Abstract
Cross-sectional data is captured by stationary induction loops, radar, or infrared sensors. The collected information is provided either directly as single-vehicle data or aggregated into macroscopic quantities. In this chapter we define the measurable and derived quantities characterizing both data formats, with special attention on the difference between temporal and spatial averages. Traffic density, a spatially defined quantity, cannot be directly measured using cross-sectional detectors, but several estimation methods are presented and discussed. Speed estimation methods are introduced to overcome the inability of single-loop detectors to directly measure vehicle speed
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Notes
- 1.
The notation \(\langle \cdot \rangle \) is used for the arithmetic average in the context of measurements and for the expected value in the context of statistical considerations (Sect. 3.3).
- 2.
Notice that the term microscopic fundamental diagram generally denotes the gap as a function of the speed for steady-state traffic flow as given by microscopic models.
- 3.
Not to be confused with the Wardrop equilibrium, a concept in transportation planning where routes are chosen according to the user equilibrium, i.e., no user is better off when choosing a different route.
- 4.
Note that the denominator equals the total travel time (vehicle-minutes) of all vehicles in the referenced spatiotemporal interval.
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Further Reading
Further Reading
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Leutzbach, W.: Introduction to the Theory of Traffic Flow. Springer, Berlin (1988)
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Helbing, D.: Traffic and related self-driven many-particle systems. Reviews of Modern Physics 73 (2001) 1067–1141
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Cassidy, M.J.: Traffic Flow and Capacity. International Series in Operations Research & Management Science. In: Handbook of Transportation Science. Springer New York (2003) 155–191
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Treiber, M., Kesting, A. (2013). Cross-Sectional Data. In: Traffic Flow Dynamics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32460-4_3
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DOI: https://doi.org/10.1007/978-3-642-32460-4_3
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