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1. Greening Electric Bike Sharing Using Solar Charging Stations

   https://doi.org/10.1145/3408308.3427621  

   Wamburu, John; Raff, Christopher; Irwin, David; Shenoy, Prashant (November 2020, Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys))

   null (Ed.)

   Electric bikes have emerged as a popular form of transportation for short trips in dense urban areas and are being increasingly adopted by bike share programs for easy accessibility to riders. Motivated by the rising popularity of electric bikes, a form of an electric vehicle, we study the research question of how to design a zero-carbon electric bike share system. Specifically we study the challenges in designing solar charging stations for electric bike systems that enable either net-zero or a fully zero-carbon operation. We design a prototype two bike solar charging station to demonstrate the feasibility of our approach. Using insights and data from our prototype solar charging station, we then conduct a data driven analysis of the costs and benefits of converting an entire bike system into one powered using solar charging stations. Using empirical analysis, we determine the panel and battery capacity for each station, and perform a feasibility evaluation of the system using 8 months of ridership data. Our results show that equipping each bike station with a single grid-tied solar panel is adequate to meet the annual charging demand from electric bikes and achieve net-zero operation using net-metering. For an off-grid setup, our analysis shows that a bike station needs twice as many solar panels, on average, along with a 1.8kWh battery, with the busiest bike station needing 6× more solar capacity than in the net-metering case. Our analysis also reveals a tradeoff between the array size and the battery size needed to achieve true-zero carbon operation for the electric bike share system. 

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2. A Discrete Model For Bike Share Inventory

   Chadwick, A; Ho, S; Y, Li; Wang, M (November 2020, International Journal of Difference Equations)

   Anderson, Douglas R; Eloe, P; Goodrich, C; Peterson, A (Ed.)

   In this paper, a discrete Markov chain model is developed to describe the inventory at a bike share station. The uniqueness of solutions is first studied. Then the model calibration is considered by investigating a constrained optimization problem. Numerical simulations involving real data are conducted to demonstrate the model effectiveness as well. 

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3. Ride Substitution Using Electric Bike Sharing: Feasibility, Cost, and Carbon Analysis

   https://doi.org/10.1145/3448081  

   Wamburu, John; Lee, Stephen; Hajiesmaili, Mohammad H.; Irwin, David; Shenoy, Prashant (March 2021, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   null (Ed.)

   While ride-sharing has emerged as a popular form of transportation in urban areas due to its on-demand convenience, it has become a major contributor to carbon emissions, with recent studies suggesting it is 47% more carbon-intensive than personal car trips. In this paper, we examine the feasibility, costs, and carbon benefits of using electric bike-sharing---a low carbon form of ride-sharing---as a potential substitute for shorter ride-sharing trips, with the overall goal of greening the ride-sharing ecosystem. Using public datasets from New York City, our analysis shows that nearly half of the taxi and rideshare trips in New York are shorts trips of less than 3.5km, and that biking is actually faster than using a car for ultra-short trips of 2km or less. We analyze the cost and carbon benefits of different levels of ride substitution under various scenarios. We find that the additional bikes required to satisfy increased demand from ride substitution increases sub-linearly and results in 6.6% carbon emission reduction for 10% taxi ride substitution. Moreover, this reduction can be achieved through a hybrid mix that requires only a quarter of the bikes to be electric bikes, which reduces system costs. We also find that expanding bike-share systems to new areas that lack bike-share coverage requires additional investments due to the need for new bike stations and bike capacity to satisfy demand but also provides substantial carbon emission reductions. Finally, frequent station repositioning can reduce the number of bikes needed in the system by up to a third for a minimal increase in carbon emissions of 2% from the trucks required to perform repositioning, providing an interesting tradeoff between capital costs and carbon emissions. 

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4. Ride Substitution Using Electric Bike Sharing: Feasibility, Cost and Carbon Analysis

   Wambouru, John; Lee, Stephen; Hajiesmaili, Mohammad; Irwin, David; Shenoy, Prashant (March 2021, Proceedings of the ACM on interactive mobile wearable and ubiquitous technologies)

   null (Ed.)

   While ride-sharing has emerged as a popular form of transportation in urban areas due to its on-demand convenience, it has become a major contributor to carbon emissions, with recent studies suggesting it is 47% more carbon-intensive than personal car trips. In this paper, we examine the feasibility, costs, and carbon benefits of using electric bike-sharing—a low carbon form of ride-sharing—as a potential substitute for shorter ride-sharing trips, with the overall goal of greening the ride-sharing ecosystem. Using public datasets from New York City, our analysis shows that nearly half of the taxi and rideshare trips in New York are shorts trips of less than 3.5km, and that biking is actually faster than using a car for ultra-short trips of 2km or less. We analyze the cost and carbon benefits of different levels of ride substitution under various scenarios. We find that the additional bikes required to satisfy increased demand from ride substitution increases sub-linearly and results in 6.6% carbon emission reduction for 10% taxi ride substitution. Moreover, this reduction can be achieved through a hybrid mix that requires only a quarter of the bikes to be electric bikes, which reduces system costs. We also find that expanding bike-share systems to new areas that lack bike-share coverage requires additional investments due to the need for new bike stations and bike capacity to satisfy demand but also provides substantial carbon emission reductions. Finally, frequent station repositioning can reduce the number of bikes needed in the system by up to a third for a minimal increase in carbon emissions of 2% from the trucks required to perform repositioning, providing an interesting tradeoff between capital costs and carbon emissions. 

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5. Fairness-Aware Demand Prediction for New Mobility

   https://doi.org/10.1609/aaai.v34i01.5458  

   Yan, An; Howe, Bill (June 2020, Proceedings of the AAAI Conference on Artificial Intelligence)

   Emerging transportation modes, including car-sharing, bike-sharing, and ride-hailing, are transforming urban mobility yet have been shown to reinforce socioeconomic inequity. These services rely on accurate demand prediction, but the demand data on which these models are trained reflect biases around demographics, socioeconomic conditions, and entrenched geographic patterns. To address these biases and improve fairness, we present FairST, a fairness-aware demand prediction model for spatiotemporal urban applications, with emphasis on new mobility. We use 1D (time-varying, space-constant), 2D (space-varying, time-constant) and 3D (both time- and space-varying) convolutional branches to integrate heterogeneous features, while including fairness metrics as a form of regularization to improve equity across demographic groups. We propose two spatiotemporal fairness metrics, region-based fairness gap (RFG), applicable when demographic information is provided as a constant for a region, and individual-based fairness gap (IFG), applicable when a continuous distribution of demographic information is available. Experimental results on bike share and ride share datasets show that FairST can reduce inequity in demand prediction for multiple sensitive attributes (i.e. race, age, and education level), while achieving better accuracy than even state-of-the-art fairness-oblivious methods. 

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