| 1. |
- Liao, Yuan, 1991, et al.
(författare)
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Impacts of charging behavior on BEV charging infrastructure needs and energy use
- 2023
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Ingår i: Transportation Research Part D: Transport and Environment. - : Elsevier BV. - 1361-9209. ; 116
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Tidskriftsartikel (refereegranskat)abstract
- Battery electric vehicles (BEVs) are vital in the sustainable future of transport systems. Increased BEV adoption makes the realistic assessment of charging infrastructure demand critical. The current literature on charging infrastructure often uses outdated charging behavior assumptions such as universal access to home chargers and the "Liquid-fuel" mental model. We simulate charging infrastructure needs using a large-scale agent-based simulation of Sweden with detailed individual characteristics, including dwelling types and activity patterns. The two state-of-art archetypes of charging behaviors, "Plan-ahead" and "Event-triggered," mirror the current infrastructure built-up, suggesting 2.3-4.5 times more public chargers per BEV than the "Liquid-fuel" mental model. We also estimate roughly 30-150 BEVs served by a slow charger may be needed for non-home residential overnight charging.
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| 2. |
- Tozluoglu, Çaglar, 1988, et al.
(författare)
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A synthetic population of Sweden: datasets of agents, households, and activity-travel patterns
- 2023
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Ingår i: Data in Brief. - 2352-3409. ; 48
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Tidskriftsartikel (refereegranskat)abstract
- A synthetic population is a simplified microscopic representation of an actual population. Statistically representative at the population level, it provides valuable inputs to simulation models (especially agent-based models) in research areas such as transportation, land use, economics, and epidemiology. This article describes the datasets from the Synthetic Sweden Mobility (SySMo) model using the state-of-art methodology, including machine learning (ML), iterative proportional fitting (IPF), and probabilistic sampling. The model provides a synthetic replica of over 10 million Swedish individuals (i.e., agents), their household characteristics, and activity-travel plans. This paper briefly explains the methodology for the three datasets: Person, Households, and Activity-travel patterns. Each agent contains socio-demographic attributes, such as age, gender, civil status, residential zone, personal income, car ownership, employment, etc. Each agent also has a household and corresponding attributes such as household size, number of children ≤ 6 years old, etc. These characteristics are the basis for the agents’ daily activity-travel schedule, including type of activity, start-end time, duration, sequence, the location of each activity, and the travel mode between activities.
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| 3. |
- Tozluoglu, Çaglar, 1988, et al.
(författare)
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Synthetic Sweden Mobility (SySMo) Model Documentation
- 2022
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- This document describes a decision support framework using a combination of several state-of-the-art computing tools and techniques in synthetic information systems, and large-scale agent-based simulations. In this work, we create a synthetic population of Sweden and their mobility patterns that are composed of three major components: population synthesis, activity generation, and location assignment. The document describes the model structure, assumptions, and validation of results.
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| 4. |
- Tozluoglu, Çaglar, 1988
(författare)
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Agent-based Transport Models as a Tool for Evaluating Mobility
- 2022
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The transportation system is undergoing fundamental transformations through emerging technologies. Some of these innovations have the potential to contribute to the sustainable transformation of the transportation system, such as electric vehicles (EVs) and shared autonomous electric vehicles (SEAVs). Before enacting policies to support these technologies or limit the use of undesirable ones, decision-makers need to better understand these innovations and the consequences of the policy to be implemented. This insight can be provided with models that are capable of reflecting the dynamics of new mobility, and interactions of travelers with each other and the infrastructure. This thesis describes the development of the Synthetic Swedish Mobility (SySMo) model that represents the travel behavior of an advanced synthetic population of Sweden, using an agent-based framework. The SySMo model provides a scaffold to build decision support tools through which present and future mobility scenarios can be analyzed and thus aid decision-makers in formulating informed policies. The SySMo model comprises a series of modules that utilize a stochastic approach combined with Neural Networks, a machine learning technique to generate a synthetic population and behaviorally realistic daily activity-travel schedules for each agent. The model first generates a synthetic replica of the population characterized by various socio-economic attributes using zone-level statistics and the national travel survey as input data. Then, daily heterogeneous activity patterns showing activity and trip features are assigned to each individual in the population with a high spatio-temporal resolution. To assess the SySMo model performance in each module, in-sample evaluations (i.e., comparing the model outputs with input data to measure the similarity of the results) and out-of-sample (i.e., comparing the model outputs with data never used in the model) evaluations are performed. The current model offers a valuable planning and visualization tool to illustrate mobility patterns of the Swedish population. The methodology can also be broadly applied to other regions with other relevant data and carefully calibrated parameters.
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