Tackling climate change with machine learning

D Rolnick, PL Donti, LH Kaack, K Kochanski… - ACM Computing …, 2022 - dl.acm.org
Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …

[HTML][HTML] A review of bicycle-sharing service planning problems

CS Shui, WY Szeto - Transportation Research Part C: Emerging …, 2020 - Elsevier
This paper reviews and systematically classifies the existing literature of bicycle-sharing
service planning problems (BSPPs) at strategic, tactical, and operational decision levels with …

Free-floating bike sharing: Solving real-life large-scale static rebalancing problems

A Pal, Y Zhang - Transportation Research Part C: Emerging …, 2017 - Elsevier
Free-floating bike sharing (FFBS) is an innovative bike sharing model. FFBS saves on start-
up cost, in comparison to station-based bike sharing (SBBS), by avoiding construction of …

Rebalancing bike sharing systems: A multi-source data smart optimization

J Liu, L Sun, W Chen, H Xiong - Proceedings of the 22nd ACM SIGKDD …, 2016 - dl.acm.org
Bike sharing systems, aiming at providing the missing links in public transportation systems,
are becoming popular in urban cities. A key to success for a bike sharing systems is the …

Dynamic cluster-based over-demand prediction in bike sharing systems

L Chen, D Zhang, L Wang, D Yang, X Ma, S Li… - Proceedings of the …, 2016 - dl.acm.org
Bike sharing is booming globally as a green transportation mode, but the occurrence of over-
demand stations that have no bikes or docks available greatly affects user experiences …

A deep reinforcement learning framework for rebalancing dockless bike sharing systems

L Pan, Q Cai, Z Fang, P Tang, L Huang - … of the AAAI conference on artificial …, 2019 - aaai.org
Bike sharing provides an environment-friendly way for traveling and is booming all over the
world. Yet, due to the high similarity of user travel patterns, the bike imbalance problem …

Dynamic repositioning to reduce lost demand in bike sharing systems

S Ghosh, P Varakantham, Y Adulyasak… - Journal of Artificial …, 2017 - jair.org
Bike Sharing Systems (BSSs) are widely adopted in major cities of the world due to
concerns associated with extensive private vehicle usage, namely, increased carbon …

Shared mobility systems: an updated survey

G Laporte, F Meunier, R Wolfler Calvo - Annals of Operations Research, 2018 - Springer
Transportation habits have been significantly modified in the past decade by the introduction
of shared mobility systems. These have emerged as a partial response to the need of …

Factors influencing dock-less E-bike-share mode substitution: Evidence from Sacramento, California

T Fukushige, DT Fitch, S Handy - Transportation Research Part D: Transport …, 2021 - Elsevier
Dock-less e-bike-share use is likely to reduce vehicle miles traveled (VMT) and related
greenhouse emissions–if it substitutes for car use. If the major mode shift comes from public …

Dynamic bike reposition: A spatio-temporal reinforcement learning approach

Y Li, Y Zheng, Q Yang - Proceedings of the 24th ACM SIGKDD …, 2018 - dl.acm.org
Bike-sharing systems are widely deployed in many major cities, while the jammed and
empty stations in them lead to severe customer loss. Currently, operators try to constantly …