XCARCITY
The XCARCITY project, funded by NWO and kicked off in 2023, will help in developing smart mobility solutions which would reduce the private car usage in the Netherlands. It is in collaboration with different stakeholders from industry, research institutes, universities and the government. The project is divided into eight work packages for which different stakeholders are responsible. The main objective is to develop digital twins (focused on Amsterdam, Almere and Metropolitan Area of Rotterdam and Den Haag) which could help in testing the impacts of different policies and strategies pertaining to low car areas, with sufficient level of detail, accuracy and efficiency.
The PhD project-Developing Integrated Transport Networks, is an important part of work package 4 of the XCARCITY project and SPTL. In this work package, the national and regional transport networks will be investigated for their potential to redesign them to cater for new/future low car areas within cities such as Amsterdam, Almere, Rotterdam and Den Haag. Societal objectives of sustainability, liveability, accessibility, inclusivity will be considered while designing the new networks. The design will be focused on unimodal and multimodal trips, including smart mobility solutions. The work package will also take input from the other work packages of the project which will develop the strategies and policies related to low car areas. The XCARCITY programme structure is presented in the figure below.
In this research, the major objective is to develop optimal solutions for authorities to make future low car areas accessible and liveable for people. To achieve this objective, the PhD will be focused on developing a multi-objective multimodal optimisation model which could deal with variables such as road capacity, as well as location of mobility hub, parking etc. Another objective would be to solve such models efficiently and as fast as possible. Focus will also be on including uncertainties related to acceptance of smart mobility, its development, travel behaviour etc in the model.