On the Selection of Charging Facility Locations for EV-Based Ride-Hailing Services: A Computational Case Study

Anastasiadis, Eleftherios; Angeloudis, Panagiotis; Ainalis, Daniel; Ye, Qiming; Hsu, Pei-Yuan; Karamanis, Renos; Macias, Jose Javier Escribano; Stettler, Marc

doi:10.3390/su13010168

Cited by 10 publications

(4 citation statements)

References 26 publications

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“…If the charge falls below the recharge threshold, the vehicle needs to finish the current trip and head to the nearest available charging station. The recharge threshold can be dynamic or constant with a safety margin based on the distribution of the charging infrastructure ( 59 ). In the proposed model, a fixed recharge threshold is set at 30%, because this was a suitable value within the scale of operation of the case study.…”

Section: Methodsmentioning

confidence: 99%

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Policy and Strategy Evaluation of Ridesharing Autonomous Vehicle Operation: A London Case Study

Cheong

Macias

Karamanis³

et al. 2023

Transportation Research Record: Journal of the Transportation R

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To understand the dynamics of an autonomous ridesharing transport mode from the perspectives of different stakeholders, a single model of such a system is essential, because this will enable policymakers and companies involved in the manufacture and operation of shared autonomous vehicles (SAVs) to develop user-centered strategies. The model needs to be based on real data, network, and traffic information and applied to real cities and situations, particularly those with complex public transportation systems. In this paper, we propose a new agent-based model for SAV deployment that enables the parametric assessment of key performance indicators from the perspective of potential SAV users, vehicle manufacturers, operators, and local authorities. This has been applied to a case study of three regions in London: central, inner, and outer. The results show there is no linear correlation between an increased ridesharing acceptance level and average trip duration. Without a fleet rebalancing algorithm, over 80% of SAVs’ energy expenditure is on picking up customers. By reducing pickup distance, SAVs could be a contender for a nonpersonal transportation system based on trip energy comparisons. The results provide a picture of future SAV systems for potential users and offer suggestions as to how operators can devise an optimal transportation strategy beyond the question of fleet size and how policymakers can improve the overall transport network and reduce its environmental impact based on energy consumption. As a result of its flexibility and parametric capability, the model can be utilized to inform any local authority how SAV services could be deployed in any city.

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Section: Methodsmentioning

confidence: 99%

“…The proposed framework builds on previous research on optimizing the distribution of charging stations (59), developing double-auction mechanisms for customervehicle assignment (67), and on modeling vehicle redistribution strategies (55). It also takes into account the baseline framework described by Karamanis et al (49).…”

Section: Agent-based Modeling Frameworkmentioning

confidence: 99%

Policy and Strategy Evaluation of Ridesharing Autonomous Vehicle Operation: A London Case Study

Cheong

Macias

Karamanis³

et al. 2023

Transportation Research Record: Journal of the Transportation R

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“…They applied the proposed methodology to the 28 countries within the European Union. Anastasiadis et al [20] conducted a computational case study on the selection of charging facility locations for electric vehicle-based ride-hailing services. They presented an optimization approach to model the placement of C.S.s to minimize the empty time travelled to the nearest C.S.…”

Section: Site Selection Work With Mcdm Methodsmentioning

confidence: 99%

The Suitability-Feasibility-Acceptability Strategy Integrated with Bayesian BWM-MARCOS Methods to Determine the Optimal Lithium Battery Plant Located in South America

et al. 2022

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This study aims to help managers develop a proper strategy and policy for their company’s future. After the global COVID-19 pandemic, developed countries decided to change their production and relocate and re-industrialize. The U.S.’s big electronics and automobile companies are not an exception to this rule. The lithium batteries are the main instrument of mobile phone and electric vehicles. The leading lithium battery supplier for the U.S mobile phone companies is China. Argentina, Bolivia, and Chile (in South America) have some of the largest lithium mines in the world; these countries are known as the lithium triangle. Among the 86 million tonnes of lithium resources worldwide, 49.9 million tonnes exist in this area. The researchers in this study surveyed the best country for constructing a battery for companies in the U.S. Because of the growth of electric vehicles and their use of the lithium battery, the world is facing astronomical prices for lithium. To emphasize this issue and help managers create good policy, this study combined multiple methods. The improved suitability-feasibility-acceptability (SFA) strategy is integrated with the Bayesian best-worst method (BBWM) and measurement of alternatives and rankings according to compromise solution (MARCOS) multicriteria methods to determine the best destination. For comparison, based on the SFA strategy, seven criteria are introduced: commercially viable reserves, national minimum wage, corporate income tax, accessibility to mining companies, accessibility to the waterway, population, and political stability index. The Bayesian BWM analysis reveals that the foremost factor is corporate income tax, whereas MARCOS’s findings indicate that Chile is the best country to construct the lithium battery industry. To verify the proposed approach, a comparison analysis also is performed.

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“…Planning problems could be classified into fleet planning and charging infrastructure planning. Fleet planning optimizes the number of EV fleet and battery capacity of each class for heterogeneous fleet, and initial fleet distribution including charge level and vehicle location [72], [73]. Charging infrastructure planning determines charging station siting and the number of charging bays with different charging rates.…”

Section: ) Planningmentioning

confidence: 99%

Fleet Management and Charging Scheduling for Shared Mobility-on-Demand System: A Systematic Review

Tan

Sun

Ding

et al. 2022

IEEE Open J. Power Energy

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Challenged by urbanization and increasing travel needs, existing transportation systems call for new mobility paradigms. In this article, we present the fleet management and charging scheduling of a shared mobility-ondemand system, whereby electric vehicle fleets are operated by a centralized platform to provide customers with mobility service. We provide a comprehensive review of system operation based on the operational objectives. The fleet scheduling strategies are categorized into four types: ⅰ) order dispatching, ⅱ) order-dispatching and rebalancing, ⅲ) orderdispatching, rebalancing and charging, and ⅳ) extended. Specifically, we first identify mathematical modeling techniques implemented in the transportation network, then analyze and summarize the solution approaches including mathematical programming, reinforcement learning, and hybrid methods. The advantages and disadvantages of different models and solution approaches are compared. Finally, we present research outlook in various directions. INDEX TERMS shared mobility-on-demand, electric vehicles, fleet management, operational objective, deep reinforcement learning.W

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On the Selection of Charging Facility Locations for EV-Based Ride-Hailing Services: A Computational Case Study

Cited by 10 publications

References 26 publications

Policy and Strategy Evaluation of Ridesharing Autonomous Vehicle Operation: A London Case Study

Policy and Strategy Evaluation of Ridesharing Autonomous Vehicle Operation: A London Case Study

The Suitability-Feasibility-Acceptability Strategy Integrated with Bayesian BWM-MARCOS Methods to Determine the Optimal Lithium Battery Plant Located in South America

Fleet Management and Charging Scheduling for Shared Mobility-on-Demand System: A Systematic Review

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