About Travel Bug

The travel and tourism sector has grown each year because of a reduction in travel cost and with growing awareness of new travel destinations due to social media. However, the process of planning a perfect getaway is still a pain for many consumers because it is difficult to calculate an accurate travel budget, knowing exactly where in the world to visit, and understanding main other critical elements involved with planning a successful trip. This is why the current $8.3 trillion travel and tourism sector is on a verge of disruption to help solve these problems.

Our project, Travel Bug, is intended to eliminate the major pain points involved with travel planning. Travel Bug will help create a travel plan by having the user answer a few questions, and then provide a recommendation list of where to travel based on specific criteria. For example, if someone has a budget of $500 and wants to travel somewhere with mountains and highly rated restaurants, Travel Bug will provide recommended locations with travel and housing options along with a budget calculator and also highlights important factors such as the crime rating in the area and ease of public transportation availability. This platform will do all the hard planning for any vacation trip, so a user can focus on the more interesting aspects of traveling. Catch the travel bug and go explore!

How we built it:

Our python backend aggregates and cleans the data from DATA.gov, Tourpedia datasets to extract restaurants, hotels and points of interest in an area (Currently only limited to London). It then extracts POIs that will be relevant to tourists (e.g. dropping Courtrooms, Emergency rooms, schools etc while keeping historic monuments, amusement parks etc.).

We then calculate hotel "density" - Number of points of interest within 5, 10, 25, and 50 miles for each hotel and sort hotels based on this density. Next, all restaurants near a 10 mile radius are extracted for each hotel. Google's place API is queried for a price range for hotel and restaurant price range, and this data is combined with Trivago's hotel price index to figure out average cost of living and eating meal near the area. Based on user's budget, we provide find the densest hotels to stay in. We have also calculated the crime rating index data and public transportation available from data gathered from living atlas, and overlay hotels on this map, so users can identify how safe the area is.

To build this web application used ArcGIS online to visualize data layers which we will pull from the Living Atlas, Google, Data.gov, Tourpedia etc.

Problems encountered:

  1. Data size: London dataset has 50,000 restaurants and 4,000 hotels with 600+ data points. To calculate density, that translates to 200,000,000 iterations
  2. Finding relevant datasets was a challenge
  3. Some of Google place api data and Tourpedia data does not match
  4. Price range is not available for all hotels/restaurants in Google's places api.

Future Scope:

  1. Expand the dataset to other areas
  2. Factor in location details like height and proximity to beach to suggest more relevant places to user's interest
  3. Use the crime index, transportation availability and other metrics to calculate a satisfaction score

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