Complex Urban Road Networks: Static Structures and Dynamic Processes.

Abstract

We first abstract road networks as complex systems and employ tools from net- work science to study their topological properties. Our multi-scale analysis in- cludes macro-, meso-, and micro-scale perspectives, deriving insights into both common and unexpected patterns in these networks. At the macro-scale, we examine the global properties of these networks. We find correlations between various metrics and capture aspects such as the efficiency and robustness of the network. At the meso-scale, we explore the existence of a sub-structure embed- ded within the road networks using two main concepts, namely community and core-periphery structures. We found that while these densely populated city road networks show particularly strong community structures (high modularity values that are not typical to other networks), they exhibit a low level of presence of core- periphery structures. This points to the cities being polycentric. Finally, at the micro-scale, we find nodal-level properties of the network. Specifically, we com- pute the various centrality measures and examine their distributions to capture the prevalent characteristics of these networks. We find different but consistent distributions for the considered centrality measures. Then we delve into a comparative analysis of the efficacy of various node re- moval strategies in terms of inducing damage to the reliability of real-world urban road networks. The impact of five strategic (deterministic) based on centrality measures and two random (stochastic) node removal strategies on network ro- bustness is assessed through an iterative node removal process. We assess the robustness of road networks of densely populated cities using three differ- ent metrics: size of the largest connected component, global efficiency, and local efficiency. Our findings suggest that targeted disruptions utilising centrality mea- sures are more effective in disrupting the network than random ones. However, some centrality measures have a strong correlation with each other and thus, requiring combinations of different removal orders to gain more comprehensive insights into the ability of the network to withstand perturbations. We find cen- trality measures considering shortest paths are more effective in degrading the robustness of the network as a whole while centrality measures that only con- sider immediate connectivity are better in disrupting the local effectiveness of the network. Interestingly, we also find that removing nodes can counter-intuitively increase the local efficiency of the network. Lastly, we advocate the use of epidemic theory to model the spreading of traffic congestion in urban cities. Specifically, we use the Susceptible - Infected - Recov- ered (SIR) model but propose to explicitly consider the road network structure in the model to understand the contagion process of road congestion. This departs from the classical SIR model where homogeneous mixing based on the law of mass action is assumed. For this purpose, we adopt the N-intertwined modeling framework for the SIR model based on continuous-time Markov chain analysis. In our evaluation, we used two real-world traffic datasets collected in California and Los Angeles. We compare our results against both classical and average-degree- based SIR models. Our results show better agreement between the model and actual congestion conditions and shed light on how congestion propagates across a road network. We see the potential application of insights gained from this work on the development of traffic congestion mitigation strategies. In conclusion, our study comprehensively examines urban road networks by analysing both their static and dynamic aspects.