Mobility - Centre for Green Shift in the Built Environment
Mobility
Task force Mobility
The transport sector is the major contributor to greenhouse gases. All transport modes need to become more sustainable, with green alternatives widely available. Through its projects, this task force will contribute to shift the way people and goods move. Key aspects include combining different modes of transport in a single journey, increasing the share of public and bicycle transport, and the development of the right incentives to drive the transition. Digitalization will be an indispensable driver for the modernization of the transport system, permitting for seamless, smart and efficient solutions.
MobilityLab Stor-Trondheim (MoST)
By 2030, the Elgeseter Innovation District will be a zero-emissions area with effective mobility solutions that look after and stimulate a variety of arenas with an innovation culture of international caliber - in close cooperation with the users of the area. NTNU provides av "Living Lab", with researchers, professors, PhDs and students contributing to the best sustainable and cost effective mobility solutions.
Partners: Miljøpakken, Trondheim Fylkeskommune, Trondheim Kommune, NTNU (Several Faculties: IV, IE, AD, ØK).
Web: MobilitetsLab Stor-Trondheim (MoST)
Contact: Agnar Johansen and Jardar Lohne
SmartRVU
Smart travel behavior data collection (SmartRVU)
SmartRVU is a project initiated by NTNU and the Norwegian Public Roads administration. The goal is to collect travel behavior data smarter – with less burden on respondents and with higher quality.
Transport planners need data on how people travel. This way planners can improve the transport system and transport services in the best possible way for the future, for the benefit of all. Travel behavior data are currently collected via phone interviews or self-reported on web.
Travel surveys collect information about how people travel in everyday life and are used for trend analyses, impact analyses related to specific changes in transport services and for estimation of transport models. This is an important basis for decision-making for planners and politicians in the field of transport. Traditional interviews, even web-assisted ones, are demanding for respondents, and recruitment for participation and completion of travel surveys has proven more challenging now than in the past.
Contact: Trude Tørset
Interpretable Models with Graph Neural Networks to support the Green Transition of Critical Infrastructures
This project is a first step to strategically position the Green Shift Centre amongst the world leading innovators for interpretable AI in the built environment. Furthermore, the project will provide technologies for achieving following four SDGs:
- SDG6-Clean Water and Sanitation
- SDG9-Industry, Innovation, and Infrastructure
- SDG11-Sustainable Cities and Communities
- SDG13-Climate Action
Finally, this project will be a starting point for future collaborations on AI for infrastructure planning between the Green Shift Centre of NTNU, TU Delft AI Lab for sustainable water management (AidroLab), and TU Graz Institute of Highway Engineering and Transport Planning.
Contact: David Steffelbauer
A Smart Mobility Service using Digital Navigation Model of the Built Environment
The proposed research delivers a novel digital navigation model which facilitates more mobility in a smarter way in the modern complex cities. The Smart Mobility Service based on the developed model provides cleaner, safer, and more resource efficient cities.
Contact: Hossein Nahavandchi
Data-driven multi-disciplinary diagnosis and solutions for settlement-induced railway damage
Railway transportation is a significant means of mass transportation with high energy efficiency and low emission compared to others. Real-time health monitoring and early warning help building a sustainable and climate-resilient railway system, and enables better maintenance protocol which will reduce maintenance and energy costs and cut carbon emission. The digitalization of all relevant elements (surface feature, track and ground information as well as statistics of track defective data) is a basis for future applications and upgrades to future high-speed railway construction, which move large volumes of traffic from air to rail.
Contact: Yutao Pan
Railway track health monitoring and predictive maintenance measured data from a train in regular traffic
At present, vehicle and railway track conditions are monitored either manually or by a dedicated measurement car. These solutions have a disadvantage as the condition of the vehicle and track will only make known infrequently. This makes both the vehicle and track vulnerable if issues happen in the interval of two measurements. This has also been one of the challenges in transition from road to railway, which is the greenest mode of transportation.
The project is about research development for railway track condition monitoring and for predictive maintenance with a regular train in regular traffic. The aim of this project is to ensure a more reliable and robust railway network which is one of the key components for the green shift transition in the transport infrastructure.
Contact: Albert Lau