TY - GEN
T1 - Machine learning from imbalanced data-sets
T2 - 7th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2021
AU - Ceccarelli, Giovanni
AU - Cantelmo, Guido
AU - Nigro, Marialisa
AU - Antoniou, Constantinos
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/6/16
Y1 - 2021/6/16
N2 - One of the major issue bike sharing operators struggle to deal with is the bicycle rebalancing activity, i.e. optimizing the fleet location reducing the related activity cost. In order to reduce operational cost generated by rebalancing and to facilitate the adoption of bike sharing by users, it is extremely important to estimate the correct value of bicycles (and available docks in case of station-based bike sharing), that is the optimal inventory level. In this paper we investigate the potential of using machine learning techniques for estimating the inventory level to address the station-based bike sharing static rebalancing in the case of imbalanced data-set. Specifically, Random Forest (RF) and Gradient Tree Boosting classifiers have been proposed, together with a new iterative approach based on RF. All the methods have been tested adopting real world data of New York City bikes together with weather data.
AB - One of the major issue bike sharing operators struggle to deal with is the bicycle rebalancing activity, i.e. optimizing the fleet location reducing the related activity cost. In order to reduce operational cost generated by rebalancing and to facilitate the adoption of bike sharing by users, it is extremely important to estimate the correct value of bicycles (and available docks in case of station-based bike sharing), that is the optimal inventory level. In this paper we investigate the potential of using machine learning techniques for estimating the inventory level to address the station-based bike sharing static rebalancing in the case of imbalanced data-set. Specifically, Random Forest (RF) and Gradient Tree Boosting classifiers have been proposed, together with a new iterative approach based on RF. All the methods have been tested adopting real world data of New York City bikes together with weather data.
KW - Bike sharing
KW - Imbalanced data
KW - Inventory level
KW - Machine learning
KW - Random Forest
KW - Rebalancing problem
UR - http://www.scopus.com/inward/record.url?scp=85115878507&partnerID=8YFLogxK
U2 - 10.1109/MT-ITS49943.2021.9529281
DO - 10.1109/MT-ITS49943.2021.9529281
M3 - Conference contribution
AN - SCOPUS:85115878507
T3 - 2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2021
BT - 2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 16 June 2021 through 17 June 2021
ER -