No abstract

The spread of the new coronavirus COVID-19, has led to unparalleled global measures such as lockdown and suspension of all retail, recreation and religious activities during the first months of 2020. Nevertheless, no scientific evidence has been reported so far with regards to the impact on road safety and driving behavior. This paper investigates the effect of COVID-19 on driving behavior and safety indicators captured through a specially developed smartphone application and transmitted to a back-end platform. These indicators are reflected with the spread of COVID-19 and the respective governmental countermeasures in two countries, namely Greece and Kingdom of Saudi Arabia (KSA), which had the most completed routes for users of the smartphone applications. It was shown that reduced traffic volumes due to lockdown, led to a slight increase in speeds by 6–11%, but more importantly to more frequent harsh acceleration and harsh braking events (up to 12% increase) as well mobile phone use (up to 42% increase) during March and April 2020, which were the months where COVID-19 spread was at its peak. On the bright side, accidents in Greece were reduced by 41% during the first month of COVID-19-induced measures and driving in the early morning hours (00:00–05:00) which are considered dangerous dropped by up to 81%. Policymakers should concentrate on establishing new speed limits and ensure larger spaces for cycling and pedestrians in order to enlarge distances between users in order to safeguard both an enhanced level of road safety and the prevention of COVID-19 spread.

Real-time risk assessment of autonomous driving at tactical and operational levels is extremely challenging since both contextual and circumferential factors should concurrently be considered. Recent methods have started to simultaneously treat the context of the traffic environment along with vehicle dynamics. In particular, interaction-aware motion models that take inter-vehicle dependencies into account by utilizing the Bayesian interference are employed to mutually control multiple factors. However, communications between vehicles are often assumed and the developed models are required many parameters to be tuned. Consequently, they are computationally very demanding. Even in the cases where these desiderata are fulfilled, current approaches cannot cope with a large volume of sequential data from organically changing traffic scenarios, especially in highly complex operational environments such as dense urban areas with heterogeneous road users. To overcome these limitations, this paper develops a new risk assessment methodology that integrates a network-level collision estimate with a vehicle-based risk estimate in real-time under the joint framework of interaction-aware motion models and Dynamic Bayesian Networks (DBN). Following the formulation and explanation of the required functions, machine learning classifiers were utilized for the real-time network-level collision prediction and the results were then incorporated into the integrated DBN model for predicting collision probabilities in real-time. Results indicated an enhancement of the interaction-aware model by up to 9%, when traffic conditions are deemed as collision-prone. Hence, it was concluded that a wellcalibrated collision prediction classifier provides a crucial hint for better risk perception by autonomous vehicles.

Current approaches to estimate the probability of a traffic collision occurring in real-time primarily depend on comparing traffic conditions just prior to collisions with normal traffic conditions. Most studies acquire pre-collision traffic conditions by matching the collision time in the national crash database with the time in the traffic database. Since the reported collision time sometimes differs from the actual time, the matching method may result in traffic conditions not representative of pre-collision traffic dynamics. In this paper, this is overcome through the use of highly disaggregated vehicle-based traffic data from a traffic micro-simulation (i.e., VISSIM) and the corresponding traffic conflicts data generated by the surrogate safety assessment model (SSAM). In particular, the idea is to use traffic conflicts as surrogate measures of traffic safety so that traffic collisions data are not needed. Three classifiers (i.e., support vector machines, k-nearest neighbours, and random forests) are then employed to examine the proposed idea. Substantial efforts are devoted to making the traffic simulation as representative of the real-world as possible by employing data from a motorway section in England. Four temporally aggregated traffic datasets (i.e., 30 s, 1 min, 3 min, and 5 min) are examined. The main results demonstrate the viability of using traffic micro-simulation along with the SSAM for real-time conflicts prediction and the superiority of random forests with 5-min temporal aggregation in the classification results. However, attention should be given to the calibration and validation of the simulation software so as to acquire more realistic traffic data, resulting in more effective prediction of conflicts.

The current study aims to investigate the impact of the COVID-19 pandemic on road traffic collisions, fatalities, and injuries using time series analyses. To that aim, a database containing road collisions, fatalities, and slight injuries data from Greece were derived from the Hellenic Statistical Authority (HSA) and covered a ten-year timeframe (from January 2010 to August 2020. The chosen time period contained normal operations, as well as the period of the first COVID-19-induced lockdown period in Greece. Three different Seasonal Autoregressive Integrated Moving Average (SARIMA) time series models were implemented in order to compare the observed measurements to forecasted values that were intended to depict assumed conditions; namely, without the appearance of the COVID-19 pandemic. Modelling results revealed that the total number of road collisions, fatalities, and slightly injured were decreased, mainly due to the sharp traffic volume decrease. However, the percentage reduction of the collision variables and traffic volume were found to be disproportionate, which probably indicates that more collisions occurred with regard to the prevailing traffic volume. An additional finding is that fatalities and slightly injured rates were significantly increased during the lockdown period and the subsequent month. Overall, it can be concluded that a worse performance was identified in terms of road safety. Since subsequent waves of COVID-19 cases and other pandemics may reappear in the future, the outcomes of the current study may be exploited for the improvement of road safety from local authorities and policymakers.