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<h2 class="hd hd-2 unit-title">5.3.1 The value and challenges of data</h2>
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<p><span lang="EN-US">In this section, we will cover the importance of data for new mobility as well as the different types of data that we can collect, the ways in which we can process that data, and what challenges emerge by its use in terms of privacy and security.<br /></span></p>
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<p>The main mobility data types are related to:</p>
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<h3 class="hd hd-2">Francisco Pereira, Ph.D. (Denmark Technical University)</h3>
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<p><span style="font-size: 30pt; font-family: 'Arial Black'; color: #33757f;"></span><span style="font-size: 30pt; font-family: 'Arial Black'; color: black;"></span>Please pause to explore these data visualization videos:</p>
<ul>
<li>Lisbon's Slow Traffic Areas in 2010: <a href="https://www.youtube.com/watch?v=WYucZEa0vr8">https://www.youtube.com/watch?v=WYucZEa0vr8</a></li>
<li>How much urban area can we monitor by putting sensors on taxis?: <a href="http://senseable.mit.edu/urban-sensing/">http://senseable.mit.edu/urban-sensing/</a></li>
<li>A Day in the Life of Americans: <a href="https://flowingdata.com/2015/12/15/a-day-in-the-life-of-american">https://flowingdata.com/2015/12/15/a-day-in-the-life-of-american</a></li>
<li>Visualizing Public Transport in 5 cities around the world: <a href="https://thecityfix.com/blog/using-open-data-visualize-public-transport-videos-joseph-paquette/">https://thecityfix.com/blog/using-open-data-visualize-public-transport-videos-joseph-paquette/</a></li>
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<h3 class="hd hd-2">The history of machine learning</h3>
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<p>Try Eliza <a href="https://www.masswerk.at/elizabot/" target="[object Object]">here</a> (http://masswerk.at/elizabot/)</p>
<p>Try Rose <a href="http://brilligunderstanding.com/rosedemo.html" target="[object Object]">here</a> (http://brilligunderstanding.com/rosedemo.html)</p>
<p>For more on Probablistic Graphical Models, read the ebook "Model-Based Machine Learning" <a href="http://www.mbmlbook.com " target="[object Object]">here</a>.</p>
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<legend id="51624bf14adc4c5db3319a2500443225_2_1-legend" class="response-fieldset-legend field-group-hd">The Bayesian framework is a tool often used in transportation-related research. It has the capability to not only predict a certain value but...</legend>
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<h2 class="hd hd-2 unit-title">5.3.2 Use of data, privacy & security</h2>
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<p>In the previous section, we learned about data analysis techniques and how the discipline of data science has evolved. Now we introduce a case study that will explain how collected data was used to solve an existing problem in freight.</p>
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<p>Prof. Cheah has just shared how her team used everything we have learned in this module to study the impacts of freight delivery on traffic in the city core. Core technologies (sensing, computing, and communication), combined with data science, allowed Prof. Cheah and her team to gain insights into truck drivers' behavior.</p>
<p>However, when using data, there are some things we must keep in mind (as mentioned briefly in Module 1). Privacy and security of data are not trivial matters, and have to be considered.</p>
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<p>We now turn again to Assaf Biderman who will share how Superpedestrian addresses data challenges. </p>
<p>Reminder: Superpedestrian is a Cambridge, Massachusetts-based startup developing lightweight electric vehicles and cloud-hosted management systems.</p>
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<h2 class="hd hd-2 unit-title">5.3.3 Avoiding biases in data</h2>
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<p>There are many different ways in which we can introduce biases unconsciously in the data collection process. These biases will give us results that don't represent adequately the reality we are trying to understand and can have severe consequences. In the content that follows, we will analyze in more depth the case of racial biases in data collection and analysis.</p>
<p>Prof. Michael Maness from the University of South Florida will discuss in detail how racial biases manifest within the data collection process and what can we do to minimize them.</p>
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<h3 class="hd hd-2">Michael Maness, Ph.D. (University of South Florida)</h3>
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<p>Because it is important to keep many of the ideas mentioned in the previous video in mind while designing new systems and technologies, Dr. Maness will now discuss the intersection of racial biases and the area of new mobility.</p>
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<p>Asking the right questions in the right ways is as important as the respondents included in our samples. More on common mistakes in the survey design process in the next video.</p>
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<p>The main goal of collecting and analyzing data is to improve the world around us. A lack of accounting for racial biases in this analysis has already had wide societal consequences. Translating knowledge into practice is key in building more fair and equitable communities.</p>
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<p>As we have learned from Prof. Maness, biases might appear in different phases of the data collection process:</p>
<ul>
<li>Sampling: by lacking a diverse sample</li>
<li>Question design: by asking questions in a format or language that it's not inclusive for all groups</li>
<li>Response collection: by not accounting for response bias</li>
<li>Measurement & weights: by not weighing responses from different groups properly in the analysis</li>
</ul>
<p>Biases probably won't ever disappear completely, but it's important to be conscious of their effects and actively pursue strategies to reduce them as much as possible.</p>
<p></p>
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<p>In your own experience, have you encountered any biases in data collection in the transportation field? What could have been done to prevent them? Please share below! <em>If you don't have an example from the transportation field, please feel free to share about biases you have seen in research from other fields.</em></p>
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<h2 class="hd hd-2 unit-title">5.3.4 Data & cities</h2>
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<p>Just like startups, cities and larger transportation companies also need to use data to evaluate investments and make improvements. Our guests reflect on the role of data to inform decision-making by city governments and the role that the private sector is playing in the administration and analysis of data.</p>
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<p>There is no doubt that data and the knowledge of how to analyze it have become extremely valuable assets.</p>
<p>Check out this <a href="https://www.forbes.com/sites/forbestechcouncil/2019/11/15/data-is-the-new-oil-and-thats-a-good-thing/#57ff0b1c7304" target="[object Object]">piece</a> via Forbes and share your reaction in the discussion below.</p>
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