The main question to address in this paper is to recommend optimal signal timing plans in real time under incidents by incorporating domain knowledge developed with the traffic signal timing plans tuned for possible incidents, and learning from historical data of both traffic and implemented signals timing. The effectiveness of traffic incident management is often limited by the late response time and excessive workload of traffic operators. This paper proposes a novel decision-making framework that learns from both data and domain knowledge to real-time recommend contingency signal plans that accommodate non-recurrent traffic, with the outputs from real-time traffic prediction at least 30 min in advance. Specifically, considering the rare occurrences of engagement of contingency signal plans for incidents, it is proposed to decompose the end-to-end recommendation task into two hierarchical models—real-time traffic prediction and plan association. The connections between the two models are learnt through metric learning, which reinforces partial-order preferences observed from historical signal engagement records. The effectiveness of this approach is demonstrated by testing this framework on the traffic network in Cranberry Township, Pennsylvania, U.S., in 2019. Results show that the recommendation system has a precision score of 96.75% and recall of 87.5% on the testing plan, and makesmore »
Multiscale Estimation of Event Arrival Times and Their Uncertainties in Hydroacoustic Records from Autonomous Oceanic Floats
ABSTRACT We describe an algorithm to pick event onsets in noisy records, characterize their error distributions, and derive confidence intervals on their timing. Our method is based on an Akaike information criterion that identifies the partition of a time series into a noise and a signal segment that maximizes the signal-to-noise ratio. The distinctive feature of our approach lies in the timing uncertainty analysis, and in its application in the time domain and in the wavelet timescale domain. Our novel data are records collected by freely floating Mobile Earthquake Recording in Marine Areas by Independent Divers (MERMAID) instruments, midcolumn hydrophones that report triggered segments of ocean-acoustic time series.
- Award ID(s):
- 1917058
- Publication Date:
- NSF-PAR ID:
- 10148312
- Journal Name:
- Bulletin of the Seismological Society of America
- ISSN:
- 0037-1106
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1785/0120190173
