The urban spatial structure represents the distribution of public and private spaces in cities and how people move within them. Although it usually evolves slowly, it can change quickly during large-scale emergency events, as well as due to urban renewal in rapidly developing countries. Here we present an approach to delineate such urban dynamics in quasi-real time through a human mobility metric, the mobility centrality index ΔKS. As a case study, we tracked the urban dynamics of eleven Spanish cities during the COVID-19 pandemic. The results revealed that their structures became more monocentric during the lockdown in the first wave, but kept their regular spatial structures during the second wave. To provide a more comprehensive understanding of mobility from home, we also introduce a dimensionless metric, KS HBT , which measures the extent of home-based travel and provides statistical insights into the transmission of COVID-19. By utilizing individual mobility data, our metrics enable the detection of changes in the urban spatial structure.

Since the outbreak of the 2019 novel coronavirus (COVID-19) pandemic, governments have been implementing containment measures aimed at mitigating the spread of the virus, including restrictions to human mobility. The ability to adapt to the pandemic and respond to containment measures can be bound by socioeconomic conditions, which are heterogeneous in large urban areas of low-income and middle-income countries. In this paper, we analyse mobility changes following the implementation of containment measures in Bogotá, Colombia. We characterise the mobility network before and during the pandemic and analyse its evolution and changes between January and July 2020. We observe a general reduction in mobility trends, but the overall connectivity between different areas of the city remains after the lockdown, reflecting the resilience of the mobility network. Then, we estimate a gravity model to assess the effect of socioeconomic conditions on mobility flows. We find that the responses to lockdown policies depend on the socioeconomic conditions of the population. Before the pandemic, the population with better socioeconomic conditions shows higher mobility flows. Since the lockdown, mobility presents a general decrease, but the population with worse socioeconomic conditions shows lower reductions in mobility flows. We conclude by deriving policy implications.

Cities around the world are turning to non-motorized transport alternatives to help solve congestion and pollution issues. This paradigm shift demands on new infrastructure that serves and boosts local cycling rates. This creates the need for novel data sources, tools, and methods that allow us to identify and prioritize locations where to intervene via properly planned cycling infrastructure. Here, we define potential demand as the total trips of the population that could be supported by bicycle paths. To that end, we use information from a phone-based travel demand and the trip distance distribution from bike apps. Next, we use percolation theory to prioritize paths with high potential demand that benefit overall connectivity if a bike path would be added. We use Bogota as a case study to demonstrate our methods. The result is a data science framework that informs interventions and improvements to an urban cycling infrastructure.

The era of the automobile has seriously degraded the quality of urban life through costly travel and visible environmental effects. A new urban planning paradigm must be at the heart of our roadmap for the years to come. The one where, within minutes, inhabitants can access their basic living needs by bike or by foot. In this work, we present novel insights of the interplay between the distributions of facilities and population that maximize accessibility over the existing road networks. Results in six cities reveal that travel costs could be reduced in half through redistributing facilities. In the optimal scenario, the average travel distance can be modeled as a functional form of the number of facilities and the population density. As an application of this finding, it is possible to estimate the number of facilities needed for reaching a desired average travel distance given the population distribution in a city.

Stories of mega-jams that last tens of hours or even days appear not only in fiction but also in reality. In this context, it is important to characterize the collapse of the network, defined as the transition from a characteristic travel time to orders of magnitude longer for the same distance traveled. In this multicity study, we unravel this complex phenomenon under various conditions of demand and translate it to the travel time of the individual drivers. First, we start with the current conditions, showing that there is a characteristic time τ that takes a representative group of commuters to arrive at their destinations once their maximum density has been reached. While this time differs from city to city, it can be explained by Γ, defined as the ratio of the vehicle miles traveled to the total vehicle distance the road network can support per hour. Modifying Γ can improve τ and directly inform planning and infrastructure interventions. In this study we focus on measuring the vulnerability of the system by increasing the volume of cars in the network, keeping the road capacity and the empirical spatial dynamics from origins to destinations unchanged. We identify three states of urban traffic, separated by two distinctive transitions. The first one describes the appearance of the first bottlenecks and the second one the collapse of the system. This collapse is marked by a given number of commuters in each city and it is formally characterized by a nonequilibrium phase transition.

Inspired by an old and almost in oblivion urban plan, we report the behavior of the Biham-Middleton-Levine (BML) model-a paradigm for studying phase transitions of traffic flow-on a hypothetical city with a perfect honeycomb street network. In contrast with the original BML model on a square lattice, the same model on a honeycomb does not show any anisotropy or intermediate states, but a single continuous phase transition between free and totally congested flow, a transition that can be completely characterized by the tools of classical percolation. Although the transition occurs at a lower density than for the conventional BML, simple modifications, like randomly stopping the cars with a very small probability or increasing the traffic light periods, drives the model to perform better on honeycomb lattices. As traffic lights and disordered perturbations are inherent in real traffic, these results question the actual role of the square gridlike designs and suggest the honeycomb topology as an interesting alternative for urban planning in real cities.

The Biham-Middleton-Levine (BML) traffic model, a cellular automaton with eastbound and northbound cars moving by turns on a square lattice, has been an underpinning model in the study of collective behavior by cars, pedestrians, and even internet packages. Contrary to initial beliefs that the model exhibits a sharp phase transition from freely flowing to fully jammed, it has been reported that it shows intermediate stable phases, where jams and freely flowing traffic coexist, but there is no clear understanding of their origin. Here, we analyze the model as an anisotropic system with a preferred fluid direction (northeast) and find that it exhibits two differentiated phase transitions: the system is either longer in the flow direction (longitudinal) or perpendicular to it (transversal). The critical densities where these transitions occur enclose the density interval of intermediate states and can be approximated by mean-field analysis, all derived from the anisotropic exponent relating the longitudinal and transversal correlation lengths. Thus, we arrive at the interesting result that the puzzling intermediate states in the original model are just a superposition of these two different behaviors of the phase transition, solving by the way most mysteries behind the BML model, which turns out to be a paradigmatic example of such anisotropic critical systems.

Some of the most important questions concerning the traffic flow theory are focused on the correct functional form of the empirical flow-density fundamental diagram. Although most cellular automata intend to reproduce this diagram by measuring the limit steady-states from the dynamic simulation, real roads are constantly perturbed by external factors, driving the system to explore a much broader phase space. Hereby, we show that a Monte Carlo sampling of all states compatible with a driving rule (previously derived for Bogota) actually reproduces the measured fundamental diagram, both in mean values and dispersion, when all such states are assumed equally probable. Even more, by using the Wardrop's relation, the same gathered data also approximates the general form of the time-mean fundamental diagrams. These results suggest that driving rules are much richer in information than usually expected and, that the assumption of equally probable states plus a finite length of road may be a first model for the statistical description of highways.

La aglomeración industrial es uno de los factores que favorecen el crecimiento económico de las naciones. El presente trabajo muestra algunos resultados empíricos que buscan determinar la existencia de aglomeraciones industriales en Bogotá junto con sus municipios aledaños. La metodología usada está basada en el modelo de aglomeración industrial “tablero de dardos” presentado por Ellison y Glaeser (1997). Para ello se tomaron microdatos del año 2005 de la Encuesta Anual Manufacturera (EAM) del Departamento Administrativo Nacional de Estadística (DANE) y se determinó la localidad de cada establecimiento. Así, tomando las variables localidad y empleo fue posible calcular el índice de aglomeración para cada industria definida en la tercera revisión del código de Clasificación Industrial Internacional Uniforme (CIIU). Los resultados revelan que en promedio no es posible hablar de gran aglomeración industrial en el Área Metropolitana de Bogotá.

We introduce cellular automaton models for both cars alone [1] and mixed traffic (cars and buses) on motorways in Bogotá. Our model includes three elements: hysteresis between acceleration and braking gaps, a delay time in the acceleration, and instantaneous braking. In addition, we include a lane changing rule and the disordered behavior of Bogotan bus drivers. The parameters of our model were obtained from direct measurements on a car and a bus in this city. We use this model to simulate the flux-density fundamental diagram for a singlelane road with car traffic and a two-lane road with mixed traffic, and compare the results with experimental data. Our simulations are in very good agreement with experimental measurements, and reproduce both the shape and the value of the maximal flux. Moreover, they show that the causes of the measured high fluxes are the short gaps that the Bogotan drivers are used to maintain to the car ahead (the agressive driving that is typical for this city).