Measuring traffic flow using real-time data.
(Proceedings) TRB Traffic Flow Theory and Characteristics Committee (AHB45) Summer Meeting 2008: The Fundamental Diagram: 75 Years (Greenshields75 Symposium).
(pp. ? - ?).
TRB: Washington DC.
The theory of traffic flow based upon speed, flow and density that vary only slowly in space and time is well established. However, matching this theory up to field observations and extracting estimates of quantities of interest is not always straightforward. Spatial density of traffic is not measured readily, and inductive loops are often used instead to measure the proportion of a sampling period for which a vehicle is present, which is known as occupancy: the relationship between occupancy and density k is k = / L , where L is the mean effective length of a vehicle at the detector. According to this, correct interpretation of occupancy depends on the composition of the traffic that is measured. Estimates of the capacity of a road are most useful when they are expressed in units that are independent of traffic composition. In this paper, we show how the value of L can be estimated from the 1-minute point observations that are available from the Highways Agency MIDAS data collected on the UK motorway network. The value of this quantity was found to vary substantially over time during the day, between lanes on the road, and according to the control status of the road thus reflecting variations in traffic composition, and variations in lane usage. The consequences of this are discussed for interpretation and use of traffic data of this kind in estimating the speed-density relationship, capacity and related properties of a road section.
|Title:||Measuring traffic flow using real-time data|
|Event:||TRB Traffic Flow Theory and Characteristics Committee (AHB45) Summer Meeting 2008: The Fundamental Diagram: 75 Years (Greenshields75 Symposium)|
|Location:||Woods Hole, Massachusetts, USA|
|Dates:||2008-07-08 - 2008-07-10|
|Keywords:||Speed-density, Traffic modelling, Road traffic, Real-time data|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Civil, Environmental and Geomatic Engineering|
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