Travel time estimation for emergency services

Emergency services has a vital function in society, and except saving lifes a functioning emergency service system provides the inhabitants of any give society with a sence of feeling secure. Because of the delicate nature of the services provided there is always an interest in improvement with regards to the performance of the system. In order to have a good system there are a variety of models that can be used as decision making support. An important component in many of these models are the travel time of an emergency vehicle. In In this study the focus lies in travel time estimation for the emergency services and how it could be estimated by using a neural network, called a deep learning process in this report. The data used in the report is map matched GPS points that have been collected by the emergency services in two counties in Sweden, Östergötland and Västergötland. The map matched data has then been matched with NVDB, which is the the national road database, adding an extra layer of information, such as roadlink geometry, number of roundabouts etc. To find the most important features to use as input in the developed model a Pearson and Spearman correlation test was performed. Even if these two tests do not capture all possible relations between features they still give an indication of what features that can be included. The deep learning process developed within this study uses route length, average weighted speed limit, resource category, and road width. It is trained with 75% of the data leaving the remaining 25% for testing of the model. The DLP gives a mean absolute error of 51.39 when trained and 59.21 seconds when presented with new data. This in comparison a simpler model which calculates the travel time by dividing the route length with the weighted averag speed limt, which gives a mean absolute error of 227.48 seconds. According to the error metrics used in order to evaluate the models the DLP performs better than the current model. However there is a dimension of complexity with the DLP which makes it sort of a black box where something goes in and out comes an estimated travel time. If the aim is to have a more comprehensive model, then the current model has its benefits over a DLP. However the potential that lies in using a DLP is entruiging, and with a more in depth analysis of features and how to classify these in combination with more data there may be room for developing more complex DLPs