Road condition monitoring application based on social media with text mining system: Case Study: East Java

2016 ◽  
Author(s):  
Istiq Septiana ◽  
Yuliana Setiowati ◽  
Arna Fariza
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Condition Monitoring ◽  
Mining System ◽  
Road Condition ◽  
Road Condition Monitoring ◽  
Monitoring Application ◽  
Text Mining System

scholarly journals An IMU-based traffic and road condition monitoring system

HardwareX ◽  
2018 ◽  
Vol 4 ◽  
pp. e00045 ◽  
Author(s):  
Tian Lei ◽  
Abduallah A. Mohamed ◽  
Christian Claudel
Keyword(s):  
Monitoring System ◽  
Condition Monitoring ◽  
Road Condition ◽  
Road Condition Monitoring ◽  
Condition Monitoring System

Live Demonstration: Passive Sensor Setup for Road Condition Monitoring

2020 ◽  
Author(s):  
Felix Kortmann ◽  
Julin Horstkotter ◽  
Alexander Warnecke ◽  
Nicolas Meier ◽  
Jens Heger ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Road Condition ◽  
Live Demonstration ◽  
Road Condition Monitoring ◽  
Passive Sensor

Traffic Impact Area Detection and Spatiotemporal Influence Assessment for Disaster Reduction Based on Social Media: A Case Study of the 2018 Beijing Rainstorm

2020 ◽  
Vol 9 (2) ◽  
pp. 136
Author(s):  
Tengfei Yang ◽  
Jibo Xie ◽  
Guoqing Li ◽  
Naixia Mou ◽  
Cuiju Chen ◽  
...  
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Global Climate ◽  
Low Cost ◽  
Disaster Reduction ◽  
Emotional Information ◽  
Road Condition ◽  
Traffic Impact ◽  
Disaster Data

The abnormal change in the global climate has increased the chance of urban rainstorm disasters, which greatly threatens people’s daily lives, especially public travel. Timely and effective disaster data sources and analysis methods are essential for disaster reduction. With the popularity of mobile devices and the development of network facilities, social media has attracted widespread attention as a new source of disaster data. The characteristics of rich disaster information, near real-time transmission channels, and low-cost data production have been favored by many researchers. These researchers have used different methods to study disaster reduction based on the different dimensions of information contained in social media, including time, location and content. However, current research is not sufficient and rarely combines specific road condition information with public emotional information to detect traffic impact areas and assess the spatiotemporal influence of these areas. Thus, in this paper, we used various methods, including natural language processing and deep learning, to extract the fine-grained road condition information and public emotional information contained in social media text to comprehensively detect and analyze traffic impact areas during a rainstorm disaster. Furthermore, we proposed a model to evaluate the spatiotemporal influence of these detected traffic impact areas. The heavy rainstorm event in Beijing, China, in 2018 was selected as a case study to verify the validity of the disaster reduction method proposed in this paper.

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