Achieving meaningful sustainability targets in Positive Energy Districts increasingly depends on understanding how mobility affects the regional energy system. Transport remains one of the most demanding energy consumers, and its gradual shift toward electrification is adding new layers of complexity. 

NTUA researchers have developed a dedicated Transport and Mobility Tool, now forming a core part of the Positive Energy District Digital Twin framework of the ExPEDite project. The tool enables districts to analyse how local travel behaviour, new transport modes, and EV charging needs interact with the energy system and how future changes in mobility might reshape demand.

The tool is built on a microscopic traffic simulation environment, allowing to recreate detailed movement patterns for individual vehicles and identify how alternative mobility scenarios and changes in infrastructure or policy could shift people’s choices. 

Figure 1 – Congested intersection inside the RTU campus as represented using microscopic traffic simulation

A distinctive strength of the ExPEDite tool is its ability to merge video-based traffic observations with structured survey data. Each data source brings advantages that help compensate for the other’s limitations. Video sensors provide continuous round-the-clock traffic counts wherever they are installed and, with AI-supported vehicle recognition, can distinguish between different vehicle types. 

The survey component provides additional information that would otherwise require an impractical and unaffordable network of sensors. By collecting responses from the RTU community, the parts of trips that cameras cannot see are captured, along with respondents’ expectations on how they might react to future mobility options. In ExPEDite, these include a mixture of attractive alternative transport options and targeted parking charges that work in combination to reduce dependence on private vehicles. Combining both data sources results in a far more complete and reliable representation of real travel behaviour.

The final outputs of the tool support planners and policymakers estimate how different policies could shift behaviour, and reveal which interventions would most effectively support sustainability goals. By linking mobility modelling with energy planning, the tool provides insight on how the transport system influences the operation and goals of a Positive Energy District.