IEICE TRANSACTIONS on Information
Prioritization of Lane-Specific Traffic Jam Detection for Automotive Navigation Framework Utilizing Suddenness Index and Automatic Threshold Determination
Summary :
Car navigation systems provide traffic jam information. In this study, we attempt to provide more detailed traffic jam information that considers the lane in which a traffic jam is in. This makes it possible for users to avoid long waits in queued traffic going toward an unintended destination. Lane-specific traffic jam detection utilizes image processing, which incurs long processing time and high cost. To reduce these, we propose a “suddenness index (SI)” to categorize candidate areas as sudden or periodic. Sudden traffic jams are prioritized as they may lead to accidents. This technology aggregates the number of connected cars for each mesh on a map and quantifies the degree of deviation from the ordinary state. In this paper, we evaluate the proposed method using actual global positioning system (GPS) data and found that the proposed index can cover 100% of sudden lane-specific traffic jams while excluding 82.2% of traffic jam candidates. We also demonstrate the effectiveness of time savings by integrating the proposed method into a demonstration framework. In addition, we improved the proposed method's ability to automatically determine the SI threshold to select the appropriate traffic jam candidates to avoid manual parameter settings.
- Publication
- IEICE TRANSACTIONS on Information Vol.E106-D No.5 pp.895-903
- Publication Date
- 2023/05/01
- Publicized
- 2023/02/03
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2022DAP0005
- Type of Manuscript
- Special Section PAPER (Special Section on Data Engineering and Information Management)
- Category
Authors
Aki HAYASHI
NTT Smart Data Science Center
Yuki YOKOHATA
NTT Smart Data Science Center
Takahiro HATA
NTT Smart Data Science Center
Kouhei MORI
NTT Smart Data Science Center
Masato KAMIYA
NTT Smart Data Science Center
Keyword
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Aki HAYASHI, Yuki YOKOHATA, Takahiro HATA, Kouhei MORI, Masato KAMIYA, "Prioritization of Lane-Specific Traffic Jam Detection for Automotive Navigation Framework Utilizing Suddenness Index and Automatic Threshold Determination" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 5, pp. 895-903, May 2023, doi: 10.1587/transinf.2022DAP0005.
Abstract: Car navigation systems provide traffic jam information. In this study, we attempt to provide more detailed traffic jam information that considers the lane in which a traffic jam is in. This makes it possible for users to avoid long waits in queued traffic going toward an unintended destination. Lane-specific traffic jam detection utilizes image processing, which incurs long processing time and high cost. To reduce these, we propose a “suddenness index (SI)” to categorize candidate areas as sudden or periodic. Sudden traffic jams are prioritized as they may lead to accidents. This technology aggregates the number of connected cars for each mesh on a map and quantifies the degree of deviation from the ordinary state. In this paper, we evaluate the proposed method using actual global positioning system (GPS) data and found that the proposed index can cover 100% of sudden lane-specific traffic jams while excluding 82.2% of traffic jam candidates. We also demonstrate the effectiveness of time savings by integrating the proposed method into a demonstration framework. In addition, we improved the proposed method's ability to automatically determine the SI threshold to select the appropriate traffic jam candidates to avoid manual parameter settings.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022DAP0005/_p
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@ARTICLE{e106-d_5_895,
author={Aki HAYASHI, Yuki YOKOHATA, Takahiro HATA, Kouhei MORI, Masato KAMIYA, },
journal={IEICE TRANSACTIONS on Information},
title={Prioritization of Lane-Specific Traffic Jam Detection for Automotive Navigation Framework Utilizing Suddenness Index and Automatic Threshold Determination},
year={2023},
volume={E106-D},
number={5},
pages={895-903},
abstract={Car navigation systems provide traffic jam information. In this study, we attempt to provide more detailed traffic jam information that considers the lane in which a traffic jam is in. This makes it possible for users to avoid long waits in queued traffic going toward an unintended destination. Lane-specific traffic jam detection utilizes image processing, which incurs long processing time and high cost. To reduce these, we propose a “suddenness index (SI)” to categorize candidate areas as sudden or periodic. Sudden traffic jams are prioritized as they may lead to accidents. This technology aggregates the number of connected cars for each mesh on a map and quantifies the degree of deviation from the ordinary state. In this paper, we evaluate the proposed method using actual global positioning system (GPS) data and found that the proposed index can cover 100% of sudden lane-specific traffic jams while excluding 82.2% of traffic jam candidates. We also demonstrate the effectiveness of time savings by integrating the proposed method into a demonstration framework. In addition, we improved the proposed method's ability to automatically determine the SI threshold to select the appropriate traffic jam candidates to avoid manual parameter settings.},
keywords={},
doi={10.1587/transinf.2022DAP0005},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - Prioritization of Lane-Specific Traffic Jam Detection for Automotive Navigation Framework Utilizing Suddenness Index and Automatic Threshold Determination
T2 - IEICE TRANSACTIONS on Information
SP - 895
EP - 903
AU - Aki HAYASHI
AU - Yuki YOKOHATA
AU - Takahiro HATA
AU - Kouhei MORI
AU - Masato KAMIYA
PY - 2023
DO - 10.1587/transinf.2022DAP0005
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2023
AB - Car navigation systems provide traffic jam information. In this study, we attempt to provide more detailed traffic jam information that considers the lane in which a traffic jam is in. This makes it possible for users to avoid long waits in queued traffic going toward an unintended destination. Lane-specific traffic jam detection utilizes image processing, which incurs long processing time and high cost. To reduce these, we propose a “suddenness index (SI)” to categorize candidate areas as sudden or periodic. Sudden traffic jams are prioritized as they may lead to accidents. This technology aggregates the number of connected cars for each mesh on a map and quantifies the degree of deviation from the ordinary state. In this paper, we evaluate the proposed method using actual global positioning system (GPS) data and found that the proposed index can cover 100% of sudden lane-specific traffic jams while excluding 82.2% of traffic jam candidates. We also demonstrate the effectiveness of time savings by integrating the proposed method into a demonstration framework. In addition, we improved the proposed method's ability to automatically determine the SI threshold to select the appropriate traffic jam candidates to avoid manual parameter settings.
ER -
English
