| 1. |
- 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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| 2. |
- 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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