Towards Developing an Integrated Microscopic Traffic Simulation Model for Large Road Networks
type: CreativeWork
description: Traffic jams have become one of the key topics in the 21st century and cause immense losses in productivity, increase CO2 emissions and affect driver stress, travel time predictability and increased wear and tear on vehicles. To test possible countermeasures, optimise existing infrastructure or develop new Intelligent Transport Systems (ITS), traffic has to be modelled. The inherent complexity which is a result of inter- and intra-driver heterogeneity, macroscopic feedback loops, local interactions, multi-modal transport and many more is tackled with simulations. By representing the (longitudinal) dynamics of individual vehicles (“microsimulation”), high model fidelity can be achieved. By aggregating the resulting trajectories, macroscopic phenomena emerge and can be incorporated to answer a wide range of traffic-related questions. Since human drivers vary in their perception of stimuli, preferences and reaction, randomness is inevitably and circumvents the construction of perfectly accurate models. Which microscopic features are needed and how they should be mathematically represented runs like a central thread through this thesis. First, it develops a systematic classification scheme to identify modelling strategies and evaluate advantages and shortcomings of (partly) discrete microsimulations. Because real-world trajectories are continuous in time and space, representing them discretely leads to artefacts which induces an upper precision boundary for all models operating on this level of discretisation. Such model-independent errors will be measured based on empirical, naturalistic and synthetically generated trajectories. It is also evaluated to which extent driver heterogeneity and randomness may be compensated with discrete components to simplify modelling and increase computational efficiency. Based on the gained insights, a new, integrated microscopic model is developed. The second major theme in this thesis are gridded, discrete road topologies (chequerboar…
abstract: Traffic jams have become one of the key topics in the 21st century and cause immense losses in productivity, increase CO2 emissions and affect driver stress, travel time predictability and increased wear and tear on vehicles. To test possible countermeasures, optimise existing infrastructure or develop new Intelligent Transport Systems (ITS), traffic has to be modelled. The inherent complexity which is a result of inter- and intra-driver heterogeneity, macroscopic feedback loops, local interactions, multi-modal transport and many more is tackled with simulations. By representing the (longitudinal) dynamics of individual vehicles (“microsimulation”), high model fidelity can be achieved. By aggregating the resulting trajectories, macroscopic phenomena emerge and can be incorporated to answer a wide range of traffic-related questions. Since human drivers vary in their perception of stimuli, preferences and reaction, randomness is inevitably and circumvents the construction of perfectly accurate models. Which microscopic features are needed and how they should be mathematically represented runs like a central thread through this thesis. First, it develops a systematic classification scheme to identify modelling strategies and evaluate advantages and shortcomings of (partly) discrete microsimulations. Because real-world trajectories are continuous in time and space, representing them discretely leads to artefacts which induces an upper precision boundary for all models operating on this level of discretisation. Such model-independent errors will be measured based on empirical, naturalistic and synthetically generated trajectories. It is also evaluated to which extent driver heterogeneity and randomness may be compensated with discrete components to simplify modelling and increase computational efficiency. Based on the gained insights, a new, integrated microscopic model is developed. The second major theme in this thesis are gridded, discrete road topologies (chequerboar…
author: Lehmann, Frank
license: https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm
date published: 2018
date modified: 2026-07-24T01:05:30Z
provider: University of Auckland
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: ResearchSpace@Auckland

PropertyValue
property ID: Handle
value: https://hdl.handle.net/2292/47096