Synthetic population and travel demand for Paris and Île-de-France based on open and publicly available data

Hörl, Sebastian; Balać, Miloš

doi:10.1016/j.trc.2021.103291

Cited by 82 publications

(58 citation statements)

References 41 publications

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“…A good overview of the current state of the art is given in [29], which expands the state of the art by building both a synthetic population and the arising travel demand with only open and publicly available data. The developed pipeline was also tested in [30].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The developed pipeline was also tested in [30]. The work in [29] makes a good case about why research in this area is needed, namely how other current approaches are rarely systematically tested and hardly transferable. While the aim and contribution are similar, some challenges are addressed in the presented paper that are not addressed in said work.…”

Section: Literature Reviewmentioning

confidence: 99%

“…While the aim and contribution are similar, some challenges are addressed in the presented paper that are not addressed in said work. For example, in [29], data concerning the commuting matrices were openly available and could be used as proxies. This is not the case in other locations (including Estonia), where other statistical information (e.g., the NACE margins) should be exploited instead.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This is not the case in other locations (including Estonia), where other statistical information (e.g., the NACE margins) should be exploited instead. Moreover, information related to businesses has a high level of detail in [29], while in this paper again statistical information and land use data are exploited as proxies to assign workplaces. Finally, the household travel surveys exploited in [29,30] had enough entries to allow correlations between socioeconomic features and commuting distances/anchors (consequently, it facilitates to build the daily activity schedules for the synthetic population).…”

Section: Literature Reviewmentioning

confidence: 99%

“…This was not the case in our work. Therefore, the method reported in this paper differs from [29], bypassing the need for an OD matrix and reliable entries in the household survey, even though both the motivation of the work and the aims are similar. Both works attempt to provide a runnable pipeline simple and as reproducible as possible, built only on publicly available data.…”

Section: Literature Reviewmentioning

confidence: 99%

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Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data

Agriesti

Roncoli

Nahmias-Biran

2022

IJGI

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Agent-based modeling has the potential to deal with the ever-growing complexity of transport systems, including future disrupting mobility technologies and services, such as automated driving, Mobility as a Service, and micromobility. Although different software dedicated to the simulation of disaggregate travel demand have emerged, the amount of needed input data, in particular the characteristics of a synthetic population, is large and not commonly available, due to legit privacy concerns. In this paper, a methodology to spatially assign a synthetic population by exploiting only publicly available aggregate data is proposed, providing a systematic approach for an efficient treatment of the data needed for activity-based demand generation. The assignment of workplaces exploits aggregate statistics for economic activities and land use classifications to properly frame origins and destination dynamics. The methodology is validated in a case study for the city of Tallinn, Estonia, and the results show that, even with very limited data, the assignment produces reliable results up to a 500 × 500 m resolution, with an error at district level generally around 5%. Both the tools needed for spatial assignment and the resulting dataset are available as open source, so that they may be exploited by fellow researchers.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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