Sensitivity analysis of the LWR model for traffic forecast on large networks using Wasserstein distance
This work provides a method for sensitivity analysis of traffic flow models on networks, relevant for traffic engineers and modelers, but the findings are incremental as they apply existing sensitivity concepts to a specific model.
The paper investigates the sensitivity of the LWR traffic model on networks to parameters and network structure, using Wasserstein distance to quantify differences. Results show high sensitivity to junction traffic distribution, network size, and topology.
In this paper we investigate the sensitivity of the LWR model on network to its parameters and to the network itself. The quantification of sensitivity is obtained by measuring the Wasserstein distance between two LWR solutions corresponding to different inputs. To this end, we propose a numerical method to approximate the Wasserstein distance between two density distributions defined on a network. We found a large sensitivity to the traffic distribution at junctions, the network size, and the network topology.