Allocation strategies in a dockless bike sharing system: a community structure-based approach

Author(s):  
J. Zhang ◽  
M. Meng ◽  
David Z. W. Wang ◽  
B. Du
2012 ◽  
Vol 65-66 ◽  
pp. 53-56 ◽  
Author(s):  
René Kaden ◽  
Eve Menger-Krug ◽  
Katja Emmerich ◽  
Kerstin Petrick ◽  
Martin Mühling ◽  
...  

2020 ◽  
Vol 41 (2) ◽  
pp. 134-159
Author(s):  
Jan Ploeger ◽  
Ruth Oldenziel

The search for “smart” or Information and Communication Technology (ICT) based mobility solutions goes back to at least the 1960s. The Provo anarchist Luud Schimmelpennink is well-known for designing mobility solutions and for being the driving force behind the 1965 “white-bike” experience. Less known is his 1968 project for shared electric cars (“Witkar”), which laid the foundations for the ICT-based bicycle sharing systems as we know today. By combining his talent for innovation with activism, he created a socially embedded design that could be part of the public transit system. Based on primary sources, we argue that these sociotechnical experiences paved the way for today’s mainstream bicycle sharing projects worldwide. We then show how since the 1990s, the Dutch railroad’s public transit bicycle (OV-fiets) has transformed Schimmelpennink’s original anarchist idea of bike sharing into a sustainable public transit system – a feat that has eluded other programmes worldwide: the integration of the bicycle’s share in a door-to-door experience.


1994 ◽  
Vol 45 (2) ◽  
pp. 243 ◽  
Author(s):  
NA O'Connor ◽  
PS Lake

The Pranjip-Creightons Creek system, a lowland stream system in north-central Victoria, contains large amounts of sand derived from agricultural activities in the upper catchment. The sand has caused long-term changes to the morphology of the upper and middle sections of the stream system-a press disturbance. During predictable winter and spring spates, sand substrata underwent regular scouring, causing large seasonal declines in macroinvertebrate species richness and numbers of individuals and marked changes in community structure. These regular short-term seasonal disturbances may be termed pulse disturbances, and their effects were most severe at mid-reach sites where sand deposits were most recent. At these sites, the press disturbance of increased sand storage also rendered the stream bed more susceptible to pulse disturbances. When winter and spring scouring spates ceased, stable communities of macroinvertebrates developed. At sampling sites on lower reaches, where the sand had yet to reach, there was little seasonal change in macroinvertebrate community structure or numbers of individuals. Seasonal variation in benthic species richness at these structurally heterogeneous sites was due to changes in the numbers of less abundant species associated with macrophytes. Current stream restoration works aimed at stemming the input of sediment should increase the seasonal stability of macroinvertebrate communities by decreasing the extent and intensity of substratum scour during winter and spring spates.


Author(s):  
Zhili Jia ◽  
Gang Xie ◽  
Jerry Gao ◽  
Shui Yu
Keyword(s):  
Big Data ◽  
System A ◽  

2020 ◽  
Author(s):  
Jianbin Huang ◽  
Heli Sun ◽  
He Li ◽  
Longji Huang ◽  
Ao Li ◽  
...  

Abstract Predicting the bike demand can help rebalance the bikes and improve the service quality of a bike-sharing system. A lot of works focus on predicting the bike demand for all the stations, which is unnecessary as the travel cost of rebalance operations increases sharply as the number of stations increases. In this paper, we propose a framework for predicting the hourly bike demand based on the central stations we define. Firstly, we propose Two-Stage Station Clustering Algorithm to assign central stations and common stations into each cluster. Secondly, we propose a hierarchical prediction model to predict the hourly bike demand for every cluster and each central station progressively. Thirdly, we use a well-studied queuing model to determine the target initial inventory for each central station. The most innovative contribution of this paper is proposing the concept of central station, the use of a novel algorithm to cluster the central stations and present a hierarchical model, containing the Time and Weather Similarity Weighted K-Nearest Neighbor Algorithm and a linear model to predict the bike demand for central stations. The experimental results on the New York citi bike system demonstrate that our proposed method is more accurate than other methods in solving existing problems.


2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Qiong Tang ◽  
Zhuo Fu ◽  
Dezhi Zhang ◽  
Hao Guo ◽  
Minyi Li

In this paper, a bike repositioning problem with stochastic demand is studied. The problem is formulated as a two-stage stochastic programming model to optimize the routing and loading/unloading decisions of the repositioning truck at each station and depot under stochastic demands. The goal of the model is to minimize the expected total sum of the transportation costs, the expected penalty costs at all stations, and the holding cost of the depot. A simulated annealing algorithm is developed to solve the model. Numerical experiments are conducted on a set of instances from 20 to 90 stations to demonstrate the effectiveness of the solution algorithm and the accuracy of the proposed two-stage stochastic model.


2019 ◽  
Vol 247 ◽  
pp. 1-12 ◽  
Author(s):  
Yujie Hu ◽  
Yongping Zhang ◽  
David Lamb ◽  
Mingming Zhang ◽  
Peng Jia
Keyword(s):  
System A ◽  

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 47
Author(s):  
Xiaoting Mo ◽  
Xinglu Liu ◽  
Wai Kin (Victor) Chan

The imbalanced distribution of shared bikes in the dockless bike-sharing system (a typical example of the resource-sharing system), which may lead to potential customer churn and lost profit, gradually becomes a vital problem for bike-sharing firms and their users. To resolve the problem, we first formulate the bike-sharing system as a Markovian queueing network with higher-demand nodes and lower-demand nodes, which can provide steady-state probabilities of having a certain number of bikes at one node. A model reduction method is then designed to reduce the complexity of the proposed model. Subsequently, we adopt an operator-based relocation strategy to optimize the reduced network. The objective of the optimization model is to maximize the total profit and act as a decision-making tool for operators to determine the optimal relocation frequency. The results reveal that it is possible for most of the shared bikes to gather at one low-demand node eventually in the long run under the influence of the various arrival rates at different nodes. However, the decrease of the number of bikes at the high-demand nodes is more sensitive to the unequal demands, especially when the size of the network and the number of bikes in the system are large. It may cause a significant loss for operators, to which they should pay attention. Meanwhile, different estimated values of parameters related with revenue and cost affect the optimization results differently.


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