Variable Response Time Lag Module for Car-Following Models: Development and Structuring with Fuzzy Set Theory

The car-following process consists of a stimulus-response relationship between vehicles in which the driver of the following vehicle reacts to the actions of the lead vehicle after a time lag. Since the 1950s, the car-following phenomenon has been studied and analyzed, resulting in various models and algorithms. Throughout this period, driver response time lag has always been assumed to be a constant value for the driver at all times, regardless of the approach and level of detail of the model. The primary shortcoming of a constant time lag is that it introduces a number of strong assumptions that do not concur with human nature. To address the problems associated with constant time lag, an independent response time lag module was developed that can be used in any car-following model or algorithm without changing its fundamental mathematical structure. One of the most appealing aspects of this module is its flexible and transparent structure that can easily be adapted to and calibrated for any model or simulation algorithm. The development and structure of the module are described, including the fuzzy definitions of driving states, fuzzy rule extraction, and fuzzy time lag assignment. Statistical and graphical evaluations of the module performance are also included by integrating the module to a proportional car-following model. In the graphical evaluation, the module improved the model performance significantly by providing more precise timing for the driver response. Both Kolmogorov–Smirnov and root-mean-square error tests confirmed that the use of the module improves the car-following model performance.

Download Full-text

Realistic Car-Following Models for Microscopic Simulation of Adaptive and Cooperative Adaptive Cruise Control Vehicles

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2623-01 ◽

2017 ◽

Vol 2623 (1) ◽

pp. 1-9 ◽

Cited By ~ 30

Author(s):

Lin Xiao ◽

Meng Wang ◽

Bart van Arem

Keyword(s):

Adaptive Cruise Control ◽

Model Performance ◽

Cruise Control ◽

Vehicle Collisions ◽

Car Following ◽

Cooperative Adaptive Cruise Control ◽

Proposed Model ◽

Wide Range ◽

Car Following Model ◽

Vehicle Response

Adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC) are important technologies for the achievement of vehicle automation, and their effect on traffic systems generally is evaluated with microscopic traffic simulations. A successful simulation requires realistic vehicle behavior and minimal vehicle collisions. However, most existing ACC-CACC simulation studies used simplified models that were not based on real vehicle response. The studies rarely addressed collision avoidance in the simulation. The study presented in this paper developed a realistic and collision-free car-following model for ACC-CACC vehicles. A multiregime model combining a realistic ACC-CACC system with driver intervention for vehicle longitudinal motions is proposed. This model assumes that a human driver resumes vehicle control either according to his or her assessment or after a collision warning asks the driver to take over. The proposed model was tested in a wide range of scenarios to explore model performance and collision possibilities. The testing scenarios included three regular scenarios of stop-and-go, approaching, and cut-out maneuvers, as well as two extreme safety-concerned maneuvers of hard brake and cut-in. The simulation results show that the proposed model is collision free in the full-speed-range operation with leader accelerations within −1 to 1 m/s2 and in approaching and cut-out scenarios. Those results indicate that the proposed ACC-CACC car-following model can produce realistic vehicle response without causing vehicle collisions in regular scenarios for vehicle string operations.

Download Full-text

STOCHASTIC CAR-FOLLOWING MODEL FOR EXPLAINING NONLINEAR TRAFFIC PHENOMENA

International Journal of Modern Physics B ◽

10.1142/s0217979211058419 ◽

2011 ◽

Vol 25 (08) ◽

pp. 1111-1120 ◽

Cited By ~ 2

Author(s):

JIANPING MENG ◽

TAO SONG ◽

LIYUN DONG ◽

SHIQIANG DAI

Keyword(s):

Response Time ◽

Present Model ◽

Random Variable ◽

Velocity Model ◽

Optimal Velocity ◽

Car Following ◽

Traffic Jams ◽

Common Time ◽

Experimental Feature ◽

Car Following Model

There is a common time parameter for representing the sensitivity or the lag (response) time of drivers in many car-following models. In the viewpoint of traffic psychology, this parameter could be considered as the perception–response time (PRT). Generally, this parameter is set to be a constant in previous models. However, PRT is actually not a constant but a random variable described by the lognormal distribution. Thus the probability can be naturally introduced into car-following models by recovering the probability of PRT. For demonstrating this idea, a specific stochastic model is constructed based on the optimal velocity model. By conducting simulations under periodic boundary conditions, it is found that some important traffic phenomena, such as the hysteresis and phantom traffic jams phenomena, can be reproduced more realistically. Especially, an interesting experimental feature of traffic jams, i.e., two moving jams propagating in parallel with constant speed stably and sustainably, is successfully captured by the present model.

Download Full-text