Clustering and association rule mining-based traffic analysis and prediction of Dhaka. Issue 4 (2018)

• Record Type: Journal Article
• Authors: Ahmed, Muyeed; Imtiaz, Mir Tahsin; Khan, Raiyan; Rahman, Rashedur M.
• Is Part Of: International journal of knowledge engineering and data mining. Volume 5:Issue 4(2018)
• Page Start: 241
• Page End: 276
• Publication Date: 2018
• Subjects: traffic analysis -- trajectory analysis -- clustering -- data mining -- association rule mining -- logistic regression; Knowledge representation (Information theory) -- Periodicals; Data mining -- Periodicals; 006.305
• Languages: English
• ISSNs: 1755-2087

Abstract

Traffic is one of the major problems for any populated city. Currently, there are many traffic alert systems available and almost all of them work with user submitted inputs to give those alerts. We have worked on developing a system that will not depend on any user's manual input. Rather it will be able to retrieve traffic and activity related data from the user's device and vehicle tracking devices automatically to predict traffic and alert users. Our system understands the user's activity using accelerometer sensor data and speed to determine whether the user is sitting at home or going somewhere by a bus or car. Once it is verified that the particular user's location and activity is related to traffic conditions, it takes that user's location related data from his or her device. Using this data from user's devices and the data from vehicle tracking devices, we predict the traffic conditions and let users know about the traffic for particular routes.