A three-dimensional macroscopic fundamental diagram for mixed bi-modal urban networks

• Published in: Transportation research. Part C, Emerging technologies Emerging technologies, 2014-05, Vol.42, p.168-181
• Main Authors: Geroliminis, Nikolas, Zheng, Nan, Ampountolas, Konstantinos
• Format: Article
• Language: English
• Subjects: Bi-modal traffic; Clustering; Macroscopic fundamental diagram; Passengers; Public transport
• ISSN: 0968-090X; 1879-2359
• DOI: 10.1016/j.trc.2014.03.004

•Investigating the existence of a three-dimensional vehicle-flow MFD for bi-modal network.•Developing an analytical model for a three-dimensional passenger-flow MFD.•Introducing a state-dependent Bus–Car Unit Equivalent value.•Integrating a partitioning algorithm to unveil flow heterogeneity in bi-modal networks. Recent research has studied the existence and the properties of a macroscopic fundamental diagram (MFD) for large urban networks. The MFD should not be universally expected as high scatter or hysteresis might appear for some type of networks, like heterogeneous networks or freeways. In this paper, we investigate if aggregated relationships can describe the performance of urban bi-modal networks with buses and cars sharing the same road infrastructure and identify how this performance is influenced by the interactions between modes and the effect of bus stops. Based on simulation data, we develop a three-dimensional vehicle MFD (3D-vMFD) relating the accumulation of cars and buses, and the total circulating vehicle flow in the network. This relation experiences low scatter and can be approximated by an exponential-family function. We also propose a parsimonious model to estimate a three-dimensional passenger MFD (3D-pMFD), which provides a different perspective of the flow characteristics in bi-modal networks, by considering that buses carry more passengers. We also show that a constant Bus–Car Unit (BCU) equivalent value cannot describe the influence of buses in the system as congestion develops. We then integrate a partitioning algorithm to cluster the network into a small number of regions with similar mode composition and level of congestion. Our results show that partitioning unveils important traffic properties of flow heterogeneity in the studied network. Interactions between buses and cars are different in the partitioned regions due to higher density of buses. Building on these results, various traffic management strategies in bi-modal multi-region urban networks can then be integrated, such as redistribution of urban space among different modes, perimeter signal control with preferential treatment of buses and bus priority.