Difference between revisions of "Computational Transportation Science Data Source"
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{| style="background-color:#FFFFFF ; color:#FFFFFF padding: 1px 0 0 0;" width="100%" cellspacing="0" cellpadding="0" valign="top" border="0" | | {| style="background-color:#FFFFFF ; color:#FFFFFF padding: 1px 0 0 0;" width="100%" cellspacing="0" cellpadding="0" valign="top" border="0" | | ||
| − | | style="padding:0.3em; font-family:Georgia; font-size:100%; border-bottom:2px solid #626262; border-left:2px #FFFFFF; background: #FFFFFF; text-align:left;" width="20%" | <font color=" | + | | style="padding:0.3em; font-family:Georgia; font-size:100%; border-bottom:2px solid #626262; border-left:2px #FFFFFF; background: #FFFFFF; text-align:left;" width="20%" | <font color="#FE2EC8" size="3em">Source<br></font> |
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For the project, Land Transport Authority (LTA) provides the data sets through LARC research labs. The dataset is a weeks’ worth of smart card (EZ-Link) transaction used in Singapore’s public transport. The data consist of both bus and also MRT transaction. For this project we will require only MRT transactions. | For the project, Land Transport Authority (LTA) provides the data sets through LARC research labs. The dataset is a weeks’ worth of smart card (EZ-Link) transaction used in Singapore’s public transport. The data consist of both bus and also MRT transaction. For this project we will require only MRT transactions. | ||
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{| style="background-color:#FFFFFF ; color:#FFFFFF padding: 1px 0 0 0;" width="100%" cellspacing="0" cellpadding="0" valign="top" border="0" | | {| style="background-color:#FFFFFF ; color:#FFFFFF padding: 1px 0 0 0;" width="100%" cellspacing="0" cellpadding="0" valign="top" border="0" | | ||
| − | | style="padding:0.3em; font-family:Georgia; font-size:100%; border-bottom:2px solid #626262; border-left:2px #FFFFFF; background: #FFFFFF; text-align:left;" width="20%" | <font color=" | + | | style="padding:0.3em; font-family:Georgia; font-size:100%; border-bottom:2px solid #626262; border-left:2px #FFFFFF; background: #FFFFFF; text-align:left;" width="20%" | <font color="#FE2EC8" size="3em">Tools used<br></font> |
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For data preparation, descriptive statistics, we use SAS JMP Pro and SAS Enterprise Guide. We used both tools as we are familiar with SAS Enterprise Guide as the Analytics Foundation course uses SAS Enterprise Guide; therefore we are well versed in the tool. We use SAS JMP Pro as recommended by our project supervisor as a faster alternative. However, as we use both tools interchangeable as fit the task. | For data preparation, descriptive statistics, we use SAS JMP Pro and SAS Enterprise Guide. We used both tools as we are familiar with SAS Enterprise Guide as the Analytics Foundation course uses SAS Enterprise Guide; therefore we are well versed in the tool. We use SAS JMP Pro as recommended by our project supervisor as a faster alternative. However, as we use both tools interchangeable as fit the task. | ||
For the data-mining portion, we will use SAS Enterprise Miner as the tool for time series data mining. | For the data-mining portion, we will use SAS Enterprise Miner as the tool for time series data mining. | ||
Revision as of 15:10, 25 February 2015
| Background | Data Source | Methodology |
|---|
| Source |
For the project, Land Transport Authority (LTA) provides the data sets through LARC research labs. The dataset is a weeks’ worth of smart card (EZ-Link) transaction used in Singapore’s public transport. The data consist of both bus and also MRT transaction. For this project we will require only MRT transactions.
| Tools used |
For data preparation, descriptive statistics, we use SAS JMP Pro and SAS Enterprise Guide. We used both tools as we are familiar with SAS Enterprise Guide as the Analytics Foundation course uses SAS Enterprise Guide; therefore we are well versed in the tool. We use SAS JMP Pro as recommended by our project supervisor as a faster alternative. However, as we use both tools interchangeable as fit the task.
For the data-mining portion, we will use SAS Enterprise Miner as the tool for time series data mining.
