Lead Supervisor: Dr Nuno Pinto (University of Manchester)
Contact Email: firstname.lastname@example.org
Partners: University of Manchester
External Partners: Transport for Greater Manchester
Start Date: October 2018
The rapid change in technology we are experiencing in cities across the world, and the associated rise of flexible travel modes and new technology services, now called MaaS (mobility as a service), are challenging the ways city authorities plan for the future.
The models and data we use for this planning process haven’t really changed for decades and carry with them a number of assumptions that mean the approach is increasingly less fit for the purpose of examining the impacts of societal trends such as homeworking and mobile technologies, or trends in city centre living and the future of the green belt.
The research has two aims:
- to develop tools for data analytics that combine the more conventional datasets used in land use and transport (LUT) planning – transport flows, ticket data, road network data, land use, land registry, socioeconomic data, etc. – with other datasets generated – for example – by the new set of sensors that support many of these urban systems, including traffic sensors, cell phone data (calls or use of data plans) and social media data, Etc. These tools should include methods to deal with the inconsistencies observed between different datasets (particularly in spatial and temporal resolutions) and methods to generate new datasets for different modeling approaches used in land use and transport interactions (LUT) (from more traditional models to Agent Based Models and micro simulation).
- To develop simulation models able to use the combined datasets and to process outputs. These models are expected to be used in policy design and evaluation. Both the input datasets and outputs generated by the models should be designed to be easily integrated in a common data infrastructure that facilitates the use of both data and models in common planning processes.
Reference number MN22