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Weather Effects on Traffic Accidents in Dubai

This article will analyze Dubai weather data collected from January 2018 to March 2020 in “Predicting weather disruption of public transport” case and Dubai traffic accident data collected from June 2019 to May 2020 on Dubai Pulse Dubai Police Traffic category.

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8 thoughts on “Weather Effects on Traffic Accidents in Dubai

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    Good data analysis and problem setup, but we are missing closure, i.e. model which allows us to pair weather forecast data (from some “official” site/location) with historical rides/accident data to predict what will happen in next period (short period of hours to maybe day or two).
    That way, the government or public authority could add more buses to minimize any disruption of services.
    “Global” problems like climate change are not something we tried to solve here, but rather the influence of weather conditions on people driving cars/buses, etc.

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      I agree with your comment. I focused more on analysis instead of models. I did train Random Forest and XGBoost models which use weather forecast data to predict the number of traffic accidents during the current hour. One way to improve based on your comment would be shifting one hour of the traffic accidents data to do the training. Another important point would be interesting to add would be trying to figure out above how many accidents predicts the government should add/decrease the number of buses.

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        However, I am unable to obtain enough bus ridership data due to Dubai Pulse API restriction which only allows its citizens with ID to access. Otherwise, it would be interesting to predict the number of buses instead of the number of traffic accidents based on the weather data.

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          I agree with your statements. There are some options on how that dataset could be downloaded (Web scraping, etc.) but it would take some processing time.
          Also, I would advise using some additional datasets which were not part of the initial dataset, like aggregated daily traffic estimates on an hourly basis provided by some navigation applications because that can additionally help with model precision. We all know that bus driers should be professionals but the majority of β€œnormal” non-bus driers are not and they are heavily impacted in distracting sensor inputs (thunderstorm, rain, people cutting in, or even forgetting how to drive when weather condition changes). – I’m adding my last sentence about additional dataset to all teams focusing on this problem because no one did even consider it and that is something you can always do on any project – focus not on internal/provided data but find something to augment it πŸ˜‰

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            For other locations, there are lots of historical datasets for buses. For example, https://transitfeeds.com/p/riverside-transit-agency/531 contains GTFS data in US California Riverside Riverside Transit Agency. However, the question statement requires Dubai. I have tried Google Map API, Here API, and TomTom API. All of them only contain real-time traffic in Dubai. Historical datasets are missing. It would be interesting to know who obtain historical data in this competition.

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    @y2587wan there is the ability to download CSV for each individual bus line, but as I said, you would need to do some web scraping. It is available for non-UAE people on the link: https://www.dubaipulse.gov.ae/data/allresource/rta-bus/rta_bus_ridership-open?organisation=rta&service=rta-bus&count=1212&result_per_page=1300&as_sfid=AAAAAAVQxFA0BFeFROVV-_FUrwIfaEqwRWpoZA-y-UptqSEqxmERCKYLhWrwqWh3AfDCDdi1moQM5yS3Qjy2NzBMeMFf3DsQYwQOBarG4FRgrDCOeBE9L_Tq7J9m8CMoTDSCXIY%3D&as_fid=ff49229e06fa994326e53390b91e89d1dc5e2954. Nevertheless, you did a really good job in so short timeframe and you should look on my comment as something which can help you on your future competitions or even with your future profession πŸ˜‰

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      Thank you. I just realized when clicking “see more” on bus ridership open page, it will show more data than several days; however, during the competition, I clicked “next page” instead. Sorry for the argument above.

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    Hi, y2587wan πŸ™‚
    Your data prep is cool and is a good premise for modelling and off course for forecasting, which would be used for further decision-making… If you worked with someone, i believe you would move much further πŸ™‚

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