Comparing Future Autonomous Electric Taxis With an Existing Free-Floating Carsharing System

Dandl, Florian; Bogenberger, Klaus

doi:10.1109/tits.2018.2857208

Cited by 73 publications

(37 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Small-scale: Numerous studies [1,4,10,11,19,22,29,34,39,40,[43][44][45][46][51][52][53] have been carried out for dealing with real problems in the ET domain, e.g., charging station siting and deployment, charging schedule. However, most of the existing works are based on small-scale real-world ET data or utilize conventional gas taxi data to simulate ET scenarios.…”

Section: Short-term Investigationmentioning

confidence: 99%

Experience

Wang

Chen

Zhang

et al. 2019

The 25th Annual International Conference on Mobile Computing and Networking

View full text Add to dashboard Cite

Due to the ever-growing concerns on the air pollution and energy security, many cities have started to update their taxi fleets with electric ones. Although environmentally friendly, the rapid promotion of electric taxis raises problems to both taxi drivers and governments, e.g., prolonged waiting/charging time, unbalanced utilization of charging infrastructures and reduced taxi supply due to the long charging time. In this paper, we make the first effort to understand the long-term evolving patterns through a five-year study on one of the largest electric taxi networks in the world, i.e., the Shenzhen electric taxi network in China. In particular, we perform a comprehensive measurement investigation called ePat to explore the evolving mobility and charging patterns of electric vehicles. Our ePat is based on 4.8 TB taxi GPS data, 240 GB taxi transaction data, and metadata from 117 charging stations, during an evolving process from 427 electric taxis in 2013 to 13,178 in 2018. Moreover, ePat also explores the impacts of various contexts and benefits during the evolving process. Our ePat as a comprehensive investigation of the electric taxi network mobility and charging evolving has the potential to advance the understanding of the evolving patterns of electric taxi networks and pave the way for analyzing future shared autonomous vehicles.

show abstract

Section: Short-term Investigationmentioning

confidence: 99%

Experience

Wang

Chen

Zhang

et al. 2019

The 25th Annual International Conference on Mobile Computing and Networking

View full text Add to dashboard Cite

show abstract

“…Hence, the complexity of this approach is 96 • O(m • c), where c is the complexity of one step of the applied policy, and therefore, computationally feasible (if the underlying policy is). Mathematically speaking, a policy-application strategy with underlying policy π assigns the action (n 26) and the system dynamics and constraints in equations (3)(4)(5)(6)(7), where (n π j , p π j ) are the actions taken according to policy π at time step j with state of charge S j . In order to save computation time, we vary n i in steps of 5 in case there are at least 25 available options for n i and in steps of 10 in case there are at least 100 options.…”

Section: Pre-day Scheduling Of Charging Processesmentioning

confidence: 99%

“…Most studies investigating free-floating MoD systems in urban networks examine reactive charging strategies, i.e. vehicles are sent charging whenever they fall below a certain threshold [5], [6]. Additional to charging lowenergy vehicles, Loeb et al [7] add a rule to send vehicles charging after being idle for long time and use different charging powers according to the vehicle's state of charge (SOC).…”

Section: Introductionmentioning

confidence: 99%

Pre-Day Scheduling of Charging Processes in Mobility-on-Demand Systems Considering Electricity Price and Vehicle Utilization Forecasts

Dandl¹,

Fehn²,

Bogenberger³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

<div>Electrifying mobility-on-demand (MoD) fleets is</div><div>an important step towards a more sustainable transportation</div><div>system. With increasing fleet size, MoD operators will be</div><div>able to participate in the energy exchange market and will</div><div>have access to time-varying electricity prices. They can benefit from intelligent scheduling of charging processes considering forecasts of electricity prices and vehicle utilization. Considering a long time horizon of, e.g., a day improves scheduling decisions, but electricity prices change in a short interval of 15 minutes; hence, an optimization-based approach needs to overcome challenges regarding computational time. For this reason, we develop a macroscopic model to study the tradeoffs between electricity, battery wear and level-of-service costs. In scenarios with varying fleet size and different numbers of</div><div>charging units, we compare the performance of several reactive and scheduling policies in a simulation framework based on a macroscopic model. Overall, the results of the study show that an MoD provider with 2000 vehicles could save several thousands of euros in daily operational costs by changing from a state of charge reactive charging strategy to one adapting to the price fluctuations of the electricity exchange market.</div>

show abstract

“…Mit Hilfe von Simulationen kann dieser Effizienzgewinn gemessen werden. In einer Studie, in der die Nachfrage für einen autonomen Mobilitätsdienst auf Buchungsdaten eines Carsharing-Betreibers in München basiert, wird gezeigt, dass ein autonomes Fahrzeug in etwa drei bis vier Carsharing-Fahrzeuge ersetzen kann (Dandl and Bogenberger, 2018b). Wegen der geringeren Fahrzeuganzahl und dadurch niedrigeren Fixkosten kann davon ausgegangen werden, dass der Mobilitätsdienst wesentlich günstiger als heutiges Carsharing angeboten werden kann, allerdings zusätzliche Fahrleistung durch Leerfahrten generiert werden könnten.…”

Section: Ausblickunclassified

Carsharing in Deutschland

Schmöller

Niels

Hardt

et al. 2019

jmv

Self Cite

View full text Add to dashboard Cite

In den letzten Jahrzehnten entwickelten sich verschiedene Carsharing-Modelle ausgehend vom stationsbasierten System, das nur Rundfahrten zuließ, bis hin zu free-floating Systemen mit flexibler Fahrzeugrückgabe innerhalb eines Geschäftsgebiets. Für Betreiber ist eine raumzeitliche Analyse der Nachfrage unabdinglich, um die Fahrzeugverfügbarkeit durch Reallokationen bestmöglich der Nachfrage anzugleichen. In diesem Artikel werden Carsharing-Systeme, aktuelle Entwicklungen, Nutzer- und Nachfrageanalysen sowie Reallokationsansätze vorgestellt.

show abstract

Comparing Future Autonomous Electric Taxis With an Existing Free-Floating Carsharing System

Cited by 73 publications

References 21 publications

Experience

Experience

Pre-Day Scheduling of Charging Processes in Mobility-on-Demand Systems Considering Electricity Price and Vehicle Utilization Forecasts

Carsharing in Deutschland

Contact Info

Product

Resources

About