Assignment 3

ABSTRACT

For this project, we (Dan Pratchenko and Geoffrey Marcus) are proposing the construction and installation of a GPS tracking system of the Lane Transit District’s busing system near the University of Oregon Campus, with the end goal of creating a system in which end-users can track bus locations and time to increase the efficiency of commuting with public transportation.

To this end, we have looked at a number of other real-time tracking systems that have been implemented across the globe – specifically looking at the one that was implemented by the King County Metro system. Next we will show how our project fits in with the National Institute for Transportation and Communities’ Request for Proposals, and finally we will discuss our field methods and impacts of our data.

 

LITERATURE REVIEW

In the last decade several cities have made the push to implement real-time tracking on public transit systems. In an article by Watkins et al. extensive research was done on the use of live tracking buses in the Seattle area, where issues with heavy traffic are a constant problem. In this study the real-time data for the buses was collected by the OneBusAway transit traveller system. OneBusAway utilized the existing Automatic Vehicle Location (AVL) system setup by the King County metro to create a multiplatform application where real-time transit information could be accessed by anyone, anywhere, at anytime. The AVL was initially set up to create real time public display signs at bus stops and transit stations. These are nice, but realistically they are expensive to set up/maintain and they are not really saving anyone any time because people must wait at the station to access this info. Creating an application accessible from a mobile device to display real-time data is a far more convenient and cost effective solution. The AVL system has no GPS or map interface, the data is simply displayed with text. In the Watkins et al. study, hundreds of transit riders were interviewed about their commutes and whether or not they used OneBusAway or another means of accessing live tracking data. They study hoped to see if real time data decreased the burden of public transit and thereby increases the overall ridership on the transit system. The results were interesting. Not only did riders perceived wait time decease, there actual wait times decreased by an average of two minutes. The study also found that riders using real-time tracking apps had less frustration with public transit and felt physically safer. Our proposal will be the good next step in the live tracking of public transit. By implementing a GPS based system we will be able to add maps of the buses in route to riders and enable a notification system based on the distance between the bus and rider.

 

THEME

Our proposal to place GPS sensors on LTD buses to collect of real time data  accessible to the public via a mobile application fits the themes of the NITC in several ways:

  1. Improving safety for users:
    • Many bus stops are not in the safest locations and the danger is often compounded in the evening hours. By using our application riders will know exactly when their bus is to arrive, therefore reducing the time spent at the bus stop itself and avoiding potentially dangerous situations.
  2. Improve health of user:
    • Studies have shown that people who utilize real time transit information experience less anxiety and aggravation with their commutes.
    • Riders will feel in control of their trips.
  3. Increasing the efficiency of transit:
    • Wait times for riders will be decreased. People will have more time to do thing they want rather than be stuck at a bus stop for fear of missing their connections.
  4. Making the best use of data, performance measures, analytical tools, and new technologies:
    • GPS sensors are a cost effective solution live tracking data collection.
    • GPS data permits better spatial analysis of the transit system.
    • The application will provide innovative features like a real-time map of the buses in route and notifications when your bus is close to your location. Riders will never miss a bus again.
  5. Decrease fossil fuel emissions:
    • More people will ride the bus thus decreasing the number of pollution vehicles on the roads.

 

OBJECTIVES

Though this project we will attempt to answer the  following questions:

  1. Will visual display of live tracking data on mobile devices increase ridership around campus?
  • Anticipated Outcome: There are many variables that affect whether or not a person decides to ride the the bus. By implementing sensors and creating our mobile application we are improving several of these variables (ie. wait time, trip control, safety, etc). Therefore we are predict that ridership will increase.
  1. Will visual display of live tracking data decrease the perceived and the actual amount of time people spend waiting at bus stops around campus?
  • Anticipated Outcome:  In the Watkins et al. article a similar question was addressed and their results were positive. Even without the visual display of the data or the distance notifications there was a decrease in the perceived and actual wait time of riders. With these added technologies included in our plan we predict the rider’s perceived and actual wait times will further decrease.
  1. Do riders feel more in control of their trips and has their perceived safety increased due to the availability of real time transit information?
  • Anticipated Outcome: Rider’s will be able to see the exact location of their bus and know exactly when it will arrive. They will not have to depend on schedules (which are really just estimated times of arrival) and waste copious amounts of time waiting at bus stops. Knowledge of the buses current position allows the rider to gain back the time usually wasted waiting for the bus. With riders being given back this time to spend on activities other than waiting, we would predict that the riders sense of control of their trip would increase. Perceived safety should increase as well, because riders will also have the ability limit waiting time at potentially dangerous bus stops.

 

METHODOLOGY

For this project, we are looking three primary systems we need to prepare. The first is the GPS trackers installed on the busses, the second is an application created for the end-users, and the third is some sort of server system that will communicate with both the end-users and the GPS systems. Once we have these systems in place, we will install them on a number of campus busses, and distribute the application to a number of alpha-testers, who we will interview at the end of the study to get feedback as to the effectiveness of our system.

For the GPS system, while pre-made vehicle tracking systems exist, we are thinking of constructing our own using Arduino boards for a number of reasons. There are a number of issues with pre-constructed vehicle tracking systems – first, they tend to be more expensive than an open-source alternative, with the cheaper systems costing around $130. The next issue is that many vehicle tracking systems aren’t designed for continuous tracking, but instead only track a vehicle once they have been activated for temporary uses – usually for tracking stolen vehicles.

With an arduino system, we would need three primary pieces – the arduino board itself which usually run around $20, a GPS Shield/Logger such as the Adafruit Ultimate GPS Logger Shield to collect the GPS information, and some way of communicating the information back to the central hub – likely a GSM shield that could send it over a cell network as either data or texts. We would want to purchase an AT&T plan to transmit the data as they support the GSM connector and have fairly consistent coverage throughout Eugene (Verizon does not support GSM chips). We would like to install these only at least ten LTD busses that have routes going to and from the University of Oregon Campus. Here is the cost breakdown:

  • Adafruit FONA – Mini Cellular GSM Breakout, $45 each x 10 = $450
    • http://www.adafruit.com/products/1963
  • AT&T Cell Plan to transmit data: $25/month x 10 = $250
 Total Hardware Cost: $1350 

As far as our central server solution, as our project is only lasting two weeks initially, we will probably do something like use the free trial of Amazon Cloud Services to run our database, this way we can avoid long term server costs.

The end-user application would be a fairly simple app the did a number of things – it will show you what busses are on time, along with their approximate location on a city map. You will be able to set it up so the app gives you a reminder when a bus is a certain distance or time away from you as well. While we could likely create this app ourselves, to increase the quality of the end-user experience, we would likely higher a freelance scripter to write this simple application for us.

 

BROAD IMPACTS

Right of the bat, this project is neither state-of-the-art or groundbreaking. The research we are hoping to do here has already been done in other forms around the world – tracking busses and other public transportation is something being done many different cities. However our research will provide some new data as to whether or not this tracking system is effective in increasing rider safety, transit efficiency, decrease rider anxiety, and a variety of other variables.

The other thing that will distinguish this project from others around the world is our use of open-source equipment. If our project is successful, it will create a model for transit districts across the globe to install these systems at a substantially reduced cost to what it might have cost before when purchasing proprietary equipment. We would hope that the NITC sees the value in our project – both in improving public transit, and reducing costs to the general public.

 

REFERENCES

  1. Kari Edison Watkins, Brian Ferris, Alan Borning, G. Scott Rutherford, David Layton. “Where Is My Bus? Impact of mobile real-time information on the perceived and actual wait time of transit riders.” Transportation Research Part A: Policy and Practice: Volume 45, Issue 8, October 2011, Pages 839-848.
    (http://www.sciencedirect.com/science/article/pii/S0965856411001030)

 

 

1 Comment

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