# Identification of Driving Heterogeneity using Action-chains

arXiv: https://arxiv.org/abs/2307.16843 · HTML: https://mlsanity.com/paper/2307.16843/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 31 Jul 2023.

## Builds on (most cited first, 2 of 2)

- [Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches](https://mlsanity.com/paper/1708.08986/) (2017)
- [How Much Data is Enough? A Statistical Approach with Case Study on Longitudinal Driving Behavior](https://mlsanity.com/paper/1706.07637/) (2017)

## Built on by (most cited first, 4 of 4)

- [Investigating Personalized Driving Behaviors in Dilemma Zones: Analysis and Prediction of Stop-or-Go Decisions](https://mlsanity.com/paper/2405.03873/) (2024)
- [Performance Comparison of Deep RL Algorithms for Mixed Traffic Cooperative Lane-Changing](https://mlsanity.com/paper/2407.02521/) (2024)
- [Driving pattern interpretation based on action phases clustering](https://mlsanity.com/paper/2407.17518/) (2024)
- [An Empirical Markov Chain Car-Following (MC-CF) Model](https://mlsanity.com/paper/2603.27909/) (2026)

## Abstract (arXiv)

Current approaches to identifying driving heterogeneity face challenges in capturing the diversity of driving characteristics and understanding the fundamental patterns from a driving behaviour mechanism standpoint. This study introduces a comprehensive framework for identifying driving heterogeneity from an Action-chain perspective. First, a rule-based segmentation technique that considers the physical meanings of driving behaviour is proposed. Next, an Action phase Library including descriptions of various driving behaviour patterns is created based on the segmentation findings. The Action-chain concept is then introduced by implementing Action phase transition probability, followed by a method for evaluating driving heterogeneity. Employing real-world datasets for evaluation, our approach effectively identifies driving heterogeneity for both individual drivers and traffic flow while providing clear interpretations. These insights can aid the development of accurate driving behaviour theory and traffic flow models, ultimately benefiting traffic performance, and potentially leading to aspects such as improved road capacity and safety.