A pervasive prediction model for vehicular ad-hoc network (VANET)

The benefit of wireless mobile communication research, especially Car to Car (C2C) communication is to abandon the expensive wireline-deployed and central processing units. It is based on principles of mobile ad-hoc network (MANET) and applies to the domain of vehicles, being Vehicular ad-hoc network (VANET) which is a key component of ITS. This project builds and investigates the behaviour of a pervasive traffic simulation model in Ad-hoc network, with a particular part of it embedded into each vehicle’s equipment. It includes the data collection, aggregation and application aim to be running in all individual cars so that cars have up to date information on the traffic at all times. Moreover, those cars could predict the traffic conditions of a road section in a short time through the proposed prediction framework, especially travel speed prediction. When the car receives the current traffic information about other vehicles, the prediction system will incorporate the information, analyse the data and predict the traffic conditions of this road section for a future time. This process will be aided by car to car wireless communication technology available nowadays. To achieve this goal, a mobility model adapted to VANET needs to be generated that a realistic city scenario based on the actual traffic traces is carried out through simulation. Based on this, we investigate the necessary influencing factors for predicted results. The simulation results illustrate that the prediction model can be applied to wireless network environment for a short time prediction, and our results demonstrate the viability and effectiveness of the proposed prediction framework over Car to Car communications. Furthermore, the wireless environment and derived factors can result in decreased application performance.

• Record URL:
  http://irep.ntu.ac.uk/id/eprint/31951
• Supplemental Notes:
  • PhD thesis
• Authors:
  • Zhang, Q
• Publication Date: 2017

Media Info

• Pagination: 1 file

Subject/Index Terms

• TRT Terms: Data analysis; Forecasting; Mathematical models; Traffic flow; Vehicle to vehicle communications
• ATRI Terms: Data analysis; Forecast; Modelling; Traffic flow; Vehicle to vehicle communications
• ITRD Terms: 8735: Intelligent transport system
• Subject Areas: Operations and Traffic Management;

Filing Info

• Accession Number: 01660759
• Record Type: Publication
• Source Agency: ARRB
• Files: ATRI
• Created Date: Feb 20 2018 10:43AM