Planning Bike Lanes based on Sharing-Bikes' Trajectories

Bao, Jie; He, Tianfu; Ruan, Sijie; Li, Yanhua; Zheng, Yu

doi:10.1145/3097983.3098056

Cited by 179 publications

(93 citation statements)

References 28 publications

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“…There are few studies on DLBS systems. Bao et al [24] proposed a data-driven approach to develop bike lane plans. Dakshak Keerthi Chandra et al [9] proposed a multidimensional tensor model to address the mismatching problem for supply and demand of DLBS systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Operating Characteristics of Dockless Bike-Sharing Systems near Metro Stations: Case Study in Nanjing City, China

Zhu

Guo

2019

Sustainability

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With the growth of dockless bike-sharing (DLBS) systems, the first-and-last mile connection to public transport, such as metro and light railway stations, could be improved. DLBS systems complete the trip chain by connecting metro stations with points of interest and enhance the sustainability of urban transportation. Therefore, it is necessary to understand the trans-shipment characteristics of DLBS systems for metro stations. In this study, we collected data from the Mobike DLBS system in Nanjing City, China and applied K-means clustering to analyse the activity patterns of DLBS systems near local metro stations. Metro stations were categorised into five types on workdays and three types on weekends. An analysis of the relationships between activity patterns and spatial distribution characteristics demonstrated that the distribution of clusters possesses a strong connection with the surrounding environment. Low land development rates and a sparse distribution of metro stations cause a large range of influences. This research has direct implications for understanding the operating state of DLBS systems near metro stations and promoting the proper management of DLBS systems.

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Section: Literature Reviewmentioning

confidence: 99%

Operating Characteristics of Dockless Bike-Sharing Systems near Metro Stations: Case Study in Nanjing City, China

Zhu

Guo

2019

Sustainability

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“…At the end this paper, a list of public trajectory datasets have been given and a few future directions have been suggested.  Jie Bao and Tianfu He [2] In this paper, they proposed a data driven approach to plan bike lanes based on the real bike trajectories collected from Mobike (a major station-less bike sharing system) in the City of Shanghai. The system can address the bike lanes planning problem in a more realistic way, considering the constraints and requirements from urban planners" perspective: 1) budget limitations, 2) construction convenience, and 3) bike lane utilization.…”

Section: IImentioning

confidence: 99%

Survey of Trajectory Mining using Uncertain Sensor Data Model with Probabilistic Suffix Tree

Gayathri¹,

Nithyakalyani²

2018

International Journal of Advanced Research in Computer and Comm

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This paper describes a framework that works by collecting the trajectory data obtained from the sensors. The data is stored and processed in a way that helps in identifying events such as key activity areas, evolving activity, etc. It helps to attain better insight into the work habits of the population. Trajectory mining is either assumed that the timeordered location data recorded as trajectories are either deterministic or that the uncertainty, e.g., due to equipment or technological limitations, is removed by incorporating some pre-processing routines. Thus, the trajectories are processed as deterministic paths of mobile object location data. Probabilistic trajectory extraction and mining from uncertain trajectory data is the first phase analysis on the subject. It is also interested in identifying and developing alternative approaches with the use of which can make the approach more scalable, e.g. a trajectory compression scheme could be developed to further decrease the length of the trajectories. This paper proposes an efficient distributed mining algorithm to jointly identify a group of sensor data and discover their trajectory of sensor data in wireless sensor networks. Then, Map-Reduce algorithm (Probabilistic Suffix Tree) is introduced which utilizes the discovered group trajectory sensor data shared by the transmitting node.

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“…Therefore, it is worth studying how to guide people to use bicycles better. There are few researches on sharing bike guidance and transportation planning [2,3] . Only the method of JieBao et al in path planning proposes a method of using greedy algorithm to plan bicycle lanes based on sharing bicycle cycling trajectories [2] .…”

Section: Introductionmentioning

confidence: 99%

Study on Path Planning of Urban Sharing-bike Connecting Rail Transit

2018

2018 3rd International Conference on Materials Science, Machinery and Energy Engineering (MSMEE 2018)

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Sharing-bikes are characterized by health, greenness and environmental protection, effectively solving the "last kilometer" problem. It is of great significance to reasonably plan the sharing-bike connecting rail transit routes to meet the traveler's needs, build safe urban traffic travel environment, and guide bicycle travel. Based on the shared bicycle trip data, this paper proposes an optimal paved lane path planning model for a sharing-bike connected rail transit based on traffic network. Based on spatial clustering and improved Dijkstra's algorithm, the steps and principles for solving the model are given. The results of the example of Beijing No. 2 Line as a planning example show that this model has the characteristics of convenience of route layout and deployment cost, and has certain feasibility, which enriches the path planning method of bicycle lanes.

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Planning Bike Lanes based on Sharing-Bikes' Trajectories

Cited by 179 publications

References 28 publications

Operating Characteristics of Dockless Bike-Sharing Systems near Metro Stations: Case Study in Nanjing City, China

Operating Characteristics of Dockless Bike-Sharing Systems near Metro Stations: Case Study in Nanjing City, China

Survey of Trajectory Mining using Uncertain Sensor Data Model with Probabilistic Suffix Tree

Study on Path Planning of Urban Sharing-bike Connecting Rail Transit

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