9,770 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
A Tutorial on Event Detection using Social Media Data Analysis: Applications, Challenges, and Open Problems
In recent years, social media has become one of the most popular platforms
for communication. These platforms allow users to report real-world incidents
that might swiftly and widely circulate throughout the whole social network. A
social event is a real-world incident that is documented on social media.
Social gatherings could contain vital documentation of crisis scenarios.
Monitoring and analyzing this rich content can produce information that is
extraordinarily valuable and help people and organizations learn how to take
action. In this paper, a survey on the potential benefits and applications of
event detection with social media data analysis will be presented. Moreover,
the critical challenges and the fundamental tradeoffs in event detection will
be methodically investigated by monitoring social media stream. Then,
fundamental open questions and possible research directions will be introduced
AI on the Road: A Comprehensive Analysis of Traffic Accidents and Accident Detection System in Smart Cities
Accident detection and traffic analysis is a critical component of smart city
and autonomous transportation systems that can reduce accident frequency,
severity and improve overall traffic management. This paper presents a
comprehensive analysis of traffic accidents in different regions across the
United States using data from the National Highway Traffic Safety
Administration (NHTSA) Crash Report Sampling System (CRSS). To address the
challenges of accident detection and traffic analysis, this paper proposes a
framework that uses traffic surveillance cameras and action recognition systems
to detect and respond to traffic accidents spontaneously. Integrating the
proposed framework with emergency services will harness the power of traffic
cameras and machine learning algorithms to create an efficient solution for
responding to traffic accidents and reducing human errors. Advanced
intelligence technologies, such as the proposed accident detection systems in
smart cities, will improve traffic management and traffic accident severity.
Overall, this study provides valuable insights into traffic accidents in the US
and presents a practical solution to enhance the safety and efficiency of
transportation systems.Comment: 8,
Urban Anomaly Analytics: Description, Detection, and Prediction
Urban anomalies may result in loss of life or property if not handled properly. Automatically alerting anomalies in their early stage or even predicting anomalies before happening is of great value for populations. Recently, data-driven urban anomaly analysis frameworks have been forming, which utilize urban big data and machine learning algorithms to detect and predict urban anomalies automatically. In this survey, we make a comprehensive review of the state-of-the-art research on urban anomaly analytics. We first give an overview of four main types of urban anomalies, traffic anomaly, unexpected crowds, environment anomaly, and individual anomaly. Next, we summarize various types of urban datasets obtained from diverse devices, i.e., trajectory, trip records, CDRs, urban sensors, event records, environment data, social media and surveillance cameras. Subsequently, a comprehensive survey of issues on detecting and predicting techniques for urban anomalies is presented. Finally, research challenges and open problems as discussed.Peer reviewe
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
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