Eoin O'Mahony's research while affiliated with UCSF University of California, San Francisco and other places

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Publications (2)


Analytics and Bikes: Riding Tandem with Motivate to Improve Mobility
  • Article

September 2019

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160 Reads

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23 Citations

Interfaces

Daniel Freund

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Eoin O’Mahony

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The authors developed new optimization models, characterized their mathematical structure, and used the insights gained to design models and analytics to manage operations for bike-sharing systems and improve their performance.

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Figure 1: Example mask matrix for a Penn Station bikeshare station over 60 days.
Figure 2: Example of the two lower bounds on true demand for a station in the West Village.
Figure 3: Morning levels assigned based on different cost functions. From left to right, minimizing max gap, minimizing the sum of the gaps, minimizing the sum of the gaps squared all with six thousand bikes. The color of stations corresponds to fill percentage, blue to 10%, red to 90% and purple to 50%.
Figure 4: Average time taken by the IP and greedy approaches for different numbers of trucks on real instances.
Figure 6: Average time taken by the IP and greedy approaches for different instance sizes in random instances.
Data Analysis and Optimization for (Citi)Bike Sharing
  • Article
  • Full-text available

February 2015

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163 Reads

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108 Citations

Proceedings of the AAAI Conference on Artificial Intelligence

Bike-sharing systems are becoming increasingly prevalent in urban environments. They provide a low-cost, environmentally-friendly transportation alternative for cities. The management of these systems gives rise to many optimization problems. Chief among these problems is the issue of bicycle rebalancing. Users imbalance the system by creating demand in an asymmetric pattern. This necessitates action to put the system back in balance with the requisite levels of bicycles at each station to facilitate future use. In this paper, we tackle the problem of maintaing system balance during peak rush-hour usageas well as rebalancing overnight to prepare the systemfor rush-hour usage. We provide novel problem formulationsthat have been motivated by both a close collaborationwith the New York City bike share (Citibike) and a careful analysisof system usage data. We analyze system data to discover the best placement of bikes tofacilitate usage. We solve routing problems forovernight shifts as well as clustering problems for handlingmid rush-hour usage. The tools developed from this research are currently in daily use at NYC Bike Share LLC, operators of Citibike.

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Citations (2)


... Theoretical and empirical research consistently illustrates that targeted interventions can enhance BSS stability [85][86][87]. However, a challenge is to select the right interventions and implement them appropriately. ...

Reference:

Interpretable Bike-Sharing Activity Prediction with a Temporal Fusion Transformer to Unveil Influential Factors: A Case Study in Hamburg, Germany
Data Analysis and Optimization for (Citi)Bike Sharing

Proceedings of the AAAI Conference on Artificial Intelligence

... In this experiment, the duration is set as 1 h because we assume the expected real demand for each hour is constant, and this assumption is widely used in bike-sharing studies [44]- [48]. Therefore, testing the model under different is out of the scope of the current study. ...

Analytics and Bikes: Riding Tandem with Motivate to Improve Mobility
  • Citing Article
  • September 2019

Interfaces