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 language | English (US) |
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Article number | 103065 |
Journal | Transportation Research Part C: Emerging Technologies |
Volume | 126 |
DOIs | |
State | Published - May 2021 |
Keywords
- Clustering
- Empirical data
- Macroscopic fundamental diagram (MFD)
- Prediction
ASJC Scopus subject areas
- Transportation
- Automotive Engineering
- Civil and Structural Engineering
- Management Science and Operations Research