Delays, Inaccuracies and Anticipation in Microscopic Traffic Models

Abstract

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. We show that the destabilizing effects of reaction times and estimation errors can essentially be compensated for by spatial and temporal anticipation, that is, the combination of stabilizing and destabilizing effects results in the same qualitative macroscopic dynamics as that of the respectively underlying simple car-following model. In many cases, this justifies the use of simplified, physics-oriented models with a few parameters only. Although the qualitative dynamics is unchanged, multi-anticipation increase both spatial and temporal scales of stop-and-go waves and other complex patterns of congested traffic in agreement with real traffic data. Remarkably, the anticipation allows accident-free smooth driving in complex traffic situations even if reaction times exceed typical time headways.

Major revision of the model and the simulations. Particularly, the number of model parameters has been reduced

References in corpus (2)

• Empirical basis for car-following theory development
• Empirical Features of Congested Traffic States and Their Implications for Traffic Modeling

Cited by in corpus (23)

• Estimating Acceleration and Lane-Changing Dynamics Based on NGSIM Trajectory Data
• Delayed Feedback Control near Hopf Bifurcation
• Microscopic Calibration and Validation of Car-Following Models -- A Systematic Approach
• Theoretical vs. Empirical Classification and Prediction of Congested Traffic States
• Langevin method for a continuous stochastic car-following model and its stability conditions
• FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modeling
• The Intelligent Driver Model with Stochasticity -- New Insights Into Traffic Flow Oscillations
• A Generative Car-following Model Conditioned On Driving Styles
• An Open-Source Microscopic Traffic Simulator
• Bayesian Calibration of the Intelligent Driver Model
• Car-Following Models: A Multidisciplinary Review
• Physics-augmented models to simulate commercial adaptive cruise control (ACC) systems
• Characterizing driver heterogeneity within stochastic traffic simulation
• Calibrating Car-Following Models via Bayesian Dynamic Regression
• The Application of Driver Models in the Safety Assessment of Autonomous Vehicles: A Survey
• Calibration of Human Driving Behavior and Preference Using Naturalistic Traffic Data
• AutonoVi: Autonomous Vehicle Planning with Dynamic Maneuvers and Traffic Constraints
• Agents for Traffic Simulation
• Calibration and validation of models describing the spatiotemporal evolution of congested traffic patterns
• Theoretical Development and Numerical Validation of an Asymmetric Linear Bilateral Control Model- Case Study for an Automated Truck Platoon
• CosIn: A Statistical-based Algorithm for Computation of Space-speed Time Delay in Pedestrian Motion
• From Behavioral Psychology to Acceleration Modeling: Calibration, Validation, and Exploration of Drivers Cognitive and Safety Parameters in a Risk-Taking Environment
• Complex Dynamics of Bus, Tram and Elevator Delays in Transportation System