Who is this blog for?
If you are a transit geek, policymaker, transportation agency, programmer interested in spatial visualization, or just want to learn how transportation decisions after urban life, then this blog is for you.
What is this blog about?
This website is about better public transit, data analysis, and change in the transportation industry. If you’ve ever wondered the following, then this blog is for you.
- How can advances in data science and technology inform transportation planning and improve traffic engineering?
- How can existing transportation providers leverage advances in data science and technology to compete with transportation startups?
- What will the future of transportation profession look like?
This blog is centered around five themes:
- Better public transit
- Machine learning applications in transportation
- Data visualization
- Complete streets
- Change in the transportation industry
Better public transit
Public transportation is an important mode of transportation. In dense urban environments, public transit is the most space effective, and time efficient way to travel. Even in lower density suburban communities, public transportation enhances mobility for many people who can’t drive or who choose not to drive. Public transit is a major driver of land-use change, which is exemplified by transit-oriented development.
With the advent of self-driving vehicles, transit is posed to become doubly more efficient and doubly more important. Unfortunately, transit agencies are slow to change and full of inefficient business practices. To realize their potential as the premier mode of transportation, transit agencies need to be become more data-driven.
In this blog, I recall my experience as a transit planner to suggest how to use data to improve transit operations, processes, and performance. I’ll also talk about how to use transit as a tool for creating more liveable and enjoyable communities.
Machine learning applications
I’m excited by the advances in data science such as machine learning and AI. I believe these data-centric technologies are more than hype, and have real potential to improve transportation systems. However, as a practitioner, I’m often frustrated by most transportation analytics products, which don’t really help me to do my job better. Existing products have been designed by developers, who lack the subject matter expertise to know how to create a product that is truly useful to transportation professionals.
This blog is intended to show transportation planners, traffic engineers, and policy analyst how to implement data driven projects from a practitioner’s perspective.
If you’re a transportation professional who wants to learn machine learning, and implement your own data analytics project, this blog is for you.
Data visualization tools
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