Incorporating human-factors in car-following models : a review of recent developments and research needs
By Mohammad Saifuzzaman, Zuduo Zheng. 2014
467 citations recorded by the source corpus.
Over the past decades there has been a considerable development in the modeling of car-following (CF) behavior as a result of research undertaken by both traffic engineers and traffic psychologists. While traffic engineers seek to understand the behavior of a traffic stream, traffic psychologists seek to describe the human abilities and errors involved in the driving process. This paper provides a comprehensive review of these two research streams. It is necessary to consider human-factors in {CF} modeling for a more realistic representation of {CF} behavior in complex driving situations (for example, in traffic breakdowns, crash-prone situations, and adverse weather conditions) to improve traffic safety and to better understand widely-reported puzzling traffic flow phenomena, such as capacity drop, stop-and-go oscillations, and traffic hysteresis. While there are some excellent reviews of {CF} models available in the literature, none of these specifically focuses on the human factors in these models. This paper addresses this gap by reviewing the available literature with a specific focus on the latest advances in car-following models from both the engineering and human behavior points of view. In so doing, it analyses the benefits and limitations of various models and highlights future research needs in the area.
Subfields: Control and Systems Engineering, Safety, Risk, Reliability and Quality, Social Psychology
View Paper: https://www.semanticscholar.org/paper/a90fed020ef56b448aa9ef88efa056f7d33cc575
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Citations in the corpus
- Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions (2015, unknown): “Behavioural models can be provided for every manoeuvre that the vehicle is about to undertake (e.g. Hidas, 2002; Llorca et al., 2014; Saifuzzaman and Zheng, 2014) to flag the manoeuvre as nominal or not.”
- Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning (2018, background): “He et al. ( 27 ) proposed a simple nonparametric car-following model with k-nearest neighbor, which outputs the average of the most similar cases, i.e., the most likely driving behavior under the current circumstance.”
- A new lattice hydrodynamic model considering the effects of bilateral gaps on vehicular traffic flow (2017, background): “Traffic flowmodels can be classified as microscopic or macroscopic models [1–4].”