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Stop-and-go waves induced by correlated noise in pedestrian models without inertia

Journal of Traffic and Transportation Engineering(English Edition) 2020年第1期7卷 52-60页

作者： Antoine Tordeux Andreas Schadschneider sylvain lassarre School of Mechanical Engineering and Safety Engineering University of WuppertalWuppertal 42119Germany Institute for Theoretical Physics University of CologneCologne 50937Germany Engineering of Surface Transportation Networks and Advanced Computing Laboratory French Institute of Science and Technology for TransportDevelopment and NetworksMarne La Vallee E-77447France

Stop-and-go waves are commonly observed in traffic and pedestrian *** most microscopic traffic models,they occur through a phase transition and instability of the homogeneous solution after fine tuning of *** effects are believed to play an important role in this *** this article,we present a novel explanation for stop-and-go waves based on stochastic effects in the absence of *** model used is a first order optimal velocity(OV)model including an additive stochastic noise.A power spectral analysis for single-file pedestrian trajectories highlights the existence of Brownian speed *** use the Ornstein-Uhlenbeck process to describe such a correlated *** introduction of this specific colored noise in the first order OV model allows describing realistic stop-and-go behavior without requiring instabilities or phase transitions,the homogeneous configurations being systematically stochastically *** compare the stochastic model to deterministic unstable OV models and analyze individual speed autocorrelation to describe the nature of the waves in stationary *** apply the approach to pedestrian single-file motion and compare simulation results to real pedestrian *** simulation results are quantitatively very similar to the real *** discuss plausible values for the model parameters and their meaning.

关键词： Transportation engineering Pedestrian single-file motion Stop-and-go dynamics First order microscopic model Brownian noise Simulation

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