In Sydney, the Blue Mountains, Central Coast, the Hunter and the Illawarra area, fares are calculated based on the distance travelled from tap on to tap off, payment method, the mode of transport, concession eligibility (or free travel) and any Opal benefits such as discounts and capped fares that apply. This dataset provides the Opal Distance for all modes of transport and the Opal Fare Values for each mode of transport. The Opal Fares Business Rules and Information document provides the latest supporting information. For additional information about Opal Fares, Default fares and how Opal Fares are calculated, visit the [Transport Fares and Payments](https://transportnsw.info/tickets-opal/opal/fares-payments) website.

PTAL is a measure of connectivity by public transport, which has been used in various planning processes for many years. For any selected place, PTAL suggests how well the place is connected to public transport services. It does not cover trips by car. The PTAL model calculates December 2019 public transport accessibility for every 2016 ABS Mesh Block in New South Wales. The TfNSW PTAL model is based on the Transport for London (TfL) PTAL and was adapted for TfNSW in collaboration with the Spatial Information Science team at the Department of Earth and Environmental Sciences, Macquarie University. The [Assessing transport connectivity in London](http://content.tfl.gov.uk/connectivity-assessment-guide.pdf) explains what PTAL is, how to understand the data and more. PTAL values are simple. They range from zero to six, where the highest value represents the best connectivity. A location will have a higher PTAL if: \* It is at a short walking distance to the nearest stations or stops \* Waiting times at the nearest stations or stops are short \* More services pass at the nearest stations or stops \* There are major rail stations nearby \* Any combination of all the above. Please read the PTAL documentation for more information. The format of the data is Arcgis geodatabase. **Images below show an example of what the data looks like at 8:00am (first image) and 1:00am (second image)**.