TY - GEN
T1 - Rebalancing Autonomous Electric Vehicles for Mobility-on-Demand by Data-Driven Model Predictive Control
AU - Ali, Muhammad Sajid
AU - Tangirala, Nagacharan Teja
AU - Knoll, Alois
AU - Eckhoff, David
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The main goal of this paper is to present an end-to-end, data-driven framework for the control of Autonomous Electric Vehicles (AEV) for Mobility-on-Demand (MoD). We present a data-driven Model Predictive Control (MPC) algorithm that rebalances (i.e. preemptively repositions) the AEV fleet in order to meet the mobility demand in the near future. The algorithm consists of Mixed Integer Linear Programming (MILP) that leverages the short-term forecast of the mobility demand as well as the charging station availability in order to optimally rebalance the AEV fleet. The proposed algorithm is evaluated by means of simulations with the New York City (NYC) taxi data. The proposed algorithm outperforms other state-of-the-art rebalancing strategies by reducing the mean customer wait time by 82.3% and the number of rejected requests by 94.6% for a given fleet size and a number of charging stations.
AB - The main goal of this paper is to present an end-to-end, data-driven framework for the control of Autonomous Electric Vehicles (AEV) for Mobility-on-Demand (MoD). We present a data-driven Model Predictive Control (MPC) algorithm that rebalances (i.e. preemptively repositions) the AEV fleet in order to meet the mobility demand in the near future. The algorithm consists of Mixed Integer Linear Programming (MILP) that leverages the short-term forecast of the mobility demand as well as the charging station availability in order to optimally rebalance the AEV fleet. The proposed algorithm is evaluated by means of simulations with the New York City (NYC) taxi data. The proposed algorithm outperforms other state-of-the-art rebalancing strategies by reducing the mean customer wait time by 82.3% and the number of rejected requests by 94.6% for a given fleet size and a number of charging stations.
UR - http://www.scopus.com/inward/record.url?scp=85186503752&partnerID=8YFLogxK
U2 - 10.1109/ITSC57777.2023.10422002
DO - 10.1109/ITSC57777.2023.10422002
M3 - Conference contribution
AN - SCOPUS:85186503752
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 215
EP - 221
BT - 2023 IEEE 26th International Conference on Intelligent Transportation Systems, ITSC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 26th IEEE International Conference on Intelligent Transportation Systems, ITSC 2023
Y2 - 24 September 2023 through 28 September 2023
ER -