UC Berkeley's Marta Gonzalez presented Data Science to Study Macroscopic Dynamics in Urban Traffic Networks at the ITS Berkeley Transportation Seminar March 16, 2018.
I present a review on research related to the application of big data and information technologies to urban systems. Data sources of interest include but are not limited to: Probe/GPS data, Credit Card Transactions, Traffic and Mobile phone data. Key uses of interest are modeling, adoption of new technologies and traffic performance measurements. In a second part a present multi-city study, we unravel traffic conditions 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 that takes to a representative group of commuters to arrive to their destinations once their maximum density has reached. While this time differs from city to city, it can be explained by the ratio of the vehicle miles traveled to their available street capacity. Moreover, we systematically characterize the macroscopic dynamic of the system by increasing 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 describing the appearance of the first bottle necks, and the second the transition to a complete collapse of the system. The transition to the second state measures the resilience of the various cities and is characterized by a non-equilibrium phase transition.
Marta González left Venezuela where she grew up to pursue a PhD in Computational Physics in Stuttgart Universitaet, as a selected fellow of the DAAD, the German agency for students’ exchange. Next, she moved to the U.S. to do a postdoc in the Barabasi Lab and initiated the study of patterns of human mobility with a complex systems’ perspective. She is currently Associate Professor in the Department of City and Regional Planning, UC Berkeley and a Research Scientist at LBNL.
Prior to coming to Berkeley, she was Associate Professor of Civil and Environmental Engineering at MIT. With support from several companies, cities, and foundations from around the world, her research team developed computational models to analyze digital traces of device-mediated information and estimate the demand on urban infrastructures in relation to energy and mobility. Her recent research uses billions of mobile phone records to understand the emergence of traffic gridlocks and the integration of electric vehicles in the power grid, records of smart meter data to compare policy of solar energy adoption, and credit card transactions to identify habits in spending behavior. Her research has been published in leading journals, including Science, PNAS, Nature, and Physical Review Letters.