Traffic problem is one of the most frustrating experiences in India’s bustling cities. Mitigating this will require a multi-disciplinary and collaborative approach involving active participation from all stakeholders in the city. Real-time data will go a long way in enabling smart solutions to address these issues. Such data can come from a diverse set of public and private sources – from field sensors, crowd sourced data, traffic cameras, fleet operators, transport companies, logistics, map service providers etc. IUDX will enable all these players to safely and securely share their data, either for free or on commercial terms. Public transport needs to be made more efficient on the lines of private ridesharing companies. This will not only reduce congestion, improve occupancy & revenues, but also provide public convenience in city transport.
Use Case 1 – Data Analytics Problem Statement:
Predict the Expected Time of Arrival (ETA) of a PMPML Bus at given bus stops for a given trip.
For the Datathon, the following data from Pune City will be made available via PUDX
- Live and Archival ITMS Bus Data (from PMPML) – GPS location data is provided to detect live status of ITMS bus with route map, route ID and stop sequence
- Location of bus stops and route IDs (from PMPNL in GTFS Static File Format )
- MAP data from Google API or Open Street API can be used.
Limited trial data is already available via PUDX. Full data will be available from Nov 15, 2019.
Evaluation: Solution will be evaluated against a test dataset.
Use Case 1 – Citizen App Problem Statement
Embed the algorithm from part 1 into an app, which will be easy to use. The app can be Smartphone app or Web app.
Evaluation: App will be judged for ease of use, adaptability to new data.
Detailed problem statement can be downloaded here.
Imagine having a live Pollution score, customized for every Citizen, that gets updated periodically throughout the day, reflecting the quality of air inhaled by the citizen. Such information can be used to monitor an individual’s exposure to pollution and correlate with their health. Many cities have deployed a diverse set of pollution monitors: High end pollution sensors deployed by central and state pollution boards, medium end pollution sensors deployed by smart city and low–cost citizen deployed sensors. Calculating the pollution load, customized for a specific individual, will require combining static and dynamic data from a diverse set of sources in a way that best estimates their pollution exposure.
Use Case 2 – Data Analytics Problem Statement:
Estimate PM2.5 concentration at specific locations and times.
The following data from Pune City will be made available via PUDX
- Air Quality data from pollution sensors – The data contains PM 2.5 concentration along with other AQM parameters (e.g., C02, C0, S02, PM10 etc.) at various locations.
- Rainfall data from IITM sensors from 9 locations in Pune.
- Possible availability of WAQI data from IITM sensors from 9 locations in Pune.
- MAP data from Google API or Open Street API can be used.
Limited trial data is already available via PUDX. Full data will be available from Nov 15, 2019.
Evaluation: Solution will be evaluated against a test dataset.
Use Case 2 – Citizen App Problem Statement
Embed the algorithm from part 1 into an app, which will be easy to use. The app can be Smartphone App or Web app.
Evaluation: App will be judged for ease of use, adaptability to new data.
Detailed problem statement can be downloaded here.
Flooding has been a serious problem in Pune this year. Can we help the city authorities to better visualize this data so that they can understand it, draw insights and take steps to plan better for coming years?
Use Case 3 – Problem Statement:
Develop an app to visualize flood sensor data analysis.
The following data from PUDX is available
- Flood sensor data. The data for Mula riverbed is provided.
- Rainfall data from IITM sensors from 9 locations in Pune.
- Open Weather API data can be used.
Limited trial data is already available via PUDX. Full data will be available from Nov 15, 2019.
Evaluation: Based on visualization, kinds of analyses supported, ease of use and adaptability to new data sets.
The app can be Smartphone app or Webapp.