Mobility Analytics using Sparse Mobility Data and Open Spatial Data

The mobility of people and things is an important societal process that facilitates and affects the lives of most people. Thus, society, including industry, has a substantial interest in well-functioning outdoor and indoor mobility infrastructures that are efficient, predictable, environmentally friendly, and safe. For outdoor mobility, reduction of congestion is high on the political agenda – it is estimated that congestion costs Denmark 30 billion DKK per year. Similarly, the reduction of CO2 emissions from transportation is on the political agenda, as the transportation sector is the second largest in terms of greenhouse gas emissions. Danish municipalities are interested in understanding the potentials for integrating various types of e-bikes in transportation planning. Increased use of such bicycles may contribute substantially to the greening of transportation and may also ease congestion and thus improve travel times. For indoor mobility, corridors and elevators represent bottlenecks for mobility in large building complexes (e.g. hospitals, factories and university campuses). With the addition of mobile robots, humans and robots will also be fighting to use the same space when moving indoors. Heavy use of corridors is also a source of noise that negatively impacts building occupants.

The ongoing, sweeping digitalisation has also reached outdoor and indoor mobility. Thus, increasingly massive volumes of mobility-related data, e.g. from sensors embedded in the road and building infrastructures, networked positioning (e.g. GPS or UWB) devices (e.g. smartphones and in-vehicle navigation devices) or indoor mobile robots, are becoming available. This enables an increasingly wide range of analyses related to mobility. When combined with digital representations of road networks and building interiors, this data holds the potential for enabling a more fine-grained understanding of mobility and for enabling more efficient, predictable, and environmentally friendly mobility. Long movement times equate with congestion and bad overall experiences.

The above data foundation offers a basis for understanding how well a road network or building performs across different days and across the duration of a day, and it offers the potential for decreased movement times by means of improved mobility flows and routing. However, there is an unmet need for low-cost tools that can be used by municipalities and building providers (e.g. mobile robot manufactures) that are capable of enabling a wide range of analytics on top of mobility data.