Taxi trajectories from urban environments allow inferring various information about the transport service qualities and commuter dynamics.It is possible to associate starting and end points of taxi trips with requirem...Taxi trajectories from urban environments allow inferring various information about the transport service qualities and commuter dynamics.It is possible to associate starting and end points of taxi trips with requirements of individual groups of people and even social inequalities.Previous research shows that due to service restrictions,boro taxis have typical customer destination locations on selected Saturdays:many drop-off clusters appear near the restricted zone,where it is not allowed to pick up customers and only few drop-off clusters appear at complicated crossing.Detected crossings imply recent infrastructural modifications.We want to follow up on these results and add one additional group of commuters:Citi Bike users.For selected Saturdays in June 2015,we want to compare the destinations of boro taxi and Citi Bike users.This is challenging due to manifold differences between active mobility and motorized road users,and,due to the fact that station-based bike sharing services are restricted to stations.Start and end points of trips,as well as the volumes in between rely on specific numbers of bike sharing stations.Therefore,we introduce a novel spatiotemporal assigning procedure for areas of influence around static bike sharing stations for extending available computational methods.展开更多
文摘Taxi trajectories from urban environments allow inferring various information about the transport service qualities and commuter dynamics.It is possible to associate starting and end points of taxi trips with requirements of individual groups of people and even social inequalities.Previous research shows that due to service restrictions,boro taxis have typical customer destination locations on selected Saturdays:many drop-off clusters appear near the restricted zone,where it is not allowed to pick up customers and only few drop-off clusters appear at complicated crossing.Detected crossings imply recent infrastructural modifications.We want to follow up on these results and add one additional group of commuters:Citi Bike users.For selected Saturdays in June 2015,we want to compare the destinations of boro taxi and Citi Bike users.This is challenging due to manifold differences between active mobility and motorized road users,and,due to the fact that station-based bike sharing services are restricted to stations.Start and end points of trips,as well as the volumes in between rely on specific numbers of bike sharing stations.Therefore,we introduce a novel spatiotemporal assigning procedure for areas of influence around static bike sharing stations for extending available computational methods.
