Work Life Made Easy
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Team 18
Our team aims to provide a solution for public transportation challenges that Sydney is facing. According to the data provided by BOM and Open Data, there is an obvious correlation between weather, and busses or trains punctuality and high usage volumes. We have prepared the “Weather to go” software: a two-part solution to help out both the users and the public transportation officers.
Users can connect to the chatbot that analyzes the real-time data around the user like weather and the traffic intensity, and helps to decide what is the most optimal time to leave for the bus or train. The dashboard is our future feature, that could provide the recommendations for the governmental agencies. It would include the real-time data about the capacity, weather, and traffic. All data would be provided in easy to use, interactive graphs.
In that matter, the “Weather to go” could be a great help in planning, developing and improving the NSW public transportation.
As part of the research for our project, we verified whether there was a correlation between bad weather and transport delays based on historical data in a scientific way. We combined data about Transport for NSW Public Transport Patronage, Sydney trains performance and Bureau of Meteorology climate statistics from the same time period 2011-2018, and use the time series as the common reference to create the rain and transportation relation graph. For the bus data, we calculated the average punctuality percent from all bus companies in the same day to get more accurate punctuality rate. This data will be used for our Weather to go forecasting dashboard to generate predictions based on historical data.
Under the hood, the Weather to go chatbot is using Bureau of Meteorology - Latest Weather Observations for Sydney - Observatory Hill dataset, along with Transport NSW Roads Real Time API and Transport NSW Realtime Trip Updates API for the chatbot real time updates functionality to enable consumers to have more data to make better transport decisions.
Description of Use We used the historical observations to observe if there is a correlation between public transport usage and delays with rainy days. In the future we could also use it to predict times of the year that are more likely to rain. This data would be visualised on a dashboard to be used by transport officials to plan and forecast public transport schedules.
Description of Use Comparison of delays on Sydney trains with weather conditions.
Description of Use Comparison of variations in public transport usage with weather conditions.
Description of Use We will use the API to help users identify if there is traffic in their area.
Description of Use We are going to use the 'rain_trace' field which measures the rain since 9am help consumers make decisions on whether they should use public transport.
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