Estimation of capacity and critical density network by using re-sampled NFDs Case study: a part of Mashhad city roads network

Network Fundamental Diagram (NFD) can provide an aggregated simple vision of urban traffic networks; thereby, it is a robust tool for measuring traffic flow on the network scale. The NFDs are delineated by different parameters, amongst which the critical density and capacity are vital in the implementation of network-wide traffic control strategies such as pricing and perimeter control. Since heterogeneity effect exists in reality and casts uncertainty about the estimation of the NFD parameters, measuring and reducing the heterogeneity effect would improve the efficiency of network-wide traffic control strategies. In this paper, firstly, the NFD of Mashhad was estimated by fusion of collected data from Inductive loop detectors (ILD), Automatic Vehicle Locating system (AVL), and Automatic Fare Collection system (AFC). As NFD estimation needs data that could directly or indirectly derive average flows and densities/speeds, ILDs have been widely exploited to estimate average flows and AVLs were used to extract average speeds. Going through the current procedure, AFCs were operated for measuring dwell time and cruising bus speed. Eventually, a random re-sampled method was applied to decline the heterogeneity effect and reveal the congestion branch in re-sampled NFDs. The outcomes highlight the application of this method to estimate the critical density and capacity in a heterogeneous condition with limited data. Also, they emphasize that applying this method is quite simple and required pretty few inputs. For the validation purpose, the relative errors of estimated values are calculated. In this measurement, critical densities from daily observation were the actual values, and those from full observations were the expected values.