Allocation optimization of bicycle-sharing stations at scenic spots

Bicycle-sharing system is considered as a green option to provide a better connection between scenic spots and nearby metro/bus stations. Allocating and optimizing the layout of bicycle-sharing system inside the scenic spot and around its influencing area are focused on. It is found that the terrain, land use, nearby transport network and scenery point distribution have significant impact on the allocation of bicycle-sharing system. While the candidate bicycle-sharing stations installed at the inner scenic points, entrances/exits and metro stations are fixed, the ones installed at bus-stations and other passenger concentration buildings are adjustable. Aiming at minimizing the total cycling distance and overlapping rate, an optimization model is proposed and solved based on the idea of cluster concept and greedy heuristic. A revealed preference/stated preference （RP/SP） combined survey was conducted at Xuanwu Lake in Nanjing, China, to get an insight into the touring trip characteristics and bicycle-sharing tendency. The results reveal that 39.81% visitors accept a cycling distance of 1-3 km and 62.50% respondents think that the bicycle-sharing system should charge an appropriate fee. The sttrvey indicates that there is high possibility to carry out a bicycle-sharing system at Xuanwu Lake. Optimizing the allocation problem cluster by cluster rather than using an exhaustive search method significantly reduces the computing amount from O（2^43） to O（43 2）. The 500 m-radius-coverage rate for the alternative optimized by 500 m-radius-cluster and 800 m-radius-cluster is 89.2% and 68.5%, respectively. The final layout scheme will provide decision makers engineering guidelines and theoretical support.

Extracting commuter-specific destination hotspots from trip destination data-comparing the boro taxi service with Citi Bike in NYC

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.

On the performance of shared autonomous bicycles:A simulation study

As society faces global challenges such as population growth and climate change,rethinking cities is now more imperative than ever.The design of cities can not be abstracted from the design of their mobility systems.Therefore,efficient solutions must be found to transport people and goods throughout the city efficiently and ecologically.An autonomous bicycle-sharing system would combine the most relevant benefits of vehicle-sharing,autonomy,and micro-mobility,increasing the efficiency and convenience of bicycle-sharing systems and incentivizing more people to bike and enjoy their cities in an environmentally friendly way.Due to the novelty of introducing autonomous driving technology into bicycle-sharing systems and their inherent complexity,there is a need to quantify the potential impact of autonomy on fleet performance and user experience.This paper presents the results of an agent-based simulation that provides an in-depth understanding of the fleet behavior of autonomous bicycle-sharing systems in realistic scenarios,including a rebalancing system based on demand prediction.In addition,this work describes the impact of different parameters on system efficiency and service quality.Finally,it quantifies the extent to which an autonomous system would outperform current station-based and dockless bicycle-sharing schemes.The obtained results show that with a fleet size three and a half times smaller than a station-based system and eight times smaller than a dockless system,an autonomous system can improve overall performance and user experience even with no rebalancing.