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Cellular service carriers often employ reactive strategies to assist customers who experience non-outage related individual service degradation issues (e.g., service performance degradations that do not impact customers at scale and are likely caused by network provisioning issues for individual devices). Customers need to contact customer care to request assistance before these issues are resolved. This paper presents our experience with PACE (ProActive customer CarE), a novel, proactive system that monitors, troubleshoots and resolves individual service issues, without having to rely on customers to first contact customer care for assistance. PACE seeks to improve customer experience and care operation efficiency by automatically detecting individual (non-outage related) service issues, prioritizing repair actions by predicting customers who are likely to contact care to report their issues, and proactively triggering actions to resolve these issues. We develop three machine learning-based prediction models, and implement a fully automated system that integrates these prediction models and takes resolution actions for individual customers.We conduct a large-scale trace-driven evaluation using real-world data collected from a major cellular carrier in the US, and demonstrate that PACE is able to predict customers who are likely to contact care due to non-outage related individual service issues with high accuracy. We further deploy PACE into this cellular carrier network. Our field trial results show that PACE is effective in proactively resolving non-outage related individual customer service issues, improving customer experience, and reducing the need for customers to report their service issues.
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Enhancing Deep Neural Network-Based Trajectory Prediction: Fine-Tuning and Inherent Movement-Driven Post-Processing
As a proactive means of preventing struck-by accidents in construction, many studies have presented proximity monitoring applications using wireless sensors (e.g., RFID, UWB, and GPS) or computer vision methods. Most prior research has emphasized proximity detection rather than prediction. However, prediction can be more effective and important for contact-driven accident prevention, particularly given that the sooner workers (e.g., equipment operators and workers on foot) are informed of their proximity to each other, the more likely they are to avoid the impending collision. In earlier studies, the authors presented a trajectory prediction method leveraging a deep neural network to examine the feasibility of proximity prediction in real-world applications. In this study, we enhance the existing trajectory prediction accuracy. Specifically, we improve the trajectory prediction model by tuning its pre-trained weight parameters with construction data. Moreover, inherent movement-driven post-processing algorithm is developed to refine the trajectory prediction of a target in accordance with its inherent movement patterns such as the final position, predominant direction, and average velocity. In a test on real-site operations data, the proposed approach demonstrates the improvement in accuracy: for 5.28 seconds’ prediction, it achieves 0.39 meter average displacement error, improved by 51.43% as compared with the previous one (0.84 meters). The improved trajectory prediction method can support to predict potential contact-driven hazards in advance, which can allow for prompt feedback (e.g., visible, acoustic, and vibration alarms) to equipment operators and workers on foot. The proactive intervention can lead the workers to take prompt evasive action, thereby reducing the chance of an impending collision.
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- Award ID(s):
- 1734266
- PAR ID:
- 10201431
- Editor(s):
- El Asmar, Mounir; Grau, David; Tang, Pingbo
- Date Published:
- Journal Name:
- Construction Research Congress 2020
- Page Range / eLocation ID:
- 67 to 75
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1061/9780784482872.008
