Dynamic Traffic Assignment Using a Multi-class Continuum Model for Disordered Traffic

The traffic conditions in urban area consist of different vehicle types, which results in varying traffic dynamics for each vehicle class. The smaller vehicles can overtake larger vehicles and also move through the gaps between larger vehicles to traverse faster. The distinct dynamics of smaller and larger vehicles is a challenge to the traditional traffic assignment models which lack class-specific behavior. The routing of vehicles based on the travel time should capture these class-specific features to have a holistic view regarding typical urban traffic conditions. Traditional traffic assignment methods fail to reproduce conditions such as congestion, queue spill back, and bottleneck regions, thereby resulting in underestimation of real traffic scenarios. This study proposes a multi-class dynamic traffic assignment framework for disordered traffic to overcome the limitations of traditional traffic assignment. The framework is tested for different traffic conditions to deduce the class-specific behavior in multi-class traffic conditions. The results from the dynamic traffic assignment are compared to the traditional traffic assignment to account for the difference in travel time computations. The travel time plots obtained using dynamic traffic assignment shows that the vehicles can overcome low to mild levels of congestion by exhibiting overtaking and creeping behavior. This stands close to real traffic conditions where there is no much change in travel time unless heavy congestion. Thus, study justifies the necessity of class-specific features in travel time computation.