Dynamic Pedestrian Demand Estimation Using Data From Reidentification Sensors: A New Research Challenge.
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| Title: | Dynamic Pedestrian Demand Estimation Using Data From Reidentification Sensors: A New Research Challenge. |
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| Authors: | Díaz-Burgos, Elena1 (AUTHOR) elena.dburgos@uclm.es, Sánchez-Cambronero, Santos1 (AUTHOR), Gentili, Monica2 (AUTHOR), Rivas, Ana1 (AUTHOR), Miwa, Tomio (AUTHOR) miwa@nagoya-u.jp |
| Source: | Journal of Advanced Transportation. 12/15/2025, Vol. 2025, p1-30. 30p. |
| Subjects: | Genetic algorithms, Microsimulation modeling (Statistics), Flow sensors, Sustainable transportation, Transportation policy, Terminals (Transportation) |
| Abstract: | Many cities are currently working on the development of mobility policies aimed at improving the accessibility of transport infrastructures and the intermodality in the citizen's daily travel. Some of these policies should focus on obtaining a more sustainable modal split distribution in the access to and egress from multimodal transportation hubs. The first step to face this problem should be to obtain a good estimation of this actual modal split. Although different methods are available in the literature, this paper opens a new research challenge proposing to use models calibrated with data obtained from pedestrian reidentification devices as these models allow the direct reconstruction of pedestrian route flows. However, this topic is still a work in progress as the real data required to validate these models should, at the outset, come from reidentification sensors that are under development, and although there are cameras installed in some stations, they are not sensors that are useful for the postprocessing we are looking for. Indeed, among the few models found in the literature dealing with dynamic pedestrian demand estimation, none of them use data from reidentification sensors to reconstruct the OD‐matrix or to establish the pedestrian modal split in the access to and the egress from the station. To fill this gap, this paper sets out to establish the fundamentals of a new dynamic pedestrian estimation model using reidentification data and to propose a genetic algorithm for the determination of the best possible location of PRI sensors in an urban multimodal transportation hub. To do so, a methodology is proposed to use microsimulation tools to obtain realistic data for the development of this model as an alternative to real data until real devices are installed. To demonstrate its applicability, two small fictitious stations and the real case study of Getafe Central station are modeled to explain the method and to generate realistic scenarios that occur daily at train stations to virtually locate pedestrian recognition sensors capable of reidentifying users over several parts of their routes. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Advanced Transportation is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Header | DbId: egs DbLabel: Engineering Source An: 190257986 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Dynamic Pedestrian Demand Estimation Using Data From Reidentification Sensors: A New Research Challenge. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Díaz-Burgos%2C+Elena%22">Díaz-Burgos, Elena</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> elena.dburgos@uclm.es</i><br /><searchLink fieldCode="AR" term="%22Sánchez-Cambronero%2C+Santos%22">Sánchez-Cambronero, Santos</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gentili%2C+Monica%22">Gentili, Monica</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rivas%2C+Ana%22">Rivas, Ana</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Miwa%2C+Tomio%22">Miwa, Tomio</searchLink> (AUTHOR)<i> miwa@nagoya-u.jp</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Advanced+Transportation%22">Journal of Advanced Transportation</searchLink>. 12/15/2025, Vol. 2025, p1-30. 30p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Microsimulation+modeling+%28Statistics%29%22">Microsimulation modeling (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Flow+sensors%22">Flow sensors</searchLink><br /><searchLink fieldCode="DE" term="%22Sustainable+transportation%22">Sustainable transportation</searchLink><br /><searchLink fieldCode="DE" term="%22Transportation+policy%22">Transportation policy</searchLink><br /><searchLink fieldCode="DE" term="%22Terminals+%28Transportation%29%22">Terminals (Transportation)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Many cities are currently working on the development of mobility policies aimed at improving the accessibility of transport infrastructures and the intermodality in the citizen's daily travel. Some of these policies should focus on obtaining a more sustainable modal split distribution in the access to and egress from multimodal transportation hubs. The first step to face this problem should be to obtain a good estimation of this actual modal split. Although different methods are available in the literature, this paper opens a new research challenge proposing to use models calibrated with data obtained from pedestrian reidentification devices as these models allow the direct reconstruction of pedestrian route flows. However, this topic is still a work in progress as the real data required to validate these models should, at the outset, come from reidentification sensors that are under development, and although there are cameras installed in some stations, they are not sensors that are useful for the postprocessing we are looking for. Indeed, among the few models found in the literature dealing with dynamic pedestrian demand estimation, none of them use data from reidentification sensors to reconstruct the OD‐matrix or to establish the pedestrian modal split in the access to and the egress from the station. To fill this gap, this paper sets out to establish the fundamentals of a new dynamic pedestrian estimation model using reidentification data and to propose a genetic algorithm for the determination of the best possible location of PRI sensors in an urban multimodal transportation hub. To do so, a methodology is proposed to use microsimulation tools to obtain realistic data for the development of this model as an alternative to real data until real devices are installed. To demonstrate its applicability, two small fictitious stations and the real case study of Getafe Central station are modeled to explain the method and to generate realistic scenarios that occur daily at train stations to virtually locate pedestrian recognition sensors capable of reidentifying users over several parts of their routes. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Advanced Transportation is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1155/atr/2466045 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 30 StartPage: 1 Subjects: – SubjectFull: Genetic algorithms Type: general – SubjectFull: Microsimulation modeling (Statistics) Type: general – SubjectFull: Flow sensors Type: general – SubjectFull: Sustainable transportation Type: general – SubjectFull: Transportation policy Type: general – SubjectFull: Terminals (Transportation) Type: general Titles: – TitleFull: Dynamic Pedestrian Demand Estimation Using Data From Reidentification Sensors: A New Research Challenge. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Díaz-Burgos, Elena – PersonEntity: Name: NameFull: Sánchez-Cambronero, Santos – PersonEntity: Name: NameFull: Gentili, Monica – PersonEntity: Name: NameFull: Rivas, Ana – PersonEntity: Name: NameFull: Miwa, Tomio IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 12 Text: 12/15/2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 01976729 Numbering: – Type: volume Value: 2025 Titles: – TitleFull: Journal of Advanced Transportation Type: main |
| ResultId | 1 |
