scholarly journals Balanced Event Prediction Through Sampled Survival Analysis

2019 ◽  
Vol 2 (1) ◽  
pp. 28-38 ◽  
Author(s):  
Gargi Datta ◽  
Leigh E. Alexander ◽  
Michael A. Hinterberg ◽  
Yolanda Hagar
Author(s):  
Amin Vahedian ◽  
Xun Zhou ◽  
Ling Tong ◽  
W. Nick Street ◽  
Yanhua Li

Urban dispersal events are processes where an unusually large number of people leave the same area in a short period. Early prediction of dispersal events is important in mitigating congestion and safety risks and making better dispatching decisions for taxi and ride-sharing fleets. Existing work mostly focuses on predicting taxi demand in the near future by learning patterns from historical data. However, they fail in case of abnormality because dispersal events with abnormally high demand are non-repetitive and violate common assumptions such as smoothness in demand change over time. Instead, in this paper we argue that dispersal events follow a complex pattern of trips and other related features in the past, which can be used to predict such events. Therefore, we formulate the dispersal event prediction problem as a survival analysis problem. We propose a two-stage framework (DILSA), where a deep learning model combined with survival analysis is developed to predict the probability of a dispersal event and its demand volume. We conduct extensive case studies and experiments on the NYC Yellow taxi dataset from 20142016. Results show that DILSA can predict events in the next 5 hours with F1-score of 0:7 and with average time error of 18 minutes. It is orders of magnitude better than the state-of-the-art deep learning approaches for taxi demand prediction.


2020 ◽  
Vol 67 (6) ◽  
pp. 712-722
Author(s):  
Sebastian Gmeinwieser ◽  
Kai Sebastian Schneider ◽  
Maximilian Bardo ◽  
Timo Brockmeyer ◽  
York Hagmayer

2007 ◽  
Vol 13 (4) ◽  
pp. 530 ◽  
Author(s):  
Kyung Woo Park ◽  
Joong-Won Park ◽  
Tae Hyun Kim ◽  
Jun Il Choi ◽  
Seong Hoon Kim ◽  
...  

Author(s):  
Krittika Singh

The Internet of things is the internetworking of physical devices, vehicles, buildings, and other items—embedded with electronics, software, sensors, actuators, and network connectivity that enable these objects to collect and exchange data. The IoT allows objects to be sensed and/or controlled remotely across existing network infrastructure, creating opportunities for more direct integration of the physical world into computer-based systems, and resulting in improved efficiency, accuracy and economic benefit in addition to reduced human intervention. In this research an expert system based upon the IOT is developed in which the next event in the flight schedules due to any kind of medical emergencies is to be predicted. For this the medical data of all the patients are to be collected through WBAN.


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