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1. The E-Bike Potential: Estimating regional e-bike impacts on greenhouse gas emissions

   https://doi.org/10.1016/j.trd.2020.102482  

   McQueen, Michael; MacArthur, John; Cherry, Christopher (October 2020, Transportation Research Part D: Transport and Environment)

   null (Ed.)
2. Bike share responses to COVID-19

   https://doi.org/10.1016/j.trip.2021.100353  

   Jobe, Jeffrey; Griffin, Greg P. (June 2021, Transportation Research Interdisciplinary Perspectives)

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3. Excess demand prediction for bike sharing systems

   https://doi.org/10.1371/journal.pone.0252894  

   Liu, Xin; Pelechrinis, Konstantinos (June 2021, PLOS ONE)

   Chiabaut, Nicolas (Ed.)

   One of the most crucial elements for the long-term success of shared transportation systems (bikes, cars etc.) is their ubiquitous availability. To achieve this, and avoid having stations with no available vehicle, service operators rely on rebalancing . While different operators have different approaches to this functionality, overall it requires a demand-supply analysis of the various stations. While trip data can be used for this task, the existing methods in the literature only capture the observed demand and supply rates. However, the excess demand rates (e.g., how many customers attempted to rent a bike from an empty station) are not recorded in these data, but they are important for the in-depth understanding of the systems’ demand patterns that ultimately can inform operations like rebalancing. In this work we propose a method to estimate the excess demand and supply rates from trip and station availability data. Key to our approach is identifying what we term as excess demand pulse (EDP) in availability data as a signal for the existence of excess demand. We then proceed to build a Skellam regression model that is able to predict the difference between the total demand and supply at a given station during a specific time period. Our experiments with real data further validate the accuracy of our proposed method. 

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4. A STOCHASTIC ANALYSIS OF BIKE-SHARING SYSTEMS

   https://doi.org/10.1017/S0269964820000297  

   Tao, Shuang; Pender, Jamol (May 2020, Probability in the Engineering and Informational Sciences)

   As more people move back into densely populated cities, bike sharing is emerging as an important mode of urban mobility. In a typical bike-sharing system (BSS), riders arrive at a station and take a bike if it is available. After retrieving a bike, they ride it for a while, then return it to a station near their final destinations. Since space is limited in cities, each station has a finite capacity of docks, which cannot hold more bikes than its capacity. In this paper, we study BSSs with stations having a finite capacity. By an appropriate scaling of our stochastic model, we prove a mean-field limit and a central limit theorem for an empirical process of the number of stations with k bikes. The mean-field limit and the central limit theorem provide insight on the mean, variance, and sample path dynamics of large-scale BSSs. We also leverage our results to estimate confidence intervals for various performance measures such as the proportion of empty stations, the proportion of full stations, and the number of bikes in circulation. These performance measures have the potential to inform the operations and design of future BSSs. 

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5. Binding BIKE Errors to a Key Pair

   https://doi.org/10.1007/978-3-030-78086-9_21  

   Drucker, Nir; Gueron, Shay; Kostic, Dusan (July 2021, Lecture notes in computer science)