Markup in the Writing Classroom

Genre: bib

Student id: c12

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<DOC>
  <docHead>
    <!--required header includes metadata about the assignment (title, author, version)-->
    <title>Writing Project 1 - Annotated Bibilography</title>
    <version n="2" date="2016-07-13"/>
    <!--note that the date must be YYYY-MM-DD for the document to be valid-->
  </docHead>
  <annotated_bib>
    <problem_stmt> Every day throughout the city of Boston, thousands of commuters spend large
            portions of their drive waiting in traffic. All it takes for traffic to build up is one
            accident on a main street and cars will be backed up for miles. Part of this issue is
            due to the volume of commuters entering and leaving the city during rush hour, but part
            of the blame can also be placed on traffic control. Traffic control, specifically the
            use of traffic signals, is fairly far behind today's technology. Although traffic
            signals are monitored and timings are adjusted every five years, the results are still
            not optimal. There needs to be a way for signals to utilize current technologies in order to optimize
            commute times and allow for people's already sparse free time to be spent how they like.
            Along with people's free time being wasted, there is also a large amount of unnecessary
            fuel consumption happening while waiting in traffic. Something needs to be done about traffic issues throughout high
            population cities such as Boston. </problem_stmt>
    <citation style="MLA" n="1"><title>Activity Report for FY 2013.</title> Rep. Boston Traffic
            Management Center, n.d. Web.</citation>
    <annotation>
      <background type="source"> The Boston Traffic Management Center produces an annual
                activity report in order to publish the statistics and usage of its department. </background>
      <summary type="general">Currently, the Traffic Management Center (TMC) <q>"has computer
                    control of 544 out of the 845 traffic signals operated by the Boston
                    Transportation Department (64%)"(1).</q> The department made 31,000 real-time manual changes
                to the timing of traffic signals based on current traffic conditions. The department
                also owns 195 traffic monitoring cameras throughout the city.</summary>
      <relevance type="application">These statistics show that the Boston Transportation
                Department has part of the infrastructure to implement an automated real-time
                traffic control system. The automated system would be able to make the 31,000 manual
                changes that employees made throughout the year by itself and reduce
                traffic.</relevance>
    </annotation>
    <citation style="MLA" n="2"><author n="1">DeMarco, Peter</author>. <title>"Exploring the
                Science behind Traffic Lights."</title> Boston.com. N.p., 09 May 2010. Web. 13 July
            2016.</citation>
    <annotation>
      <background type="source">An article published by Boston.com discusses the current
                technology used in traffic signals in the Quincy area.</background>
      <summary type="general">Jack Gillon, a traffic engineer in Quincy, explained how he has
                total control of the lights from a central location. He also quickly showed how
                sensors detect cars at traffic signals.</summary>
      <summary type="approach">Most traffic signals have an electrically charged wire
                underneath the pavement. When a car passes over this wire, a change in magnetic
                field is measured and sent to the control box of the light. This tells the light
                that there is a car waiting to go through the intersection or make a turn.</summary>
      <relevance type="application">This technology could be implemented in an automated
                traffic light system by sending the readings from the charged wire to a central
                network. The central network could then take in these readings and account for the
                incoming traffic at the next lights that the car would encounter.</relevance>
    </annotation>
    <citation style="MLA" n="3"><author n="1">Gilmore, John F.</author>, <author n="2">Khalid J.
                Elibiary</author>, and <author n="3">Naohiko Abe</author>. <title>"Neural Network
                System for Traffic Flow Management."</title> AAAI Technical Report (1993): 85-95.
            Web.</citation>
    <annotation>
      <background type="author"> John Gilmore along with Khalid Elibiary of the Georgia Tech
                Research Institute produced a paper for Association for the Advancement of
                Artificial Intelligence regarding the use of neural networks to control traffic in
                1993 with the assistance of Naohiko Abe of Mitsubishi Heavy Industries in Japan. </background>
      <summary type="general"> Gilmore and his associates go into depth about how neural
                networks can be implemented into a traffic control system to improve traffic
                conditions. They introduce the idea of using the Hopfield neural network model in
                order to allow the network to learn and build off of its knowledge of previous
                congestion situations to improve traffic conditions.</summary>
      <summary type="approach">The group forms an energy function which allows them to assign
                lights and traffic conditions different weights. Higher weighted traits, such as how
                busy an intersection generally is, allow lights to have a higher priority and change
                faster. </summary>
      <relevance type="application"> The usage of a neural network as described by Gilmore,
                Elibiary, and Abe can be used as a base for a city wide traffic control system,
                where the system learns and adapts based on previous traffic data. This could cut
                out a large amount of traffic by optimizing the traffic flow.</relevance>
    </annotation>
    <citation style="MLA" n="4"><author n="1">Santi, Paolo</author>, and <author n="2">Carlo
                Ratti</author>. <title>"Revisiting Street Intersections Using Slot-Based
                Systems."</title> PLOS ONE. N.p., n.d. Web. 13 July 2016.</citation>
    <annotation>
      <background type="author">Paolo Santi of the Senseable City Lab and Carlo Ratti of MIT
                outlined a plan to eliminate the need for traffic lights at all using automation in
                cars.</background>
      <summary type="general">For Santi's system, called a slot-based system, to work, cars
                need to be autonomously operated. The cars need to be able to communicate with each
                other to determine where they are on the road and what their planned route is at
                each light. The cars would then decide amongst themselves which car is turning when
                with no need for traffic lights. </summary>
      <relevance type="application">If all cars could communicate and turn without interfering
                with each other, it would eliminate the need for stop lights and would greatly
                decrease traffic given that the cars will harmoniously move through the
                streets.</relevance>
      <relevance type="value_stmt">Although this seems like the optimal solution, it would
                first require that all cars be autonomous, so this would not be a solution for the
                    near-term.</relevance>
    </annotation>
    <citation style="MLA" n="5"><author n="1">Srinivasan, Dipti</author>, <author n="2">Min Chee
                Choy</author>, and <author n="3">Ruey Long Cheu</author>. <title>"Neural Networks
                for Real-Time Traffic Signal Control."</title> IEEE Trans. Intell. Transport. Syst.
            IEEE Transactions on Intelligent Transportation Systems 7.3 (2006): 261-72.
            Web.</citation>
    <annotation>
      <background type="author">Dipti Srinivasan and her colleagues explore the use of
                real-time neural networks in order to assist with traffic control in this paper for
                an Institute of Electrical and Electronics Engineers journal on Intelligent
                Transportation Systems. </background>
      <summary type="general">Srinivasan uses the concept of a neural network to take in
                real-time traffic statistics and adjust traffic lights accordingly. The current
                conditions of traffic are available to the system and can be included in
                calculations made to determine how long to have lights turned green.</summary>
      <relevance type="value_stmt">Her results showed that the implementation of a real-time
                neural network can allow a greater number of cars to pass through congested
                    intersections.</relevance>
      <relevance type="application">Dipti's real-time neural network could be applied in a
                city like Boston where sensors could be installed to acquire real-time data used in
                the program to control the lights. Traffic control based on current conditions would
                produce much better traffic flow than average timed lights.</relevance>
    </annotation>
  </annotated_bib>
</DOC>

  

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