Impact of vehicle automation and electric propulsion on production costs for mobility services worldwide

Becker, Henrik; Bécker, Félix; Abe, Ryosuke; Bekhor, Shlomo; Belgiawan, Prawira Fajarindra; Compostella, Junia; Frazzoli, Emilio; Fulton, Lewis; Bicudo, Davi Guggisberg; Gurumurthy, Krishna Murthy; Hensher, David A.; Joubert, Johan W.; Kockelman, Kara M.; Kröger, Lars; Vine, Scott Le; Malik, Jai; Marczuk, Katarzyna; Nasution, Reza Ashari; Rich, Jeppe; Carrone, Andrea Vanesa Papu; Shen, Danqi; Shiftan, Yoram; Tirachini, Alejandro; Wong, Yale Z.; Zhang, Mengmeng; Bösch, Patrick M.

doi:10.1016/j.tra.2020.04.021

Cited by 40 publications

(34 citation statements)

References 28 publications

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“…Fares impact the outcome of the mode choice for each person, and SAV demand is likely to be influenced by low-cost fares in the future. Studies have shown that the likely costs for operation are about 50¢/mi to $1/mi (Becker et al, 2020;Bösch et al, 2018;Loeb & Kockelman, 2019). To simplify the analyses, a per-mile fare is used such that the average cost of operation is exactly 50¢/mi.…”

Section: Shared Autonomous Vehiclesmentioning

confidence: 99%

“…Studies have shown that AV technology is likely to first be taken up by fleet operators (Bansal & Kockelman, 2017;Quarles et al, 2020), much like current-day transportation network companies (TNCs), who wish to employ shared fleets of AVs and turn a higher profit by avoiding driver-related costs. Long-term cost estimates for shared AV (SAV) fleets are in the range of $0.50 to $1 per mile (Becker et al, 2020;Bösch et al, 2018;Loeb & Kockelman, 2019), making them an attractive mode alternative to personally owned vehicles today, which cost about $0.50-$0.80 per mile for a gasolinepowered sedan depending on use (American Automobile Association, 2019).…”

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confidence: 99%

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A system of shared autonomous vehicles for Chicago: Understanding the effects of geofencing the service

Gurumurthy

Auld

Kockelman

2021

JTLU

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With autonomous vehicles (AVs) still in the testing phase, researchers and planners must resort to simulation techniques to explore possible futures regarding shared and automated mobility. An agent-based discrete-event transport simulator, POLARIS, is used in this study to simulate travel in the 20-county Chicago region with a shared AV (SAV) mobility option. Using this framework, the effect of an SAV fleet on system performance when constrained to serve within geofences is studied under four distinct scenarios: service restricted to the city, to the city plus suburban core, to the core plus exurban areas, and to the entire region — along with the choice of dynamic ridesharing (DRS) versus solo travel in an SAV. Results indicate that service areas need a balanced mix of trip generators and attractors, and an SAV fleet’s empty VMT (eVMT) can be noticeably reduced through suitable geofencing and DRS. Geofences can also help lower response times, reduce systemwide VMT across all modes, and ensure uniform access to SAVs. DRS is most useful in lowering VMT and %eVMT that arises from sprawled land development, but with insufficient demand to share rides, savings from the use of geofences is higher. Geofences targeting neighborhoods with high trip density bring about low response times and %eVMT, but fleet sizes in these regions need to be designed for uniformly low response times throughout a large region, as opposed to maximizing vehicle use in a 24-hour day.

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Section: Shared Autonomous Vehiclesmentioning

confidence: 99%

mentioning

confidence: 99%

A system of shared autonomous vehicles for Chicago: Understanding the effects of geofencing the service

Gurumurthy

Auld

Kockelman

2021

JTLU

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“…The third experiment makes use of a detailed cost model of automated mobility [15]. It has been successfully applied to the case of Zurich, Switzerland and has recently been applied to a range of cities worldwide [33]. Depending on the fleet utilization, empty distance, fleet sizes, among other components, the cost model calculates a passenger price per kilometre that covers the expenses of maintaining the service.…”

Section: B Future Scenariosmentioning

confidence: 99%

Dynamic demand estimation for an AMoD system in Paris

Hörl

Balać

Axhausen

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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A simulation framework is presented that equilibrates a given automated taxi fleet with (a) consistent prices to provide the service and (b) customer behaviour that reacts to costs and level of service alike. In a first attempt, a hypothetical AMoD service within the highway ring of Paris is considered. The "dynamic demand" case yields a demand of around 1.2M trips per day for such a service at the optimal fleet size of 25k vehicles. This number is considerably lower than 2.3M trips that potentially could be served in a "static maximum demand" case which has often been used as a basis for previous fleet sizing studies. While the authors acknowledge a multitude of assumptions that constitute the present model, clear pathways to its improvement, methodologically and datawise, are provided.

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“…Along with the trends in the global economy, the interest of scientists in issues related to the automotive industry is also growing dynamically. The latest research concepts concern mainly issues related to the methods of constructing autonomous vehicles based solely on electric drives, but also to the use, operation and recycling of the currently most commonly used batteries used to power electric and hybrid vehicles, i.e., lithium-ion batteries [1,4]. In the last two decades, the number of published articles related to LiBs has increased from several dozen to several thousand publications annually [1].…”

Section: Introductionmentioning

confidence: 99%

The Necessity of Recycling of Waste Li-Ion Batteries Used in Electric Vehicles as Objects Posing a Threat to Human Health and the Environment

et al. 2021

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The automotive industry is one of the fastest-growing sectors of the modern economy. Growing customer expectations, implementing solutions related to electromobility, and increasingly stringent legal restrictions in the field of environmental protection, determine the development and introduction of innovative technologies in the field of car production. To power the most modern vehicles that include electric and hybrid cars, packages of various types of lithium-ion cells are used, the number of which is constantly growing. After use, these batteries, due to their complex chemical composition, constitute hazardous waste that is difficult to manage and must be recycled in modern technological lines. The article presents the morphological characteristics of the currently used types of Li-ion cells, and the threats to the safety of people and the environment that may occur in the event of improper use of Li-ion batteries and accumulators have been identified and described on the basis of the Regulation of the European Parliament and Council (EC) No. 1272/2008 of 16 December 2008 and No. 1907/2006 of 18 December 2006 on the classification, labeling and packaging of substances and mixtures and the registration, evaluation, authorization and restriction of chemicals (REACH), establishing the European Chemicals Agency.

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Impact of vehicle automation and electric propulsion on production costs for mobility services worldwide

Cited by 40 publications

References 28 publications

A system of shared autonomous vehicles for Chicago: Understanding the effects of geofencing the service

A system of shared autonomous vehicles for Chicago: Understanding the effects of geofencing the service

Dynamic demand estimation for an AMoD system in Paris

The Necessity of Recycling of Waste Li-Ion Batteries Used in Electric Vehicles as Objects Posing a Threat to Human Health and the Environment

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