Understanding the relation between patterns of human mobility and the scaling of dynamical features of urban environments is a great importance for today's society. Although recent advancements have shed light on the characteristics of individual mobility, the role and importance of emerging human collective phenomena across time and space are still unclear. In this Article, we show by using two independent data-analysis techniques that the traffic in London is a combination of intertwined clusters, spanning the whole city and effectively behaving as a single correlated unit. This is due to algebraically decaying spatio-temporal correlations, that are akin to those shown by systems near a critical point. We describe these correlations in terms of Taylor's law for fluctuations and interpret them as the emerging result of an underlying spatial synchronisation. Finally, our results provide the first evidence for a large-scale spatial human system reaching a self-organized critical state. ince their appearance, cities have been one of the main catalysts of human economical, social and cultural development. In recent years, the formation and internal dynamics of urban environments 1 have been progressively regarded as the outcome of multilayered interactions of economic, infrastructural and social factors: a network of networks 2 very much akin to highly structured, biological organisms, and also sharing similar vulnerabilities 3 . In this analogy, transportation networks naturally identify with circulatory systems, supporting the urban ecosystem through enhanced mobility of people and goods [4] [5] [6] . Thus, in order to guarantee its smooth functioning, it is extremely important to be able to correctly describe and predict traffic dynamics at the metropolitan scale. In the eighty years since Greenshields's seminal paper on the Fundamental Diagram 7 , the relation between traffic density and flow, great leaps have been taken toward the understanding of the complicated non-linear phenomena that precede and follow the break down of free flow giving way to a variety of different congested states: stop-and-go waves, wide moving jams and extended jams 8, 9 . Alongside traffic flow theory, the impact of different travel behaviours and traffic control schemes have been studied in depth 10 , for example in connection with the effect of pre-travel and real time travel information and variable travel demands 11, 12 , real and perceived information uncertainty 13,14 and its cognitive cost , inequality among drivers 16 or different user responses to travel information 17 . More recently, attention shifted to the information feedback effects of advanced traffic control systems 18-20 and the most promising (non equilibrium assignment) routing methods based on decentralised collection and projection of real-time information [21] [22] [23] , usually inspired by swarm or ant colony methods . All of these methods focus on devising the best strategy to collect traffic information and forecast traffic conditions in order to minimise travel times. Data-driven studies on urban traffic at the city level are instead less common, due to the inherent technical difficulties in recording and managing large quantities of traffic data. Although recently this tendency has reversed due to increased availability of data for large urban areas, a macroscopic description of traffic in dense complex environments like modern world cities is still an open problem, warranting approaches inspired by a combination of traffic flow theory and statistical mechanics. The emblematic example is the current discussion about the existence and definition of a meaningful Macroscopic Fundamental Diagram (MFD), the relation between urban traffic density and resulting flow at the city level. Various proposals for a well-defined state space
Shortly, it is further confirmed in the sense of complex network based experiments that traffic fluctuations in London are correlated over the whole city REF .
Entangled communities and spatial synchronization lead to criticality in urban traffic
Scientific Reports
Scientific Reports
Computer Science Medicine