Approximative Network Partitioning for MFDs from Stationary Sensor Data

Abstract

The macroscopic fundamental diagram (MFD) measures network-level traffic performance of urban road networks. Large-scale networks are normally partitioned into homogeneous regions in relation to road network topology and traffic dynamics. Existing partitioning algorithms rely on unbiased data. Unfortunately, widely available stationary traffic sensors introduce a spatial bias and may fail to identify meaningful regions for MFD estimations. Thus, it is crucial to revisit and develop stationary-sensor-based partitioning algorithm. This paper proposes an alternative two-step partitioning algorithm for MFD estimations based on information collected solely from stationary sensors. In a first step, possible partitioning outcomes are generated in the road networks using random walks. In a second step, the regions’ MFDs are estimated under every possible partitioning outcome. Based on previous work, an indicator is proposed to evaluate the traffic heterogeneity in regions. The proposed partitioning approach is tested with an abstract grid network and empirical data from Zurich. In addition, the results are compared with an algorithm that disregards stationary detectors’ biases. The results demonstrate that the proposed approach performs well for obtaining the quasi-optimal network partitions yielding the lowest heterogeneity among all possible partition outcomes. The presented approach not only complements existing literature, but also offers practice-oriented solutions for transport authorities to estimate the MFDs with their available data.