Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
3-Dimensional Multi-Linear Transformation Based Multimedia Cryptosystem Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Traffic Flow Prediction Using Regression and Deep Learning Approach

Authors : Savita Lonare, R. Bhramaramba

Published in: New Trends in Computational Vision and Bio-inspired Computing

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Accurate prediction of traffic makes it easy to make decisions of travelling route, travelling schedule, travel vehicles choice for a commuter. The surveillance systems, GPS system installed on road way are the abundant source of traffic data. This huge amount of traffic data and increased computing power definitely motivates researchers to analyze the data to solve the road traffic and transportation problems. Deep learning always proved to be a good solution for prediction problems such as audio classification, signal processing, image classification and Network traffic prediction. This research shows that Long short term memory network can be used to design a traffic forecast model. Many researchers have proved that deep sigmoidal networks could be trained to produce good results for many tasks such as audio classification, signal processing, image classification and Network traffic prediction. In this work comparison of LSTM with Linear Regression, Logistics Regression and ARIMA was done.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Comparative Study on Various Techniques Involved in Designing a Computer Aided Diagnosis (CAD) System for Mammogram Classification
next chapter A Comparative Study on Assessment of Carotid Artery Using Various Techniques
Literature
1.
Levin, M., and Tsao Y.: On forecasting freeway occupancies and volumes. Transportation Research Record, Volume 773, pp. 47–49, Transportation Research Board (1980)
2.
Farokhi Sadabadi Kaveh Hamedi, Masoud Haghani, Ali, A.: Evaluating moving average techniques in short-term travel time prediction using an AVI data set, Transportation Research Board 89th Annual Meeting, Washington DC (2010)
3.
Mascha, V., D., Voort, Mark Dougherty, Susan, W.: Combining Kohonen maps with ARIMA time series models to forecast traffic flow, Transport. Res. C, pp 307–318 (1996)
4.
Lee, S., Fambro, D.: Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting, Trans. Res. rec, pp. 179–188 (1999)
5.
Williams, B., Multivariate vehicular traffic flow prediction: evaluation of ARIMAX modeling, Trans. Res. rec, vol 1776, pp. 194–200 (2001)
6.
Karlaftis, M.G., Vlahogianni, E.I.: Statistical methods versus neural networks in transportation research: Differences, similarities and some insights, Transport. Res. C Emerg. Technol., Vol 19, Issue 3, pp. 387–399 (2010)
7.
Okutani, I., Stephanedes, Y.J.: Dynamic prediction of traffic volume through kalman filtering theory, Transport. Res. B Methodol., vol-18, (1), pp. 1–11 (1984)
8.
Liu, H., Zuylen, H.J.V., Lint, H.V., et al.: Predicting urban arterial travel time with state-space neural networks and kalman filters, Transport. Res. Record, 1968, pp. 99–108 (2006)
9.
Andreas Rosenblad, J.J. Faraway.: Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models, Chapman and Hall, vol. 24, pp 369–370, Springer-Verlag (2009)
10.
Zhang, Y., Liu, Y.: Traffic forecasting using least squares support vector machines, Transportmetrica, vol 5, Issue 3, pp. 193–213 (2009)
11.
Yanru Zhang, Ali Haghani: A gradient boosting method to improve travel time prediction, Transport. Res. C Emer. Technol, 58, pp. 308–324, Elsevier (2015)
12.
Zou, Y., Hua, X., Zhang, Y., et al.: A Hybrid Method for Short-term Freeway Travel
13.
Markov, C., Can J., Civ. E.: Time Prediction Based on Wavelet Neural Network and vol 45, Issue 2, pp. 77–86 (2017)
14.
Li, S., Liu L.J., Zhai, M.: Prediction for short-term traffic flow based on modified PSO optimized BP neural network, Syst. Eng. Theory Practice, vol 9, pp. 2045–2049 (2012)
15.
Li Ying-hong, Liu Le-min, Wang Yu-quan: Short-term traffic flow prediction based on combination of predictive models, J. Transport. Syst. Eng. Inf. Tech., vol 13, issue 2, pp. 34–41 (2013)
16.
Zheng Zhao, Weihai Chen, Xingming Wu, Peter C. Y. Chen, Jingmeng Liu: LSTM network: a deep learning approach for short-term traffic forecast, IET Intelligent Transport Systems, vol 11, issue 2, pp. 68-75 (2017)
17.
Yipeng Liu, Haifeng Zheng, Xinxin Feng: Short-term traffic flow prediction with Conv-LSTM, WCSP-9, pp. 1-6, IEEE-China (2017)
18.
Arezou M-K. and Mo J.: Constructing a Deep Regression Model Utilizing Cascaded Sparse Autoencoders and Stochastic Gradient Descent, ICMLA, pp. 559-564 (2016)
19.
Arief, K., Ridha, S., Fakhreddine, K.: Improving Traffic Flow Prediction With Weather Information in Connected Cars: A Deep Learning Approach, TVT, vol 65, issue 12, pp. 9508-9517 (2016)
20.
Joko, L. B., Victor, H., Ahmad S, Saprina, M.: Generalized Regression Neural Network for Predicting Traffic Flow, ICTS 2016, pp. 199-202, IEEE (2016)
Metadata
Title
Traffic Flow Prediction Using Regression and Deep Learning Approach
Authors
Savita Lonare
R. Bhramaramba
Copyright Year
2020
Publisher
Springer International Publishing
Book
New Trends in Computational Vision and Bio-inspired Computing

Print ISBN: 978-3-030-41861-8

Electronic ISBN: 978-3-030-41862-5

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-41862-5_63

Premium Partner

    Image Credits