Understanding sudden traffic jams: From emergence to impact

Road traffic jams are a major problem in most cities of the world, resulting in massive delays, increased fuel wastage, and monetary and productivity losses. Unlike conventional computer networks, which experience congestion due to excessive traffic, road transportation networks can experience traffic jams over prolonged periods due to traffic bursts over short time scales that push the traffic density beyond a threshold jam density. We observe that the emergence of such jams can happen over a very short duration, hence we term them as sudden traffic jams. We provide a formalism for understanding the phenomena of sudden traffic jams and show evidence of its existence using loop detector data from New York City. Further, we show the signature of sudden jams when observed at hourly resolution. We also provide a method to compute the traffic curve in a situation where we do not have access to fine-grained flow and density information. With this method, using only hourly speed data from Uber, we compute traffic curves for the road segments in Nairobi, São Paulo, and New York City, which is, by our knowledge, the first attempt to do so for signalized road networks. Running our analysis on the Uber movement speed data for the three cities, we show numerous instances of jams that last several hours, and sometimes as long as 2–3 days. Empirically, we find that Nairobi experiences 3x the mean jam time per road segment as compared to São Paulo and New York City. Based on key development metrics, we find that the ratio of traffic load per road segment for São Paulo, New York City, and Nairobi is approximately 1:2:3. We propose that chaotic driving patterns and traffic mismanagement in the developing world cities lead to tighter traffic curves, more intense jams and overall lower road capacity utilization, which explains the observed data. We posit that the problem of traffic congestion in developing countries cannot be solved entirely by building new infrastructure, but also requires smart management of existing road infrastructure.