A Streaming Algorithm for Online Estimation of Temporal and Spatial Extent of Delays
Knowing traffic congestion and its impact on travel time in advance is vital for proactive travel planning as well as advanced traffic management. This paper proposes a streaming algorithm to estimate temporal and spatial extent of delays online which can be deployed with roadside sensors. First, th...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2017/4018409 |
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| author | Kittipong Hiriotappa Suttipong Thajchayapong Pimwadee Chaovalit Suporn Pongnumkul |
| author_facet | Kittipong Hiriotappa Suttipong Thajchayapong Pimwadee Chaovalit Suporn Pongnumkul |
| author_sort | Kittipong Hiriotappa |
| collection | DOAJ |
| description | Knowing traffic congestion and its impact on travel time in advance is vital for proactive travel planning as well as advanced traffic management. This paper proposes a streaming algorithm to estimate temporal and spatial extent of delays online which can be deployed with roadside sensors. First, the proposed algorithm uses streaming input from individual sensors to detect a deviation from normal traffic patterns, referred to as anomalies, which is used as an early indication of delay occurrence. Then, a group of consecutive sensors that detect anomalies are used to temporally and spatially estimate extent of delay associated with the detected anomalies. Performance evaluations are conducted using a real-world data set collected by roadside sensors in Bangkok, Thailand, and the NGSIM data set collected in California, USA. Using NGSIM data, it is shown qualitatively that the proposed algorithm can detect consecutive occurrences of shockwaves and estimate their associated delays. Then, using a data set from Thailand, it is shown quantitatively that the proposed algorithm can detect and estimate delays associated with both recurring congestion and incident-induced nonrecurring congestion. The proposed algorithm also outperforms the previously proposed streaming algorithm. |
| format | Article |
| id | doaj-art-2ef4e99c6b264c21bc123d1fad51983a |
| institution | OA Journals |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-2ef4e99c6b264c21bc123d1fad51983a2025-08-20T02:38:52ZengWileyJournal of Advanced Transportation0197-67292042-31952017-01-01201710.1155/2017/40184094018409A Streaming Algorithm for Online Estimation of Temporal and Spatial Extent of DelaysKittipong Hiriotappa0Suttipong Thajchayapong1Pimwadee Chaovalit2Suporn Pongnumkul3National Electronics and Computer Technology Center (NECTEC), 112 Thailand Science Park, Phahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani 12120, ThailandNational Electronics and Computer Technology Center (NECTEC), 112 Thailand Science Park, Phahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani 12120, ThailandNational Electronics and Computer Technology Center (NECTEC), 112 Thailand Science Park, Phahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani 12120, ThailandNational Electronics and Computer Technology Center (NECTEC), 112 Thailand Science Park, Phahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani 12120, ThailandKnowing traffic congestion and its impact on travel time in advance is vital for proactive travel planning as well as advanced traffic management. This paper proposes a streaming algorithm to estimate temporal and spatial extent of delays online which can be deployed with roadside sensors. First, the proposed algorithm uses streaming input from individual sensors to detect a deviation from normal traffic patterns, referred to as anomalies, which is used as an early indication of delay occurrence. Then, a group of consecutive sensors that detect anomalies are used to temporally and spatially estimate extent of delay associated with the detected anomalies. Performance evaluations are conducted using a real-world data set collected by roadside sensors in Bangkok, Thailand, and the NGSIM data set collected in California, USA. Using NGSIM data, it is shown qualitatively that the proposed algorithm can detect consecutive occurrences of shockwaves and estimate their associated delays. Then, using a data set from Thailand, it is shown quantitatively that the proposed algorithm can detect and estimate delays associated with both recurring congestion and incident-induced nonrecurring congestion. The proposed algorithm also outperforms the previously proposed streaming algorithm.http://dx.doi.org/10.1155/2017/4018409 |
| spellingShingle | Kittipong Hiriotappa Suttipong Thajchayapong Pimwadee Chaovalit Suporn Pongnumkul A Streaming Algorithm for Online Estimation of Temporal and Spatial Extent of Delays Journal of Advanced Transportation |
| title | A Streaming Algorithm for Online Estimation of Temporal and Spatial Extent of Delays |
| title_full | A Streaming Algorithm for Online Estimation of Temporal and Spatial Extent of Delays |
| title_fullStr | A Streaming Algorithm for Online Estimation of Temporal and Spatial Extent of Delays |
| title_full_unstemmed | A Streaming Algorithm for Online Estimation of Temporal and Spatial Extent of Delays |
| title_short | A Streaming Algorithm for Online Estimation of Temporal and Spatial Extent of Delays |
| title_sort | streaming algorithm for online estimation of temporal and spatial extent of delays |
| url | http://dx.doi.org/10.1155/2017/4018409 |
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