Macroscopic Transport Model
EO Capability Benefits
In the absence of other existing datasets, an EO-derived Land Use Land Cover dataset helps the transport planner determine where trips are generated and where they might be going. As most land uses cannot themselves be deduced from space, this information is often corroborated through non-EO reference data. To then estimate how many trips are being generated, not only are employment and car ownership rates important, but also the precise location of populations by age group, as well as the employment, educational and leisure potential of non-residential areas. In data-poor environments, Earth Observation products, in combination with crowd-sourced datasets such as OpenStreetMap, can help transport planners answer many of these questions. In addition, Population Density usually coarsely aggregated into census districts, can be proportionally re-distributed into much finer spatial units of, e.g. 10x10m pixels based, among other things, on land cover and Building Height derived from EO. This data could in turn be re-aggregated into more numerous Traffic Analysis Zones smaller than the original census districts, thus increasing the spatial fidelity of the model.
EO Capability Description
Macroscopic transport models are high-level models that provide a broad view of transportation systems. They typically cover large areas like entire cities or metropolitan regions, assuming a uniform traffic behaviour between individual Traffic Analysis Zones following general rules. They are useful for evaluating the impact of major infrastructure projects (e.g. a new subway line) on demand and the modal split. They therefore provide important information for developing land use and transportation plans covering 10 to 20 year planning horizons. They incorporate economic, socio-demographic and Earth Observation-derived data, most notably the Transport Network and Land Use Land Cover, but also Population Density.
They differ from the more granular meso- and microscopic transport models. Mesoscopic models are more computationally intensive as traffic is represented as discrete units (such as groups of similar vehicles known as “platoons”) instead of a continuous flow. They also incorporate household survey data and traffic statistics, and are better able to capture congestion and incident effects, for instance, from road closures caused by flooding (which can be estimated using past Flood Extent data). Microscopic models are the most detailed and computationally intensive as they represent individual vehicles and drivers. They are best suited for small-scale analysis of isolated intersections or road segments.
