Development of Dynamic Traffic Assignment Framework for Heterogeneous Traffic Lacking Lane Discipline

Dynamic Traffic Assignment (DTA) works on an iterative framework to reach dynamic user equilibrium which states that all routes used by the travellers leaving the same origin at the same time for the same destination will have equal and minimum travel time. The prime component of any DTA model is the underlying traffic flow model which computes travel time of vehicles. For a heterogeneous traffic flow network with lacking lane discipline conditions, traditional DTA models fail to predict travel time output even with an acceptable level of accuracy. Any traffic management measures based on this incorrect output could actually worsen the traffic congestion problem. This paper uses a recently proposed multi-class continuum model for traffic flow propagation in DTA, and the framework is tested on a hypothetical network under three traffic scenarios. The simulated network-level traffic variables, namely, inflow and travel time on paths and total system travel time, are compared with that from a microsimulation result. The paper shows that the proposed DTA framework, with its macroscopic traffic flow modelling approach, yields accurate and more intuitive prediction of network-level traffic variables in the context of heterogeneous traffic lacking lane discipline.