Demand Forecasting model based on artificial neural networks for Passenger Transportation Projects
Bibliographic Data
| ID | 5944309 |
|---|---|
| Authors | Victor Silva Vasconcelos (0000-0003-1112-8504, Universidade Nove de Julho), Filipe Quevedo-Silva (0000-0001-7494-4651, Universidade Federal de Mato Grosso do Sul), Ricardo Leonardo Rovai (0000-0001-9603-5096, Universidade Nove de Julho) |
| Year | 2021 |
| Volume | 13 |
| Publication date | 2021-01-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | urbe Revista Brasileira de Gestão Urbana (JOURNAL) |
| Journal identifiers | ISSN: 2175-3369 • E-ISSN: 2175-3369 |
| Publisher | FapUNIFESP (SciELO) (PUBLISHER) |
| DOI | 10.1590/2175-3369.013.e20200160 |
| OpenAlex | W3180051665 |
| SciELO | S2175-33692021000100244 |
| Language | EN |
| References cited | 20 |
Whereas passenger demand is one of the main risks in passenger transport infrastructure projects on track, this paper aims to propose a demand forecasting model based on artificial neural networks (ANN) in order to contribute to the project management still in its early planning stages. For this, the design of ex post facto type was used in a descriptive research with quantitative approach, where the research group was composed by subway and train stations in the metropolitan region of São Paulo-Brazil. In total, 12 ANN were proposed architectures with 15 different configurations, totaling 180 training processes, testing and validation. For each architecture has been identified the lowest mean square error percentage obtained; and the best architecture, with a hidden layer, was performed relevance analysis by Garson method, the model 4 input variables: the population; the school enrolment; the number of jobs; and per capita income. With the proposed model, one expects to contribute to the theory by adding to demand forecasting models using a robust methodology and, for managers, serve as a tool in studies of economic and financial viability of these projects, still in its planning phase anticipated as an investment decision-making tool
Artificial neural network · Demand forecasting · Economics · Geography · Metropolitan area · Operations research · Per capita · Population · Supply and demand · Transport engineering · Computer Science · Engineering · Traffic Prediction and Management Techniques · Transportation Planning and Optimization · Urban and Freight Transport Logistics · Artificial Intelligence
| Highly cited | No |
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