doaj-art-0e1d7d6e16394692af82eb7bb9e90a8a |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2014-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-0e1d7d6e16394692af82eb7bb9e90a8a2025-08-20T03:54:29ZengWileyInternational Journal of Distributed Sensor Networks1550-14772014-03-011010.1155/2014/943845943845Trajectory-Based Road-Geometry and Crash-Risk Estimation with Smartphone-Assisted Sensor NetworksDongwook Lee0Ilmin Kim1Minsoo Hahn2 Digital Media Laboratory, KAIST, ICC Campus, Munji-dong, Yuseong-gu, Daejeon 305-732, Republic of Korea Computer Science Department, Hansung University, Samseongyoro-16 gil, Seongbuk-gu, Seoul 136-792, Republic of Korea Digital Media Laboratory, KAIST, ICC Campus, Munji-dong, Yuseong-gu, Daejeon 305-732, Republic of KoreaAs mobile devices came into wide use, it became practical to collect travel data in personal logs. Many studies have been conducted to extract meaningful information from this trend. In this study, we present a system for monitoring road-geometry and crash-risk estimation, based on trajectories created using a smartphone-aided sensor network. The proposed system consists of a number of node vehicles with smartphone applications for GPS data collection and a map server which aggregates the collected GPS trajectories and estimates road conditions. In order to estimate road geometry and crash risk information, the trajectories were segmented and categorized into groups according to their headings. Based on the processed trajectories, the geometry of the road section was estimated using the principal curve method. The crash risk of the road section was estimated from the constructed road geometry and the density map of the trajectories. Our system was evaluated using bicycle trajectories collected from segregated bicycle tracks in Seoul, Korea. Constructed geometry and crash-risk information of the track was compared with real track geometry and crash data. As a result, the estimated road geometry showed over 74% similarity and the calculated crash risk (61%) matched the real crash data.https://doi.org/10.1155/2014/943845 |
| spellingShingle | Dongwook Lee Ilmin Kim Minsoo Hahn Trajectory-Based Road-Geometry and Crash-Risk Estimation with Smartphone-Assisted Sensor Networks International Journal of Distributed Sensor Networks |
| title | Trajectory-Based Road-Geometry and Crash-Risk Estimation with Smartphone-Assisted Sensor Networks |
| title_full | Trajectory-Based Road-Geometry and Crash-Risk Estimation with Smartphone-Assisted Sensor Networks |
| title_fullStr | Trajectory-Based Road-Geometry and Crash-Risk Estimation with Smartphone-Assisted Sensor Networks |
| title_full_unstemmed | Trajectory-Based Road-Geometry and Crash-Risk Estimation with Smartphone-Assisted Sensor Networks |
| title_short | Trajectory-Based Road-Geometry and Crash-Risk Estimation with Smartphone-Assisted Sensor Networks |
| title_sort | trajectory based road geometry and crash risk estimation with smartphone assisted sensor networks |
| url | https://doi.org/10.1155/2014/943845 |
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