Optimization of Dynamic Congestion Pricing Strategy – A Case Study of Greater Manchester City

Abstract: With increasing urbanization, a future vision of urban mobility focuses on traffic
conditions and congestion mitigation on the road network. Traffic congestion,
particularly during peak hours, must be addressed in car-oriented cities; tolling
strategies are one of the critical tools to mitigate traffic. As a result, the goal of
this research is to formulate the optimal congestion pricing strategy by incorporating the
system's dynamic congestion into an optimization model. This study introduces a
dynamic congestion pricing strategy to improve system efficiency, which also
encourages users to make more effective travel decisions, which helps in traffic
management. To formulate the optimization, model this research employs a dynamic
traffic assignment model that incorporates the dynamic user equilibrium assignment for
the route choice. An objective function is formulated in an optimization model. The
objective function considered the dynamic congestion, parameters related to dynamic
traffic demand, and traffic emissions. Further, the optimization model is validated in
the mesoscopic simulation model in the network of Greater Manchester city in AIMSUN
Next software (traffic simulation software). We incorporated the results of the
simulation into the optimization model. We have utilized a state-of-art model-based
optimization paradigm named Bayesian optimization which tries to model the
underlying black-box function based on prior-posterior causality. The output of
the simulation and optimization model showed valuable insights in the traffic parameters
like traffic delay, and emissions in congested areas during peak hours. The
evolution in overall traffic performance with dynamic congestion toll is assessed with the
macroscopic fundamental diagram.