Urban Transportation Network Design and Impacts of Automated Driving
Automated Driving is a trend that will evolve over time, both in the market penetration rate of Automated Vehicles and the level of automation. In the transition period to full automation, there will be a mix of vehicles with different automation levels with different requirements for safe and efficient automated driving. In order to provide insight into the possible future scenarios, we need new models to explain how automated vehicles and urban infrastructure interact with each other. This includes modelling travel behaviour of automated vehicles, modelling new traffic flow dynamics resulting from these new behavioural patterns, network optimization methods for optimizing urban transportation networks, and stochastic methods to deal with uncertain future development path of automated driving.
First scientific challenge in this project is developing accurate and efficient traffic flow models to capture the behavioural differences of automated vehicles. Next challenge is developing efficient stochastic optimization methods to find reasonable trade-offs between investments for network design and benefits obtained by these investments while considering various uncertainties with respect to development path of automated driving (e.g., technology development path, policy making, users’ response and adoption rate).
Urban planners and transport authorities need insight into the possible future scenarios and potential impacts of their decisions in order to define their policies regarding automated driving. This research can provide them with a decision support tool and aid them in assessing the impacts of the choices they face concerning the future of automated driving.
Start/end date: April, 2016 - April, 2020