Identifying the ideal locations to implement mobility hubs or closing missing links in the cycling network in order to maximize CO2 reduction
Routes with saving potential identified
in cycling network mapped
Tons of CO2 saving potential calculated
Learn how we achieved these results together:
While Tallinn has a robust cycling network, gaps in the network affect its utility. We built a model of existing usage, then identified corridors in the city where trips under 5 km were disproportionately taken by car. Using shared mobility data from electric bikes and scooters, we identified 50 gaps in the cycle path network and simulated the effect of different growth in usage of the corridors, for both shared devices and private bicycles.
While the center of Amsterdam sees exceptionally high cycling and public transport usage, there are outlying areas that remain car-dependant. In order to achieve targets, the City hopes to shift these users to shared bikes, mopeds, and cars through the use of mobility hubs. Therefore we built two sets of 30 recommendations for mobility hub locations, using a mix of CO2 savings potential, predicted demand, and app opens which did not lead to trips. Our model proposed location optimisation for two different objectives: mobility hubs which maximise potential CO2 savings and hubs which can support better public space management by organising demand.
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