QROWD—A Platform for Integrating Citizens in Smart City Data Analytics

Ibáñez, Luis‐Daniel; Maddalena, Eddy; Gomer, Richard; Simperl, Elena; Zeni, Mattia; Bignotti, Enrico; Chenu-Abente, Ronald; Giunchiglia, Fausto; Westphal, Patrick; Stadler, Claus; Dziwis, Gordian; Lehmann, Jens; Yumuşak, Semih; Voigt, Martin; Sanguino, Maria-Angeles; Villazan, Javier; Ruiz, Ricardo Machado; Pariente-Lobo, Tomas

doi:10.1007/978-3-031-08815-5_16

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…To process big data on the operation of urban transport systems when studying sustainable active mobility such as Mobility-on-Demand systems [12,13], bike sharing [14], and others [15], machine learning methods, including neural network technologies, are used.…”

Section: Introductionmentioning

confidence: 99%

Studying Spatial Unevenness of Transport Demand in Cities Using Machine Learning Methods

Chainikov,

Zakharov,

Kozin

et al. 2024

Applied Sciences

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The article discusses the issues of spatial unevenness of transport demand in the city by various transport modes. It describes the creation of models using an artificial neural network to estimate the travel time and share by private and public transport in a large city that does not have off-street transport. The city transport macromodel in PTV Visum (V.18) was used as a data source, from which data were obtained on 50 basic parameters taken into account in the specialized software during the development of the transport model. In total, 50 factors act as independent variables that do not have linear relationships with each other and with the dependent variable, which significantly complicates the use of other models. These models allow assessing the influence degree of the most important factors. Further, the article shows the uneven spatial distribution of the shares of trips by private and public transport across the areas of a city. Using machine learning methods, the transport areas of Tyumen were clustered into nine classes belonging to the central sector, where the share of public transport is significantly higher than at the city border. The dependence of the trip share by cars and shuttle buses on the average travel time and distance by private and public transport for each class of transport areas has been established. The research results can be used when creating new transport areas in the city macromodel and when adjusting transport planning documents. The methods used for analyzing big data on the operation of the transport complex can be implemented in the digital twin of the city and the Intelligent Transport System.

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