Dynamic clustering and propagation of congestion in heterogeneously congested urban traffic networks
Abstract
Developing a static clustering that minimises heterogeneity and guarantees connectivity. Formulating and solving an MILP that truncates a feasible set of snakes with the aforementioned objectives. Extending the method to dynamic clustering that considers spatial interactions. Merging or Splitting allows for a better fine-tuning in the dynamic framework. Fast computation and proper integration of physical properties of congestion propagation.
- Publication:
-
Transportation Research Part B: Methodological
- Pub Date:
- November 2017
- DOI:
- 10.1016/j.trb.2017.08.021
- Bibcode:
- 2017TRPB..105..193S
- Keywords:
-
- Graph partitioning;
- Congestion propagation;
- Asggregate modeling;
- Mixed integer linear programming;
- Snake algorithm