Stochastic models for unsignalized road traffic intersections

In this thesis, we contribute to the modeling and analysis of unsignalized priority-controlled road traffic intersections, on which vehicles on a ‘major' road have priority over vehicles on a ‘minor' road. Our models for such intersections are stochastic in nature to capture the inherent uncertainty about future arrivals of vehicles and their characteristics such as inter-vehicle-type heterogeneity (due to the vehicle’s features, in particular the size and acceleration speed of the vehicle), inter-driver heterogeneity (due to the driver characteristics such as age, gender, years of driving experience), intra-driver heterogeneity (due to the driver’s behavior in different situations), etc. In this thesis, these intersections are modeled by mathematical stochastic models, so-called gap-acceptance models in the framework of queueing theory. The basis of these models is the assumption that the crossing decision of a driver on the minor road is based on the time gap between two successive vehicles on the major road. These models can also be applied in other contexts as well, e.g. when analyzing freeways and pedestrian crossings. Specifically, we study a single server queueing model with batch arrivals and semi-Markov service times to investigate performance measures on the minor road such as the capacity, the queue length distribution and the delay distribution, and show the impact of the gap-acceptance strategy and the merging behavior of low-priority drivers on these performance measures. The main techniques used in the thesis are probability generating function analysis, heavy-traffic scaling and phase-type modeling.


  • English

Media Info

  • Pagination: 189p

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Filing Info

  • Accession Number: 01711595
  • Record Type: Publication
  • Source Agency: ARRB Group Limited
  • Files: ATRI
  • Created Date: Jul 22 2019 7:56AM