SavedAdd your journal

Journals / Sigma Mühendislik ve Fen Bilimleri Dergisi / 2017 / Cilt: 35 - Sayı: 1

A COMPREHENSIVE REVIEW FOR ARTIFICAL NEURAL NETWORK APPLICATION TO PUBLIC TRANSPORTATION

Engin PEKEL
Selin SONER KARA
Pages
157–179
Publication Date
2017-03-01
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This paper guides future research directions and presents the methods to be exerted in PT for input determination.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This paper presents a comprehensive review of research studies related to the application of artificial neural networks (ANNs) to public transportation (PT) since 2000.
📊
Key Findings & Data:More than a thousand studies have been viewed, however, 72 studies of PT are related to ANN.
💡
Conclusion & Impact:This paper guides future research directions and presents the methods to be exerted in PT for input determination.
🔬Design:Review & Systematic Survey
📍Scope:2000 Verileri
⚙️Variables:Artificial neural network · multi-layer perceptron · public transportation

Abstract

This paper presents a comprehensive review of research studies related to the application of artificial neural networks (ANNs) to public transportation (PT) since 2000. PT applications with ANNs have a great prominence because it provides an opportunity of prediction, comparison and evaluation in PT. A short introduction for applied studies in public transportation based on NN is included to guide the unfamiliar readers and a detailed review table has been presented in the paper. More than a thousand studies have been viewed, however, 72 studies of PT are related to ANN. It is observed that multi-layer feed forward network with gradient descent training has been commonly used by now. In contrast, the other less known methods are prone to increase. This paper guides future research directions and presents the methods to be exerted in PT for input determination.

🔖

Cite This Article

PEKEL, E., KARA, S. S. (2017). A COMPREHENSIVE REVIEW FOR ARTIFICAL NEURAL NETWORK APPLICATION TO PUBLIC TRANSPORTATION. Sigma Mühendislik ve Fen Bilimleri Dergisi, Cilt: 35 - Sayı: 1, 157–179. https://ajindex.com/articles/415885

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (10)

  1. Alan T. Murray, Rex Davis, Robert J. Stimson and Luis Ferreira. Public Transportation Access. Transportation Research Part D: Transport and Environment 1998; 3(5), 319-328, Doi: 10.1016/S1361-9209(98)00010-8
  2. James F. Sallis, Lawrence D. Frank, Brian E. Saelens and M. Katherine Kraft. Active transportation and physical activity: opportunities for collaboration on transportation and public health research. Transportation Research Part A: Policy and Practice 2004; 38(4), 249-268, Doi: 10.1016/j.tra.2003.11.003
  3. Anil K. Jain, Jianchang Mao and Mohiuddin K.M. Artificial neural networks: a tutorial. Computer 1996; 29(3), 31-44, Doi: 10.1109/2.485891
  4. Colin Fyfe. Artificial Neural Networks. Department of Computing and Information Systems, 1996
  5. Mei Chen, Xiaobo Liu, Jingxin Xia and Steven I. Chien. A Dynamic Bus-Arrival Time Prediction Model Based on APC Data. Computer-Aided Civil and Infrastructure Engineering 2004; 19(5), 364-376, Doi: 10.1111/j.1467-8667.2004.00363.x
  6. Abbas Khosravi, Ehsan Mazloumi, Saeid Nahavandi, Doug Creighton and J.W.C Van Lint. A genetic algorithm-based method for improving quality of travel time prediction intervals. Transportation Research Part C 2011; 19(6), 1364-1376, Doi: 10.1016/j.trc.2011.04.002
  7. B. Gültekin Çetiner, Murat Sari and Oğuz Borat. A Neural Network Based Traffic-Flow Prediction Model. Mathematical and Computational Applications 2010; 15(2), 269-278 [8] Sudarmanto Budi Nugroho, Akimasa Fujiwara and Junyi Zhang. An empirical analysis of the impact of a bus rapid transit system on the concentration of secondary pollutants in the roadside areas of the TransJakarta corridors. Stochastic Environmental Research and Risk Assessment 2011;25(5), 655-669, Doi: 10.1007/s00477-011-0472-x
  8. Fabio Stella, Vittorio Vigano, Davide Bogni and Matteo Benzoni. An Integrated Forecasting and Regularization Framework for Light Rail Transit Systems. Journal of Intelligent Transportation Systems 2006; 10(2), 59-73,Doi: 10.1080/15472450600626240
  9. Ehsan Mazloumi, Geoff Rose, Graham Currie and Majid Sarvi. An Integrated Framework to Predict Bus Travel Time and Its Variability Using Traffic Flow Data. Journal of Intelligent Transportation Systems 2011;15(2), 75-90, Doi: 10.1080/15472450.2011.570109
  10. J.K.K. Yuen, E.W.M. Lee, S.M. Lo and R.K.K. Yuen. An Intelligence-Based Optimization Model of Passenger Flow in a Transportation Station. IEEE Transactions on Intelligent Transportation Systems 2013; 14(3), 1290