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Title: An Agent-Based Model to Simulate Motorcycle Behaviour in Mixed Traffic Flow
Author: Lee, Tzu-Chang
Awarding Body: Imperial College London (University of London)
Current Institution: Imperial College London
Date of Award: 2007
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This thesis presents a new model to simulate the movements ofmotorcycles in mixed traffic and to evaluate their impacts on the flow. The scope of this study covers several mathematical models for describing the vehicle-following and path choice behaviour of motorcycles, and a computer based simulation model to visually represent these mathematical models and to' obtain the simulation results. The new model of motorcycle behaviour consists ofa three behavioural components. Two vehicle-following models, based on collision avoidance principles, were developed to describe longitudinal following behaviour and oblique following behaviour. The longitudinal vehicle following model describes the gap maintenance behaviour of a motorcycle when it is progressing behind another vehicle in a lane, focusing, in particular, on the fact that a motorcycle can swerve easily to avoid the collision as the leading vehicle brakes suddenly. The oblique vehicle-following model describes the gap maintenance behaviour when a motorcycle is positioned at the rear left or rear right ofthe preceding vehicle. It was developed assuming that the safety distance was a function of the following angle, the following speed and the speed difference betweep the two vehicles. Finally, a path choice model describes the decision 'making behaviour of a motorcyclist when choosing a route to overtake the preceding vehicle. This overtaking behaviour was modelled using a discrete choice model. The parameters of these component models were estim~ed based using data from an extensive video survey of motorcycle activity undertaken in London. These models were implemented in an agent-based simulation program developed to simulate the behaviour of mixed traffic flow consisting ofmotorcycles and the other type ofvehicles. The simulation model was validated against real work video data and then used to explore the implications of a number of policy scenarios.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available