Smart Sustainable Mobility
To this day traditional urban mobility relies primarily on internal combustion (IC) engine vehicles. This mobility setup brings with it four well-known social negatives. First, traditional road transport contributes substantially to the global GHG emission balance sheet. Second, pollution in the form of NOx, HC, PM and other emissions poses serious health hazards to urban populations. Third, road traffic is a major safety concern with close to 1.3m people dying in road accidents each year across the globe. Finally, road transport is highly inefficient, as utilization of passenger cars is low, thus requiring many cars to provide mobility to comparatively small numbers of passengers. This results in massive space requirements for roads and parking as well as traffic congestion.
To combat these issues the concept of CASE (connected, autonomous, shared, electrical) vehicles has gained traction. CASE vehicles promise a deep transformation of the mobility sector and a solution to the sector’s most pressing challenges:
- Connected & Autonomous: recent advances in Deep Learning for computer vision have enabled decisive steps towards fully autonomous vehicles that do not require a human driver. Autonomy is expected to substantially improve road safety and minimize traffic accidents but will also open up opportunities for multi-modal transport, optimized routing, inter-vehicle coordination (e.g. platooning), shared mobility and parking and many other innovations.
- Shared: vehicles will increasingly be shared (e.g. in the form of free-floating car sharing options, ride sharing or ride hailing) leading to much higher utilization (currently c. 90% idle time) and correspondingly less cars in cities, less space utilization (for driving and parking) and less congestion. This will require cloud-based communication between vehicles and with the environment (roads, parking spots, other consumers etc.) driven by fast and ubiquitous internet access and mutually beneficial sharing platform and business model designs.
- Electrical: the future light-duty vehicle fleet will be comprised of vehicles powered by varying electricity-based fuel types with focus on battery electric vehicles (BEV) but potentially also incl. hybrids (PHEV), fuel cell vehicles and alternative fuel vehicles (power-to-gas, power-to-fuel). This electrification is expected to be a large part of the solution to tackle CO2emissions and inner-city pollution, given a growing renewable share in the electricity production mix across the globe.
Yet, building a CASE mobility future also entails significant hurdles. For example, autonomous mobility will lead to a significant decrease in cost for car-based transport (likely below that of public transport by some estimates). This will result in increased demand for road transport which could worsen the congestion problem. Similarly, there are major challenges associated with integrating electrical vehicles into the grid. Uncoordinated EV adoption and charging will most certainly overstrain the electricity grid.
Reshaping the mobility sector can therefore be seen as a wicked problem, a problem of societal scale with multiple cross-sector and cross-disciplinary interfaces that require comprehensive system-level solutions. The digitalized and data-driven nature of the CASE mobility future makes it especially well-suited for information-system-based solutions. Smart sustainable mobility research challenges for the IS discipline are indeed abundant and the community is gearing up to the task.Selected research areas include: big behavioral data (BBD)-based modeling and prediction of mobility and charging preferences; smart (transactive) mechanisms for coordination with (1) the smart grid (e.g. smart charging), (2) other mobility resources, modes and vehicles (e.g. smart markets/platforms), (3) other mobility-intensive sectors (e.g. last mile logistics), (4) local infrastructure (e.g. road usage, parking spot allocation, charging point allocation), multi-modal transaction management (e.g. via distributed ledger technologies), data-driven mobility business models, and others.
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Willing, C., Klemmer, K., Brandt, T., & Neumann, D. (2017). Moving in time and space – Location intelligence for carsharing decision support. Decision Support Systems, 99, 75–85.