The future of road transport : implications of automated, connected, low-carbon and shared mobility

Maria, Alonso Raposo; Ciuffo, Biagio; Patricia, Alves Dias; Ardente, Fulvio; Philippe, Aurambout Jean; Baldini, Gianmarco; Claudia, Baranzelli; Darina, Blagoeva; Bobba, Silvia; Robert, Braun; Giulia, Cassio Laura; Pravir, Chawdhry; Christidis, Panayotis; Aris, Christodoulou; Corrado, Sara; Duboz, Amandine; Nestor, Duch Brown; Sofia, Felici; Enrique, Fernandez Macias; Jaime, Ferragut Martinez Vara De Rey; Gianluca, Fulli; Cristina, Galassi Maria; Georgakaki, Aliki; Gkoumas, Konstantinos; Grosso, Monica; Jonatan, Gomez Vilchez; Marton, Hajdu; Maria, Iglesias Portela; Maria, Julea Andreea; Krause, Jette; Kriston, Ákos; Lavalle, Carlo; Laura, Lonza; Alexandre, Rocha Pinto Lucas; Michail, Makridis; Antonios, Marinopoulos; Marmier, A.; Fabio, Marques Dos Santos; Bertin, Martens; Mattas, Konstadinos; Mathieux, Fabrice; Menzel, Gerhard; Fabrizio, Minarini; Silvia, Mondello; Moretto, Pietro; Barbara, Mortara; Elena, Navajas Cawood; Paffumi, Elena; Pasimeni, Francesco; Claudiu, Pavel; Ferenc, Pekar; Enrico, Pisoni; Ioan, Raileanu; Sala, Serenella; Saveyn, Bert; Harald, Scholz; Natalia, Serra; Tamba, Marie; Christian, Thiel; Trentadue, Germana; Tecchio, Paolo; Anastasios, Tsakalidis; Uihlein, Andreas; Mitchell, Van Balen; Vandecasteele, Ine

doi:10.2760/668964

Cited by 27 publications

(13 citation statements)

References 98 publications

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“…by the beta nor by the Weibull distribution. Although the ( ) = √ combined with beta distribution has larger p-value than ( ) = 3 √ , the Q-Q plot indicates that it fits worse the charging pools with low energy consumption. Thus, considering p-values, the P-P plots and the Q-Q plots, we concluded as the most satisfactory fit the beta distribution (with parameter values = 2.576 and = 4.528) combined with the transformation ( ) = 3 √ .…”

Section: Number Of Companies Where Was Electric Energy Consumption Me...mentioning

confidence: 95%

“…To model the distribution of energy consumed at charging pools, we considered original data and five transformations ( 2 , 3 , √ , 3 √ and ( )) and parametrized density functions of three known probabilistic distributions (Weibull, beta and gamma). We used the Kolmogorov-Smirnov goodness of fit test together with the inspection of the P-P and the Q-Q plots [59] to conclude that the transformation 3 √ combined…”

Section: Distribution Of Consumed Energy Over Charging Poolsmentioning

confidence: 99%

“…Considering the Kolmogorov-Smirnov goodness-of-fit test together with the inspection of the P-P and the Q-Q plots [59], we concluded that the most satisfactory fit of the consumed energy at charging pools is obtained by transforming the data with the function ( ) = 3 √ (the function is applied to the vector element-wise) and using the beta distribution (see section S3 of the SI file). The beta distribution is defined on the interval ⟨0, 1⟩.…”

Section: A Appendix: Fitting the Energy Consumption With The Transfor...mentioning

confidence: 99%

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Explaining the distribution of energy consumption at slow charging infrastructure for electric vehicles from socio-economic data

Straka,

Carvalho,

van der Poel

et al. 2020

Preprint

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Section: Number Of Companies Where Was Electric Energy Consumption Me...mentioning

confidence: 95%

Section: Distribution Of Consumed Energy Over Charging Poolsmentioning

confidence: 99%

Section: A Appendix: Fitting the Energy Consumption With The Transfor...mentioning

confidence: 99%

See 1 more Smart Citation

Explaining the distribution of energy consumption at slow charging infrastructure for electric vehicles from socio-economic data

Straka,

Carvalho,

van der Poel

et al. 2020

Preprint

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show abstract

“…Assuming an occupancy rate of 1.6 for cars (Fraunhofer ISI 2020) we can estimate 34.4 million passengers using the corridor from Lyon to Moscow within one year. Using these passenger flows as a baseline, an annual growth rate of 2% for aviation and a growth rate of 0.75% for road travel is assumed (Eurocontrol 2018;Alonso Raposo et al 2019).…”

Section: Factor 1: Difference In Emission Factorsmentioning

confidence: 99%

10. COVID-19 and the Corporate Digital Divide

Rückert¹,

Veugelers²,

Virginie³

et al. 2021

Open Reports Series

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His main expertise is in international political economy (IPE), in particular the problem of uneven deveolopment and the new challenges posed by rising countries to the current global governance scheme.Andrea Brasili is a Senior Economist at the EIB (Luxembourg) where his research interests are both micro (firm level) data analysis and macroeconomic developments, in particular those related to fiscal policy. He received his PhD in Public Economics from the University of Pavia (Italy). Before joining the EIB, he worked in the private sector (in Italian banks and asset management companies) as a research economist, still collaborating with academia.

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“…However, in recent years, more and more commercially available vehicle models are equipped with Automated Driver Assistance Systems that support the driver by taking over particular driving tasks and enhance safety standards. The ongoing evolution towards full automation and vehicle connectivity will affect the characteristics of road transportation substantially [1].…”

Section: Introductionmentioning

confidence: 99%

Comparing the Observable Response Times of ACC and CACC Systems

Brunner

Makridis

Kouvelas

2022

IEEE Trans. Intell. Transport. Syst.

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This paper analyzes trajectory observations from vehicles driving in platoon formation and they are equipped with Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC) systems; aiming to quantify response delays. When a preceding vehicle induces a perturbation, the delay until the reaction of the following vehicle, often quoted as observable response time, can have negative implication to the traffic flow and other factors such as energy consumption, stability and safety. Quantifying such delays can help towards more realistic traffic simulation modeling. The analysis is performed based on empirical observations from three well-known experimental campaigns in the literature with data from ACC-driven and CACC-driven vehicle platoons. Three state-of-the-art techniques were implemented to provide quantitative results for the observed response times. The benefits and downsides of each technique are discussed as well. The results show that ACC systems do not exhibit a significant improvement compared to human drivers, yet, it can be concluded that the additional vehicle-to-vehicle communication incorporated in the CACC systems allows for a substantially higher traffic flow and possibly other benefits.

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The future of road transport : implications of automated, connected, low-carbon and shared mobility

Cited by 27 publications

References 98 publications

Explaining the distribution of energy consumption at slow charging infrastructure for electric vehicles from socio-economic data

Explaining the distribution of energy consumption at slow charging infrastructure for electric vehicles from socio-economic data

10. COVID-19 and the Corporate Digital Divide

Comparing the Observable Response Times of ACC and CACC Systems

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