The efficiency of road transport is typically influenced by factors such as, weather, choice of road, and time of day, and day of the week. Knowledge about interactions between different traffic-and transport related factors and their influence on the execution of transport is important in transport planning. The purpose of this paper is to study the impact of different factors on the performance of road transport. We aim to contribute to improved transport planning by analysing traffic and transport data obtained from different sources in order to support data driven decision making. Through a review of existing literature and discussions with a Swedish road transport operator, we identified factors that could be relevant to consider when planning a transport, e.g., related to weather, location of roads where the transport will take place, and planned time of the transport. As a result of variation in size, type and volume of the data representing these factors, suitable machine learning algorithms were selected, such as Decision Stump, M5 model tree, M5 regression tree, RepTree, M5 rules, and linear regression in order to study the data. Our experimental results illustrate the complexity associated to the performance of road transport systems mainly because of the dependency between the choices of influencing factors and geographic location of the road segment.