Wondering which residents in your city have the most trouble getting to work? Or in which neighbourhoods you can best roll out your sharing concept? The multimodal accessibility of areas and the development opportunities of specific groups of people are important when making considered choices. We generate these insights by combining various data sources.
Different data sources
Some examples of data we use are observed travel times for car traffic from FCD-data, timetables for public transport from GFTS, and for bicycle traffic calculated travel times or based on GPS. Depending on the question, we link these travel times to information about traffic density, accidents and other relevant data in the spatial environment. Examples include the number of inhabitants and jobs, locations of public transport hubs, bicycle parking facilities, etc.
Differentiate by target group
We can also differentiate the accessibility of inhabitants and workplaces by target group, using a synthetic population of the Dutch population. In addition, by linking to traffic models, we can consider multiple realities and forecast scenarios. In this way, we provide a detailed insight into the blind spots of the current mobility system and can test possible solutions.
A better starting point for mobility policy
We make the results transparent in map images, which we make accessible via our web-based application OmniTRANS Analytics. These map images offer a clearly visualised starting point for policy design, a monitoring task or a mobility vision. The data-driven insight into current accessibility that is obtained, contributes to the development of an effective and clearly substantiated mobility policy.