Comparison of the Flow Rate and Speed of Vehicles on a Representative Road Section before and after the Implementation of Measures in Connection with COVID-19

Transport is an inseparable part of the life of all citizens. At the beginning of the year, the COVID-19 pandemic hit the world. Individual states have taken strict measures to prevent its spread among the population. Due to this fact, the government of the Slovak Republic has issued restrictions on the closure of public spaces (schools, shopping centres, restaurants, bars, etc.). These restrictions have had an impact not only on the economic activity of the population but also on their mobility in the form of reduced traffic. This is due to the drastically reduced mobility associated with the coronavirus, such as commuting trips and extremely limited leisure opportunities. Reduced mobility of the population (reduction of the number of vehicles in the traffic flow) can bring positive effects not only on overloaded road network (increased vehicle speed, lower flow) but also on the environment (reduction of noise, emissions, etc.). This article aims at finding out what effect the measures taken had on the quality of traffic flow. The quality of movement was examined in the form of the flow and speed of vehicles on one of the busiest first-class road sections. Descriptive statistics were used to examine the state of the restrictions. The results show that after the introduction of measures against the spread of coronavirus, the intensity and speed of vehicles in the measured section decreased.


Introduction
Transportation is a complicated stochastic process. Nowadays, it is an inseparable part of each of us-whether when traveling to work, for relaxation, transportation of goods, etc. Rising living standards of the population as well as favourable conditions when travelling across countries are some of the key indicators reflected in the increase in demand for transport. The consumer lifestyle has also affected our society. The social trend in the automotive industry is particularly obvious. Previously, car ownership in our region was a sign of luxury, wealth and higher social status. Today, many more people can buy it. The fact that nowadays buying a vehicle is much easier than some years ago does not only affect the environmental aspect in the form of pollution (emissions), noise, vibration. This factor mainly affects the increase in the number of vehicles on the roads, which often causes congestions on the road network [1,2].
Due to increasing flows, especially on highways and first-class roads, the smoothness of traffic flow moves away from free traffic flow and the part of traffic flow with a load degree heading to 1 increases [3][4][5]. The time between the origin and the destination of the trip increases significantly. Travel time is considered one of the main indicators of road network permeability [6]. Travel time is

Traffic Flow
In theory, traffic flow can be understood as a flow composed of different types of vehicles that have specific attributes. These attributes distinguish it from similar phenomena (flows), known for example in physics. Therefore, it is necessary to examine it separately. Several factors affect the traffic flow. On the other hand, the traffic flow influences its surroundings through its behaviour, both in qualitative and quantitative ways [31,32]. The traffic flow is understood as the unity of temporal and spatial characteristics but also of the transport and motoric characteristics of vehicles on the road. The movement of vehicles in traffic flow is affected by other vehicles in the traffic and is therefore monitored as a whole and not as the movement of an individual vehicle [33][34][35].

Basic Characteristics of the Traffic Flow
Three interdependent variables are considered as these characteristics, namely speed v (d, t), flow q (d, t) and density k (d, t). All of these variables are dependent on place and time. The quantity of the traffic flow is its flow and the quality expresses the speed and fluency in the given conditions. The research and description of the relationships between these three variables is the basis of the traffic flow theory [33,35,36].

Speed
The speed v is dependent directly on the distance s and indirectly on the time t. It is most often stated in the basic units of the SI (The International System of Units), i.e., in m/s or km/h [37]. where: • v is the speed of the traffic flow [km/h], • d is the distance of the monitored section of road [km], • t is the time [s].

Flow
Flow is the most important characteristic of traffic flow because the largest flow that communication can transmit is the capacity of road communication. We define the flow rate as the number of vehicles passing a certain point in a given time period in one or both directions [33]. where: • N is number of vehicles [veh].

Density
Density is the number of vehicles situated on a unit length of a roadway at a given time. At low density, vehicles are allowed to move freely, and drivers can choose the speed of their choice. On the contrary, at high density, the driver is influenced by other road users and congestion occurs [33]. where: • l is a monitored section of road [km].

Basic Equation between Traffic Flow Characteristics
There is a connection between the basic characteristics, and it is given by the continuity equation: Sustainability 2020, 12, 7216 4 of 19 Provided that these characteristics were obtained by spatial-temporal observation. Empirically, the natural dependence of speed on density is verified. There is a maximum speed at the minimum density and a maximum density at zero speed. It follows that density is also dependent on the flow [38].
The principles of descriptive statistics are most often used to evaluate the measured data from traffic surveys. Specifically, these are characteristics or measures of position. The most used characteristics of the position of one-dimensional distributions are the mean value (arithmetic mean), median, mode and quantiles [39,40].

•
The mean value is the best-known characteristic of the position, it describes the place on the numerical axis around which the values of the random variable fluctuate randomly. It is also referred to as expected value or mathematical hope: where: • x is random variable, • n is number of variables.

•
The median is a value that divides a series of ascending results into two equally numerous halves: x = a + h· 0.5 − r−1 n=1 f i f x (6) where: • a is lower limit of median class, • h is range of median class, • r−1 n=1 f i is proceeding cumulative frequency from median class. • f x is frequency of median class.

•
The mode is the most frequently occurring value in the statistics file.

Analysis of the Addressed Section of the Road I/11
Traffic flow monitoring was performed on the first-class road I/11, which is part of the European road E75. The first-class road I/11 connects northern and southern Europe [41]. It enters Slovakia via Svrčinovec border crossing and leads to the town of Žilina with a length of 36.8 km. It is relatively busy not only with individual passenger transport but also with freight transport, as it forms a significant transit junction to two neighbouring states. Long traffic jams (sometimes more than 10 km long) are an everyday part of this section of the road [42][43][44]. Traffic flow monitoring was performed at two points of the road in the Kysucké Nové Mesto district ( Figure 1). This section was chosen due to the high volume of traffic. The intersection near Kysucké Nové Mesto plays a key role in the quality of the traffic flow. A few years ago, this intersection was changed from a roundabout to an intelligent light-controlled intersection.
A large number of people worked from home (home office) during the strict restrictions related to the spread of coronavirus. This has reduced the number of passengers in public transport, but also the number of vehicles on the road network. In public transport vehicles, the number of seats had been reduced and it had been and still is obligatory to have face protection. All cultural and sports events have been cancelled. Some sports matches were held but without the presence of spectators. This also had an impact on the number of vehicles on the roads and the number of passengers in public transport vehicles. In terms of reduced demand for transport, several connections in public transport were cancelled and the demand for freight transport was also reduced. Unfortunately, specific numbers are not yet available. During strict measures, state borders were closed, and it was not possible to travel abroad. However, freight vehicles were allowed to cross the border, but drivers were tested to see if they were infected. The survey was carried out online and it was necessary to monitor the individual measured values during the day and record them at 15-min intervals. Subsequently, graphs and tables were made. A large number of people worked from home (home office) during the strict restrictions related to the spread of coronavirus. This has reduced the number of passengers in public transport, but also the number of vehicles on the road network. In public transport vehicles, the number of seats had been reduced and it had been and still is obligatory to have face protection. All cultural and sports events have been cancelled. Some sports matches were held but without the presence of spectators. This also had an impact on the number of vehicles on the roads and the number of passengers in public transport vehicles. In terms of reduced demand for transport, several connections in public transport were cancelled and the demand for freight transport was also reduced. Unfortunately, specific numbers are not yet available. During strict measures, state borders were closed, and it was not possible to travel abroad. However, freight vehicles were allowed to cross the border, but drivers were tested to see if they were infected. The survey was carried out online and it was necessary to monitor the individual measured values during the day and record them at 15-minute intervals. Subsequently, graphs and tables were made.

Traffic Data Collection
The monitored road section is one of the main road sections. Traffic detectors are located in two places (point 1 and 2- Figure 1), which record the average speed and flow (Figure 2) of the vehicles at 15-minute intervals.
The marked points in the picture are 6.5 km apart. Point 1 is 3.8 km from junction A and point 2 is 2.7 km away. This traffic information is available 24 hours a day and is processed by the National Traffic Information System. Then, they are published on the webpage odoprave.info. The National Traffic Information System (NSDI) is a comprehensive system environment for the obtaining, processing,

Traffic Data Collection
The monitored road section is one of the main road sections. Traffic detectors are located in two places (point 1 and 2- Figure 1), which record the average speed and flow ( Figure 2) of the vehicles at 15-min intervals. A large number of people worked from home (home office) during the strict restrictions related to the spread of coronavirus. This has reduced the number of passengers in public transport, but also the number of vehicles on the road network. In public transport vehicles, the number of seats had been reduced and it had been and still is obligatory to have face protection. All cultural and sports events have been cancelled. Some sports matches were held but without the presence of spectators. This also had an impact on the number of vehicles on the roads and the number of passengers in public transport vehicles. In terms of reduced demand for transport, several connections in public transport were cancelled and the demand for freight transport was also reduced. Unfortunately, specific numbers are not yet available. During strict measures, state borders were closed, and it was not possible to travel abroad. However, freight vehicles were allowed to cross the border, but drivers were tested to see if they were infected. The survey was carried out online and it was necessary to monitor the individual measured values during the day and record them at 15-minute intervals. Subsequently, graphs and tables were made.

Traffic Data Collection
The monitored road section is one of the main road sections. Traffic detectors are located in two places (point 1 and 2- Figure 1), which record the average speed and flow ( Figure 2) of the vehicles at 15-minute intervals.
The marked points in the picture are 6.5 km apart. Point 1 is 3.8 km from junction A and point 2 is 2.7 km away. This traffic information is available 24 hours a day and is processed by the National Traffic Information System. Then, they are published on the webpage odoprave.info. The National Traffic Information System (NSDI) is a comprehensive system environment for the obtaining, processing, provision, publication and distribution of traffic information and data on the current traffic situation on the whole road network in Slovakia [34,45]. The webpage provides comprehensive information The marked points in the picture are 6.5 km apart. Point 1 is 3.8 km from junction A and point 2 is 2.7 km away.
This traffic information is available 24 h a day and is processed by the National Traffic Information System. Then, they are published on the webpage odoprave.info. The National Traffic Information System (NSDI) is a comprehensive system environment for the obtaining, processing, provision, publication and distribution of traffic information and data on the current traffic situation on the whole road network in Slovakia [34,45]. The webpage provides comprehensive information on the current traffic situation, in particular on road traffic restrictions, traffic accidents and driving conditions. Records from this online traffic survey were used for analysis and subsequent comparison [31,45].

Evaluation of Online Traffic Surveys
The surveys aimed to obtain information about traffic on the monitored section, then compare and evaluate it. Two traffic surveys were carried out. Both surveys lasted 12 h, from 6:00 to 18:00. The maximum speed at the monitoring point was 90 km/h. The first traffic survey was carried out on the Sustainability 2020, 12, 7216 6 of 19 5th of March 2020, before the introduction of measures against the spread of coronavirus. The second was carried out four weeks later-the 2nd of April 2020. Clear to partly cloudy weather was during both surveys. The first survey was carried out spontaneously to obtain information about the traffic situation in the monitored section. However, after the introduction of the restrictions, a second survey was deliberately carried out and compared with the first one. The green columns in figures represent the peak hour.

Evaluation of Online Traffic Surveys
The surveys aimed to obtain information about traffic on the monitored section, then compare and evaluate it. Two traffic surveys were carried out. Both surveys lasted 12 h, from 6:00 to 18:00. The maximum speed at the monitoring point was 90 km/h. The first traffic survey was carried out on the 5th of March 2020, before the introduction of measures against the spread of coronavirus. The second was carried out four weeks later-the 2nd of April 2020. Clear to partly cloudy weather was during both surveys. The first survey was carried out spontaneously to obtain information about the traffic situation in the monitored section. However, after the introduction of the restrictions, a second survey was deliberately carried out and compared with the first one. The green columns in figures represent the peak hour.

Evaluation of Online Traffic Surveys
The surveys aimed to obtain information about traffic on the monitored section, then compare and evaluate it. Two traffic surveys were carried out. Both surveys lasted 12 h, from 6:00 to 18:00. The maximum speed at the monitoring point was 90 km/h. The first traffic survey was carried out on the 5th of March 2020, before the introduction of measures against the spread of coronavirus. The second was carried out four weeks later-the 2nd of April 2020. Clear to partly cloudy weather was during both surveys. The first survey was carried out spontaneously to obtain information about the traffic situation in the monitored section. However, after the introduction of the restrictions, a second survey was deliberately carried out and compared with the first one. The green columns in figures represent the peak hour.

The First Traffic Survey (5.3.2020)
The total number of monitored vehicles in 12 hours in point 1 was 2906 vehicles in both directions, which represents an average of 242 vehicles per hour. The following figures show the daily variation of the flow and speed of vehicles at point 1 for both directions ( Figure 3).    A total of 4527 vehicles passed point 2 during the survey (377 veh/h). The following figures show the flow and speed of vehicles for both directions at this point.
In the first case ( Figure 5), the peak hour was from 6:45 to 7:45-289 vehicles. The maximum number of vehicles in 15 minutes interval was between 7:15-7:30 (86 vehicles). The speed at the monitored point was about steady until 14:15 then there was a decrease. In the opposite direction ( Figure 6), the peak hour was between 8:15 and 9:15-245 vehicles. The speed at the monitored point did not change significantly. The maximum number of vehicles in 15 minutes interval was from 6:15 to 6:30-92 vehicles.      A total of 4527 vehicles passed point 2 during the survey (377 veh/h). The following figures show the flow and speed of vehicles for both directions at this point.
In the first case ( Figure 5), the peak hour was from 6:45 to 7:45-289 vehicles. The maximum number of vehicles in 15 minutes interval was between 7:15-7:30 (86 vehicles). The speed at the monitored point was about steady until 14:15 then there was a decrease. In the opposite direction ( Figure 6), the peak hour was between 8:15 and 9:15-245 vehicles. The speed at the monitored point did not change significantly. The maximum number of vehicles in 15 minutes interval was from 6:15 to 6:30-92 vehicles.

Result and Discussion
The previous figures show the recorded values in monitored points 1 and 2 separately for both days of the survey. Figures 11-16 show a comparison of the recorded values at the monitored points during 5.3.2020 and 2.4.2020.
In fact, it is a graphical comparison of the recorded intensity and speed at point 1, 2 separately according to the direction of the traffic flow. It is a combination of histograms (Figures 3-10).

Result and Discussion
The previous figures show the recorded values in monitored points 1 and 2 separately for both days of the survey. Figures 11-16 show a comparison of the recorded values at the monitored points during 5 March 2020 and 2 April 2020.

Result and Discussion
The previous figures show the recorded values in monitored points 1 and 2 separately for both days of the survey. Figures 11-16 show a comparison of the recorded values at the monitored points during 5.3.2020 and 2.4.2020.
In fact, it is a graphical comparison of the recorded intensity and speed at point 1, 2 separately according to the direction of the traffic flow. It is a combination of histograms (Figures 3-10). In the first measurement (Figure 11), the flow reached a maximum value of 75 veh/15 min. However, in the second measurement, its maximum value was 20% lower (60 veh/15 min). In the first measurement, the speed of the vehicles had a fluctuating tendency during the day. During the morning, the lowest speed was 22 km/h. This was caused by regular traffic jams, which arise in front of the crossroads near Kysucké Nové Mesto. During the second measurement, due to the lower flow of the vehicles, the speed did not change significantly. The highest recorded value was 79 km/h, but in the first survey, the maximum speed was 73 km/h.   In fact, it is a graphical comparison of the recorded intensity and speed at point 1, 2 separately according to the direction of the traffic flow. It is a combination of histograms (Figures 3-10). In the first measurement (Figure 11), the flow reached a maximum value of 75 veh/15 min. However, in the second measurement, its maximum value was 20% lower (60 veh/15 min). In the first measurement, the speed of the vehicles had a fluctuating tendency during the day. During the morning, the lowest speed was 22 km/h. This was caused by regular traffic jams, which arise in front of the crossroads near Kysucké Nové Mesto. During the second measurement, due to the lower flow of the vehicles, the speed did not change significantly. The highest recorded value was 79 km/h, but in the first survey, the maximum speed was 73 km/h.   In the opposite direction (Figure 12), the decrease in the flow is also clearly visible in the second measurement. The maximum flow was 41 veh/15 min. It was approximately 30.5% higher (59 veh/15 min) during the first measurement. The speed did not change significantly during the second measurement. However, the difference in maximum speed between measurements is around 17%. In the second measurement, it reached a value of 84 km/h.
In conclusion, the flow has decreased after the introduction of measures against the spread of coronavirus. The same statement can be made with the achieved values in the second monitored point. The flow measured during the first survey (blue colour) reached higher values than can be seen from the graphic processing. The maximum flow at the second measurement was reached in the interval 8:30-8:45 (70 veh/15 min-2.4.). At the first measurement, the maximum flow was almost 18% higher. The speed in both cases was around 80 km/h. However, the speed at the first measurement slowed down significantly after 14:00, which was caused by the formation of a traffic jam in front of the intersection.    The flow measured during the first survey (blue colour) reached higher values than can be seen from the graphic processing. The maximum flow at the second measurement was reached in the interval 8:30-8:45 (70 veh/15 min-2.4.). At the first measurement, the maximum flow was almost 18% higher. The speed in both cases was around 80 km/h. However, the speed at the first measurement slowed down significantly after 14:00, which was caused by the formation of a traffic jam in front of the intersection.      Figures 15 and 16 show a graphical course of the flow and speed recorded during the surveys at the monitored points for both lanes. It can be seen from the figures that the flow at both monitored points reached higher values during the first measurement. Following the introduction of measures against the spread of coronavirus, the flow decreased. On the contrary, due to the decrease in the flow, the speed of vehicles has increased.       Figures 15 and 16 show a graphical course of the flow and speed recorded during the surveys at the monitored points for both lanes. It can be seen from the figures that the flow at both monitored points reached higher values during the first measurement. Following the introduction of measures against the spread of coronavirus, the flow decreased. On the contrary, due to the decrease in the flow, the speed of vehicles has increased.     In fact, it is a graphical comparison of the recorded intensity and speed at point 1, 2 separately according to the direction of the traffic flow. It is a combination of histograms (Figures 3-10).

Comparison of the Flow and Speed at Monitored Points
In the first measurement (Figure 11), the flow reached a maximum value of 75 veh/15 min. However, in the second measurement, its maximum value was 20% lower (60 veh/15 min). In the first measurement, the speed of the vehicles had a fluctuating tendency during the day. During the morning, the lowest speed was 22 km/h. This was caused by regular traffic jams, which arise in front of the crossroads near Kysucké Nové Mesto. During the second measurement, due to the lower flow of the vehicles, the speed did not change significantly. The highest recorded value was 79 km/h, but in the first survey, the maximum speed was 73 km/h.
In the opposite direction (Figure 12), the decrease in the flow is also clearly visible in the second measurement. The maximum flow was 41 veh/15 min. It was approximately 30.5% higher (59 veh/15 min) during the first measurement. The speed did not change significantly during the second measurement. However, the difference in maximum speed between measurements is around 17%. In the second measurement, it reached a value of 84 km/h.
In conclusion, the flow has decreased after the introduction of measures against the spread of coronavirus. The same statement can be made with the achieved values in the second monitored point.
The flow measured during the first survey (blue colour) reached higher values than can be seen from the graphic processing. The maximum flow at the second measurement was reached in the interval 8:30-8:45 (70 veh/15 min-2.4.). At the first measurement, the maximum flow was almost 18% higher. The speed in both cases was around 80 km/h. However, the speed at the first measurement slowed down significantly after 14:00, which was caused by the formation of a traffic jam in front of the intersection.
In the opposite direction, the highest value of flow was 92 veh/15 min, recorded in the time interval 6:15-6:30. In the second survey, the maximum flow was 30% lower (64 veh/15 min). The speed of the vehicles in both measurements was around 80 km/h. All of the above comparisons were table processed using descriptive statistics. For the flow and speed, the basic characteristics of the position (mean value, median and mode) were calculated.
The final values and their comparison are shown in the following tables. Table 1 shows the average flow for both measurements and their subsequent comparison and evaluation. The average flow value at the first measurement in point 1 reached 29 veh/15 min. The median was 26 veh/15 min. After the introduction of measures against the spread of coronavirus, the second survey found out that the average flow value increased by one. The average and median reached the same values, 30 veh/15 min. Only in this case, the flow did not decrease, but on the contrary, increased by 3.87%. This may be because, in the first measurement, more intervals were recorded with the lower flow but large variance. On the contrary, in the second survey, most of the intervals reached lower values but with smaller variance. In contrast to the average values, the mode reached a lower value in the second measurement. The most frequently recurring flow value for the 15-min interval was 28 veh/15 min in the first measurement, and 15 veh/15 min in the second measurement.
In other directions, the average flow of vehicles decreased after the introduction of restrictions. In relative terms, this decrease represented a variance of 24%-40%.
The previous Table 2 shows the average speed values for the individual directions. The average speed of vehicles increased, which caused a decrease in the flow. The number of vehicles on the road decreased, which led to an increase in the quality of traffic flow in the form of increased speed. The relative decrease in the average speed between the surveys is shown in Table 2. The highest increase in speed was found in the direction Kysucký Lieskovec-Kysucké Nové Mesto, up to 42.51%. The highest average value of speed during the first measurement was 80 km/h and the second measurement was 84 km/h in the direction Kysucké Nové Mesto-Budatín.   The average flow of vehicles in the first survey reached the value of 61 veh/15min in point 1. In the second survey, it reached a value of 49 veh/15min, it is a decrease of about 19%. In point 2, there was a decrease of almost 26% between the two surveys. The mode of flow in the first measurement reached 68 veh/15min and in the second measurement, it decreased to 46 veh/15min. Due to the decrease in the intensity of vehicles at the monitored points, the average speed increased on average by 12.38% in point 2, and by as much as 22.25% in point 1.
The following figures (Figures 17 and 18) show a graphical course of the flow and speed between 2019 and 2020, specifically 2 April recorded at the monitored points for both lanes a comparison made. d 2020, specifically 2 April recorded at the monitored points for both lanes a comparison made.         Table 4, as in the previous case.
The average value of the traffic flow in 2019 reached in point 1. 158 veh/15min and in point 2. to 186 veh/15 min.The last comparison shows a huge decrease in the flow of vehicles in 2020 compared to 2019. This decrease represents almost by 70% fewer vehicles in 2020 than on the same day in 2019. However, the average speed in point 2 did not show a very large difference of 9.23%, on the contrary in point 1 it increased by more than 20%.
Due to the fact that there was an increased fluency of the traffic flow (increased speed and a decrease in the flow of vehicles), it is possible to state a positive effect of the restrictions on the quality of movement at the monitored points. However, the crisis has also shown that it is possible for governments can take measures that have a great impact that also have the support of the people. This is important: The people are the ones who experience the benefits of clean air and the disappearance of congestion [46].
Already in the graphical comparison of surveys, a decrease in the flow of vehicles, and an increase in the speed of the current traffic flow can be seen. Using descriptive statistics, the positions of the monitored quantities were calculated and compared with each other. These comparisons were made in two ways: separately for each direction and together for both directions. The analysis and comparison of individual directions showed that in one case the number of vehicles increased on average by 3.87%. In other cases, there was a strong decrease in the average flow of vehicles in the range of 23.97% to 39.28%. The highest decrease (39.28%) was found in the direction Kysucké Nové Mesto-Kysucký Lieskovec (point 1). In our case, however, there is a decrease in the number of vehicles on one road. But, for example, when comparing 2019 and 2020, the decrease in traffic flow reached almost 70% at both points, similar to [15,22,23,25].
The traffic flow has decreased in cities around the world. Somewhere traffic flow drops by more than 80% [47]. In China, for example, there has been a dramatic reduction in road traffic during the control period. The flow of commercial vehicles and buses in the Beijing-Tianjin-Hebei region and its surrounding areas decreased by 77% and 39%, respectively, during the control period [25]. In the UK, data shows motor traffic dropped by 73% on 29 March [23] compared with pre-outbreak levels. The analysis shows the number of road miles travelled has not been this low since 1955. Bucsky states that mobility was severely reduced, at least by 51% and maximally by 64%, and the middle estimate suggests a reduction of 57% in Budapest for the second half of March [48]. In our study, we did not look at the reduction of traffic throughout the Slovak Republic, but we focused on one path. Therefore, it can be assumed that the decrease in the flow of vehicles in road transport could be even lower, as reported by other studies.
The decrease in the average flow of vehicles caused an increase in the average speed in the range of 4.8-42.5%. It is interesting that in the direction Kysucký Lieskovec-Kysucké Nové Mesto, where the flow rate increased, there was the largest increase in speed. In the case of the analysis of the average values of the traffic flow in both directions, there was a decrease in the flow rate and an increase in speed in the monitored points 1 and 2. In point 1, the average flow rate decreased by almost 19%, in the second point by almost 26%. This fact and the interaction between the monitored variables caused an increase in the speed of 12.38% in point 2 and 22.25% in point 1. Several studies [13,[49][50][51] also confirm an increase in speed on the roads in response to a reduction in traffic flow but also points to an increase in average speeds between 2019 and 2020.
In addition to the decrease in the flow rate, air quality records in the Slovak Republic also showed a decrease in pollutants. In cities, changes in NO 2 concentrations from the 10-year average range from −6 to −41%, with the average for all stations at −24%. Changes in NO x concentrations from the 10-year average are more pronounced from +29% to −53%, with an average of −25%. The observed trends are less pronounced compared to 2019, where the average decrease from all stations is at −23% for NO 2 and −21% for NO x . PM10 concentrations in the considered period of 2020 compared to the average of 2010-2019 decreased, on average, by −14% and compared to 2019 increased by +1% [52]. Results from the study [53] given at a resolution of 20 km show decreases in NO 2 concentrations ranging from −30% to −50% in all Western European countries. The reduction in NO 2 concentrations in Madrid (Spain), under COVID-19 lockdown during March 2020 were 50% and 62%, respectively [54]. Others authors in Barcelona (Spain) assessed air quality changes during the lockdown in the city of Barcelona and observed a 31% and 51% reduction of particulate matter (PM10) and nitrogen dioxide (NO 2 ), respectively, during the lockdown compared to the month before the lockdown [55]. The NO 2 levels of São Paulo decreased during the partial lockdown: −45% compared to the same period in 2019 [26] and e.g., in Almaty (Kazakhstan), CO and NO 2 concentrations reduced by 49% and 35%, respectively [56]. In one study from Morocco, it was found that during social distance, NO 2 levels fell by up to 96% and −75% for PM10 [57]. Sharma et al. [58] observed a 31% reduction of particulate matter (PM10), respectively, during the lockdown compared to the same time period of the past four years in India.
In the Slovak Republic, the decrease in NO x and NO 2 concentrations in the urban area could be due to two factors over the period considered-favourable dispersion conditions or a decrease in emissions due to the measures under consideration, in particular in transport and to a lesser extent in industry. For PM10, the situation is more complicated. These concentrations are more affected by cross-border transmission (significant cross-border transmission was observed at the beginning of measures with a likely source of dust in the Karakum Desert and around the Caspian Sea) and the most significant source of their household emissions-over 60%, followed by agriculture, transport (around 9%) and industry.
During the COVID-19 epidemic, various public health measures, such as encouraging social distancing, lockdown of cities, and travel restrictions. This could be explained by the fact that the emergency measures (lockdowns), related to the cessation of industrial and transportation activities, had as a consequence a limitation in NO 2 emission from both industrial production and vehicle exhaust, which has implicated a decrease in NO 2 concentrations during this period. Air quality also improves with the reduction of production activities and human mobility. At the same time, the lockdowns during COVID-19 are stricter, and the compliance of residents is better than usual. Reduced traffic in cities is also a major benefit in terms of reducing pollution in NO 2 and other local pollutants. Therefore, the air quality index decreases more after the private vehicles are restricted during this period. Comparing the air quality in 2019 and 2020, Cadotte [59] found that governments can improve air quality through policy change. Hepburn et al. consider the possible positive and negative effects of COVID-19 on climate change and tend to be very optimistic [60].

Conclusions
Currently, transport is a basic and very important part of society. In general, increasing the flow of vehicles increases the load on the road network to unfavourable values and the use of roads reaches the maximum capacity. However, an important task is to design and build roads that will suit the current as well as the future flow of vehicles.
The article aimed to point out how the measures against the spread of COVID-19 limited the quality of the traffic flow on the selected section of the road. Specifically, it was the first-class road I/11, which connects the cities of Žilina andČadca. It is one of the most congested road sections in Slovakia. The flow and speed data from the National Traffic Information System were used as a basis for analysis and evaluation. The flow and speed of the traffic flow were monitored at two points in both directions during two surveys (before and after the introduction of measures). In addition to traffic surveys, a comparison was made between 2019 and 2020 for the same day, 2 April. In this case, there was a significant decrease in the number of vehicles. Based on our results, it can be argued that the lockdown and social distancing had a great impact on the decline in traffic not only in our country but also around the world, as describe in other studies.
Based on the findings, it can be argued that the measures related to COVID-19 had a positive effect on improving the quality of traffic flow in the monitored road section. In addition to reducing the flow of traffic on roads and in cities around the world, there has been a reduction in air pollutants in the Slovak Republic and other countries. However, several scientific studies have shown that this state of emergency has affected not only road transport but all modes of transport worldwide. In this way, the negative effects of traffic and industry such as particulate matter such as emissions, noise, etc. have been reduced. Although the partial lockdown has contributed to a positive impact on air quality, reducing the number of vehicles and our planet has become "greener and healthier" for a while. It is important to consider the negative impacts on social aspects, considering the deaths caused by COVID-19 and also the economic effects. Funding: This research was funded by the Slovak scientific grant agency VEGA of the Ministry of Education-no. 1/0436/18-Externalities in road transport, an origin, causes and economic impacts of transport measures.

Conflicts of Interest:
The authors declare no conflict of interest.