Riding the wave: Predicting the use of the bike-sharing system in Barcelona before and during COVID-19

Bustamante, Xavier; Federo, Ryan; Fernández‐i‐Marín, Xavier

doi:10.1016/j.scs.2022.103929

Cited by 33 publications

(35 citation statements)

References 92 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Accelerating to compensate for increased visibility is in line with other research, which noted that women develop specific strategies when cycling at night (Pellicer- Chenoll et al, 2021). Furthermore, it should be noted that bike-sharing trips duration might be constrained by their fee structure, with which individuals who surpass 30-min rides are charged (Bustamante et al, 2022).…”

Section: Discussionsupporting

confidence: 81%

“…The membership has an annual cost of €50 and offers unlimited free-of-charge trips for journeys shorter than 30 min. Each additional 30 min of use has a fee of €0.7 and trips longer than 2 h are charged €5/h (Bustamante et al, 2022). In contrast, the municipality does not currently offer an e-scooter-sharing platform nor does it allow private e-scooter enterprises to operate within the city limits.…”

Section: Study Settingmentioning

confidence: 99%

See 1 more Smart Citation

Gendered travel behaviour in micromobility? Travel speed and route choice through the lens of intersecting identities

Cubells

Miralles‐Guasch

Marquet

2023

Journal of Transport Geography

View full text Add to dashboard Cite

Section: Discussionsupporting

confidence: 81%

Section: Study Settingmentioning

confidence: 99%

Gendered travel behaviour in micromobility? Travel speed and route choice through the lens of intersecting identities

Cubells

Miralles‐Guasch

Marquet

2023

Journal of Transport Geography

View full text Add to dashboard Cite

“…They found that both subway ridership and bike-sharing usage plummeted at the beginning; however, bike-sharing usage has almost returned to normal, whereas subway ridership has remained substantially below pre-pandemic levels. Other recent studies [18,19] also revealed that bike-sharing platform usage in many cities has reached or surpassed pre-pandemic levels [20].…”

Section: Introductionmentioning

confidence: 94%

When crisis hits: Bike-Sharing platforms amid the Covid-19 pandemic

et al. 2023

View full text Add to dashboard Cite

In this work, we examine the changes in demand for bike-sharing platforms with the onset of the Covid-19 pandemic. Using the fixed-effects regression formulation of difference-in-differences, we evaluate how the demand for bike-sharing platforms changed after the first cases of Covid were discovered and after the first executive orders were implemented. Accounting for weather conditions, socio-economic characteristics, time trends, and fixed effects across cities, our findings indicate that there is an increase in daily bike-sharing trips by 22% on average after the first Covid-19 case diagnosis, and a decrease of 30% after the first executive order implementation in each municipality, using the data up to August 2020. Moreover, we observe a 22% increase in weekday-specific trip frequency after the first Covid-19 case diagnosis and a 28% decrease in weekend-specific trip frequency after the first executive order implementation. Finally, we find that there is an increase in the frequency of trips on bike-sharing platforms in more bike-friendly, transit-friendly, and pedestrian-friendly cities upon both the first Covid-19 case diagnosis and the first executive order implementation.

show abstract

“…They use Random Forest and Least-Squares Boosting as univariate regression to model the number of available bike stations. Bustamante et al [9] employ multiple machine learning algorithms using probabilistic programming through Bayesian inference. Their work study the impact of COVID 19 or ridership.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Bike Usage and Optimizing Operations at Repair Shops in Bike Sharing Systems

Alzaman

Aljuneidi

2023

IEEE Access

View full text Add to dashboard Cite

Supply chain responsiveness and big data analytics (BDA) have garnered considerable interest in academia and among practitioners. BDA helps researchers understand the current challenges in data management, including the high volume, velocity, and variety of data. This study is concerned with improving the responsiveness of supply chain networks to bike-sharing systems (BSS), which exhibit BDA characteristics. To address the challenges of forecasting bike usage and accordingly optimizing repair shop operations, we analyze multi-factor BSS data (Data from Washington D.C. BSS available to public), wherein attributes, such as weather conditions, registration, humidity, date, and time, are present. We use machine learning algorithms, such as neural networks, decision-tree-based regression, K-nearest neighbor, support vectors, and ensemble random forest, to predict bike usage and repair. This work contests the results and demonstrates the effectiveness of combining machine learning with supply chain network design. Supply chain networks model bike repairs by means of capacity extensions, which entails a nonlinear problem. In this study, we utilize a gradient search to solve a nonlinear supply chain network model. By enabling capacity extension, bike repair shops within the BSS exhibit a promising 50 % reduction in lead repair time. Furthermore, a 25 % overall throughput increase in BSS is achieved. Ultimately, this study demonstrates the importance of operational flexibility in responding to big data challenges.INDEX TERMS Big data, bike sharing, flexibility, machine leaning, supply chain network design.

show abstract

Riding the wave: Predicting the use of the bike-sharing system in Barcelona before and during COVID-19

Cited by 33 publications

References 92 publications

Gendered travel behaviour in micromobility? Travel speed and route choice through the lens of intersecting identities

Gendered travel behaviour in micromobility? Travel speed and route choice through the lens of intersecting identities

When crisis hits: Bike-Sharing platforms amid the Covid-19 pandemic

Predicting Bike Usage and Optimizing Operations at Repair Shops in Bike Sharing Systems

Contact Info

Product

Resources

About