Data-Driven Utilization-Aware Trip Advisor for Bike-Sharing Systems

2017 ◽  
Author(s):  
Ji Hu ◽  
Zidong Yang ◽  
Yuanchao Shu ◽  
Peng Cheng ◽  
Jiming Chen
Keyword(s):  
Data Driven ◽  
Bike Sharing

scholarly journals Bike-sharing mobility patterns: a data-driven analysis for the city of Lisbon

2021 ◽  
pp. 169580
Author(s):  
Vitória Albuquerque ◽  
Francisco Andrade ◽  
João Ferreira ◽  
Miguel Dias ◽  
Fernando Bacao
Keyword(s):  
Data Driven ◽  
Mobility Patterns ◽  
Bike Sharing ◽  
The City

Mobility Modeling and Data-Driven Closed-Loop Prediction in Bike-Sharing Systems

2019 ◽  
Vol 20 (12) ◽  
pp. 4488-4499 ◽  
Author(s):  
Zidong Yang ◽  
Jiming Chen ◽  
Ji Hu ◽  
Yuanchao Shu ◽  
Peng Cheng
Keyword(s):  
Closed Loop ◽  
Data Driven ◽  
Mobility Modeling ◽  
Loop Prediction ◽  
Bike Sharing

scholarly journals Built Environment Factors Affecting Bike Sharing Ridership: Data-Driven Approach for Multiple Cities

2019 ◽  
Vol 2673 (12) ◽  
pp. 55-68 ◽  
Author(s):  
David Duran-Rodas ◽  
Emmanouil Chaniotakis ◽  
Constantinos Antoniou
Keyword(s):  
Built Environment ◽  
National Level ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Factors Affecting ◽  
Environment Factors ◽  
Learning Techniques ◽  
Bike Sharing ◽  
Data Driven Approach

Identification of factors influencing ridership is necessary for policy-making, as well as, when examining transferability and aspects of performance and reliability. In this work, a data-driven method is formulated to correlate arrivals and departures of station-based bike sharing systems with built environment factors in multiple cities. Ridership data from stations of multiple cities are pooled in one data set regardless of their geographic boundaries. The method bundles the collection, analysis, and processing of data, as well as, the model’s estimation using statistical and machine learning techniques. The method was applied on a national level in six cities in Germany, and also on an international level in three cities in Europe and North America. The results suggest that the model’s performance did not depend on clustering cities by size but by the relative daily distribution of the rentals. Selected statistically significant factors were identified to vary temporally (e.g., nightclubs were significant during the night). The most influencing variables were related to the city population, distance to city center, leisure-related establishments, and transport-related infrastructure. This data-driven method can help as a support decision-making tool to implement or expand bike sharing systems.

scholarly journals A Data-Driven Based Dynamic Rebalancing Methodology for Bike Sharing Systems

2021 ◽  
Vol 11 (15) ◽  
pp. 6967
Author(s):  
Marco Cipriano ◽  
Luca Colomba ◽  
Paolo Garza
Keyword(s):  
Decision Making ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Real Data ◽  
Frequent Pattern ◽  
Data Driven ◽  
System Usability ◽  
Bike Sharing ◽  
Data Driven Approach

Mobility in cities is a fundamental asset and opens several problems in decision making and the creation of new services for citizens. In the last years, transportation sharing systems have been continuously growing. Among these, bike sharing systems became commonly adopted. There exist two different categories of bike sharing systems: station-based systems and free-floating services. In this paper, we concentrate our analyses on station-based systems. Such systems require periodic rebalancing operations to guarantee good quality of service and system usability by moving bicycles from full stations to empty stations. In particular, in this paper, we propose a dynamic bicycle rebalancing methodology based on frequent pattern mining and its implementation. The extracted patterns represent frequent unbalanced situations among nearby stations. They are used to predict upcoming critical statuses and plan the most effective rebalancing operations using an entirely data-driven approach. Experiments performed on real data of the Barcelona bike sharing system show the effectiveness of the proposed approach.

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