14,338 research outputs found
DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic Congestion
Non-recurring traffic congestion is caused by temporary disruptions, such as
accidents, sports games, adverse weather, etc. We use data related to real-time
traffic speed, jam factors (a traffic congestion indicator), and events
collected over a year from Nashville, TN to train a multi-layered deep neural
network. The traffic dataset contains over 900 million data records. The
network is thereafter used to classify the real-time data and identify
anomalous operations. Compared with traditional approaches of using statistical
or machine learning techniques, our model reaches an accuracy of 98.73 percent
when identifying traffic congestion caused by football games. Our approach
first encodes the traffic across a region as a scaled image. After that the
image data from different timestamps is fused with event- and time-related
data. Then a crossover operator is used as a data augmentation method to
generate training datasets with more balanced classes. Finally, we use the
receiver operating characteristic (ROC) analysis to tune the sensitivity of the
classifier. We present the analysis of the training time and the inference time
separately
Leveraging Personal Navigation Assistant Systems Using Automated Social Media Traffic Reporting
Modern urbanization is demanding smarter technologies to improve a variety of
applications in intelligent transportation systems to relieve the increasing
amount of vehicular traffic congestion and incidents. Existing incident
detection techniques are limited to the use of sensors in the transportation
network and hang on human-inputs. Despite of its data abundance, social media
is not well-exploited in such context. In this paper, we develop an automated
traffic alert system based on Natural Language Processing (NLP) that filters
this flood of information and extract important traffic-related bullets. To
this end, we employ the fine-tuning Bidirectional Encoder Representations from
Transformers (BERT) language embedding model to filter the related traffic
information from social media. Then, we apply a question-answering model to
extract necessary information characterizing the report event such as its exact
location, occurrence time, and nature of the events. We demonstrate the adopted
NLP approaches outperform other existing approach and, after effectively
training them, we focus on real-world situation and show how the developed
approach can, in real-time, extract traffic-related information and
automatically convert them into alerts for navigation assistance applications
such as navigation apps.Comment: This paper is accepted for publication in IEEE Technology Engineering
Management Society International Conference (TEMSCON'20), Metro Detroit,
Michigan (USA
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Network emulation focusing on QoS-Oriented satellite communication
This chapter proposes network emulation basics and a complete case study of QoS-oriented Satellite Communication
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