Inspiration
If you've travelled a couple times in your life, it's not uncommon to have found yourself spending extra for an authentic, premium experience abroad just to recieve mediocre service with questionably cultural roots. It's not your fault either! Many businesses will maintain high ratings and volume of customers while upcharging their customers for the sole crime of trying to enjoy their holidays. And while it's certainly not a crime to use this business model, it can leave travellers feeling cheated out of their coin, and discouraged in further exploring all the world has to offer. With this in mind, TrapTrack was made as a way to provide a simple and easy solution to help travellers get the most bang for their buck while on vacation, being so accessible that anyone could use it.
What it does
TrapTrack is a Google Chrome extension that obtains establishment information from Google Maps and assigns them scores based on how likely they are to be targeted towards "tourists" using an algorithm. It can also scan all nearby establishments and provide a brief summary of nearby options and their "tourist trap likelihood" ratings.
How we built it
We used VS code to build a google extension framework for our project, as well as Node.js to connect and query establishment information from Google's Places API. CSS, HTML, and Javascript was used throughout the implementation of TrapTrack.
Challenges we ran into
The main challenge our group faced was centered around the creation of Google Chrome extensions and coordinating them with APIs. No one in our group had any previous experience with creating extensions, so the first challenge we faced was quickly learning how to get a simple extension working. Afterwards, the largest challenge we faced, and the bulk of our development time, arose from implementing and coordinating Google's Places API with our extension to perform tasks like currating information and returning said formatted data.
Accomplishments that we're proud of
Our team was constantly making breakthroughs throughout these 24 hours, as none of us had any prior experience with hackathons, let alone many of the tools we ended up working with. As a result we were constantly celebrating small victories, like getting our rudimentary Chrome extension working, to larger successes like when we completed a main function of TrapTrack (the scoring and returning of restaurant risk ratings). Even with constant setbacks along the way, such as creating empty displays and ridiculous algorithm ratings, we were still proud of ourselves as we saw proof of our effort and progress over these 24 hours.
What we learned
Being beginners, we learnt many things from NewHacks 2025. In particular, we learnt how to work with programming languages like JavaScript, HTML, and CSS. Furthermore, we learnt how to create our own Google Extensions and work with some of Google's APIs, as well as working with servers through Node.js.
What's next for TrapTrack
Currently TrapTrack's "trap likelihood" algorithm is limited by the capabilities of Google's Places API. Our algorithm currently heavily relies on the reviews left by others, however the Places API limits us to querying 5 reviews per location. Testing the algorithm and ensuring that results were generally accurate became significantly more difficult because of this. Moving forward, we'd like to improve the algorithm by expanding its scope to query a wider scope of reviews (through Yelp API, for example) to ensure that TrapTrack can provide helpful and accurate information to the user.
Log in or sign up for Devpost to join the conversation.