Artificial Neural Network Based Real-Time Urban Road Traffic State Estimation Framework

  • Ayalew Belay Habtie
  • Ajith Abraham
  • Dida Midekso
Chapter
First Online:
Part of the Studies in Computational Intelligence book series (SCI, volume 676)

Abstract

With the rapid increase of urban development and the surge in vehicle ownership, urban road transport problems like traffic accident and congestion caused huge waste of time, property damage and environmental pollution in recent years. To address these problems, use of information communication technology-based transport systems that can support maximum utilization of the existing road transport infrastructure has been proposed by different researchers. Road monitoring systems are one of these solutions which support road users to make informed decisions. However, the current road traffic monitoring systems use road side infrastructures for road traffic data collection and these technologies lack accurate and up-to-date traffic data covering the whole road network. By comparison, cellular networks are already widely deployed and can provide large road network coverage. Besides, 3G and 4G cellular networks provide mobile phone positioning facility with better performance accuracy and this opportunity can help to obtain accurate traffic flow information in cost effective manner on the entire road networks. The purpose of this chapter is to present our approach for real-time road traffic state estimation framework using the existing cellular network for road traffic data source and a neural network state estimation model. To evaluate the performance of the Artificial Neural Network model (ANN) both simulation and real world data is applied. The estimation accuracy using MAE and estimation availability indicated that reliable link speed estimation can be generated using this model and the estimated data can help to indicate real-time urban road traffic condition.

Keywords

Cellular network Positioning technology Artificial Neural Network Framework State estimation 
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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ayalew Belay Habtie
    • 1
  • Ajith Abraham
    • 2
  • Dida Midekso
    • 1
  1. 1.Department of Computer ScienceAddis Ababa UniversityAddis AbabaEthiopia
  2. 2.Machine Intelligence Research Labs (MIR Labs)WashingtonUSA

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