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Thread: What is a true data scientist in transportation planning?

  1. #1
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    What is a true data scientist in transportation planning?

    In an article published by BloombergBussiness, the autor wrote, "So-called unstructured data can include e-mails, videos, photos, social media, and other user-generated content. Data scientists write algorithms to extract insights from these troves of information. But “true data scientists are rare,” says Ricard Benjamins, head of business intelligence and big data at Telefónica, Europe’s second-largest phone company, which employs more than 200 of them. Says Stan Humphries, chief economist at Zillow, the real estate listings site: 'You can find a great developer and a great researcher who has a background in statistics, and maybe you can find a great problem solver, but to find that in the same person is hard.'"

    Asking the pros out there, what would it mean to be a true data scientist working in transportation planning. What skills or talents would the best big data transportation planners possess? The same as what Humphries stated?

    I'm heading into the University of Glasgow's Urban Transport MSc program which sits on a platform that involves using big data. Would love the insight.

    Thank you all!


    http://www.bloomberg.com/news/articl...-belts-in-data

  2. #2
    Cyburbian The District's avatar
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    the term "big data" gets misused a lot. it can literally mean BIG data (measured in terabytes, etc.) or it can mean what the author referred to here--unstructured data. but unstructured data is not necessarily big. the same can be said for "data science"--this term also gets used all sorts of different ways.

    let me respond to both of these types of "big data".

    1-transportation planners potentially work with a lot of data, but it's likely managed by an IT person, who is responsible for making the data accessible for analysis.

    2-i would think that a transportation planner is not going to be working with a lot of unstructured data. think about the examples used in the article (email, photos, texts). these are all human-generated. most data that transportation planners work with is data from a vehicle, in the form of digits. it's all very mathematical in the traditional sense--your data is already in an easy-to-mathematize format. working with unstructured data is a very different animal.

    i think there's definitely a place in transportation engineering and planning for big data and data science. on the big data side, it involves being able to work effectively with large data sets, learning to store them in a way that still makes them accessible and readable at a relatively high speed. data science would involve (as an example) using that big data to predict accidents based on weather and day of the week/time of year. i would recommend doing some google-ing for "prediction" and "transportation planning" or "big data" and "transportation planning". you get the idea.

  3. #3
    Cyburbian
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    Traditionally in transportation planning "data" have been mostly quantitative and vehicle sourced. Some examples are:
    • Traffic Engineering: Level of Service and Queueing for vehicle flow, capacity analysis, and throughput
    • Transit Planning: Dwell times, headways, wait times, travel times, and other service, routing and performance measures
    • Freight and Cargo Planning: Truck and airport data for supply chain management (not familiar with this type of planning so I won't say much but I know there's a huge field out there for this)

    Now, transportation planning data collection is becoming more and more qualitative and political as we mix in more modes: bicycle planning, pedestrian counts, which streets get paved.

    Big data is also used in emerging shared mobility planning, such as tracking where bike-share bikes, Zipcars, Ubers and Lyfts go.

    All the best for your Master's!

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    Thank you

    Thanks guys for the input. Your insightful comments are noted and appreciated.

    Following your third line akshali, I've used public bike share data for my quantitative methods course at the University of Glasgow. It's been a real pleasure to study impacts on system usage and changes over time. Recently, I've been putting throught into ways in which additional dataset can be layered into bike share data to provide additional insight into cycling/active tranportation in cities.

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