A data-driven approach to enhancing cyclist bike share efficiency

Navneet Kumar Rajpoot · Prabh Deep Singh · Vikas Tripathi · Bhasker Pant · Akash Singh Chauhan

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Urban Transport and Accessibility Transportation Planning and Optimization Transportation and Mobility Innovations

Abstract

Cyclist bike share programs are now a big part of how cities look and will look in the future. These programs are meant to provide a handy and environmentally friendly way to travel short distances, could help ease traffic, get people to live healthier lives, and make the environment cleaner. As these bike share systems grow, they produce a lot of data that can be used to make them more efficient, easier to use, and have a bigger effect overall. This paper examines the cyclist bike share data, using data analysis techniques, visualization tools, and statistical methods to look at system usage patterns, user demographics, popular routes, seasonal changes, transportation impacts, accessibility and equity, maintenance challenges, and future works. This study promises to give city planners, transportation authorities, and bike share operators useful information. This will help these programs work better, be more accessible, and last longer in cities.

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References

2018Utilization-Aware Trip Advisor in Bike-Sharing Systems Based on User Behavior Analysis 2021Station-level Demand Prediction for Bike-Sharing System 2023Competitive or complementary? Analyzing bike-sharing use between public transport stops: A case study in Budapest 2020Locally Designed Campus Smart Bike Sharing System: Lessons Learned and Design Optimization for Thailand 2020The Empirical Evaluation of Models Predicting Bike Sharing Demand 2020Station Correlation Attention Learning for Data-driven Bike Sharing System Usage Prediction 2019Central Station Based Demand Prediction in a Bike Sharing System 2018Data-Driven Smart Bike-Sharing System by Implementing Machine Learning Algorithms

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