Demo entry 5862345

Brent Feas


Submitted by anonymous on Aug 03, 2016 at 20:20
Language: XML. Code size: 9.9 kB.

<?xml version="1.0" encoding="UTF-8"?>
<?xml-model href="../schema_3302.rng" type="application/xml" schematypens=""?>
<!--the second line in the document associates the schema, so be sure not to change it-->
        <!--required header includes metadata about the assignment (title, author, version)-->
        <title>Writing Project 2</title>
        <author xml:id="c12">Brent Christy</author>
        <version n="1" date="2016-08-01"/>

        <page type="ttl">
            <title>Boston Traffic Signal Neural Network</title>
            <subTitle>Feasibility Report</subTitle>
            <date>August 1st, 2016</date>
            <contributors>Brent Christy</contributors>
        <page type="tbl_contents">
            <sect n="1" lvl="1">Executive Summary</sect>
            <sect n="2" lvl="1">Preliminary Requirements</sect>
            <sect n="3" lvl="1">Feasibility</sect>
            <sect n="4" lvl="2">Technical</sect>
            <sect n="5" lvl="2">Economic</sect>
            <sect n="6" lvl="1">Success Criteria</sect>
            <sect n="7" lvl="1">Design</sect>
            <sect n="8" lvl="1">Cost Estimate</sect>
            <sect n="9" lvl="1">Risk Analysis</sect>
            <sect n="10" lvl="1">Conclusion</sect>

        <page type="exec_sum">A network linked traffic signal control system is proposed throughout
            the city of Boston. The network will autonomously adjust traffic signal timing depending
            on the current traffic conditions. The goal of using autonomously adjusting traffic
            signals as opposed to timed signals is to reduce the amount of time spent in traffic in
            the city. Less time spent in traffic will result in lower fossil fuel emissions as well
            as a higher quality of life for commuters. The current traffic signal system in Boston
            is well set up to be adapted and take on the proposed neural network system of signals. </page>

        <page type="pre_req_ana">
            <app_ovr type="obj">The objective of the proposed system is to provide an overall
                decrease in traffic throughout the city of Boston, especially during peak travel
            <app_ovr type="synergy"> The proposed system will utilize the current traffic control
                system which has central control of 544 of 845 lights operated by the Boston
                Transportation Department. The program will also utilize several sensors present at
                some stop lights throughout the city.</app_ovr>
            <app_ovr type="rls">The proposed system will have to comply will all state and federal
                laws regarding traffic signals.</app_ovr>

        <page type="fsb">
            <fsb_hlgt type="tcnl"> The proposed neural network will require several improvements to
                the current control system. Currently, most traffic signals are controlled by preset
                timers. These timers are set by traffic control engineers once every five years
                after observing traffic for several days. The other traffic signals use both the
                timers and some sensors to determine how many cars are currently at the
                intersection. For the neural network traffic system, three major actions will need
                to take place. First, the remaining 301 of 845 lights will need to be connected to
                the central traffic system. This will allow the central program to control all
                lights in the city. Second, sensors will have to be places at large intersections in
                the city. These sensors will tell the central network how many cars are at major
                intersections and allow the network to adjust traffic flow depending on this
                traffic. The sensors can be applied either on traffic poles or underneath the
                pavement. As there will be a fairly large number of sensors, most will have to be
                applied to poles as the roads should not be dug up. Last, the neural network will
                need to be implemented. This neural network will be connected to all of the traffic
                lights and receive the sensor data from the main intersections. The network can
                leverage several papers already written on the subject of traffic control with
                neural networks. </fsb_hlgt>

        <page type="fsb">
            <fsb_hlgt type="econ">The effective costs of the project will go to three categories:
                connecting the lights to the central network, adding sensors to main intersections,
                and coding the neural network. The majority of the cost will go towards the
                development of the neural network. Based on the Boston Transportation Department's
                current budget, there is a sizeable amount of money that could be used towards these
                purposes each year over the course of the next ten years. </fsb_hlgt>

        <page type="sccs_crt">
                <req n="1"> 1. Connect all traffic lights to central network </req>
                <req n="2"> 2. Sensors installed at major intersections </req>
                <req n="3"> 3. Network autonomously operates </req>
                <req n="4"> 4. Timings adjust depending on current conditions </req>

        <page type="dsn">

        <page type="cost">
            <est type="fnc">$1,000 per light to connect each to the central network. (301 lights) =
            <est type="fnc">$2,000 per intersection that needs sensors (50 intersections) =
            <est type="fnc">$500,000 to develop neural network</est>

        <page type="rsk_ana"> There are several potential risks to this system. One risk is lights
            getting disconnected from the central system. This can be combated by having preset
            timers as backups to the network timing. Another risk is the introduction of self
            driving cars. Self driving cars may eliminate the need for traffic signals which would
            prove the system useless. </page>

        <page type="conc">
            <fsb>Given the current budget and technology, it appears to be reasonable to complete
                this task within the proposed ten year time frame. With much research already done on
                the subject of neural networks, it would be reasonable to create over the course of
                several years.</fsb>
            <cond>Despite budget concerns throughout the city, this would be a good allocation of
                funds to increase the quality of life of workers around the area.</cond>
            <sum>The project appears to be technically feasible in that all proposed actions can be
                completed given the correct time and resources. Given some room in the Boston
                Transportation Department's budget, there is also enough monetary resources for the
                project to work. Last, the large time period proposed for the project will allow the
                system to be implemented in a timely manor. Overall, it appears reasonable that the
                system can be implemented.</sum>


        <reviewer type="peer" xml:id="m20">Chi Mo</reviewer>
        <report type="peer" resp="#m20">
                After reading this feasibility report about the traffic lights neural network, I can see that the feasibility report 
                is well organized and easy to understand. After a glance, the report looks feasible. It looks like the author is trying 
                to make the Boston’s traffic signal more efficient. I don’t understand where does the number 544 of traffic lights come 
                from? It doesn’t have any explanation of it. It has two feasibility pages which I’m confused so I went through the XML 
                page and realized that they are in different types. I feel like it can be one page with two paragraphs. In this draft, 
                it doesn’t have the report page and financial page. I think it won’t hurt to have these two pages since they are the part 
                of the schema and may influence the audience’s decision after they read the report. In the risk analysis page, the author 
                should not only mention the risk but also explain how will this project reduce some of the risk. You can’t let people know 
                the risk but no solving plans to it. Also, how is the current Boston traffic signal set up? Why does it need to be changed 
                into a neural network? I think it will be better if the author mentioned these. The author uses most of the schema in the 
                same way that we think when we develop the schema. In the success criteria, I’m not sure it we need more words to explain 
                it or just do the same as the author did in bullet points? The schema is well formed and easy to use, thus, the rules of 
                the schema is easy to follow as the author did.  
        <reviewer type="instructor" xml:id="kgs"/>
        <report type="instructor" resp="#kgs">
            <p>Comments on assignment will appear here.</p>

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