Stochastic macroscopic analysis and modelling for traffic management

Research output: ThesisDissertation (TU Delft)

28 Downloads (Pure)

Abstract

When congestion becomes a problem on a road or road network, there are generally three main solution areas available to tackle it: construction, pricing or traffic management. Traffic management became an increasingly preferred option towards the end of the twentieth century as an alternative to construction in many cases. Traffic management proves a more efficient alternative and focusses on influencing traffic flows such that the existing road and network capacity is more effectively utilised resulting in a reduction in congestion. The effectiveness of traffic management is dependent on the ability to influence traffic flow. However, traffic contains a relatively large amount of stochastic behaviour, which is connected to human driving behaviour. The fluctuations that occur in traffic flow due to this stochastic behaviour have a large effect on the effectiveness of traffic management. Furthermore, uncertainty between time dependant scenarios has also shown to have a large influence on the outcome of the analysis of traffic management measures. In the past, little attention has been paid to these effects. Therefore, the main objective of this thesis is to give insight into the stochastic fluctuations and uncertainty in traffic flow for the application of traffic management measures and to propose tools that allow these effects to be analysed and subsequently modelled in aggregated macroscopic flows. In doing this, the necessity to consider uncertainty and fluctuations for traffic management is also demonstrated. Stochastic processes are considered as uncertainty, which describes day-to-day uncertainties between traffic flows, and fluctuations, which describes microscopic variability in the traffic flow. Three main areas are focussed on: the analysis of variations in traffic, modelling fluctuations and uncertainty in traffic, and the visual communication of uncertainty from traffic models.
Original languageEnglish
Supervisors/Advisors
  • Hoogendoorn, S.P., Supervisor
  • Taale, H., Advisor
  • Snelder, M., Advisor
Award date26 May 2016
Place of PublicationDelft
Publisher
Print ISBNs978-90-5584-201-8
DOIs
Publication statusPublished - 26 May 2016

Fingerprint Dive into the research topics of 'Stochastic macroscopic analysis and modelling for traffic management'. Together they form a unique fingerprint.

  • Cite this