Estimation of Macroscopic Fundamental Diagram in Heterogeneous Traffic Conditions

In recent years, urban mobility gained importance for commuters' smooth, reliable, and safer movement. Traffic congestion, gridlock, and travel time delays are common in urban road networks, particularly in CBD. Efficient traffic planning and management are essential for the optimal performance of the transport system. The Macroscopic Fundamental Diagram (MFD) is an established model for assessing the network performance on a given demand. There are two approaches for MFD estimation, i.e., analytical and empirical. However, with the increasing complexity of traffic dynamics and new data collection techniques, the empirical methods gained greater support due to their practical application in large-scale road networks. In contrast, the analytical methods are limited to isolated case studies. Moreover, the uniqueness of an empirically estimated MFD cannot be guaranteed due to heterogeneity in the traffic conditions of the road network.

The heterogeneity in the traffic conditions affects the accuracy of the empirical MFD estimation. For accurate MFD estimation, homogeneity should be maintained in the road network. In the literature, various methods tried to decrease the heterogeneity, but maintaining homogeneity in urban road networks is impossible. The other way round is to include the effect of heterogeneity in the MFD estimation. The doctoral thesis is focused on empirical MFD estimation. In the first step, a model will be developed to include heterogeneity in the MFD estimation to determine the locations of traffic data collection. Further, the effect of road coverage of Loop Detector Data (LDD), road coverage and penetration rate of Floating Car Data (FCD) will be assessed on the MFD estimation of the urban road network.

Ultimately, the research outcome will highlight the locations of traffic data collection and optimum road coverage of LDD and FCD at different penetration rates of FCD for the accurate MFD estimation of heterogeneous urban road networks.