Study case in LA
This work focuses on traffic congestion and unifies different approaches, perspectives, and fields into a single science of traffic. A century passed the age of the automobile, traffic networks have been described as engines of global growth and prosperity. However, their uninformed expansion is a leading cause of pollution and other negative externalities. The collapse of the traffic network, i.e. network-wide congestion, causes a loss in social and economic opportunities and increased carbon emissions; however, it is common across cities worldwide. In our endeavor towards greener and more livable cities, a clear understanding of traffic network congestion is necessary, to simplify science informed policy. A unified science of traffic networks facilitates the planning of cities from a social, environmental, and economic perspective.
Current models of urban traffic congestion implement either a network level or link level perspective. Both perspectives have proven useful in understanding urban traffic congestion – the network level perspective capturing the aggregated global system dynamics and the link level perspective accounting for the dynamics of individual roads. Despite the abundance of literature on both perspectives, they have not yet been unified into a single science of traffic consistently describing the dynamics of the congestion at both scales simultaneously. Traffic research combines diverse fields such as engineering, city planning, and physics. This has resulted in different fields preferring different analysis metrics which further hampers communication and research cohesion. In this work, we address these issues by connecting the two perspectives (link and network) for five major cities worldwide: Boston, Los Angeles, and San Francisco Bay Area in the United States, Rio de Janeiro in Brazil, and Lisbon in Portugal. We begin by modeling the traffic networks through state-of-the-art simulations spanning an extended morning traffic peak.
Study case in Rio de Janeiro
We then assess and connect different approaches and perspectives used by various fields to study traffic. In particular, we uncover a correspondence between network level traffic performance measures including the macroscopic fundamental diagram (MFD) as discussed by Helbing in Eur. Phys. J. B 70, 229-241 (2009) and the flow over capacity measure as proposed by Çolak et al. in Nature Comm. 7:10793 (2016). Due to its ability to assess the network state with minimal data, the MFD has received a lot of attention by traffic engineers since it allows to parsimoniously model, manage, and optimize traffic in urban networks. On the other hand, \Gamma, as proposed by network scientists, measures traffic network efficiency in terms of vehicles-travelled kilometers, thus appealing to urban planners. Therefore, by combining these measures, we unify efforts by different fields to understand and model traffic at the network level into a single actionable framework.Study case in Rio de Janeiro
Examples of link and network level perspectives compared.
Building upon this unification process, we use percolation theory, inspired by Li et al. in PNAS 112, 669-672 (2015), to characterize the growth of link level congestion from small isolated pockets of congestion to the sudden emergence of a single giant connected cluster of congestion. We then correlate this process with the evolution of the network level measures. Through this, for the first time, we directly connect the link level and network level perspectives as well as uncover the deeper meaning of features observed in the network level measures. Furthermore, because percolation is a tool for assessing network resilience, our results have the potential to enable planners to take advantage of the minimal data requirements of the network level measures to assess road network resilience. While traffic is typically studied with urban planning in mind, the actual impact of traffic congestion is on individual drivers. Thus, we relate our network level measures to their impact on individual drivers through the average percent congestion (equivalent to the TomTom Traffic Index). This process reveals a universal relationship which holds between cities around the world and should allow the same sparse network level data and resiliency models to be translated to the average driver.
Each of these results is interesting in its own right; when combined their implications are even more significant. The unification of perspectives, approaches and fields of study into a single Science of Traffic updates our understanding of urban traffic substantially. It allows traffic scientists to draw on knowledge from a coherent set of models and approaches and to communicate their results more efficiently to other researchers in the field.
Ambühl, L., Olmos, L. E., Menendez, M., & Gonzalez, M. C. (2020). The collapse of urban traffic networks (in preparation)
Lukas Ambuehl (ETH Zuerich), James Parker (UC Berkeley), Monica Menendez (NYU Abu Dhabi), Marta C. Gonzalez (UC Berkeley). Towards a science of traffic networks: A unifying approach (in review)