The current paper aims to identify and investigate the most significant factors that influenced driving behavior during 2020, a year that behavior was heavily influenced by the COVID-19 pandemic. Both COVID-19 metrics (i.e., COVID − 19 cases, fatalities, and reproduction rate) and restrictions (i.e., stringency index and lockdown measures) were taken into account to identify their relationship with driving behavior. The XGBoost algorithm was chosen as the analysis method and the results suggest a strong correlation between COVID-19 metrics and restriction measures with driving behavior. Furthermore, different patterns were revealed for both harsh events among three examined conditions, i.e., without restrictions, 1st lockdown, and 2nd lockdown.
Modelling results demonstrated that there are three common crucial factors that influenced HA and HB events the most during the pandemic. These factors were distance, mobile use/ driving time, and driving requests (requested in Apple Maps). More specifically, trip distance and mobile use duration were the two most important factors out of the eight examined variables that influence HA and HB. Τrip distance had a great impact on HA and HB events probably due to the fact that the longer trips were driven on highways and rural roads than trips within urban environment. Hence, the change in road type probably influences the drivers’ braking and acceleration patterns with more or less frequent harsh events. Another causal factor for the correlation between harsh events and duration was the increasing fatigue by increasing the trip distance. However, these assumptions need further research in order to be validated. Additionally, mobile phone use shows the importance of drivers being undistracted in order to avoid HA and HB events. After trip duration and mobile phone use, driving requests follow which are a driving exposure measurement and is an indication of the prevailing traffic volumes. This finding reveals the relation between this exposure measurement with HA and HB events. A higher value of exposure indicates a greater density of traffic and by extension, it changes the probabilities of the driver being involved in a harsh event for instance with more dense surrounding traffic. A small contribution to HA and HB was also provided by driving during risky nighttime hours indicating that there was a change in events during nighttime driving (00:00–05:00) due to the lighting conditions themselves as well as it was probably affected due to the prohibitions imposed by the Greek government during the nighttime and essentially reduced trips during risky hours (Katrakazas et al. 2021).
The three aforementioned variables are evidently not directly related to the pandemic. Nevertheless, four COVID-19-related variables were found to impact HA and HB events. New COVID-19 cases in Greece were found to prevail compared to other COVID-19-related variables in terms of HA events. On contrary to HA, COVID-19 Reproduction Rate was found to influence HB events the most. The most influential pandemic-related factors for HA and HB events in Greece were COVID-19 Reproduction Rate, Stringency Index, and New COVID-19 Fatalities and Cases. This is in line with existing literature. For example, the studies of (Dong et al. 2022; Lee et al. 2020; Vanlaar et al. 2021) found that COVID-19 restrictions negatively affected risky driving behaviors such as speeding, and distracted driving.
With regards to traffic exposure during 2020, it can be concluded from Fig. 1 that driving requests were significantly decreased during both lockdowns compared to the baseline of no restrictions. The greatest reduction was observed in the first lockdown compared to the second. This means that the traffic volume during the 1st lockdown was lower than in the other conditions (i.e., during the 2nd lockdown, and without restrictions). Hence, with fewer vehicles ahead, the drivers could accelerate more easily and this can be revealed in Fig. 2 (a), where the upper quartile was higher than in other conditions. Additionally, in Fig. 2 (b) for trips with harsh accelerations occurrence, the median was higher during the 1st lockdown than the other conditions (i.e., during 2nd lockdown, and without restrictions) meaning that the HA events were more frequent. This finding can be related partly to speeding, an increase was revealed in the spatial extent of speeding, and in the level of speeding as well as statistically significant differences in speeding before and after the COVID-19 outbreak (Lee et al. 2020). With regards to the 2nd lockdown, for trips with harsh accelerations, the median was higher compared to conditions without restrictions, as a result of the decreased traffic volume but not at the same magnitude as the 1st lockdown, in which the traffic volume was much lower.
With regards to HB events, in Fig. 3 (a), again, the upper quartile is greater during the 1st lockdown than other conditions (i.e., during 2nd lockdown, and without restrictions) and combining Fig. 3 (b), the median is higher for trips with HB occurrence and this implies that the HB events were more frequent. This finding is also consistent with the literature (Katrakazas et al. 2020). This can be explained, for instance, as the traffic volume during the 1st lockdown was lower than the other conditions and hence with fewer vehicles ahead, the drivers could maintain higher speeds, as stated in (Katrakazas et al. 2020). With higher speeds, the drivers were more probable to be involved in a harsh braking event with potential traffic obstacles ahead (i.e., pedestrians, bikes, scooters and traffic control signs or signals), especially during the lockdowns that the active transport was increased (Linares-Rendón and Garrido-Cumbrera 2021). With regards to the 2nd lockdown following the same logic as HA, for trips with harsh brakings, the median was higher compared to no restrictions as a result of the decreased traffic volume but not the same magnitude as the 1st in which the traffic volume was lower.
The results of the exploratory analysis by XGBoost indicate a correlation of COVID-19 metrics and restrictive measures with harsh brakings and accelerations. This correlation could be explained as COVID-19 metrics and restriction measures affected commuters by leading them to stay at home. Consequently, the stay at home restrictions led to a decreased traffic volume, this can be validated by Fig. 1, and thus the traffic volumes affected directly driving behavior. This phenomenon can substantiate why the driving requests are a more important factor in the analysis than COVID-19-related variables.
Nevertheless, this work is not without shortcomings, and therefore, future research could focus on covering the remaining gaps that this work did not cover. Initially, future studies could concentrate on more sophisticated models, such as deep neural networks, e.g., Convolutional neural networks (CNNs) or Artificial Neural Networks (ANNs), which probably can accomplish lower errors and give more insights into driving behavior variables. In addition, more variables with regards to driving behavior, i.e., speeding, speeding duration, and speed, could be exploited using the same method in order to give in the same context results. These variables were tested but they led to models with large errors and therefore, were not included in this work. Finally, additional data with geolocation and road type information could also enhance the current methodology, leading to spatial analyses of the examined variables.