Understanding heavy goods vehicles’ behaviour by means floating car data

Transition from current mobility model to a smart city model raises several non-trivial challenges. When implementing policy measures, it is crucial to base decisions on objectively identified insights concerning transport behaviour and patterns. An understanding of how travel behaviour and freight flows impact the liveability of cities is essential. Our research attempts to introduce indicators that are relatively straightforward to measure from overgrowing data sources. The framework enables cities and municipalities to gain improved insights in the impact of urban transport.
In the context of a road tax, based on driven kilometres for heavy good vehicles, each truck in Belgium has been equipped with an On-Board Unit (OBU). The on-board unit of each vehicle reports time, position, velocity and direction of the vehicle every thirty seconds. Furthermore, the OBU data includes characteristics of each truck such as their weight category, plate’s country code and European emission standards classification of the engine (Euro class). In this research, we investigate what are the interesting indicators for a municipality, which can be derived from this big data set which has around two hundred million observations per day. In this case, the municipality for whom the indicators have been derived is Brussels Capital Region (BCR). We investigate the indicators with respect to entering, leaving and driving distance and times in the considered municipality. We study distribution over time of the hours that trucks enter and leave. Moreover, we explore the entry points used more commonly by trucks. Thereafter, we discuss the definition of a spot used for loading and unloading, and demonstrate the hotspots used by trucks for this objective. Finally, the origin and destination of trucks is categorized.