Car Following Markov Regime Classification and Calibration

The car following behavior has recently gained much attention due to its wide variety of applications. This includes accident analysis, driver assessment, support systems, and road design. In this paper, we present a model that leverages Markov regime switching models to classify various car following regimes. The detected car following regimes are then mined to calibrate the parameters of drivers to be dependent on the driver's current driving regime. A two stage Markov regime switching model is utilized to detect different car following regimes. The first stage discriminates normal car following regimes from abnormal ones, while the second stage classifies normal car following regimes to their fine-grained regimes like braking, accelerating, standing, free-flowing, and normal following. A genetic algorithm is then employed to the observed driver data in each car following regime to optimize car following model parameter values of the driver in each regime. Experimental evaluation of the proposed model using a real dataset shows that it can detect up-normal (rare and short time) events. In addition, it can infer the switching process dynamics such as the expected duration, the probability of moving from one regime to another and the switching parameters of each regime. Finally, the model is able to accurately calibrate the parameters of drivers according to their driving regimes, so we can achieve a better understanding of drivers behavior and better simulation of driving situation.

[1]  James D. Hamilton.  Regime switching models , 2010 .

[2]  Walid Gomaa,et al.  Car following regime taxonomy based on Markov switching , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[3]  Serge P. Hoogendoorn,et al.  Adaptive Car-Following Behavior Identification by Unscented Particle Filtering , 2007 .

[4]  Serge Hoogendoorn,et al.  Calibration of microscopic traffic-flow models using multiple data sources , 2010, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[5]  Eric Moulines,et al.  Inference in hidden Markov models , 2010, Springer series in statistics.

[6]  Álvaro Seco,et al.  Calibration of the Gipps Car-following Model Using Trajectory Data , 2014 .

[7]  Vincenzo Punzo,et al.  Comparison of Simulation-Based and Model-Based Calibrations of Traffic-Flow Microsimulation Models , 2008 .

[8]  P. G. Gipps,et al.  A behavioural car-following model for computer simulation , 1981 .

[9]  Walid Gomaa,et al.  Multi-Agent Reinforcement Learning Control for Ramp Metering , 2014, ICSEng.

[10]  A. Sathyanarayana,et al.  Driver behavior analysis and route recognition by Hidden Markov Models , 2008, 2008 IEEE International Conference on Vehicular Electronics and Safety.

[11]  Xiaoliang Ma,et al.  A Neural-Fuzzy Framework for Modeling Car-following Behavior , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[12]  Jesus Gonzalo,et al.  Estimation and inference in threshold type regime switching models , 2013 .

[13]  Jonathan D. Cryer,et al.  Time Series Analysis , 1986 .

[14]  Helmut Prendinger,et al.  Tokyo Virtual Living Lab: Designing Smart Cities Based on the 3D Internet , 2013, IEEE Internet Computing.

[15]  Marcelo Perlin.  MS_Regress - The MATLAB Package for Markov Regime Switching Models , 2015 .

[16]  Hussein Dia,et al.  Neural Agent Car-Following Models , 2007, IEEE Transactions on Intelligent Transportation Systems.

[17]  Xiaoliang Ma,et al.  Behavior Measurement, Analysis, and Regime Classification in Car Following , 2007, IEEE Transactions on Intelligent Transportation Systems.

[18]  Y. Kishimoto,et al.  A modeling method for predicting driving behavior concerning with driver’s past movements , 2008, 2008 IEEE International Conference on Vehicular Electronics and Safety.

[19]  L. A. Pipes.  An Operational Analysis of Traffic Dynamics , 1953 .

[20]  Helbing,et al.  Congested traffic states in empirical observations and microscopic simulations , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[21]  R. Jiang,et al.  Full velocity difference model for a car-following theory. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[22]  Wolfgang Krautter,et al.  TRAFFIC SIMULATION SUPPORTING URBAN CONTROL SYSTEM DEVELOPMENT , 1997 .

[23]  Chang‐Jin Kim,et al.  State-Space Models with Regime-Switching: Classical and Gibbs Sampling Approaches with Applications , 1999 .

[24]  Li Li,et al.  Calibration of MITSIM and IDM car-following model based on NGSIM trajectory datasets , 2010, Proceedings of 2010 IEEE International Conference on Vehicular Electronics and Safety.

[25]  K. Arrow,et al.  The New Palgrave Dictionary of Economics , 2020 .