Building Automation into Urban and Metropolitan Mobility Planning

Arem, Bart van; Ackerman, Aernout Aki; Chang, Tilly; Riggs, William; Wegscheider, Augustin; Smith, Scott; Rupprecht, Siegfried

doi:10.1007/978-3-030-22933-7_13

Cited by 8 publications

(2 citation statements)

References 14 publications

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“…Since strong interactions between the AEV market and the six connected urban sub-systems exist, there is an increasing need to investigate the diffusion of AEVs from a systematic and dynamic perspective, taking the interactions into account (Hawkins and Nurul Habib, 2019;van Arem et al, 2019). In response, some researchers have proposed to explore the diffusion of AVs or EVs using integrated urban models, such as land use and transport interaction models (Hawkins and Nurul Habib, 2019;Moreno, 2017;Zhuge et al, 2019).…”

Section: Interactions Between the Aev Market And Those Associated Urb...mentioning

confidence: 99%

Integrated modelling of autonomous electric vehicle diffusion: From review to conceptual design

Zhuge

Wang

2021

Transportation Research Part D: Transport and Environment

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Section: Interactions Between the Aev Market And Those Associated Urb...mentioning

confidence: 99%

Integrated modelling of autonomous electric vehicle diffusion: From review to conceptual design

Zhuge

Wang

2021

Transportation Research Part D: Transport and Environment

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“…In the last decade, numerous mobility systems, such as shared bicycles and scooters, automated cars, ride-hailing services, electric bicycles, and other personal light electric vehicles have been developed. Such new mobility systems could potentially change the way our urban areas look substantially in terms of spatial use, sustainability, health, equity, safety, and economic opportunities [1][2][3][4]. For instance, it is estimated that the yearly impact of automated vehicles (AV) alone could approach 4,000 generalized US dollars per person per year, including economic benefts, crash cost savings, travel time reductions (due to a reduction in congestion), and lower parking costs [1].…”

Section: Introductionmentioning

confidence: 99%

Estimating the Potential Modal Split of Any Future Mode Using Revealed Preference Data

Clercq

Binsbergen

Arem

et al. 2022

Journal of Advanced Transportation

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Mode choice behaviour is often modelled by discrete choice models, in which the utility of each mode is characterized by mode-specific parameters reflecting how strongly the utility of that mode depends on attributes such as travel speed and cost, and a mode-specific constant value. For new modes, the mode-specific parameters and the constant in the utility function of discrete choice models are not known and are difficult to estimate on the basis of stated preferences data/choice experiments and cannot be estimated on the basis of revealed preference data. This paper demonstrates how revealed preference data can be used to estimate a discrete mode choice model without using mode-specific constants and mode-specific parameters. This establishes a method that can be used to analyze any new mode using revealed preference data and discrete choice models and is demonstrated using the OViN 2017 dataset with trips throughout the Netherlands using a multinomial and nested logit model. This results in a utility function without any alternative specific constants or parameters, with a rho-squared of 0.828 and an accuracy of 0.758. The parameters from this model are used to calculate the future modal split of shared autonomous vehicles and electric steps, leading to a potential modal split range of 24–30% and 37–44% when using a multinomial logit model, and 15–20% and 33–40% when using a nested logit model. An overestimation of the future modal split occurs due to the partial similarities between different transport modes when using a multinomial logit model. It can therefore be concluded that a nested logit model is better suited for estimating the potential modal split of a future mode than a multinomial logit model. To the authors’ knowledge, this is the first time that the future modal split of shared autonomous vehicles and electric steps has been calculated using revealed preference data from existing modes using an unlabelled mode modelling approach.

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Enhancing the Energy Performance of Passive Building Through the Internet of Things

Khattabi¹,

Diouri²,

Mharzi

et al. 2020

Artificial Intelligence and Industrial Applications

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Building Automation into Urban and Metropolitan Mobility Planning

Cited by 8 publications

References 14 publications

Integrated modelling of autonomous electric vehicle diffusion: From review to conceptual design

Integrated modelling of autonomous electric vehicle diffusion: From review to conceptual design

Estimating the Potential Modal Split of Any Future Mode Using Revealed Preference Data

Enhancing the Energy Performance of Passive Building Through the Internet of Things

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