The spatial variability of vehicle densities as determinant of urban network capacity

By Amin Mazloumian, Nikolaos Geroliminis, Dirk Helbing. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2010

348 citations recorded by the source corpus.

Due to the complexity of the traffic flow dynamics in urban road networks, most quantitative descriptions of city traffic so far have been based on computer simulations. This contribution pursues a macroscopic (fluid-dynamic) simulation approach, which facilitates a simple simulation of congestion spreading in cities. First, we show that a quantization of the macroscopic turning flows into units of single vehicles is necessary to obtain realistic fluctuations in the traffic variables, and how this can be implemented in a fluid-dynamic model. Then, we propose a new method to simulate destination flows without the requirement of individual route assignments. Combining both methods allows us to study a variety of different simulation scenarios. These reveal fundamental relationships between the average flow, the average density and the variability of the vehicle densities. Considering the inhomogeneity of traffic as an independent variable can eliminate the scattering of congested flow measurements. The variability also turns out to be a key variable of urban traffic performance. Our results can be explained through the number of full links of the road network, and approximated by a simple analytical formula.

Subfields: Building and Construction, Control and Systems Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/336539a19d1e9d4e5fb9023fa9aa9a9518fe219b

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Citations in the corpus

Optimal Perimeter Control for Two Urban Regions With Macroscopic Fundamental Diagrams: A Model Predictive Approach (2013, background): “Property 2 is important for control purposes, because efficient active traffic management schemes can be developed without a detailed knowledge of origin–destination (OD) tables.”
Perimeter and boundary flow control in multi-reservoir heterogeneous networks (2013, background): “This is in accordance with previous observations, see e.g. Mazloumian et al. (2010) for the effect of variance, Gayah and Daganzo (2011b) and Mahmassani et al. (2013) for the efficiency of simple networks with somewhat adaptive drivers and the effect of DTA, respectively.”
A Bathtub Model of Downtown Traffic Congestion (2013, background): “Thus, (21) reduces to α− [ρ+ βt](v + kv ′) L = 0 .”
On the spatial partitioning of urban transportation networks (2012, background): “With respect to property (3), recent findings from empirical and simulated data (Geroliminis and Sun, 2011; Mazloumian et al., 2010) have identified the spatial distribution of vehicle density in the network as one of the key components that affect the scatter of an MFD and its shape.”
Stochastic Approximations for the Macroscopic Fundamental Diagram of Urban Networks (2015, background): “…Ladier, 2009; Daganzo et al., 2011), (ii) analytical (Daganzo and Geroliminis, 2008; Leclercq et al., 2014), and (iii) simulation (Ji et al., 2010; Mazlomian et al., 2010; Geroliminis and Boyacı, 2013; Haddad and Geroliminis, 2012; Haddad et al., 2013; Knoop and Hoogendoorn, 2011; Knoop et al.,…”
Hysteresis Phenomena of a Macroscopic Fundamental Diagram in Freeway Networks (2011, background): “Despite the fact that capacity drop is not observed in individual arterial links because of the signals effect, arterial networks can also experience hysteresis loops if there is large heterogeneity in the density distribution (see for example Mazloumian et al., 2010 or Daganzo et al., 2011).”
Traffic jams: dynamics and control (2010, methodology): “For example, using the FD in figure 3a for road sections between traffic lights, one may determine 'integrated' FDs for the whole network and study the effects of different route planning and signalling strategies (Daganzo & Geroliminis 2008; Helbing 2009; Mazloumian et al. 2010).”
Exploring Properties of Networkwide Flow–Density Relations in a Freeway Network (2012, background): “Other studies provided insight on the effect of variability in urban systems on network capacity (32, 33).”
Robust Constrained Control of Uncertain Macroscopic Fundamental Diagram Networks (2015, background): “…for the existence of well-defined MFDs for homogeneous urban networks with small variance of link densities Godfrey (1969); Daganzo (2007); Geroliminis and Daganzo (2008); Geroliminis and Sun (2011b); Buisson and Ladier (2009); Ji et al. (2010); Mazloumian et al. (2010); ∗ Corresponding author.”
Dynamics of heterogeneity in urban networks: aggregated traffic modeling and hierarchical control (2015, background): “While various studies have investigated the effect of heterogeneity in the MFD e.g. Geroliminis and Sun (2011a), Mazloumian et al. (2010), Knoop et al. (2013), and Mahmassani et al. (2013) a functional form is necessary as these MFD dynamics have to be integrated in a control framework.”