Comparison of Microscopic Drivers' Probabilistic Lane-changing Models With Real Traffic Microscopic Data

Abstract

The difficulties of microscopic-level simulation models to accurately reproduce real traffic phenomena stem not only from the complexity of calibration and validation operations, but also from the structural inadequacies of the sub-models themselves. Both of these drawbacks originate from the scant information available on real phenomena because of the difficulty in gathering accurate field data. This paper studies the traffic behaviour of individual drivers utilizing vehicle trajectory data extracted from digital images collected from freeways in Iran. These data are used to evaluate the four proposed microscopic traffic models. One of the models is based on the traffic regulations in Iran and the three others are probabilistic models that use a decision factor for calculating the probability of choosing a position on the freeway by a driver. The decision factors for three probabilistic models are increasing speed, decreasing risk of collision, and increasing speed combined with decreasing risk of collision. The models are simulated by a cellular automata simulator and compared with the real data. It is shown that the model based on driving regulations is not valid, but that other models appear useful for predicting the driver’s behaviour on freeway segments in Iran during noncongested conditions.

Effects of Weather Conditions on Motorway Lane Flow Distributions

“…Dynamic speed limits affect lane flows and speed distributions, resulting in improved capac-ity utilisation [6]. Traffic control technology is not the scope of this paper, but it should be noted that in the future this technology will be significant since intelligent speed adaptation systems in vehicles, or autonomous vehicles, will perform better than human drivers [7].…”

Multi-lane Changing Model with Coupling Driving Intention and Inclination

“…The contributions of these studies focus on the analysis and modelling of lane changing behaviours in specific areas using traffic flow theory. In terms of research means, Hoseini uses probability theory, and Cellular Automaton (CA) to analyse the impact of lane changing behaviour on the traffic flow [7]. The results indicate that the model based on driving regulations is not valid, but that three probabilistic models appear useful for predicting the driver's behaviour on freeway segments during non-congested conditions.…”