Disentangling the city traffic rhythms: A longitudinal analysis of MFD patterns over a year

Lukas Ambühl, Allister Loder, Ludovic Leclercq, Monica Menendez

Research output: Contribution to journalArticlepeer-review

Abstract

Urban road transportation performance is the result of a complex interplay between the network supply and the travel demand. Fortunately, the framework around the macroscopic fundamental diagram (MFD) provides an efficient description of network-wide traffic performance. In this paper, we show how temporal patterns of vehicle traffic define the performance of urban road networks. We present two high-resolution traffic datasets covering a year each. We introduce a methodology to quantify the similarity of macroscopic traffic patterns. We do so by using the concepts of the MFD and a dynamic time warping (DTW) based algorithm for time series. This allows us to derive a few representative MFD clusters that capture the essential macroscopic traffic patterns. We then provide an in-depth analysis of traffic heterogeneity in the network which is indicative of the previously found clusters. Thereupon, we define a parsimonious classification approach to predict the expected MFD clusters early in the morning with high accuracy.

Original languageEnglish (US)
Article number103065
JournalTransportation Research Part C: Emerging Technologies
Volume126
DOIs
StatePublished - May 2021

Keywords

  • Clustering
  • Empirical data
  • Macroscopic fundamental diagram (MFD)
  • Prediction

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Automotive Engineering
  • Transportation
  • Computer Science Applications

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