Adding Statistics to Transportation Decision Supporting System


In our daily lives, there is one thing that we cannot avoid is moving. It is important as eating, drinking, and breathing. Though today is era of the internet, people still need to move from their home to the office or the supermarket every day. Nowadays, we have so many ways to move, including walking, riding a bicycle, and taking various vehicles. Therefore, it is essential for transportation administration in the government to build a system that can gather all traffic information and help officials to make better decisions.

In Taiwan, such system is called TTDSS (Taiwan Transportation Decision Supporting System), which is built and maintained by IOT (Institute of Transportation), a research institute under Ministry of Transportation and Communications. Initiated in 2009, TTDSS and its integrated database have been continuously maintained and improved until today. Recently, facing the rapid development of data science and visualization, IOT believes it is necessary to integrate more innovative analytical methods to strengthen the original system.


In order to achieve the goal mentioned in Scenario, the TTDSS team in IOT decided to integrate advanced statistical features to the current system, making users absorb the information intuitively and rapidly. To make the system more easy-to-use, the newly added features not only should allow users to query and analyze data from the database but also should be able to do cross-analysis and export the result with web browsers. In addition, to help users compare different types of data and do more cross-analyses, the overall design should be flexible.


Authorized by the TTDSS team, Supergeo Project Team decided to achieve the goal by completing following works:

  1. Establish a basic web GIS platform with TGOS MAP API powered by Ministry of the Interior, Taiwan. After the platform was successfully built, it can be further used as a base to apply the Statistical Map API and develop advanced statistical features.
  2. To meet the needs from IOT, the project team should add the following features by using the Statistical Map API.
  3. After integrating the budget, socio-economic data, and general traffic data, the GIS platform can display them as statistical maps and charts on the GIS platform.
  4. The function of generating statistical maps includes basic statistical maps (such as choropleth maps) and hybrid statistical maps.
  5. The function of generating statistical charts includes basic statistical charts (such as bar charts) and cross-analysis charts.
  6. For innovative methods, the space-time data visualization was also augmented to the system. 

After adding these new features, the overall improvement of TTDSS is significant. Now, the researchers and analysts of IOT can quickly integrate various kinds of data with traffic data and explore hidden patterns more efficiently. Therefore, the analysts and researchers can help the policy makers more perspective decisions.

In the following paragraphs, the powerful core capabilities of advanced statistical analysis will be introduced.

Data Analysis:
Users can select the needed data and display it as analytical charts on the platform in real-time, helping users to read and comprehend the information.

Fig.1 Select needed data and display it in real-time

Rich charts and tables export:
By selecting different data analysis methods, users can process data in various ways and export it as charts or tables. Furthermore, users can click on the graph to find out the detailed information and download the graph as an image.

Fig.2 Statistical charts could be displayed as regular charts (left) or hybrid charts (right)

Spatial-temporal data display:
While the data covers multiple periods, users can utilize a switch to display the statistical charts from different times with ease.

Fig.3 Users can show data from different periods at the same time

Data Visualization:
By integrating GIS, users can visualize the data that has spatial attributes as maps, absorbing and analyzing spatial data in a more effective way.

Fig.4 Integrating the statistics with maps helps people to absorb information more effectively
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