A model framework for discovering the spatio-temporal usage patterns of public free-floating bike-sharing system

2019 ◽  
Vol 103 ◽  
pp. 39-55 ◽  
Author(s):  
Yuchuan Du ◽  
Fuwen Deng ◽  
Feixiong Liao
Keyword(s):  
Model Framework ◽  
Bike Sharing ◽  
Usage Patterns ◽  
Spatio Temporal

scholarly journals Spatio-Temporal Usage Patterns of Dockless Bike-Sharing Service Linking to a Metro Station: A Case Study in Shanghai, China

2020 ◽  
Vol 12 (3) ◽  
pp. 851 ◽  
Author(s):  
Qiang Yan ◽  
Kun Gao ◽  
Lijun Sun ◽  
Minhua Shao
Keyword(s):  
Demand Management ◽  
Land Development ◽  
Travel Distance ◽  
Metro Station ◽  
Suburban Areas ◽  
Distance Distributions ◽  
Bike Sharing ◽  
Usage Patterns ◽  
Spatio Temporal ◽  
Distribution Family

The dockless bike-sharing (DLBS) system serves as a link between metro stations and travelers’ destinations (or originations). This paper aims to uncover spatio-temporal usage patterns of dockless bike-sharing service linking to metro stations for supporting scientific planning and management of the dockless bike-sharing system. A powerful visualization tool was used to analyze the differences in usage patterns in workdays and weekends. The travel distance distributions of using dockless bike-sharing near metro stations were investigated to shed light on the service area of the dockless bike-sharing system. Agglomerative hierarchical clustering was applied to analyze differences in usage patterns of metro stations located in different areas. The results show that the usage patterns of dockless bike-sharing on weekends are different from those on workdays. The average travel distance using the dockless bike-sharing system at weekends is significantly larger than that of workdays. The travel distance distribution could be nicely fitted by the Fréchet distribution of the Generalized Extreme Value (GEV) distribution family. The usage characteristics of shared bikes are correlated with land use and population density around metro stations. No matter in urban or suburban areas, there is a great demand for bike-sharing in densely populated areas with intensive land development, such as university towns in suburban areas. This study improves the understandings regarding the usage patterns of the DLBS system serving as a link between the final destinations (or originations) and metro stations. The results can be helpful to the operation and demand management of DLBS.

scholarly journals Quantification of potential recruitment of large woody debris in mountain catchments considering the effects of vegetation on hydraulic and geotechnical bank erosion and shallow landslides

2018 ◽  
Vol 40 ◽  
pp. 02046 ◽  
Author(s):  
Eric Gasser ◽  
Andrew Simon ◽  
Paolo Perona ◽  
Luuk Dorren ◽  
Johannes Hübl ◽  
...  
Keyword(s):  
Woody Debris ◽  
Probabilistic Approach ◽  
Temporal Distribution ◽  
Bank Erosion ◽  
Shallow Landslides ◽  
Large Woody Debris ◽  
Correction Coefficient ◽  
Model Framework ◽  
Modelling Framework ◽  
Spatio Temporal

Large woody debris (LWD) exacerbates flood damages near civil structures and in urbanized areas and the awareness of LWD as a risk is becoming more and more relevant. The recruitment of “fresh” large woody debris has been documented to play a significant role of the total amount of wood transported during flood events in mountain catchments. Predominately, LWD recruitment due to hydraulic and geotechnical bank erosion and shallow landslides contribute to high volumes of wood during floods. Quantifying the effects of vegetation on channel and slope processes is extremely complex. This manuscript therefore presents the concepts that are being implemented in a new modelling framework that aims to improve the quantification of vegetation effects on LWD recruitment processes. One of the focuses of the model framework is the implementation of the effect of spatio-temporal distribution of root reinforcement in recruitment processes such as bank erosion and shallow landslides in mountain catchments. Further, spatio-temporal precipitation patterns will be considered using a probabilistic approach to account for the spatio-temporal precipitation variability to estimate a LWD recruitment correction coefficient. Preliminary results are herein presented and discussed in form of a case study in the Swiss Prealps.

Understanding spatio-temporal heterogeneity of bike-sharing and scooter-sharing mobility

2020 ◽  
Vol 81 ◽  
pp. 101483 ◽  
Author(s):  
Rui Zhu ◽  
Xiaohu Zhang ◽  
Dániel Kondor ◽  
Paolo Santi ◽  
Carlo Ratti
Keyword(s):  
Temporal Heterogeneity ◽  
Bike Sharing ◽  
Spatio Temporal

scholarly journals Monitoring the first dockless bike sharing system in Greece: Understanding user perceptions, usage patterns and adoption barriers

2019 ◽  
Vol 33 ◽  
pp. 100432 ◽  
Author(s):  
Efthimios Bakogiannis ◽  
Maria Siti ◽  
Stefanos Tsigdinos ◽  
Avgi Vassi ◽  
Alexandros Nikitas
Keyword(s):  
User Perceptions ◽  
Bike Sharing ◽  
Usage Patterns

scholarly journals Pay-as-you-go City’: New Forms of Domesticity in a Technological Society

2019 ◽  
Author(s):  
Jeroen van Ameijde ◽  
Zineb Sentissi
Keyword(s):  
Housing Market ◽  
New Technologies ◽  
Sharing Economy ◽  
Economic Interests ◽  
Family Structures ◽  
Technological Society ◽  
Gig Economy ◽  
Challenges And Opportunities ◽  
Bike Sharing ◽  
Spatio Temporal

Ongoing urbanization, combined with market fundamentalism as the prevailing mode of political management, is leading to the spatial and social segregation of economic classes in cities. The housing market, being driven by economic interests rather than public policy, favors inflexible forms of ownership or tenancy that are increasingly incompatible with the more diverse forms of live-work patterns and family structures occurring in the society. This paper presents a research-by-design project that explores a speculative future scenario of housing, based on current developments in digital technologies and their impact on the mobility and accessibility to services enjoyed by urban residents. It references technology platforms that underpin the 'sharing economy' or 'gig economy', such as 'pay-as-you-go' car and bike sharing programs or internet and smartphone-based services for taxis or temporary accommodation. The study explores how new forms of participation in the housing market could circumvent the current segregation of different communities across the city. It describes a speculative system of distributed residential spaces, accessible to all on a 'pay-for-time-used' basis. By offering freedom of choice across domestic functions of greater range and accessibility than found within existing housing or hotel accommodation, the system would enable opportunistic or nomadic forms of living linked to the dynamic spatio-temporal occurrences of social, cultural or economic opportunities. The research references how new forms of social networking create new challenges and opportunities to participate in communities and explores how new technologies, applied to housing, can help to find a 'sense of belonging' within the technological society.

scholarly journals Smart Recommendations for Renting Bikes in Bike-Sharing Systems

2021 ◽  
Vol 11 (20) ◽  
pp. 9654
Author(s):  
Holger Billhardt ◽  
Alberto Fernández ◽  
Sascha Ossowski
Keyword(s):  
Queuing Theory ◽  
Real Data ◽  
Transport Systems ◽  
Bike Sharing ◽  
Usage Patterns ◽  
One Step ◽  
The One ◽  
The Individual ◽  
Recommendation Strategy ◽  
Better Than

Vehicle-sharing systems—such as bike-, car-, or motorcycle-sharing systems—have become increasingly popular in big cities in recent years. On the one hand, they provide a cheaper and environmentally friendlier means of transportation than private cars, and on the other hand, they satisfy the individual mobility demands of citizens better than traditional public transport systems. One of their advantages in this regard is their availability, e.g., the possibility of taking (or leaving) a vehicle almost anywhere in a city. This availability obviously depends on different strategic and operational management decisions and policies, such as the dimension of the fleet or the (re)distribution of vehicles. Agglutination problems—where, due to usage patterns, available vehicles are concentrated in certain areas, whereas no vehicles are available in others—are quite common in such systems, and need to be dealt with. Research has been dedicated to this problem, specifying different techniques to reduce imbalanced situations. In this paper, we present and compare strategies for recommending stations to users who wish to rent or return bikes in station-based bike-sharing systems. Our first contribution is a novel recommendation strategy based on queuing theory that recommends stations based on their utility to the user in terms of lower distance and higher probability of finding a bike or slot. Then, we go one step further, defining a strategy that recommends stations by combining the utility of a particular user with the utility of the global system, measured in terms of the improvement in the distribution of bikes and slots with respect to the expected future demand, with the aim of implicitly avoiding or alleviating balancing problems. We present several experiments to evaluate our proposal with real data from the bike sharing system BiciMAD in Madrid.

scholarly journals Dynamic Rebalancing Dockless Bike-Sharing System based on Station Community Discovery

2021 ◽  
Author(s):  
Jingjing Li ◽  
Qiang Wang ◽  
Wenqi Zhang ◽  
Donghai Shi ◽  
Zhiwei Qin
Keyword(s):  
Supply And Demand ◽  
Service Level ◽  
Integer Program ◽  
Mixed Integer ◽  
Mixed Integer Program ◽  
Real World Data ◽  
Community Discovery ◽  
Bike Sharing ◽  
Total Profit ◽  
Spatio Temporal

Influenced by the era of the sharing economy and mobile payment, Dockless Bike-Sharing System (Dockless BSS) is expanding in many major cities. The mobility of users constantly leads to supply and demand imbalance, which seriously affects the total profit and customer satisfaction. In this paper, we propose the Spatio-Temporal Mixed Integer Program (STMIP) with Flow-graphed Community Discovery (FCD) approach to rebalancing the system. Different from existing studies that ignore the route of trucks and adopt a centralized rebalancing, our approach considers the spatio-temporal information of trucks and discovers station communities for truck-based rebalancing. First, we propose the FCD algorithm to detect station communities. Significantly, rebalancing communities decomposes the centralized system into a distributed multi-communities system. Then, by considering the routing and velocity of trucks, we design the STMIP model with the objective of maximizing total profit, to find a repositioning policy for each station community. We design a simulator built on real-world data from DiDi Chuxing to test the algorithm performance. The extensive experimental results demonstrate that our approach outperforms in terms of service level, profit, and complexity compared with the state-of-the-art approach.

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