An efficient approach to create agent-based transport simulation scenarios based on ubiquitous Big Data and a new, aspatial activity-scheduling model
| Autor: | Billy Charlton, Sebastian Hörl, Kai Nagel, Dominik Ziemke |
|---|---|
| Přispěvatelé: | Technische Universität Berlin (TU), Hasselt University (UHasselt), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Process (engineering) media_common.quotation_subject Distributed computing transport model Big data 02 engineering and technology [SPI.GCIV.IT]Engineering Sciences [physics]/Civil Engineering/Infrastructures de transport big data 0502 economics and business 0202 electrical engineering electronic engineering information engineering Quality (business) Agent-based transport simulation media_common 050210 logistics & transportation Scope (project management) activity-based demand models business.industry 05 social sciences Low input Simulation modeling Activity scheduling [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation Work (electrical) 020201 artificial intelligence & image processing business cell-phone data [PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis Statistics and Probability [physics.data-an] |
| Zdroj: | Transportation Research Procedia 23rd EURO Working Group on Transportation Meeting, EWGT 2020 23rd EURO Working Group on Transportation Meeting, EWGT 2020, Sep 2020, Paphos, Cyprus. pp.613-620, ⟨10.1016/j.trpro.2021.01.073⟩ |
| ISSN: | 2352-1465 |
| DOI: | 10.1016/j.trpro.2021.01.073⟩ |
| Popis: | International audience; Agent-based transport simulation models are a particularly useful tool to analyze demand-oriented transport policies and new mobility services, which have both gained significant attention lately. Since travel diaries, a traditional source to create the transport demand in agent-based transport models, are often hard to procure and not policy-sensitive, alternative approaches to creating travel demand representations for simulation scenarios are sought. In this study, a particularly efficient approach based on Big Data and a new, aspatial activity-based demand model with comparatively low input data requirements is established. Home, work, and education locations are informed based on mobile-phone-based origin-destination matrices. Other activity locations are modeled within the scope of the coevolutionary algorithm of the agent-based transport model, which is also responsible for finding suitable travel options of the modeled individuals. As a result, a comparatively lightweight process chain to create an agent-based transport simulation scenario is established, which is transferable to other regions. A basic quality evaluation of the created tool chain is carried out against a well-validated transport simulation model of the same region. |
| Databáze: | OpenAIRE |
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