Research On Routes Of Intensive Service That Can Replace Bike Sharing Travel In Winter Using Data-driven Methods

As a non-motorized green travel mode,dockless bicycle sharing plays a positive role in reducing traffic pollution and expanding the radius of public transportation services,and is a travel option that is both flexible and convenient.However,shared bicycle travel is greatly affected by weather,especially in cold regions where bicycle use plummets in winter,and many operators choose to stop service in winter due to cost considerations.As a result,most users lack a travel option that is comparable to the flexibility and convenience of bike-sharing in winter.Based on the above background,this research proposes an intensive alternative service that enables shared bicycle users with similar riding paths to share one vehicle,which satisfies the flexible and convenient features of shared bicycle travel on the one hand,and overcomes the riding drawbacks of shared bicycles in winter on the other.Reasonable service routes have an important impact on the intensive alternative service.This research mines the service routes of the intensive alternative service through a data-driven approach and briefly describes the design of specific service routes in the subsequent operation process on this basis.The core and key of the intensive alternative service route mining is to determine the frequent paths of shared bicycle trips,which is the focus of this research.Firstly,according to the characteristics of intensive alternative service operation proposed in this research,the obtained shared bicycle trajectory data and road network data are pre-processed.Among them,the road segment division operation in the pre-processing of road network data takes into account factors such as user drop-off and pick-up point settings,acceptable walking distance of users and reasonable starting and stopping distance of service vehicles,and divides the road segments in the original road network into sub-segments of more uniform length.Then the trajectory is matched to the processed road network by the map matching algorithm based on the road network adjacency relationship,and the point sequence trajectory data is transformed into the edge sequence data composed of sub-sections.In order to make the service routes cover users’ travel demand to the maximum extent,a spatio-temporal trajectory clustering algorithm,named PATHSCAN clustering algorithm,is proposed to define the core starting point sub-sections,set the section trajectory frequency threshold,and perform spatio-temporal clustering analysis on one-day shared bicycle trajectory edge sequence data to get the initial route cluster of intensive alternative service.Then the initial route clusters results of multiple days are processed by frequent pattern mining to obtain frequent pattern route clusters,i.e.,service routes,at different times of the day,as well as the passenger flow between sub-sections within each cluster.Finally,on the basis of the service routes,a brief description of how to develop a specific design plan for the intensive alternative service routes is presented,taking into account various factors such as fleet size and vehicle capacity.This research proposes an intensive alternative service with reference to the existing intensive transportation service model with high flexibility,which solves the problem that the use of shared bicycles is restricted in cold regions into winter,and users lack flexible and convenient travel options,which in turn leads to poor travel experience.In addition,this research mines the service routes of the intensive alternative service through a data-driven approach,solving the problem of lack of consideration of real road network conditions in previous studies of intensive transportation-related routes.Finally,this research selects ten central administrative regions of a northern 10 million population city in China as the case study area,and by applying the data-driven route mining method proposed in this paper and combining it with the actual trajectory big data,we get the service routes of intensive alternative services in the study area,and briefly describe the specific route design scheme on this basis.The results show that the data-driven method can obtain reasonable service routes for intensive alternative services,which can provide a basis for further design of specific service routes,and has important practical significance for promoting the diversification of urban transportation travel modes and improving people’s travel experience.