Day 13 and 14 WK-3

Kim and I continued work on our literature review today. We met with our mentor Dr. Huang and decided to catergorize our research as societal, algorithms/technology, apps, carbon footprint, defining GPS. We will not only look at data being collected by out RET team but also take a sample of the data collected from taxi's in Shanghai. The RET team met in the library for additional literature review research as well.

Day 11 WK-3 and Day 12 WK-3

We spent time reviewing our literature and getting a better understanding of how Google Earth works. Took time to explore many of the features rarely used in everyday applications. Turns out that Google Earth has a lot of powerful features built into it.

Several of the GPS-31 trackers exhibited problems that we seemed to clear up by reformatting the SD card and resetting the complete unit.

Dr. Thompson went to Maryland and took one of the GPS trackers with her to gather GPS data. We were so excited about the data obtained from the unit but upon inspection no data was captured and written to the data card. I had Jason look at the unit to determine if he could figure out if any issue was resident.

Day 10 - WK 2

Today we were working on developing a fun and interactive lesson plan to help engage the students and encourage and get them excited to collect data understand how to look at data.  
I had the idea to have students take rc cars outside equip them with GPS sensors and have students drive a mock city setup to complete a set of tasks. To incorporate this with my Physics course I want to have the students first complete the activity using measurements by hand and vector addition and look at distance and displacement, then have them go back and create GPS data of their tasks and look at the data gathered on the GPS through numerical and GIS files and calculate their carbon emissions on the trip. I want students to then go back and look to see if there was a way to reduce their trip, and their emissions.  

The project part will be for the teams to collect GPS data for 2-5 days and compile it into one file possibly using GPSbabel.  I spoke with Jason and he is going to help us develop a code to turn the files into XML files that can then be uploaded by multiple team members and overlay each person's track in a different color to visualize and compare their routes to create a model and presentation about ways to calculate and create rideshare opportunities within their school, or community, or for some students see if they can find a better more effective bus route.
The next step for me is to research different cities maps to determine what cities might work best to scale up and recreate its basic highway system.  Also look at different rc cars and costs and GPS costs to implement this in my classes.  I need to run more tests with the rc vehicle to determine how accurate and to what extent we can collect data with few errors.  I actually saw a sight where students used GPS to write their names once the data was uploaded into the GIS application.

Day 9 - WK 2

Brought in an RC vehicle because the snap rover did not have a high enough rate of speed to log on the GPS.  The RC car was able to log a significant amount of data with a surprisingly few outliers.  The data actually was able to track the vehicle down one side of the sidewalk and back up the other side.  We were also able to park in a parking spot and back out.  We collected the data then imported it into Google earth, which showed our path very precisely, with only a few small outliers.  We also drove the rc car back into the building, up the and around the vending area outside the Ee department.  You can see in the images our path and even our detour to the vending area, there are some greater deviations from the actual track once in the building, but still a fairly good representation of our track.


We had a morning meeting where we discussed the requirements for the research paper and presentations.  Midterm presentations will need to contain most of our literature reveiw, methods and any results we have obtained.  IEEE.org actually has a paper format for teams interested in possibly publishing thier work after RET is completed.

In the afternoon I found a calculation for converting car mileage into emissions quatnities.  I was able to obtain this data from the Sightline Institute.org.  The following table shows the values based on car size.

vehicle	conversion factor	lbs CO2 per mile traveled
small	0.59
medium	1.1
SUV	1.57

Afternoon lecture on wireless senesor networks and the TEO - Texas environmental Observatory by Dr. Fu and Dr. Acevedo.  

Dr. Fu

Dr. Acevedo

Day 9 WK 2


We ended the day with presentations from Dr. Fu and Dr. Acevedo. Dr Fu enlightened us over his research in Wireless communications and Sensor Networks while Dr. Acevedo provided information on the Environmental projects using the technology presented by Dr. Fu.

After finding we were not able to collect data from the RC Snap Rover because of minimum and file size settings on the GPS 31, Kim brought her radio controlled truck. We were able to put the tracker into the truck and drive through the parking lot and along the second floor at Discovery Park. We were able to collect some good data by making the aforementioned modifications. We will analyze the trajectories  and document the results later this week.

Day 8 - WK 2

One of our GPS devices is giving off corrupted files so we worked to reset the GPS device, it seems to a bad formatted data card.  We have now been collecting data for 8 days.  Two of our GPS participants are now ridesharing, so we will be giving a device to another teacher.   Worked on the lesson plan today then we went to the library for research time after lunch.  We want to design a PBL, project based lesson for my students to work in teams, collect GPS data through various devices.  The first step for students will be to look at the benefits of ridesharing and the inhibitors to its success and discuss as a class.  I want students to become familiar with GPS and data collection and how it is processed by doing a geocaching activity with vectors and data collection. Research ridesharing techniques in various cities, the social issues of ridesharing, methods used to determine the best routes and how GPS calculates your routes.  Students will then develop a model and plan to reduce the carbon footprint from transportation and promote ridesharing in their school and community using their data results to support their model.
At this point we have no knowledge of how GIS could be incorporated to our project.  We do not have any data patterns other than looking at our data in lat/lon by date and time or in Google earth or on a map.
 Found articles over the social aspects of ridesharing and a website with research and connections for dynamic ridesharing  dynamicridesharing.org.  Found an interesting atricle that noticed a social pattern that women are more likely to ride share unless it was with a stranger. Men were more likely to rideshare with a stranger but in total it was less than 20 adults total willing to rideshare with a stranger. This was in the article Leveraging Social Networks to Embed Trust in Rideshare Programs. Another article actually had a formula they used to calculate CO2 emissions,the article Estimating the environmental benefits of ride-sharing: A case study of Dublin has several formulas that could be useful as we move forward to find a method to turn our distances traveled for each GPS data track into carbon footprint calculation and as well as a practical format that students and children at Techfest will understand.  One example might be how many trees it takes to absorb your CO2 emissions for a time period.


This is a picture of Jesse's track around the second floor of Discovery Park walking after our snap rover data collection failed today. Will bring in a faster RC car to attempt data collection again.

Day 8 Week 2

Kim and I test drove the rover through the building to see if we could collect some tracking data via the rover. It appears that the speed of the rover is not sufficient to trigger any readings on the GPS device other than the initial location of Discovery Park. We repeated the test with the same GPS unit and walked the same path and did collect data. We will look to repeat the test at a later time using a faster remote controlled  rover/vehicle.

Jason mentioned we will use Excel to look at small segments of data to determine/find common trajectories. Using this algorithm, a software program may be written to use on larger amounts of data depicting similar patterns.