[HTML][HTML] An in-depth analysis of electric vehicle charging station infrastructure, policy implications, and future trends
A significant transformation occurs globally as transportation switches from fossil fuel-
powered to zero and ultra-low tailpipe emissions vehicles. The transition to the electric …
powered to zero and ultra-low tailpipe emissions vehicles. The transition to the electric …
Modeling the preference of electric shared mobility drivers in choosing charging stations
Electric vehicles for urban shared mobility services are important customers at public
charging stations, and understanding their charging behavior is essential to the charging …
charging stations, and understanding their charging behavior is essential to the charging …
Data-driven distributionally robust electric vehicle balancing for autonomous mobility-on-demand systems under demand and supply uncertainties
Electric vehicles (EVs) are being rapidly adopted due to their economic and societal
benefits. Autonomous mobility-on-demand (AMoD) systems also embrace this trend …
benefits. Autonomous mobility-on-demand (AMoD) systems also embrace this trend …
[HTML][HTML] An exact approach for the multi-depot electric bus scheduling problem with time windows
This study extends the multi-depot vehicle scheduling problem with time windows
(MDVSPTW) to the case of electric vehicles which can recharge at charging stations located …
(MDVSPTW) to the case of electric vehicles which can recharge at charging stations located …
[PDF][PDF] A robust and constrained multi-agent reinforcement learning framework for electric vehicle amod systems
Electric vehicles (EVs) play critical roles in autonomous mobility-on-demand (AMoD)
systems, but their unique charging patterns increase the model uncertainties in AMoD …
systems, but their unique charging patterns increase the model uncertainties in AMoD …
FairCharge: A data-driven fairness-aware charging recommendation system for large-scale electric taxi fleets
Our society is witnessing a rapid taxi electrification process. Compared to conventional gas
taxis, a key drawback of electric taxis is their prolonged charging time, which potentially …
taxis, a key drawback of electric taxis is their prolonged charging time, which potentially …
Joint charging and relocation recommendation for e-taxi drivers via multi-agent mean field hierarchical reinforcement learning
Nowadays, most of the taxi drivers have become users of the relocation recommendation
service offered by online ride-hailing platforms (eg, Uber and Didi Chuxing), which could …
service offered by online ride-hailing platforms (eg, Uber and Didi Chuxing), which could …
Robust electric vehicle balancing of autonomous mobility-on-demand system: A multi-agent reinforcement learning approach
Electric autonomous vehicles (EAVs) are getting attention in future autonomous mobility-on-
demand (AMoD) systems due to their economic and societal benefits. However, EAVs' …
demand (AMoD) systems due to their economic and societal benefits. However, EAVs' …
CODE-V: Multi-hop computation offloading in Vehicular Fog Computing
MM Hussain, MMS Beg - Future Generation Computer Systems, 2021 - Elsevier
Abstract Vehicular Fog Computing (VFC) is an extension of fog computing in Intelligent
Transportation Systems (ITS). It is an emerging computing model that leverages latency …
Transportation Systems (ITS). It is an emerging computing model that leverages latency …
Data-driven fairness-aware vehicle displacement for large-scale electric taxi fleets
We are witnessing a rapid taxi electrification process due to the ever-increasing concern
about urban air quality and energy security. A key difference between conventional gas taxis …
about urban air quality and energy security. A key difference between conventional gas taxis …