A predictive analysis of citizen hotlines 1999 and traffic accidents: A case study of Taoyuan city

Lin, Bo-Huei; Tseng, Shu-Fen

doi:10.1109/bigcomp.2017.7881696

Cited by 7 publications

(2 citation statements)

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“…In 2017, a study in Taoyuan, Taiwan, used a data analytic approach that included correlation and regression analysis; [8] the results showed that regions with a high frequency of road potholes had a higher rate of traffic accidents. Potholes caused irrevocable damage to pizzas during delivery.…”

Section: Literature Reviewmentioning

confidence: 99%

Autonomous Car for Indian Terrain

Patil,

Aher,

Bhate

et al. 2022

imiens

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In recent years, autonomous vehicle (AV) technology has improved dramatically. Self-driving cars have the potential to transform urban mobility in India by offering sustainable, convenient, and congestion-free transportation. However, India confronts challenges such as potholes and the need for enhanced lane detection to make autonomous vehicles a reality. The project's central goal is to create a Convolution Neural Network (CNN) model that can scan and identify its surroundings and move. This paper proposes a project which is accomplished by training CNN with a dataset of images and videos to perform advanced lane identification, pothole recognition, and sophisticated object detection.

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Section: Literature Reviewmentioning

confidence: 99%

Autonomous Car for Indian Terrain

Patil,

Aher,

Bhate

et al. 2022

imiens

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“…In the past research on solving the problem of automatic incidence detection with a large amount of data, beginning with a 2017 study by Bo-Huei Lin, Shu-Fen Tseng [1] analyzed data that the government allows the public to participate in collecting data by 1,2 The authors are with Faculty of Engineering, Rajamangala University of Technology Phra Nakhon, E-mail: Korn.p@rmutp.ac.th and Natworapol.b@rmutp.ac.th informing them via hotline to report on problems encountered on the way to compare with road accidents by the results in the same year's large-scale. Computing research, Lin Cao [2] presented a big data platform to collect data for public trac services.…”

Section: Introductionmentioning

confidence: 99%

Collection of Road Traffic Incidents in Bangkok from Twitter Data based on Deep Learning Algorithm

Puangnak

Rachsiriwatcharabul

2022

ECTI-CIT Transactions

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Text processing technology from Twitter to report notification formats that are known in many countries with verification on different languages. This research presents the development of a neural network memory learning model. To solve the problem of classifying incidence patterns and identifying severity of incidents from Thai social media messages. For gathering incident data and reporting incidents externally from a single reporting platform by using deep learning models like MLP, CNN and LSTM which is designed by dividing the study into 3 types, including examination traffic incidence identification pattern that can identify the report as general news or traffic reporting Incident Identification Patterns. These include traffic conditions, accidents, disasters, damaged roads, or other than the aforementioned patterns, and the pattern indicating the severity of the incidence consists of normal level, medium level and lane blocking or stationary levels. The results demonstrated the ability of LSTM learning with the best results in incidence detection and incidence pattern identification at 93.44% and 87.40%, respectively, and the CNN method was able to State the severity of the incidence at best, reaching 91.42%.

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Open Data in Prediction Using Machine Learning: A Systematic Review

Ismail

Yusof

2021

Lecture Notes on Data Engineering and Communications Technologies

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A predictive analysis of citizen hotlines 1999 and traffic accidents: A case study of Taoyuan city

Cited by 7 publications

References 0 publications

Autonomous Car for Indian Terrain

Autonomous Car for Indian Terrain

Collection of Road Traffic Incidents in Bangkok from Twitter Data based on Deep Learning Algorithm

Open Data in Prediction Using Machine Learning: A Systematic Review

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