Understanding Sudden Traffic Jams : A Tale of Two Cities

Road traffic jams are a major problem in most cities around the world, resulting in massive delays, increased fuel wastage and monetary losses to the tune of $100 billion in the US alone. Unlike conventional networks in computer systems, 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 illustrate this using real data from two different cities – New York and Nairobi. We provide a formalism for understanding the phenomena of traffic collapse and sudden jams. We also provide a method to compute the traffic curve in a situation where we do not have access to fine-grained data and density information. We observe from our analysis of the NYC data that road segments can be clustered into a certain finite set of categories, and that certain clusters are more susceptible to sudden jams than others. We also observe numerous instances of sudden jams in Nairobi from the Uber movement speed data, that result in congestion that last several hours, sometimes as high as 8-10 hours. Upon clustering of the segments into low-, medium- and high-congestion clusters, we notice that jams last the longest in the low-congestion clusters, comprising of highways