Dockless bicycle-sharing is one of the novel transportation modes that emerged in recent years. As a component of the public transit system, it plays an important part in connecting the urban metro system but also generates some problems. With the low parking turnover rate, the oversupply of bicycles will generate parking demand and occupy a large amount of urban public land. To make the dockless bicycle-sharing service a more effective travel mode for connecting the metro system, it is necessary to implement policy zoning with different management strategies for efficient land utilization in the dockless bicycle-sharing system. In this paper, the bicycle-sharing trips data and the metro trips data are utilized to extract indexes to assess the connection performance and operation potential between the bicycle-sharing system and metro transit system. A methodology of coverage area division and policy zoning based on cycling attraction area areas is used. Applying the methodology proposed to a case study in Shanghai, the metro stations in Shanghai are classified into four clusters with different characteristics, including stations with effective land utilization, stations with the potential to promote BS service, stations with deficiencies in BS service, and stations with the potential to improve the land utilization efficiency. According to the different demand patterns and operation performance of the four clusters, corresponding policy suggestions are proposed.

Dockless bicycle sharing is one of the low-carbon transportation modes towards sustainable mobilities. Electric fences, as an effective solution for parking management, may have a high potential in guiding the usage of dockless bicycles at a low operation cost. However, new issues arise with the implementation of electric fences. The location of electric fences in hotspots fails to match the parking demand, leading the parking congestion in urban central areas. In this paper, a novel methodology of bicycle hotspots identification and classification is proposed to support parking management. An evaluation framework for bicycle hotspots is also proposed covering three aspects: demand and supply, unbalance, and land use. The methodology is applied to the case of Xiamen Island by using the trip data covering 53,629 bicycles during morning peak hours. Applying the methodology proposed, 47 pick-up hotspots and 53 return hotspots are identified, respectively. The total parking overload of return hotspots during the morning peak is 12,587 bicycles in Xiamen Island. The 53 return hotspots are classified into three clusters, including (1) hotspots where bicycle sharing is in overload status, (2) hotspots where bicycle sharing service quality needs to be improved, and (3) hotspots where bicycle sharing is in stable status. Based on the demand and land use characteristics, parking management schemes and policy implications are proposed. The result of this paper provides guidance for the layout of dockless bicycle sharing electric fences in different areas.