Predictive Demand Service for Public Transit Using CNN/ Atendimento Preditivo de Demanda do Transporte Público Coletivo Usando CNN

Authors

  • Willgnner Ferreira Santos
  • Leonardo Guerra de Rezende Guedes
  • Olegário Corrêa da Silva Neto
  • Euge?nio Julio Messala Ca?ndido Carvalho
  • Douglas Vieira do Nascimento

DOI:

https://doi.org/10.38152/bjtv3n4-003

Keywords:

Public transport, CNN and Computer Simulation.

Abstract

Several cities in Brazil undergo a territorial expansion and inhabitants constantly, this process is called urbanization. An uncontrolled urbanization generates many difficulties, highlighting the mobility of public transport, since many citizens depend on this mobility, we have, for example, public transport in Goiânia, which directly affects the living conditions of passengers. For your foreknowledge, a model capable of mirroring the performance of your demand is essential, providing that the system meets users in an acceptable way. A two-dimensional CNN is a CNN model that has a hidden convolutional layer that operates on a 1D sequence, it is a convenient mechanism to simulate a univariate forecast of time series of the predictive service of Goiânia's public transport. The method is equivalent to an analysis of the focal parts that make up the public transport system and how to represent it in the 1D convolutional neural network. Actual data of the systems and their results were compared to those expected, showing the model's effectiveness. This work manifests a forecast of the demand for public transport in Goiânia, to make it susceptible to users of the system.

 

References

SILVA, Julia Borges Correia et al. Algorithms applied to the multimodal transport problem. 2018.

PANDO, Luciano Urgal et at. Evaluation of estimation techniques of the origin-destination matrix of vehicle traffic in cities. 2018. Master Thesis. Universidade Tecnológica Federal do Paraná.

GABRIEL DA SILVA SOUSA, C. et al. Predictability of Poissonian Urban Public Traffic. Brazilian Journal of Development, Curitiba, p. 20, 2019. ISSN ISSN 2525-8761.

KHAZAII, Javad. Genetic Algorithm Optimization. In: Advanced Decision Making for HVAC Engineers. Springer International Publishing, 2016.

CARVALHO, C. H. R. D. Urban Mobility: Advances, Challenges and Perspectives. Brasília – DF: Livraria Ipea, 2016.

GUEDES, Ítalo César Montalvão et al. Probabilistic model for investigating the influence of bus stops on urban vehicle traffic noise. 2018.

CASTRO, G.M. The impact of public infrastructure components on the growth of Brazilian cities: a spatial analysis from the period from 1970 to 2010. 2016. Doctoral Thesis. Universidade de São Paulo.

BRAGA, M. L., SANTOS, A. J., PEDROZA, C. P., COSTA, L. H. K. M., Route Planning with Anytime Algorithms in Vehicle Networks on the Raspberry Pi Platform, IV Simpósio Brasileiro de Engenharia de Sistemas Computacionais, 2014.

GAVIRA, Muriel de Oliveira. Computer Simulation as a Knowledge Acquisition Tool. 2003. 163 f. Master Thesis - Production Engineering Course, Universidade de São Paulo, São Carlos, 2003. Disponível em: <https://teses.usp.br/teses/disponiveis/18/18140/tde-20052003-004345/publico/Gavira1.pdf>. Access in 03 mar. 2020.

SET, Union of Urban Passenger Transport Companies in Goiânia, disponível em http://www.sitpass.com.br/site/institucionaI/seV, Access in 01/03/2017.

M. D. Zeiler and R. Fergus, Computer Vision – ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I. Springer International Publishing, 2014, ch. Visualizingand Understanding Convolutional Networks, pp. 818–833.

Downloads

Published

2020-11-16

How to Cite

Santos, W. F., Guedes, L. G. de R., Neto, O. C. da S., Carvalho, E. J. M. C., & Nascimento, D. V. do. (2020). Predictive Demand Service for Public Transit Using CNN/ Atendimento Preditivo de Demanda do Transporte Público Coletivo Usando CNN. Brazilian Journal of Technology, 3(4), 146–159. https://doi.org/10.38152/bjtv3n4-003

Issue

Section

Original articles