Mobility and energy systems generate a wealth of data. These rich datasets provide infinite opportunities to learn, leverage and build solutions. Enhancing mobility experiences or improving energy systems for society hinges on the ability to interact with this unique data. We at 99P Labs believe that a collaborative research community produces the best outcomes and that is why we have opened this data through our 99P Labs Developer Portal to address the problems of the changing mobility landscape.

“The Developer Portal unlocks innumerable data opportunities”

On the 99P Labs Developer Portal, we serve various types of mobility datasets such as data from Telematics or V2X enabled vehicles…


Written By Adam Huth, Isabel Zavian, and Ebru Odok

In our last article, we discussed how we processed our data and overcame challenges to successfully pull in over 35 million rows of data. From there, we developed a KNN model for vehicle dwell-time predictions and used DBScan for creating dwell time clusters. In this article, we will explore our journey with building our location prediction model.

Figuring out which model to implement for vehicle location predictions was extremely challenging. After our preliminary research, we originally decided to create a Hidden Markov Model, but within the first few weeks of the…


Written by Adam Huth, Isabel Zavian, Ebru Odok and Charlie Duarte

Predicting how long a vehicle is going to remain parked (dwell time), and then where it’s going next, is no Sunday afternoon drive. In our last article, we discussed our journey of building a vehicle prediction model with 99P Labs. Millions of observations are collected for just one vehicle’s trip, and while having a large amount of data grants us the ability to create more robust models, it has also led to various difficulties setting up both models.

Our team successfully pulled in data utilizing the 99P Labs API…


99P Labs Developer Portal provides its community members access to data sets such as mobility, transit, and automotive behavior of the future. Providing access to the data is not enough. The users also need to understand how to explore, utilize, and apply the data to real-world problems or their use cases quickly. This becomes a challenge when you consider the diversity of expertise levels of participating users. Our problem-opportunity is trying to define a “Getting Started” framework that will make the users experience seamless to adopt the data sets and apply it to the problem at hand. To tackle this…


serjosoza from Unsplash

Check out this webinar on “The Future of Smart Mobility” by Prof. Atkinson at The Ohio State University. We at 99P Labs are excited about this topic and working closely with our partners such as OSU to deeply think and realize this future.


Homepage for 99P Labs Current Developer Portal

Our Challenge

At 99P Labs, we believe that innovation in the Mobility space requires collaborative execution, applying and learning. To enable this collaborative research community around the mobility data ecosystem, we launched our 99P Labs Developer Portal in September 2020. Our community has grown to about 100+ members from Universities and Public & Private entities in a short time surfacing very exciting problems and challenges. This community is very diverse not only in domain knowledge but also in data skills and experience and we’re very fortunate to be able to work closely on both domain and technical challenges. …


Written by Adam Huth, Isabel Zavian and Ebru Odok

It’s a beautiful Tuesday afternoon — the sky is blue, the birds are singing, and unfortunately for you, you’re running errands. It’s 3:17PM — a full grocery list sits on your lap, and you’re supposed to be picking up the kids at four before dropping them off at soccer practice. Oh great; the cleats still haven’t come in yet. It will have to wait — groceries first! You frantically run into the store, reminding yourself to not forget eggs this time.

4:12PM, and you’re pulling into the soccer field’s parking lot…


Written By: Ben Davis

Photo By Goran Ivos

Our plans started out as many others with wanting to put an API in front of our Data Lake. We ended up with a GraphQL API that returns rows of data in JSON format. This setup works well up to a point. Eventually, you run into practical limits on how much data can be sent back to a client as text. In our case, we have python notebook clients submitting requests and generating result sets that are in the million-row range and up. In ordinary times, connected to gigabit ethernet at the office, large result sets…


The Electrion Team

Accelerating the Adoption of Portable Clean Energy

When we look at the world around us, what do we see? The legacy of those that came before. Good or bad, every person leaves behind a legacy that impacts those who follow in their steps. If we are not leaving behind a better world for future generations, then what is our legacy? It is our belief that everything we do should be about sustainability. With unlimited access to knowledge, our generation and those to come not only have the opportunity, but the responsibility to drive positive behavior changes through innovation in sustainability. This is where Electrion finds its home.


Written By: Robert Sunderhaft

Apache Superset

INTRO:

One of the essential tasks of a data scientist is to obtain useful results from large data sets. Preferably, these findings are showcased through a simple and sleek visualization. With the right graph, layout, and information, data can transform from numbers into a form of art. To accomplish this though, a data scientist needs a platform to build upon.

There are many applications to start with, but my introduction to data science visualizations was through the up and coming Apache Superset. Since I am new to the field and haven’t used any other software, I can…

99P Labs

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