TY - JOUR
T1 - Understanding sudden traffic jams
T2 - From emergence to impact
AU - Bhardwaj, Ankit
AU - Iyer, Shiva R.
AU - Ramesh, Sriram
AU - White, Jerome
AU - Subramanian, Lakshminarayanan
N1 - Funding Information:
The work done by the authors Ankit Bhardwaj, Shiva Iyer, Sriram Ramesh, Jerome White, and Lakshminarayanan Subramanian in this paper has been supported by funding in part from the NYU CTED , 4 4 and the industrial affiliates in the NYUWIRELESS research group 5 5 that funded Shiva Iyer in part as well as the air quality sensors used in the study. Shiva was also funded in part by an NSF Grant (award number OAC-2004572 ) titled “A Data-informed Framework for the Representation of Sub-grid Scale Gravity Waves to Improve Climate Prediction”. Shiva Iyer has also been supported by the Dean’s Dissertation Fellowship and Graduate Teaching Assistantship in the Graduate School of Arts and Sciences at New York University. Ankit Bhardwaj was funded by the New York University Henry M. MacCracken Fellowship . Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of New York University or Wadhwani AI.
Publisher Copyright:
© 2022 The Authors
PY - 2023/11
Y1 - 2023/11
N2 - 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.
AB - 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.
KW - Road traffic
KW - Traffic curve
KW - Traffic jams
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U2 - 10.1016/j.deveng.2022.100105
DO - 10.1016/j.deveng.2022.100105
M3 - Article
AN - SCOPUS:85144561113
SN - 2352-7285
VL - 8
JO - Development Engineering
JF - Development Engineering
M1 - 100105
ER -