Trip Cost Calculator

Provide post-trip informatics after car rides

Mobile Ubiquitous Behaviour
  • Team Size: 3


Driving is the second highest expense for the average American household. Yet few people know the value of their vehicles, and most cannot estimate accurately how much a common driving trip (like a daily commute) costs. There are an increasing number of viable alternatives for personal transportation available today. Arguably, cost is one of the primary factors in transportation mode choice, and awareness of the cost of driving is useful in making better informed decisions. To bridge this awareness gap, we built and deployed a mobile system that makes the total cost of each driving trip (beyond just fuel expenses) visible to the user. After this intervention, participants were able to more accurately and confidently estimate costs of their driving commutes, and extrapolate this knowledge to other trips for which they had not seen a cost.
However, this newfound knowledge was not always enough to really change the car owners behaviours. Although they could now estimate better, their method of countering this was not necessarily as expected, i.e. not all of them reconsidered continuing their daily trips. In tandem there's a need to address the believability of what is being represented to them. In light of all of this we now set out to provide a better nudge to these car owners that can actually lead them to take more informed decisions beyond just building their knowledge.

My Role

Caleb Southern a PhD associated with the Ubicomp Lab at Georgia Tech, began the ground laying work on this project in the Spring of 2016. I joined the project a month into Fall Semester of 2016, to help him with the redesigning and rebuilding the tool itself.

UX/UI Designer
In my earlier projects, I had some experience with working on (and using) personal informatics applications that encourage users to modify their behaviours via subtle persuasion. Revamping the method of intervention for the users was of prime importance to the next steps for this project, and I was strongly involved in this. Another significant influence of mine was on creating interactions for the user that helps them view the information more tangibly.
Development Engineer
Having significant development experience in my repertoire I was steering the actual construction of the new tools we planned to test. This included Android development for the smartphone utility that provides the post-trip interventions to the user. Along with this I also worked in implementing the systems infrastructure for the primary researchers to execute their study remotely.
Note: These roles are not necessarily exclusive to me nor are they exhaustive in describing everything I did in the project

Past Work

The Tool Itself

The system aims to frame the unit of analysis of measure for car owners on a per-trip basis. After every car trip the user is nudged with meaningful information for that trip itself. The model aggregates both fixed and per-trip costs, including ownership (depreciation), fuel, maintenance, insurance, taxes, and fees, and produces a personalized cost estimate for each driving trip, based on the user’s specific vehicle and actual fuel use and distance traveled.

The cost model can be broken down as follows:

Fuel Cost
= (Fuel Cost per Gallon / Fuel Miles per Gallon) x Trip Length
This is the most "obvious" expense of driving a car. The only variable that is a function of the trip is the length itself. The fuel cost, though not strictly invariant, can still be perceptually considered an average stable value (or one that changes slowly).
Other Costs
= Operating Costs + Ownership Costs
This is the invisible value we are trying to draw the user's attention too. The "hidden cost" is also varies as a function of the trip length and generally adds to the expected fuel cost. Most of the persuasive argument suggested by the system hinges on revealing this cost to the car owners.

The multiple operands used to calculate these costs are either retrieved on-the-fly (such as trip length), sourced from databases like that Kelly Blue Book (such as yearly depreciation of a given car) or manually calculated and loaded to the system by the researchers.

The apparatus that was used to create this solution and study its effectiveness relies on two major components:

OBD Interface
A Bluetooth- enabled On-Board Diagnostics (OBD) interface was attached to the vehicle. The OBD device is used to indirectly determine the fuel usage and detect the start and stop of each driving trip. This needed to be manually calibrated for each vehicle in the study.
The Android application on the user's phone retrieves the data from the OBD (using third-party APIs), takes GPS information for the trip variables and audibly alerts the user to view their trip cost at the end of the car ride.

A display of the Old Interface nudged a user at the end of the trip

The First Study

The participants of the study were asked to follow the following protocol:

  1. Each participant completed an entry survey, where they gave an estimate of the average cost of a one-way trip as well as how confident they are in that answer.
  2. The system is installed on the user vehicle, the model coefficients of the participant’s personal vehicle are adjusted on the application settings based on how that specific vehicle’s OBD protocol reported
  3. Immediately after each driving trip, the system allowed participants to see the estimated total cost of that trip. Participants could not review the trip costs later, so our evaluation reflected their memories of a trip cost meter that appeared when they turned on their vehicles.
  4. The participants completed the exit interview with the same questions as the first interview. Apart from this the participants were also asked to discuss their overall impression of the system, additional thoughts and reflections.
  5. 9 participants (of the 14 enrolled) completed the study
    7 days of deployment
    145 driving trips were completed during the study
    4 participants took part in a follow-up interview four months later

    It is safe to say that the results for this first study were promising. In general the participants showed an improved accuracy and confidence when compared at the stages before and after the study deployment.

    A representation of how the 9 participants that completed the study saw their trip cost Cost Break Down
    The accuracy of almost all the participants improves after the study Pre-Post Accuracy Analysis
    The confidence of almost all the participants improves after the study Pre-Post Confidence Analysis

    New Requirements

    What already has been established by the study is that highlighting the hidden costs augments an individuals knowledge about the cost of owning and operating their own vehicle. What needs to be further explored however, is that there is a connection between increased awareness of driving costs with the choice of which kind of transport an individual may decide to use daily.

    Currently users view the breakdown of the per trip cost, via the static segmented gauge that shows the fuel and "other costs". The other costs which one might consider to be "fixed costs" could be interpreted to be static. Due to this, even after becoming aware of the hidden cost, individuals might think of it as "a cost they will incur anyway" - so they might as well try to increase the per-trip cost (and decrease the percentage of the other cost segment) to get maximum value of each trip.
    Rough sketch
    In-Depth Breakdown
    Of the 9 participants 7 wanted to understand better what the "Other Costs" are. 4 of them specifically asked for the difference between per trip costs and "fixed costs". Even though they understood that there were extra costs, fundamentally knowing where the values would emerge from could help them learn better and accordingly evaluate their choices better.
    The way the previous apparatus was deployed was with a clearly visible OBD and a smartphone that quietly pulled information and performed actions in the background. The point being, the participant was aware that there are some devices with them, in the car that would be calculating these costs correctly. Would the reason they believe the information shown in the smartphone to be true because it was with them through the journey? Or would logging their trip on an online dashboard yield the same result? Or are they just primed to believe the data on the phone because of study biases?
    This connection between the user's interpretation and the actionable information provided to them needs to explored better.

    New Improvements

    Dynamic Story-Telling
    Instead of using the static horizontal bar, that immediately pops up at the user at end of each trip, we implemented a narrative method to send the point of the information across more effectively.
    The first change in this was to use relatable _metaphors_. The first of these is to use a vertical bar to represent the fact that costs "rise". Naturally, we needed to exemplify rising costs better - this was done by showing a animated meter to the user instead of the old static one.
    Also, to improve the story of the hidden costs to your trips, the cost segments are ordered in a very specific manner. First the fuel cost is shown, then there's a momentary pause, then the meter rises to represent the operating cost, another pause and then finally we see the ownership costs rise on top of that.
    The motivation of using this method is to always give the user a feeling of increase and stacking over. Such an attempt communicates the aim of the persuasive information far better than the old UI. It is more complex in terms of elements, but also more intuitive for the user to understand and make a sensible inference.
    An animated stacked bar graph column tells the story much better
    Drill Down Interactions
    Many participants voiced that they want the tool to let them be more incisive about the information they are presented. There was a need to get further details when it came to the extra costs in particular. The new interface gets rid of the ambiguous "other costs" and replaces it with the far more explanatory ownership and operating costs.
    This issue is addressed by allowing the users to engage with the visual elements of the screen. They can now tap on a segment to drill down into how that segment's value is calculated and what it really means.
    Another possibility could be to show them another vertical meter representing the different elements of a segment when they click on it. Think of it as a recursive set of gauges the participant can keep drilling into based on how inquisitive they are.
    Power users can inspect cost segments with a simple tap


    The nitty-gritty of a future study are still to be finalized. More importantly because of the ongoing academic nature of this work, most of the details for this are confidential.
    This study is expected to be conducted sometime in Spring 2017. The learnings from this would propel us towards the next iteration where we would also explore how to replace the utility of the OBD with a more scalable solution. Eventually we are looking to deploy this tool for a greater sample set, and obtain more conclusive results.