Autonomous vehicles: The next jump in accessibilities?

Meyer, Jonas; Becker, Henrik; Bösch, Patrick M.

doi:10.1016/j.retrec.2017.03.005

Cited by 279 publications

(133 citation statements)

References 19 publications

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“…Driverless cars or autonomous vehicles (AVs) represent a transformative technology that may have important implications for society, urban mobility, economy, and environment Kockelman, 2015, 2014;Greenblatt and Shaheen, 2015). The fact that this technology is driverless and accordingly available every time and every place makes us assume that they are rather shared and consequently easily accessible and affordable for all Harper et al, 2016;Meyer et al, 2017). After several years of growing interest in AVs by car manufacturers investing millions of dollars to develop driverless cars, gaining expertise of operation of future shared autonomous vehicles (SAVs) is a major concern.…”

Section: Introductionmentioning

confidence: 99%

Shared autonomous vehicle simulation and service design

Vosooghi

Puchinger

Janković

et al. 2019

Transportation Research Part C: Emerging Technologies

101

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Today, driverless cars, as a new technology that allows a more accessible, dynamic and intelligent form of Shared Mobility, are expected to revolutionize urban transportation. One of the conceivable mobility services based on driverless cars is shared autonomous vehicles (SAVs). This service could merge cabs, carsharing, and ridesharing systems into a singular transportation mode. However, the success and competitiveness of future SAV services depend on their operational models, which are linked intrinsically to the service configuration and fleet specification. In addition, any change in operational models will result in a different demand. Using a comprehensive framework of SAV simulation in a multi-modal dynamic demand system with integrated SAV user taste variation, this study evaluates the performance of various SAV fleets and vehicle capacities serving travelers across the Rouen Normandie metropolitan area in France. Also, the impact of ridesharing and rebalancing strategies on service performance is investigated.Research results suggest that the performance of SAV is strongly correlated with the fleet size and the strategy of individual or shared rides. Further analysis indicates that for the pricing scheme proposed in this study (i.e., 20% lower for ridesharing scenario), the standard 4-seats car with shared ride remains the best option among all scenarios. The results also underline that enabling vehicle-rebalancing strategies may have an important effect on both user and service-related metrics. The estimated SAV average and maximum driven distance prove the importance of vehicle range and charging station deployment.

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Section: Introductionmentioning

confidence: 99%

Shared autonomous vehicle simulation and service design

Vosooghi

Puchinger

Janković

et al. 2019

Transportation Research Part C: Emerging Technologies

101

View full text Add to dashboard Cite

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“…On the one hand, these options can support multi-modal traffic in urban areas and thereby reduce the number of vehicle-km [137,138]. On the other hand, they may favor urban sprawl and compete with public transport, leading to increased travel demand [139][140][141]. Currently, the main barrier to a large-scale adoption of autonomous vehicles is the high costs, which are expected to reduce significantly [142].…”

Section: Me In Vehiclesmentioning

confidence: 99%

Material efficiency strategies to reducing greenhouse gas emissions associated with buildings, vehicles, and electronics—a review

Hertwich

Ali

Ciacci

et al. 2019

Environ. Res. Lett.

254

153

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As one quarter of global energy use serves the production of materials, the more efficient use of these materials presents a significant opportunity for the mitigation of greenhouse gas (GHG) emissions. With the renewed interest of policy makers in the circular economy, material efficiency (ME) strategies such as light-weighting and downsizing of and lifetime extension for products, reuse and recycling of materials, and appropriate material choice are being promoted. Yet, the emissions savings from ME remain poorly understood, owing in part to the multitude of material uses and diversity of circumstances and in part to a lack of analytical effort. We have reviewed emissions reductions from ME strategies applied to buildings, cars, and electronics. We find that there can be a systematic trade-off between material use in the production of buildings, vehicles, and appliances and energy use in their operation, requiring a careful life cycle assessment of ME strategies. We find that the largest potential emission reductions quantified in the literature result from more intensive use of and lifetime extension for buildings and the light-weighting and reduced size of vehicles. Replacing metals and concrete with timber in construction can result in significant GHG benefits, but trade-offs and limitations to the potential supply of timber need to be recognized. Repair and remanufacturing of products can also result in emission reductions, which have been quantified only on a case-by-case basis and are difficult to generalize. The recovery of steel, aluminum, and copper from building demolition waste and the end-of-life vehicles and appliances already results in the recycling of base metals, which achieves significant emission reductions. Higher collection rates, sorting efficiencies, and the alloy-specific sorting of metals to preserve the function of alloying elements while avoiding the contamination of base metals are important steps to further reduce emissions.

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“…For example, l o2 = l o1 +l 12 . For arc (1,3), an AEV may drive through it by path (123) or (153). However, under Assumption [A2], an AEV driving from node 1 to node 3 without getting charged between them will only choose the shortest path (123) to minimize its costs.…”

Section: A Transportation Network Expansionmentioning

confidence: 99%

Joint Fleet Sizing and Charging System Planning for Autonomous Electric Vehicles

Zhang

Sheppard

Lipman

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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This paper studies the joint fleet sizing and charging system planning problem for a company operating a fleet of autonomous electric vehicles (AEVs) for passenger and goods transportation. Most of the relevant published papers focus on intracity scenarios and adopt heuristic approaches, e.g., agent based simulation, which do not guarantee optimality. In contrast, we propose a mixed integer linear programming model for intercity scenarios. This model incorporates comprehensive considerations of 1) limited AEV driving range; 2) optimal AEV routing and relocating operations; 3) time-varying origindestination transport demands; and 4) differentiated operation cost structure of passenger and goods transportation. The proposed model can be computational expensive when the scale of the transportation network is large. We then exploit the structure of this program to expedite its solution. Numerical experiments are conducted to validate the proposed method. Our experimental results show that AEVs in passenger and goods transportation have remarkable planning and operation differences. We also demonstrate that intelligent routing and relocating operations, charging system and vehicle parameters, e.g., charging power, battery capacity, driving speed etc., can significantly affect the economic efficiency and the planning results of an AEV fleet.

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Autonomous vehicles: The next jump in accessibilities?

Cited by 279 publications

References 19 publications

Shared autonomous vehicle simulation and service design

Shared autonomous vehicle simulation and service design

Material efficiency strategies to reducing greenhouse gas emissions associated with buildings, vehicles, and electronics—a review

Joint Fleet Sizing and Charging System Planning for Autonomous Electric Vehicles

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