Assessing influential factors for lane change behavior using full real-world vehicle-by-vehicle data

ABSTRACT

Understanding the underlying reasons for potential human risky driving behaviors is crucial for improving road safety. Recent technologies allow the analysis of driving behaviors at a microscopic level, allowing a naturalistic observation of such phenomenon through information collected unobtrusively. This paper assesses the factors that influence discretionary lane changes on an urban highway in Santiago, Chile, employing an interpretable machine learning approach. We use full real-world vehicle-by-vehicle data gathered from Automatic Vehicle Identification technology and individual public information of the vehicles and their owners, which allows us to understand patterns that might have different characteristics when performed in simulated environments. We provide insights about the causes that increase the likelihood of lane changes. For example, we find that: (i) the older the car, the less likely it is to change lane, (ii) younger drivers change lane more often, and (iii) motorcycles drivers are the most likely to change lane.

KEYWORDS:

• Drivers’ behaviors
• Interpretable Machine Learning
• Automatic Vehicle Identification
• lane change
• safety

Acknowledgments

We thank Autopista Central for providing us with data and collaborating enthusiastically with the project.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. https://www.registrocivil.cl.

2. To support this point, we conducted a survey that comprises eight questions regarding car usage. The online questionnaire was carried out from August 19th, 2021 to September 1st, 2021 using the official digital platforms of Pontificia Universidad Católica de Valparaíso and Instituto Sistemas Complejos de Ingeniería for diffusion purposes. In summary, the questionnaire was answered by 406 persons, of which 360 had a driver’s license. From this number, 83.6% of the persons stated that they are the owner of the vehicle they drive frequently, 13.1% is borrowed, 0.3% is rented, and 3.1% corresponds to other situations. These results are in line with the finding of a study developed by Comisión Nacional de Productividad of Chile, which indicated that 80% and 75% of the drivers from Uber and Beat are the owners of the vehicles they use, respectively (CNP 2019).

Additional information

Funding

Franco Basso gratefully acknowledges the financial support from both the Complex Engineering Systems Institute, ISCI (grant ANID PIA/BASAL AFB180003) and a grant from Science, Technology, Knowledge, and Innovation Ministry of Chile (FONDECYT Project 11200167). Raúl Pezoa thanks doctoral scholarship to ANID-PFCHA/Doctorado Nacional/2018-21181528. Mauricio Varas thanks a grant from Science, Technology, Knowledge, and Innovation Ministry of Chile (FONDECYT Project 11190892).