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The imbalance between the supply and demand of shared bikes is prominent in many urban rail transit stations, which urgently requires an efficient vehicle deployment strategy. In this paper, we propose an integrated model to optimize the deployment of shared bikes around urban rail transit stations, incorporating a seasonal autoregressive integrated moving average with long short-term memory (SARIMA-LSTM) hybrid model that is used to predict the heterogeneous demand for shared bikes in space and time. The shared bike deployment strategy was formulated based on the actual deployment process and under the principle of cost minimization involving labor and transportation. The model is applied using the big data of shared bikes in Xicheng District, Beijing. Results show that the SARIMA-LSTM hybrid model has great advantages in predicting the demand for shared bikes. The proposed allocation strategy provides a new way to solve the imbalance challenge between the supply and demand of shared bikes and contributes to the development of a sustainable transportation system. [ABSTRACT FROM AUTHOR]

• Environmental impact of large-scale free-floating bike sharing (FFBS) was estimated. • Explosive growth of FFBS increased per capita GHG emissions in Beijing. • FFBS can obtain desirable environmental benefits under effective governance. • Increasing recycling rate and lifespan of bikes can significantly improve the sustainability of FFBS. • FFBS seems to be less environmentally friendly than station-based bike sharing. Free-floating bike sharing (FFBS) is clearly becoming a global phenomenon. However, the actual environmental impact of FFBS remain controversial. Using life cycle assessment (LCA) and bottom-up method, this study quantitatively estimates the impact of Beijing large scale FFBS program on greenhouse gas (GHG) emissions from transportation system based on the real-world transportation big data. The results show that FFBS can obtain desirable environmental benefits, but the premise is reasonable and effective market supervision. In the period of explosive growth (2017∼2018), due to oversupply and short service life of the shared bike, FFBS has become the transportation mode with the highest GHG emission-intensity. Compared with before the advent of FFBS (i.e., 2016), the total GHG emissions and per capita GHG emissions increased by about 0.33 million tons CO 2 -eq and 25.63 kg CO 2 -eq respectively. Subsequently, in the orderly development stage, the operational efficiency of FFBS has been significantly improved under the supervision and governance. The total GHG emissions and per capita GHG emissions in 2019 decreased by about 0.33∼0.41 million tons CO 2 -eq and about 11.58∼15.85 kg CO 2 -eq respectively, compared with 2016. This study helps to better understand the environment impact of FFBS and constitutes a valuable reference for improving urban transportation sustainability. [ABSTRACT FROM AUTHOR]

• We quantitatively explore the impacts of COVID-19 on the usage of bike sharing. • A novel method is proposed to estimate trip distances and trajectories of bike sharing. • Complex network theory is employed to explore the transformation of user behaviors. • COVID-19 impacts the user behaviors and environmental benefits of bike sharing significantly. The COVID-19 pandemic spreads rapidly around the world, and has given rise to huge impacts on all aspects of human society. This study utilizes big data techniques to analyze the impacts of COVID-19 on the user behaviors and environmental benefits of bike sharing. In this study, a novel method is proposed to calculate the trip distances and trajectories via a python package OSMnx so as to accurately estimate the environmental benefits of bike sharing. In addition, we employ the topological indices arising from complex network theory to quantitatively analyze the transformation of user behavior pattern of bike sharing during the COVID-19 pandemic. The results show that this pandemic has impacted the user behaviors and environmental benefits of bike sharing in Beijing significantly. During the pandemic, the estimated reductions of energy consumption and emissions on 6th Feb decreased to approximately 1 in 17 of those on a normal day, and the environmental benefits at most recovered to 70% of those in normal days. The impacts of COVID-19 on the environmental benefits in different districts are different. Furthermore, the decline of average strength and strength distribution obeying exponential distribution but with different slope rates suggests that people are less likely to take bike sharing to the places where were popular before. The pandemic has also increased the average trip time of bike sharing. Our research may facilitate the understanding of the impacts of COVID-19 pandemic on our society and environment, and also provide clues to adapt to this unprecedented pandemic so as to respond to similar events in the future. [ABSTRACT FROM AUTHOR]

Bike sharing, such as Mobike, are thriving throughout the past three years. Mobike allows users to rent bikes left by previous users. This mobility mode generates self-organized bike distribution over the city. We analyze real data from Mobike bike sharing system in Beijing and Citibike bike sharing system in New York. In this paper, we study the spatial pattern of sharing activity from this new business model. We construct an Origin–Destination spatial network of bike sharing traffic flow based on trip data and study the characteristic of unbalance in the systems. The unbalance of nodes in Mobike bike mobility OD network is revealed to follows a power law distribution with different exponents on weekdays and weekends. We also found that there exists a scaling between maximum unbalance and network degree in both systems. These features of bike sharing can provide a better understanding of sharing mobility behaviors and help to develop more efficient management methods. • We construct Origin–Destination spatial network for bike sharing systems. • We find that the unbalance of Mobike OD Network follows a power law distribution. • We discover a scaling between maximum unbalance and network degree. [ABSTRACT FROM AUTHOR]

It has become increasingly clear that a promising way to address waste-related issues resides in waste prevention by resource conversation. Research has therefore started to focus on preventing waste production by improving resource conservation capabilities. This study investigates how the societal transition to the fourth generation of bike-sharing system, known as free-floating bike-sharing (FFBS), presents not only a technological leap but also an environmental one by fostering stronger resource conservation capabilities which can be promoted by marketers. Taking Beijing as an example, a quantitative analysis compares the changes in resources utilization before and after the emergence of an FFBS scheme as compared to the third generation of bike-sharing systems, called station-based bike-sharing (SBBS), and of privately-owned bikes (POB). The results show that FFBS can improve the resources utilization of the urban bicycle system. It can reduce aluminium consumption by 15.3%, steel consumption by 10.6%, plastic consumption by 13.0% and rubber consumption by 18.4% for each bicycle trip throughout the city. FFBS presents, therefore, transformative properties for both society and the environment by showing greater potential in resource conservation especially in comparison to SBBS. • Resources utilization analysis before and after the emergence of free-floating bike-sharing (FFBS) in Beijing. • FFBS can increase the utilization rate of bicycle resources within urban settings by 43%. • FFBS curbs the use of 2 540 tons of aluminum, 1 310 tons of steel, 570 tons of plastic and 510 tons of rubber annually. • FFBS shows greater potential in resource conservation than station-based bike-sharing. [ABSTRACT FROM AUTHOR]