Dockless Bike-Sharing Reallocation Based on Data Analysis: Solving Complex Problem with Simple Method

Liu, Ming; Xu, Xifen

doi:10.1109/dsc.2018.00072

Cited by 10 publications

(7 citation statements)

References 8 publications

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“…In particular, four simulation models are developed to analyze the impacts of different redistribution strategies with different levels of bike rental information. In [12], the Authors use the Big Data analytics to produce an integrated method for reallocating dockless bikes. They collect and preprocess the GPS data of dockless bikes and cluster these bikes into several groups, examining large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information.…”

Section: Related Workmentioning

confidence: 99%

“…These works related to the bike reallocation problem do not consider how the bike data is recovered, and which is the best solution for bike tracking. Indeed, in [10], [11], [12] is clear the need to track the bike but the technology used to gather this information is not presented or, in the case of [10], the authors consider a dataset with only the docking stations position.…”

Section: Related Workmentioning

confidence: 99%

“…This data can be used to collect information about transportation routes and user riding profiles as well as offering additional services, such as enroute business promotions or pick-up and delivery [6], [7], [8], [9]. Finally, new dockless bike sharing systems also bring complex management problems such as how to reallocate the bikes after a whole day operation [10], [11], [12].…”

Section: Introductionmentioning

confidence: 99%

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Performance of LoRa for Bike-Sharing Systems

Croce

Garlisi

Giuliano

et al. 2019

2019 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE)

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Today bike sharing systems are becoming popular in many cities as short-distance transit vehicles. More than 18 million bicycles are available worldwide for public use and one of the main problems that afflicts such sharing systems is the loss of bikes, which can be stolen or simply left in unknown locations. Thus, many bikes are docked or tracked using GPS and costly cellular connections.In this paper, we consider the emerging Long Range (LoRa) technology for use in bike sharing systems. LoRa exploits free ISM bands and has been conceived for low power and low data rate applications. Additionally, LoRa is characterized by large cells and heterogeneous application domains, which may lead to extremely high numbers of devices coexisting in the same cell. Thus, in this paper we study the scalability limits of a typical LoRa cell, showing that the performance of LoRa in crowded scenarios can be quite limited when using high spreading factors (SFs). Then, we design and realize a prototype of LoRa tracker module that can be embedded in a bike and we test its use in a large area of Palermo city, Italy. According to our knowledge, this is the first paper that study the performance of LoRa for bike sharing systems.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance of LoRa for Bike-Sharing Systems

Croce

Garlisi

Giuliano

et al. 2019

2019 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE)

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“…However, the existing studies about spatial and temporal characteristics based on origin-destination (O-D) points have shortcomings. First, studies concentrate on bike-sharing in large-scale cities [5] and hinge on outdated data or less-than-a-month data, and few studies target small-scale cities, such as prefectures [6,7], and employ current more-than-a-month data. Data that covers a time length greater than a month is more conducive in reflecting the daily changes in mobility behavior.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, this paper analyzes the spatial and temporal travel characteristics of citizens in the central area of Tengzhou City based on real-time captured GPS data from May to July in 2018 in the central area of Tengzhou City, Shandong Province. This study is based on the station-free bike-sharing system (SFBSS), which is unique especially in China for its strong flexibility and convenience in usage [6][7][8]. The objectives are: (1) to analyze the overall trip patterns of shared electric bikes (e-bikes);…”

Section: Introductionmentioning

confidence: 99%

Analysis of Spatial and Temporal Characteristics of Citizens’ Mobility Based on E-Bike GPS Trajectory Data in Tengzhou City, China

Dai

Zhu

et al. 2019

Sustainability

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Environmentally friendly shared transit systems have become ubiquitous at present. As a result, analyzing the ranges and tracts of human activities and gatherings based on bike share data is scientifically useful. This paper investigates the spatial and temporal travel characteristics of citizens based on real-time-extracted electric bikes (e-bikes) Global Positioning System (GPS) data from May to July in 2018 in the central area of Tengzhou City, Shandong Province, China. The research is conducive for the exploration of citizens’ changes in mobility behaviors, for the analysis of relationships between mobility changes and environmental or other possible factors, and for advancing policy proposals. The main conclusions of the study are as follows. First, in general, citizens’ travelling is featured by rides that are less than 10 min, shorter than 5 km, and with a speed between 5 km/h and 20 km/h. Second, in terms of temporal characteristics, monthly e-bike usage and citizens’ mobility are positively correlated with temperature in May and negatively correlated with temperature in July; an overall negative correlation is also manifested between the e-bike usage (mobility) and air quality index; daily usage reaches a trough on Tuesday and a peak on Friday, indicating the extent of mobility on respective days; e-bike usage and human outdoor behaviors are significantly lowered in rainy weather than in sunny weather; hourly rides reach a peak at 18:00 (more human activities) and a trough at 2:00 (less activities), and average hourly riding speed maximizes at 5:00 and minimizes around 8:00 and 17:00. Third, for spatial characteristics, destinations (D points) during morning rush hour and regions where e-bikes are densely employed are concentrated mainly in mid-north and middle parts of the central area (major human gatherings), and the rides have a diffusing pattern; e-bike origin–destination (O–D) trajectories radiate mostly towards the mid-north and the east during evening rush hour. In addition, 9.4% of the total trips to work areas during morning rush hour represent spillover commuting, indicating that separations between jobs and residential are not severe in the central area of Tengzhou City and commuting is relatively convenient.

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GPS data in urban bicycle-sharing: Dynamic electric fence planning with assessment of resource-saving and potential energy consumption increasement

Xie²,

Liu³

et al. 2022

Applied Energy

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Dockless Bike-Sharing Reallocation Based on Data Analysis: Solving Complex Problem with Simple Method

Cited by 10 publications

References 8 publications

Performance of LoRa for Bike-Sharing Systems

Performance of LoRa for Bike-Sharing Systems

Analysis of Spatial and Temporal Characteristics of Citizens’ Mobility Based on E-Bike GPS Trajectory Data in Tengzhou City, China

GPS data in urban bicycle-sharing: Dynamic electric fence planning with assessment of resource-saving and potential energy consumption increasement

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