Researchers Marianthi Kallidoni, Christos Katrakazas and George Yannis from Department of Transportation Planning and Engineering, National Technical University of Athens, Greece, published an article in The European Journal of Transport and Infrastructure Research (EJTIR), on 31 of May 2022, regarding the relationship between covid-19 restrictive measures and mobility patterns. The article provides an a-up to-date review on this issue. Here are some of the key issues.
As countermeasures to the high transmissibility of COVID-19, “social distancing” and “lockdown” measures were imposed globally in order to diminish the infections and thus the emergency hospitalizations. The restrictive measures mainly concerned the closure of schools and workplaces, limits on gatherings, orders to “stay at home” and restrictions on internal movements and on international travel.
The aforementioned countermeasures, as well as the fear of exposure to the virus, had a direct impact on travel behavior. Public transport users and overall mobility have been radically reduced. For instance, in the European Union driving was reduced up to 89%, while the use of public transportation fell up to 93% in the first months of the pandemic. At the same time, in many European cities an increased interest in cycling, shared bicycles and walking was observed. As a result, the mobility trends have changed leading to new unknown patterns.
Factors that determined mode choice in pre-coronavirus era, such as travel time saving, comfort and cost, became less priority during the pandemic. During the first “wave” of COVID-19 in China, commuters’ choice of mode of transport was dictated by the possibility of getting infected. Many surveys indicated an important shift from usage of public modes of transport to private ones.
With respect to walking volumes, a similar reduction was identified, although to a lesser degree than the massive reduction of transit trips. Reports noticed an important shift from motorized modes to walking and cycling. Walking was preferred for short distances as a safer and healthier mode of transport. A survey conducted in Germany revealed an important shift from regular transport use to walking, cycling and gradually driving, resulting in an increased proportion of walking trips compared to the pre-pandemic era.
Time-series analysis has been repeatedly used in a wide range of transportation studies, in order to predict future conditions from observed past data. Especially during COVID-19 crisis, many researchers have implemented seasonal time-series models to analyze the daily effect of the pandemic on travel behavior and road safety.
Regarding the present analysis, the available time-series were split into several components (i.e. trend-cycle, seasonal and residual) to detect the underlying patterns. For this research, data were extracted from the mobility trend report of Apple (Apple, 2021), in which route requests are measured and divided into driving, walking and public transport use.
Orders to “stay at home”, also known as lockdown measures, were probably the most restrictive policy and were widely implemented in the European Union. Orders to “stay at home” are limited additionally to public events, crowded gatherings, public transport use, commuting to or from workplaces and internal movements. Reports showed a major drop in road fatalities in young people aged 0- 17 years occurred due to school closing.
Restrictions on international travel were imposed in all countries, especially those that did not implement other measures, to prevent the transmission of the virus from other countries.
Authors stress that results demonstrate the direct impact of the applied restrictive measures on travel behavior in the majority of European countries, underlining the alteration of mobility patterns due to the pandemic. The sharp decline of traffic in the spring of 2020 is linked to the national restrictive strategies, while the easing of the imposed measures contributed to the gradual increase of drivers and pedestrians flows in the summer of 2020.
The researchers point out that from a policy perspective, these findings are extremely worthy for the subsequent waves of COVID-19 cases or future crises. Through the estimated models, the current research suggests the most adequate strategies in pan-European and national level for controlling the disease spread. For example, governments along with traffic management centers can evaluate the different mobility evolutions and identify popular areas, where specific measures could be taken to restrict the spread of the virus.
Trends in mobility and the corresponding correlation with COVID-19 countermeasures could also act as a surrogate for virus transmission especially in times when cases are increasing. Consequently, if mobility patterns are increasing, governments and local authorities could impose the most significant measures as these are shown by the developed models to stop the spread.
The study highlights that the understanding of the different mobility evolutions with similar countermeasures would help decision makers to enforce or lift the confinement measures after the required period. Towards that end, local and regional observatories which observe mobility and disease trends could be initiated in order to proactively detect the effect of COVID-19 and other diseases and the relationship with mobility and the corresponding disease-restricting countermeasures.
Since different mobility results imply also different severity of the countermeasures between countries, international guidelines could be set in order to declare the most effective countermeasures based on mobility patterns between countries, especially on those with close business and touristic relationships. Furthermore, the transferability of this study allows governments and policy makers to devise their pandemic response depending on the results of countries with similar demographic and geographic characteristics. Hence, the analysis could provide useful insights also for countries that were not studied in the current paper but present similar cultural, demographic and geographic attributes.
Finally, a smartphone application could be developed based on the previous insights to provide citizens appropriate advice for the crowding avoidance, by examining the response of countries with similar countermeasures.
Nevertheless, the authors agree that this paper is not without shortcomings. Utilized data from the mobility trend report of Apple refer to a specific sample of drivers and pedestrians (i.e., users of Apple), which are only a sub-group of the national populations and may not resemble the total travel behavior. Apple does not hold demographic information of users and the representativeness of the sample compared to the general population is not available. Moreover, these data cover a short time span (i.e., February 2020 – February 2021) with only one day baseline (i.e. the 13th of January 2020) which dismisses the data seasonality within a year. An analysis of the traffic volumes during the pandemic compared to the previous years should be conducted to examine the magnitude of the impact due to the pandemic. Furthermore, the association of mobility with the confinement measures is important, but still indirect. Using the number of confirmed cases and deaths, the time series models could have a better fit and provide better forecasts for the evolution of driving and walking during the pandemic.
The researchers emphasize that further research should consider the combination of restrictive measures, the strictness scale and the evolution of confirmed cases and deaths, as mentioned above, using multivariate forecasting models e.g., Vector AutoRegression (VAR) in order to gain further insights on the impact of COVID-19 on travel behavior. Moreover, expanding the time frame of the study and analyzing mobility of the next pandemic “waves” may provide better and more precise outcomes.