Junction design rules: Improving junction design choices in urban traffic networks

Erwin Marco Bezembinder

Research output: ThesisPhD Thesis - Research external, graduation UT

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This thesis provides a solution for the two main deficiencies of the junction design assessment process, which is used to determine the best junction design for a specific location. First, junction design rules, as well as a suitable approach to determine and evaluate them, are provided which can be used to identify junction alternatives in the first stage of the assessment process. Datasets were generated containing the operational, safety and environmental performances for a wide range of junction design alternatives and traffic flow patterns. Pareto optimal sets of junction design alternatives were determined for each traffic flow pattern in the data. Decision tree methods were used in order to derive new rules from the data. Existing and new junction design rules were evaluated for two cases. Second, insights in the network effects of local selection methods and new rule based selection methods which can be used to determine network strategies are provided. The operational, safety and environmental network performances of different selection methods were determined and analysed using various modelling approaches incorporating route choice behaviour. Models for traffic assignment and local and global optimisation (formulated as a specific case of the road NDP) were used to evaluate existing and new (rule based) selection methods for two cases.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • University of Twente
  • van Berkum, Eric C., Supervisor
  • Wismans, Luc J.J., Co-Supervisor
Thesis sponsors
Award date5 Nov 2021
Place of PublicationEnschede
Print ISBNs978-90-365-5244-8
Electronic ISBNs978-90-365-5244-8
Publication statusPublished - 13 Oct 2021


  • Junction design
  • Decision Trees
  • Traffic assignment
  • Network design problem
  • Pareto optimal solutions
  • Genetic Algorithm
  • Method of successive weighted averages


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