An open data-driven approach for travel demand synthesis: an application to São Paulo

Autor: Miloš Balać, Aurore Sallard, Sebastian Hörl
Přispěvatelé: Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), IRT SystemX (IRT SystemX)
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Economics and Econometrics
Sociology and Political Science
Computer science
transport scenario
Geography
Planning and Development
transport simulation
agent-based models
eqasim
São Paulo
02 engineering and technology
Transport engineering
Regional economics. Space in economics
[SPI.GCIV.IT]Engineering Sciences [physics]/Civil Engineering/Infrastructures de transport
Regional planning
Downstream (manufacturing)
0502 economics and business
0202 electrical engineering
electronic engineering
information engineering
050210 logistics & transportation
05 social sciences
HT390-395
Metropolitan area
Pipeline (software)
[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
Open data
HT388
020201 artificial intelligence & image processing
Raw data
[PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis
Statistics and Probability [physics.data-an]
Zdroj: Regional Studies, Regional Sciences
Regional Studies, Regional Sciences, Taylor and Francis, 2021, 8 (1), pp.371-386. ⟨10.1080/21681376.2021.1968941⟩
Regional Studies, Regional Science, Vol 8, Iss 1, Pp 371-386 (2021)
Regional Studies, Regional Science, 8 (1)
ISSN: 2168-1376
Popis: This paper presents a synthetic travel demand for the Greater Sao Paulo Metropolitan Region of Brazil, entirely based on open data and representative of the observed travel demand. The open-source and extendable pipeline creates a path from raw data to the synthetic travel demand and, further, to the downstream agent-based mobility simulation. An advantage of this approach is that it enables the reproduction of the synthetic travel demand and, therefore, provides the foundation of repeatability of downstream studies. Furthermore, as the methodology is based on open data, the study's outcomes are easily accessible to the broad research and practice-oriented community.
Regional Studies, Regional Science, 8 (1)
ISSN:2168-1376
Databáze: OpenAIRE
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