Delays, inaccuracies and anticipation in microscopic traffic models

By Martin Treiber, Arne Kesting, Dirk Helbing. 2004

588 citations recorded by the source corpus.

We generalize a wide class of time-continuous microscopic traffic models to include essential aspects of driver behaviour not captured by these models. Specifically, we consider (i) finite reaction times, (ii) estimation errors, (iii) looking several vehicles ahead (spatial anticipation), and (iv) temporal anticipation. The estimation errors are modelled as stochastic Wiener processes and lead to time-correlated fluctuations of the acceleration.

Subfields: Control and Systems Engineering, Safety, Risk, Reliability and Quality, Transportation

View Paper: https://www.semanticscholar.org/paper/51ba013f34b461f4babbef47c3443bbcc3f718db

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In Control and Systems Engineering

In Safety, Risk, Reliability and Quality

In Transportation

Citations in the corpus

Stability and Stabilization of Systems with Time Delay. Limitations and Opportunities (2010, unknown): “188–202, 2004. [S12] R. J. Peterka, “Postural control model interpretation of stabilogram diffusion analysis,” Biol. Cybe rn., vol. 82, pp. 335–343, 2000. [S13] G. Stepan, Ed., “Delay effects in brain dynamics,” Philos. Trans. R. Soc. A, vol. 367, pp. 1059–1212, 2009. [S14] A. Goldbeter, Biochemical”
General Lane-Changing Model MOBIL for Car-Following Models (2007, background): “A more generic stochastic approach would be based on a car-following model that explicitly takes into account perception errors that lead to a subjective estimate for the utility and safety of a lane change (29).”
A new fundamental diagram theory with the individual difference of the driver’s perception ability (2012, methodology): “The classical fundamental diagram theory has been used to study the complex traffic phenomena from many different perspectives, such as the macroscopic model [33–37, 39–63, 66, 68–71] and microscopic model [38, 64, 65, 67].”
Incorporating human-factors in car-following models : a review of recent developments and research needs (2014, background): “Although the notion behind the model is a realistic one, this research direction received little attention in the literature until recently, when multi-vehicle interaction has re-gained some attention (Hoogendoorn and Ossen, 2005; Lenz et al., 1999; Peng and Sun, 2010; Treiber et al., 2006).”
Calibrating Car-Following Models by Using Trajectory Data (2008, background): “[14] according to x(t − Tr) = βxt−n−1 + (1 − β)xt−n, (14) where x denotes any input quantity such as s, v or ∆v (cf.”
Flow: A Modular Learning Framework for Mixed Autonomy Traffic (2017, background): “These equilibria have high velocities, which is desirable, but they are unstable due to properties of human driving behavior [106], and thus do not naturally occur.”
Adaptive cruise control design for active congestion avoidance (2008, methodology): “This is also relevant for manual driving because human drivers generally respond to the local traffic conditions as well (Treiber et al., 2006b).”
Estimating Acceleration and Lane-Changing Dynamics from Next Generation Simulation Trajectory Data (2008, background): “From this it can be concluded that lane-changing decisions are based not only on gaps and velocities but also on velocity differences and, possibly, on accelerations ( 19 ).”