TY - GEN
T1 - DeepRisk
T2 - 15th Annual International Conference on Distributed Computing in Sensor Systems, DCOSS 2019
AU - Zhang, Yang
AU - Wang, Hongxiao
AU - Zhang, Daniel
AU - Wang, Dong
N1 - Funding Information:
This research is supported in part by the National Science Foundation under Grant No. CNS-1831669, CBET-1637251, CNS-1566465 and IIS-1447795, Army Research Office under Grant W911NF-17-1-0409, Google 2017 Faculty Research Award. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - This paper focuses on the migratable traffic risk estimation problem in intelligent transportation systems using the social (human-centric) sensing. The goal is to accurately estimate the traffic risk of a target area where the ground truth traffic accident reports are not available by leveraging an estimation model from a source area where such data is available. Two important challenges exist. The first challenge lies in the discrepancy between source and target areas (e.g., layouts, road conditions, and local regulations) and such discrepancy would prevent a direct application of a model from the source area to the target area. The second challenge lies in the difficulty of identifying all potential features in the migratable traffic risk estimation problem and decide the importance of identified features due to the lack of ground truth labels in the target area. To address these challenges, we develop DeepRisk, a social sensing based migratable traffic risk estimation scheme using deep transfer learning techniques. The evaluation results on a real world dataset in New York City show the DeepRisk significantly outperforms the state-of-the-art baselines in accurately estimating the traffic risk of locations in a city.
AB - This paper focuses on the migratable traffic risk estimation problem in intelligent transportation systems using the social (human-centric) sensing. The goal is to accurately estimate the traffic risk of a target area where the ground truth traffic accident reports are not available by leveraging an estimation model from a source area where such data is available. Two important challenges exist. The first challenge lies in the discrepancy between source and target areas (e.g., layouts, road conditions, and local regulations) and such discrepancy would prevent a direct application of a model from the source area to the target area. The second challenge lies in the difficulty of identifying all potential features in the migratable traffic risk estimation problem and decide the importance of identified features due to the lack of ground truth labels in the target area. To address these challenges, we develop DeepRisk, a social sensing based migratable traffic risk estimation scheme using deep transfer learning techniques. The evaluation results on a real world dataset in New York City show the DeepRisk significantly outperforms the state-of-the-art baselines in accurately estimating the traffic risk of locations in a city.
KW - Deep Transfer Learning
KW - Intelligent Transportation
KW - Migratable Traffic Risk Estimation
KW - Social Sensing
UR - http://www.scopus.com/inward/record.url?scp=85071957225&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071957225&partnerID=8YFLogxK
U2 - 10.1109/DCOSS.2019.00039
DO - 10.1109/DCOSS.2019.00039
M3 - Conference contribution
AN - SCOPUS:85071957225
T3 - Proceedings - 15th Annual International Conference on Distributed Computing in Sensor Systems, DCOSS 2019
SP - 123
EP - 130
BT - Proceedings - 15th Annual International Conference on Distributed Computing in Sensor Systems, DCOSS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 May 2019 through 31 May 2019
ER -