Disentangling the city traffic rhythms: A longitudinal analysis of MFD patterns over a year. (May 2021)

• Record Type: Journal Article
• Authors: Ambühl, Lukas; Loder, Allister; Leclercq, Ludovic; Menendez, Monica
• Is Part Of: Transportation research. Volume 126(2021)
• Publication Date: 2021-05
• Subjects: Macroscopic fundamental diagram (MFD) -- Clustering -- Empirical data -- Prediction; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2021.103065
• Languages: English
• ISSNs: 0968-090X

Abstract

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. Highlights: Proposing a similarity measure for MFDs based on dynamic time warping. Application to two one-year empirical loop detector datasets from Lucerne and Zurich. Clustering MFD patterns and link them to bottleneck activation patterns in the city.