Difference between revisions of "Group16 Proposal"
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== <big>References</big> == | == <big>References</big> == | ||
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| − | + | 1. [https://www.microsoft.com/en-us/research/publication/t-drive-trajectory-data-sample/#!related_info Dataset source website]<br> | |
| + | 2. [http://toddwschneider.com/posts/analyzing-1-1-billion-nyc-taxi-and-uber-trips-with-a-vengeance/ An NYC trajectory data visualization to learn from]<br> | ||
| + | 3. [https://github.com/roryhr/taxi-trajectories Previous visualization project on the same dataset]<br> | ||
| + | 4. [https://ieee-dataport.org/documents/beijing-taxi-trip-datasample Potential data to be used, waiting for reply from data provider] | ||
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Revision as of 22:18, 17 June 2018
Urban Pulse: A Case Study on Beijing's Traffic
Background
As China urbanizes, more and more people flood into first-tier cities, which has boosted China's economy while at the same time brought many problems. And Traffic Congestion is obviously one of them.
According to the report from ChinaDaily in early 2018, Beijing comes as the second top traffic congested city in mainland China, right following Jinan, Shandong. Statistics form 2015 suggests that the rush hour delay index of Beijing reaches as 2.046, with the speed of rush hour reaching 21.91 Km/h. Given such severe situation, Chinese government has been working hard to trace the traffic situation in Beijing and come up with implementable suggestions.
With real-time data as backup, the visualization of Beijing's taxi trajectory would help to have a holistic view on the traffic status. With further drill-down look to specific districts and areas, government can find the possible causes for the congestion and then give possible solutions.
Data Source
Our data is from Microsoft, see the link https://www.microsoft.com/en-us/research/publication/t-drive-trajectory-data-sample/
The dataset contains the GPS trajectories of 10,357 taxis during the period of Feb. 2 to Feb. 8, 2008
within Beijing. The total number of points in this dataset is about 15 million and the total distance of
the trajectories reaches to 9 million kilometers. The average sampling interval is about 177 seconds with a distance
of about 623 meters. Each file is named by the taxi ID, and contains the trajectories of one taxi.
Objectives
With real-time taxi traffic data of 1 week, we can discover traffic patterns such as what is the peak time of traffic in a certain area, what is the direction of traffic stream, and what is the average time of traffic congestion given a specific site and time point.
This part is to give reasons for all the patterns found in the previous stage. (i.e. How is it that the peak time for that area is 12:00-12:30 am? How is it that the traffic stream flow from site A to site B at 10:00-12:00 am? And how is it that the average time of traffic congestion is higher in site A than site B?)
- For Government: Due to limited information for Beijing's city construction and road planning, we may only give suggestions based on our cognition. The suggestions may not be applicable in real world, but it gives a general direction of how to mitigate congestion by improving the traffic arrangements.
- For Taxi Company: We will suggest taxi companies in Beijing how to allocate taxis in a more efficient way.
- For Commuters: Commuters may have a more clear view of the traffic condition at different time and site to make cleverer decisions on their transportation planning.
