Data Driven Transportation Planning

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This quarter the UCLA library gave a guest lecture and workshop on open data and Tableau. Tableau is an excellent data visualization tool that allows planners to easily share dashboards with the public that automatically update as new data become available. In this post I will discuss some new sources of data for transportation planners, tools to understand and communicate the meaning of the data, and how new mobility companies are collecting and using big data in their transportation operations.

Originally created by LADOT in 2018 to standardize real-time communications between new mobility companies and the city through a set of application program interfaces (MDS). The data standard has been collaboratively updated as other city governments and new mobility companies work with LADOT to improve the standard and add functionality. Currently MDS is used in multiple cities to provide real-time data sharing and communications between permitted micromobility companies and city governments.

Although companies have raised issues about trip data and privacy, MDS in some form is likely to remain a valuable tool for collecting real-time transportation data. During my time as a student worked in SFMTA’s bikeshare team, SFMTA data scientists successfully implemented MDS with permitted micromobility companies Scoot, Skip, and JUMP (owned by Uber). Staff have used MDS data in pilot permit program evaluation, enforcement, and bike lane efficacy studies.

Tableau
This brings me back to Tableau and how it can be used with MDS and other data visualization tools to automatically update planners in real time how new mobility options are affecting travel in the public right-of-way. SFMTA built multiple dashboards populated by the real time data collected via the MDS APIs.


Source: SFMTA
Python for GIS and location intelligence
This quarter I also took a course on python for GIS. Did you know that open source geospatial library GDAL (geographic data abstraction library) is the basis for both ESRI’s proprietary ArcMap GIS software and the free open source QGIS platform? For planners who want to automate repetitive tasks in GIS or query massive datasets (including all present and future MDS datasets), learning python can help planners understand trip data without resorting to an outside consultant or relying on new mobility companies themselves. Who better to have the tools to use and understand massive amounts of travel data than transportation planners themselves? Certainly not data scientists at companies whose interest does not always align with the public’s. For those who don’t want to take time and effort to use these tools, your department can use Tableau and MDS data to give you all the information in a user friendly form.

Source: SFMTA

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