The previous research available to predict travel speed is wide and has been extensively studied. What currently is missing from the previous work is to estimate the travel speed when different non-recurrent events occur, such as car accidents and road maintenance work. This research implements a machine learning model to predict the average speed on a road segment with and without road accidents. The model would assist in (1) planning the most efficient route which could reduce CO2 emissions and travel time (2) the drivers in traffic could get an estimate of when the traffic will open up again (3) the authorities could take safety measures if drivers are expected to be stuck for too long. In our work, we conducted a review to determine some of the optimal machine learning models to predict on time series data. What we found by comparing GRU (Gated Recurrent Unit) and LSTM (Long Short Term Memory) on travel speed data over a road in Sweden provided by the Swedish Transport Administration, is that there is no major difference in performance between the LSTM and GRU algorithms to predict the average travel speed. We also study the impact of using weather, date and accident related parameters on the model’s predictions. What we found is that we obtained much better results when including the weather data. Furthermore, the inclusion of road events vaguely hints that it could improve performance, but can not be verified due to the low number of road accidents in our dataset.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:mau-59273 |
| Date | January 2023 |
| Creators | Höjmark, André, Singh, Vivek |
| Publisher | Malmö universitet, Institutionen för datavetenskap och medieteknik (DVMT) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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