Ride Substitution Using Electric Bike Sharing

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.

[1]  David B. Shmoys,et al.  Data Analysis and Optimization for (Citi)Bike Sharing , 2015, AAAI.

[2]  Sang Hyuk Son,et al.  BRAVO , 2018 .

[3]  Patrick Jaillet,et al.  Dynamic Repositioning to Reduce Lost Demand in Bike Sharing Systems , 2017, J. Artif. Intell. Res..

[4]  Lukasz Golab,et al.  Analyzing the usage patterns of electric bicycles , 2016 .

[5]  Sang Hyuk Son,et al.  Towards Efficient Sharing: A Usage Balancing Mechanism for Bike Sharing Systems , 2019, WWW.

[6]  Hermann de Meer,et al.  Range prediction for electric bicycles , 2016, e-Energy.

[7]  Kristina Lerman,et al.  Analyzing Uber's Ride-sharing Economy , 2017, WWW.

[8]  L. Bertazzi,et al.  A two-stage stochastic optimization model for the Bike sharing allocation and rebalancing problem , 2018 .

[9]  Arthur Zimek,et al.  ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg" , 2019, ArXiv.

[10]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[11]  Susan L Handy,et al.  Experiences of electric bicycle users in the Sacramento, California area , 2014 .

[12]  Christopher R. Cherry,et al.  Use characteristics and mode choice behavior of electric bike users in China , 2007 .

[13]  Xiaohu Zhang,et al.  Understanding the usage of dockless bike sharing in Singapore , 2018 .

[14]  Yu Zheng,et al.  Traffic prediction in a bike-sharing system , 2015, SIGSPATIAL/GIS.

[15]  Finbarr Brereton,et al.  The economic contribution of public bike-share to the sustainability and efficient functioning of cities , 2017 .

[16]  David B. Shmoys,et al.  Minimizing Multimodular Functions and Allocating Capacity in Bike‐Sharing Systems , 2016, IPCO.

[17]  Markus Strohmaier,et al.  Discovering and Characterizing Mobility Patterns in Urban Spaces: A Study of Manhattan Taxi Data , 2016, WWW.

[18]  Yu Zheng,et al.  Citywide Bike Usage Prediction in a Bike-Sharing System , 2020, IEEE Transactions on Knowledge and Data Engineering.

[19]  Zhaohui Wu,et al.  Dynamic cluster-based over-demand prediction in bike sharing systems , 2016, UbiComp.

[20]  Yu Zheng,et al.  Dynamic Bike Reposition: A Spatio-Temporal Reinforcement Learning Approach , 2018, KDD.

[21]  I-Lin Wang,et al.  Models for Effective Deployment and Redistribution of Bicycles Within Public Bicycle-Sharing Systems , 2013, Oper. Res..

[22]  U. Davis The Transition To Electric Bikes In China: History And Key Reasons For Rapid Growth , 2006 .

[23]  Christo Wilson,et al.  On Ridesharing Competition and Accessibility: Evidence from Uber, Lyft, and Taxi , 2018, WWW.

[24]  Yuan Tian,et al.  Understanding intra-urban trip patterns from taxi trajectory data , 2012, J. Geogr. Syst..

[25]  Xiaoyan Hong,et al.  Analysis of mobility patterns for urban taxi cabs , 2012, 2012 International Conference on Computing, Networking and Communications (ICNC).

[26]  Clayton Lane PhillyCarShare , 2005 .

[27]  Patrick Jaillet,et al.  Online Repositioning in Bike Sharing Systems , 2017, ICAPS.

[28]  Oar,et al.  Greenhouse Gas Equivalencies Calculator , 2015 .

[29]  Carlo Ratti,et al.  Exploring human movements in Singapore: a comparative analysis based on mobile phone and taxicab usages , 2013, UrbComp '13.

[30]  Alejandro Henao,et al.  Impacts of Ridesourcing - Lyft and Uber - on Transportation Including VMT, Mode Replacement, Parking, and Travel Behavior , 2017 .

[31]  Jiawei Han,et al.  Inferring human mobility patterns from taxicab location traces , 2013, UbiComp.

[32]  Kang An,et al.  Travel Characteristics of E-bike Users: Survey and Analysis in Shanghai☆ , 2013 .

[33]  Gang Pan,et al.  Bike sharing station placement leveraging heterogeneous urban open data , 2015, UbiComp.

[34]  Clayton Lane,et al.  PhillyCarShare : First-year social and mobility impacts of carsharing in philadelphia, pennsylvania , 2005 .

[35]  Philip S. Yu,et al.  Bicycle-sharing systems expansion: station re-deployment through crowd planning , 2016, SIGSPATIAL/GIS.