Macroscopic Fundamental Diagrams: A cross-comparison of estimation methods

• Only Edie’s definitions applied on full trajectory data guaranty a proper estimation of the NFD.
• Mean network speed (or density) estimated from loop detectors can be significantly biased.
• Probe vehicles provide accurate estimate for mean network speed even for low sampling rate.
• A simple correction method can be applied to improve mean network density estimation from loop detectors.

This paper aims to cross-compare existing estimation methods for the Macroscopic Fundamental Diagram. Raw data are provided by a mesoscopic simulation tool for two typical networks that mimic an urban corridor and a meshed urban center. We mainly focus on homogenous network loading in order to fairly cross-compare the different methods with the analytical reference. It appears that the only way to estimate the MFD without bias is to have the full information of vehicle trajectories over the network and to apply Edie’s definitions. Combining information from probes (mean network speed) and loop detectors (mean network flow) also provides accurate results even for low sampling rate (<10%). Loop detectors fail to provide a good estimation for mean network speed or density because they cannot capture the traffic spatial dynamics over links. This paper proposes a simple adjustment technic in order to reduce the discrepancy when only loop detectors are available.

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