Autonomous driving is designed to enhance the overall performance of vehicular traffic. The primary objective of this study is to develop an improved car-following model for adaptive cruise control (ACC) by integrating conventional automated logic with human factors. Specifically, a modeling framework is proposed with a generalization of a classical action point paradigm describing drivers’ psychological reactions and expectations. The action points are determined from collected data, from which the unconscious and conscious reaction regimes can be identified. Based on this identification, the human-like driving mode is carried out, and its corresponding acceleration model is developed to reproduce empirical car-following spiral within the unconscious regime, whereas the conventional autonomous mode driven by the Intelligent Driver Model (IDM) is used to calculate changes in acceleration for the conscious regime. To integrate the two modes, a switching rule considering the random occurrence of action points is also determined. The results of numerical experiments reveal that simulated macroscopic traffic is compatible with the three-phase theory. Moreover, traffic mobility in terms of throughput and speed is significantly improved compared to that achieved by the classical IDM. Asymptotic stability is achieved in four typical fluctuation scenarios, as the amplitudes of fluctuations converge while travelling along the vehicular platoon. However, the classical IDM fails to maintain such stability. The proposed model enables ACC to drive in accordance with drivers’ psychological traits, and consequently has the potential to increase the acceptance of autonomous driving. It also has the ability to help ACC enhance traffic efficiency and safety.

Driving safety is considered to have a strong relationship with traffic flow characteristics. However, very few studies have addressed the safety impacts in the three-phase traffic theory that has been demonstrated to be an advancement in explaining the empirical features of traffic flow. Another important issue affecting safety is driver experience heterogeneity, especially in developing countries experiencing a dramatic growth in the number of novice drivers. Thus, the primary objective of the current study is to develop a microsimulation environment for evaluating safety performance considering the presence of novice drivers in the framework of three-phase theory. First, a car-following model is developed by incorporating human physiological factors into the classical Intelligent Driver Model (IDM). Moreover, a surrogate safety measure based on the integration concept is modified to evaluate rear-end crashes in terms of probability and severity simultaneously. Based on a vehicle-mounted experiment, the field data of car-following behavior are collected by dividing the subjects into a novice group and an experienced group. These data are used to calibrate the proposed car-following model to explain driver experience heterogeneity. The results indicate that our simulation environment is capable of reproducing the three-phase theory, and the changes in the modified surrogate safety measure are highly correlated with traffic phases. We also discover that the presence of novice drivers leads to different safety performance outcomes across various traffic phases. The effect of driver experience heterogeneity is found to increase the probability of the rear-end crashes as well as the corresponding severity. The results of this study are expected to provide a scientific understanding of the mechanisms of crash occurrences and to provide application suggestions for improving traffic safety performance.

The primary objective of this study is to quantitatively evaluate the impacts of driver population heterogeneity on capacity and traffic performance at basic roadway segments. Drivers were classified as two categories including novice drivers and experienced drivers, and they mainly differs in driving experience. On-road experiments were conducted for measuring the car-following data associated with the test drivers. Based on a nonlinear optimization procedure, the improved intelligent driver model was calibrated by minimizing the deviation between the observed driving trajectories and the simulated dynamics. Capacity and traffic performance were analyzed with respect to different driver population compositions by using the calibrated simulation model. The simulation results revealed that the mixture rate of novice drivers can result in capacity reduction and poor traffic performance. Specifically, the reduction rate of capacity may reached up to 28%, and most of other key measures representing traffic performance characteristics also significantly deteriorated.

This study extends the conventional stochastic regret minimization model and incorporates it into the path choice utility function as an expected regret psychological influence factor. This happens because of the limited explanatory capacity of the purely rational path choice-based model regarding actual travel. The weakness in the classic model's inference of the intensity of regret is fixed by the updated model, which employs the utility difference rather than the actual amount of the utility value. Two IIA characteristics-the path objective utility and the subjective regret psychological intensity brought on by the absolute utility difference-can be lessened by combining the improved utility function with the generalized logit model. The outcomes of the arithmetic examples demonstrate that the new model more accurately captures the influence of subjective regret psychology in path selection while resolving the IIA problems of the logit model. Furthermore, the enhanced model may simultaneously address the distributional implications of the random utility maximization and random regret minimization models, improving its capacity to elucidate actual choice behavior. When calibrated and applied to conventional network arithmetic allocation, the enhanced model's accuracy can reach 9.85%, capturing the network traffic flow allocation raw more accurately.