From behavioral psychology to acceleration modeling: Calibration, validation, and exploration of drivers’ cognitive and safety parameters in a risk-taking environment

Abstract

The first contribution of this research is capturing driver behavior in congested and incident-prone situations, thus incorporating drivers' risk-taking attitude in the model equations. The model formulated in this paper does not exogenously impose safety constraints to prevent accidents from occurring. Models used in practice typically preclude accidents, contrary to real-life situations. One more implication of this contribution is capturing that drivers do not perfectly register existing stimuli without subjectively weighing different alternatives based on their personality (aggressive versus conservative drivers). This allows risky behavior as an inherent result of the model. Moreover, the corresponding acceleration choice emerges as a probabilistic decision making process facing uncertainty; the method by which the resulting accident causing behavior is weighed can be calibrated based on recorded traffic data.From a practitioner stand point, the main challenge in realizing the above contribution and incorporating the corresponding parameters is the degree of complexity that would be added to the eventual model which would preclude its usefulness in actual practice. Accordingly, the second contribution of this research is to put forward a "logic" that is robust enough to advance the state of knowledge related to the driving task but simpleand fastenough so that it can be readily implemented, calibrated and validated. The resulting model is intended to provide a competitive stochastic alternative to existing simpler models that lack cognitive dimensions. Highlights• A car-following model is formulated using a prospect theory value function.• Risk taking and proneness to accidents are quantified through the offered framework.• Realistic traffic properties are reproduced after extensive numerical approximations.• While capturing heterogeneity, the model is calibrated against NGSIM trajectory data.• Traffic break-down followed by congestion flow-density data scattering are observed ABSTRACTWe investigate a utility-based approach for driver car-following behavioral modeling while analyzing different aspects of the model characteristics especially in terms of capturing different fundamental diagram regions and safety proxy indices. The adopted model came from an elementary thought where drivers associate subjective utilities for accelerations (i.e. gain in travel times) and subjective dis-utilities for decelerations (i.e. loss in travel time) with a perceived probability of being involved in rear-end collision crashes. Following the testing of the model general structure, the authors translate the corresponding behavioral psychology theory -prospect theory -into an efficientmicroscopic traffic modeling with more elaborate stochastic characteristics considered in a risk-taking environment.After model formulation, we explore different model disaggregate and aggregate characteristics making sure that fidelity is kept in terms of equilibrium properties. Significant effort is then dedicated to calibrating and validat...

Will Automated Vehicles Negatively Impact Traffic Flow?

“…Other attempts describe more sophisticated human-factor mechanisms. For example, Hamdar and coauthors [77] propose a car-following model based on prospect theory in which drivers increase travel time to prevent the risk of rear-end collisions, and several authors have experimented with Fullers' Task-Capacity-Interface model [78][79][80]. Incorporating human factors in car-following models remains a very lively research field.…”

Noise-Induced Stop-and-Go Dynamics in Pedestrian Single-file Motion

“…environment, experience, culture, psychology, etc. Stochastic effects and noises are the main emphases of many pedestrian or road traffic modelling approaches (see, e.g., white noises [31,17], pink-noise [32], action-point [33], or again inaccuracies or risk-taking behaviour [34,35]).…”

Noise-Induced Stop-and-Go Dynamics

“…experience, culture, environment, psychology, etc. Stochastic effects and notion of noise are the main emphasis of many pedestrian or road traffic modelling approaches (see, e.g., white noises [13,30], pink-noise [29], action-point [36], or again inaccuracies or risk-taking behavior [33,10]). Fig.…”