**Reference:**

A. Jamshidnejad and
B. De Schutter,
"An algorithm for estimating the generalized fundamental traffic
variables from point measurements using initial conditions,"
*Transportmetrica B: Transport Dynamics*, 2018. To appear.

**Abstract:**

Fundamental macroscopic traffic variables (flow, density, and average
speed) have been defined and formulated in two different ways: the
classical definitions (defined as either temporal or spatial averages)
and the generalized definitions (defined as temporal-spatial
averages). The available literature has considered estimation of the
classical variables, while estimation of the generalized variables is
still missing. This paper proposes a new efficient sequential
algorithm for estimating the generalized traffic variables using point
measurements. The algorithm takes into account those vehicles that
stay between two consecutive measurement points for more than one
sampling cycle and that are thus not detected during these sampling
cycles. The algorithm is introduced for single-lane roads first, and
then is extended to multi-lane roads. For evaluation of the proposed
approach, NGSIM data, which provides detailed information on
trajectories of the vehicles on a segment of the interstate freeway
I-80 in San Francisco, California is used. The simulation results
illustrate the excellent performance of the sequential procedure for
estimating the generalized traffic variables compared with other
approaches.

Online version of the paper

@article{JamDeS:17-001,

author={A. Jamshidnejad and B. {D}e Schutter},

title={An algorithm for estimating the generalized fundamental traffic variables from point measurements using initial conditions},

journal={Transportmetrica B: Transport Dynamics},

year={2018},

note={To appear},

doi={10.1080/21680566.2017.1279991}

}

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