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Data Mining for Traffic Prediction and Intelligent Traffic Management System

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Data Mining is an important method of extracting implicit, unknown and useful information from a dataset or collection of data. Currently, Dhaka is the most densely populated city in the whole world. Almost 16 million people live here. And, the number is increasing day by day. Also, the number of vehicles- both public and private transport is increasing.

As a result, most of our working hours get affected by traffic jam and it is creating a bad impact on the economic growth. To reduce traffic jam, several flyovers are constructed and metro-rail project is already underway. But, it is matter of fact that people are bound to spend lazy time on the busy roads on pick hour. The objective is to have a complete infrastructure to monitor the traffic characteristic of particular roads on busy hours and updating them regularly, so that driver can get an idea of selecting an easier route to face less traffic jam.

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In recent times the collection of information on traffic volumes has become a significant portion of the work of road planning programs in terms of both personnel and cost. Traffic data is partitioned into different regimes by identifying breakpoints for traffic variables in the data. In two-regime traffic models, critical occupancy is used to separate free flow and congested flow conditions. Observations with occupancy values smaller than critical occupancy are assumed to be in the free flow regime and observations with occupancy values greater than critical occupancy are assumed to be in the congested flow regime. Identifying the critical occupancy value from field observations is not trivial. Data mining is the process of discovering interesting knowledge, such as patterns, associations, changes, anomalies and significant structure from large amounts of data stored in databases, data warehouses, or other information repositories.

A case study was shown to prove that the use of tiered cluster study can be cast-off to routinely find time-of-day (TOD) breaks, based on the facts that care the plan of a TOD signal controller system. The group investigation tactic is able to apply a high-resolution system state that takes full benefit of the widespread set of instruments positioned in a traffic signal system and group validation chains the premises accessible.

Intelligent transportation system (ITS) contains large figures of traffic radars that assemble huge numbers of data in an effort to offer data for the sustenance and development of gesture timing actions. Progressive forms of signal control, such as another and third group control, are reliant on the device data complete by ITS.

Data mining tools are desired to abstract the applicable data from the data. Owing to incomplete storage resources, the lack of available study tools, the fact that the device data is not needed for the provision of TOD signal control. This is unlucky, specially mean while it is sensible to use the device data not only for liberal methods of switch, but again for the most communal method of signal control TOD. Thus, there is a need to use inspection tools that prove the value of this data, and authorize the project of systems with swollen storage abilities. Data mining tools used to review and critical info from large sets of data are typically secret as ― data mining tools. This describes study that is inventing a procedure for developing, understanding and treatment traffic signal judgment plans using nearby data mining tools. The data together by signal control arrangements can be used to improve system design and courses for the present tactics of traffic control. The data-mining tool that helps as the basis in this study for sign idea progress is sorted cluster analysis, while society may be cast-off for inspection plan efficiency.

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