1. Introduction
On 5 April 2021, the World Health Organization (WHO) announced that there were approximately 131 million confirmed COVID-19 cases all over the world [
1]. The U.S., with more than 30 million COVID-19 cases and 554,064 total deaths, ranked first in the globe. Among the U.S. states, Florida ranked third after California and Texas in terms of the highest number of cases [
2]. The Florida Department of Health announced 2,085,306 cases and 33,710 deaths due to coronavirus throughout the state, as of 5 April 2021 [
3]. This issue becomes even more challenging when aging populations are considered, due to their cognitive, behavioral, and health limitations [
4]. According to the Centers for Disease Control and Prevention (CDC), the older population (65+) and those with serious medical conditions such as lung disease, diabetes, and liver disease are at a higher risk of COVID-19 infection [
2,
5]. This is especially a serious issue in Florida, since more than 20% of the total population in the state are 65 years and older [
6]. The expected growth among aging Floridians justifies a need to study the impact of the COVID-19 pandemic on mobility in the State of Florida, given its unique demographic characteristics and associated risks for the roadway users. In addition to the older population’s (65+) special needs and vulnerability to severe crashes, many researchers have recognized the need to study the severity and frequency of youth-involved roadway crashes and the relevant behavioral factors. For example, young drivers (aged 16 to 25) were found to be at greater risk of being involved in a crash that led to casualties compared with other age groups, and this greater danger was usually related to their propensity to take risks while driving [
7] and lacking enough experience to handle critical adverse conditions while driving in various type of crashes [
8]. Although most of the existing research has focused on the noticeable changes in mobility pattern during COVID-19 to limit person-to-person interaction, several important concerns remain unaddressed. In this study, we utilize the existing crash data during the COVID-19 pandemic to answer the following questions:
- (1)
How, and to what extent, did the COVID-19-induced decrease in traffic flow impact the pattern of the crash density?
- (2)
Were there any significant crash pattern differences in selected demographically different Florida counties during the pandemic?
As such, with an extensive suite of spatial and statistical models, this research examines the impacts of the COVID-19 pandemic on the crash density patterns in four Florida counties; namely, Escambia (a mid-size county), Hillsborough (a metropolitan county), Leon (a mid-size college-oriented county which includes the state capital), and Liberty (a rural county) between 15 March 2020 and 2 June 2020 (we name this time period as “2020 After COVID”). Note that these counties have been selected due to their distinct demographic differences. We compare these patterns with those associated with three different periods: (a) 26 December 2019 and 14 March 2020 (2020 Before COVID), (b) the same period in 2019, and (c) the same period in 2018. To the authors’ knowledge, this type of comparative work has not been done previously. As a potential application, the findings of this study can assist state and local agencies in strategic planning efforts for coping with unpredictable COVID-19 impacts on mobility patterns, to improve safety and enhance mobility for road users from different age groups with certain characteristics. This type of analysis can help planners and emergency officials distinguish more vulnerable age groups and impose more efficient countermeasures during the COVID-19 pandemic for targeted populations.
2. Literature Review
There is a vast amount of literature focusing on mobility and crashes; however, this paper specifically focuses on those works that study COVID-19-related traffic safety and operations studies. For more information on the crash literature, please refer to: [
9,
10,
11,
12,
13]. Specifically, GIS-based crash clustering has been utilized by many agencies to identify roadway segments and intersections that pose a high crash risk [
14,
15,
16]. There are several clustering methods found in the literature, including Getis-Ord (Gi*) statistics [
17,
18], Bayesian spatiotemporal modeling [
19], k-nearest neighbor (KNN) algorithm [
20,
21], kernel density estimation (KDE) [
22], spatial weights matrices [
23]. One of the common methodologies used for such a spatial analysis is kernel density estimation (KDE) which can identify the density of events. KDE is also adopted by this study to calculate the density differences between each pair of datasets, given the frequent and successful utilization of this method by previous studies [
24,
25,
26].
Recent studies investigate the COVID-19 pandemic impacts on transportation from various aspects, including spatiotemporal of migration pattern during restrictions induced by the COVID-19 pandemic [
27], accessibility to healthcare facilities [
28,
29], and traffic crashes and operations. According to Road Ecology Center at the University of California-Davis, the number of traffic crashes, crash-related injuries, and deaths were reduced by half during the first three weeks of the shelter-in-place order issued in California. Moreover, they reported a 55% reduction in the number of vehicles on California’s highways and a saving of USD 40 million per day [
30,
31]. Similarly, a 60% decline in traffic crashes, a 43% decline in deaths, and a 64% decline in injuries were observed due to the COVID-19 curfew in Turkey [
32]. Brodeur et al. (2020) also found a 50% reduction in traffic collisions and USD 7 billion to USD 24 billion savings due to avoided car collisions after a stay-at-home order was issued in the states of Alabama, Connecticut, Kentucky, Missouri, and Vermont [
33]. Moreover, Alabama Law Enforcement Agency stated a 48% decline in crashes in April 2020 compared to April 2019 [
34]. The Alabama Department of Transportation also reported a 50% decrease in traffic volumes from 5 April 2020 to 23 April 2020, compared to the same period of the previous year [
34]. Furthermore, the North Carolina Department of Transportation reported a drastic decline in the number of total crashes after the pandemic compared to the same period of 2019 in both urban and rural areas [
35].
Moreover, National Police Foundation statistics showed a drastic decrease in the total number of total crashes in Florida, Iowa, Ohio, Massachusetts, and Missouri during the COVID-19 pandemic, specifically in March and April 2020. However, the findings indicated an increase in the fatality rates compared to the same period in 2019. They suggested that reduced traffic congestion would lead to higher speeds and reckless driving on the roads [
36]. Similarly, the Indianapolis Star newspaper reported an average of 39% decrease in the traffic in Indianapolis, Indiana, from 2 March to 30 March 2020. Moreover, the number of crashes across the state dropped from 15,800 in March 2019 to 11,200 crashes in March 2020 [
37]. Parr et al. [
38] investigated the traffic volume patterns on urban and rural roadways across Florida during the COVID-19 pandemic in March 2020 and compared them with similar dates in 2019. Their findings indicated that the traffic volumes were reduced by 47.5% statewide. Furthermore, they found that the traffic decline in South Florida was less than in other areas in the state and people in southern parts of the state traveled more, although they were at a higher risk due to the more concentration of COVID-19 cases. They did not, however, investigate how, and to what extent, this drastic change in traffic volume patterns during the pandemic impact crash occurrences.
COVID-19 global pandemic has also significantly changed human mobility and travel patterns [
39], resulting in a reduction in vehicle miles travel [
40] and daily travel time [
41]. Information technology-based activities (e.g., telecommuting, telemedicine, telehealth, telelearning) have been offering safer alternatives to physical traveling during the pandemic [
42]. An analysis of traffic patterns during this period has identified that reduction in VMT has a significant negative relationship with COVID-19 cases and deaths across the USA [
43]. This shows that people tend to avoid unnecessary travel and reduce social interactions that would occur during transportation [
44]. Indeed, Doucette et al. (2021) showed that the mean daily VMT was reduced by 43% during the COVID-19 pandemic. Results also reveal that this decrease in VMT also led to less crashes, regardless of the level of injury [
45]. Some researchers focused on safety performance functions (SPF), which demonstrated a positive correlation between AADT and crash counts [
46,
47].
The issue of reduced vehicle usage and ownership and its corresponding impact on various aspects of sustainable development (e.g., air pollution, economics, and traffic operations) have also been widely explored in the literature [
48,
49,
50]. Zero emission policies have also encouraged many people to actively contribute to decreasing private vehicle usage through promoting alternative modes of transportation (i.e., public transit and biking) [
51]. The COVID-19 pandemic may fade away soon or later; however, there is a potential that its impact on people’s tendency toward staying at home, working remotely, and lower private vehicle usage could last for a long time [
52]. This will result in reduced vehicle usage and there is a need to investigate how demographically different areas would respond to this in terms of traffic operations and safety.
On the contrary to existing work, this study intends to investigate crash density reduction patterns in areas with different demographic characteristics. From this perspective, Florida is a particularly interesting case study to assess the traffic safety impacts of the COVID-19 due to (a) a high number of traffic crashes (i.e., Florida is among the top three states in the U.S.), (b) a high number of COVID-19 cases, and (c) a significant percentage of the senior population living in the state [
38].
The remainder of the paper is as follows. First, the required crash data are described, followed by a brief review of the developed GIS-based methodology. The Kruskal–Wallis test results obtained from the differences of crash density are also provided to indicate whether these differences are statistically different or not. The KDE results for each county are, then, presented as separate maps for each time pair to indicate the pattern of crash density from a county-wide perspective. The sets of time series are also provided to (a) depict the temporal distribution of the total number of crashes that occurred during the COVID-19 pandemic, and (b) make a comparison possible for the same number of days in 2019, 2018, and 2020 before the COVID-19 pandemic. Moreover, an index is defined to compare crash patterns between different years to confirm that the crash density difference in 2020 was mainly due to the COVID-19 pandemic, not any other factors, including safety improvement measurements. Furthermore, three separate negative binomial regression (NBR) models were developed to statistically investigate the contribution of socio-demographic and transportation-related parameters on the crash count decrease (CCD) during the COVID-19 pandemic. Finally, the paper concludes with a summary, limitations, and recommendations for future research.
6. Conclusions
The COVID-19 pandemic has been affecting our lives drastically, and many people have been going about their daily lives remotely. With an extensive suite of spatial and statistical models, this study investigated the impacts of the noticeable change in mobility during the COVID-19 pandemic by analyzing its impact on the spatiotemporal patterns of crashes in four demographically different counties in Florida. We tried to evaluate how demographically different areas respond to policies that intend to reduce travel. The results obtained from the Kruskal–Wallis test indicate that COVID-19 conditions led to statistically significant reductions in crash densities in all counties. KDE-based spatial visualization reveals the highest crash density decreases mostly occurred within city limits regardless of different demographic characteristics of counties.
In order to examine the possibility of different responses to CCD from each County with various demographic and transportation-related factors, three separate negative binomial regression models have been developed. Among all these factors, the age-related variables have the most noticeable correlation with differences in crash density distribution during the COVID-19 pandemic. Although both are mid-size counties, NBR models for Escambia and Leon provide different results. The contribution of the youth (18–29) population ratio in the model reveals a percentage CCD change of 65% for every unit increase in the ratio of youth population living in census block groups of Leon County. This is mainly because of the county’s college-oriented nature.
Moreover, in Hillsborough County, we see interesting results regarding the aging (65+) population. The census block groups populated with more aging people seem to have a lower crash count decrease during the COVID-19 pandemic. This may possibly be due to the fact that these seniors did not change their daily travel habits during the COVID-19 pandemic. This may also show a need for the governments to teach them new technologies related to communication, shopping and medicine, so that they can avoid unnecessary trips. The findings for Leon County also reveal that remote working and other telecommunication methods, including e-shopping, decrease trip generation, particularly in the context of bike/walk mode choice “to-work” trips. This leads to more crash count decreases in areas that utilize these types of transportation modes due to COVID-19.
7. Limitations and Future Work
Since the topic is focusing on the crash count decrease during the COVID-19 pandemic, we needed to add some additional variables to the dataset to justify whether the changes in crash frequency and distribution are attributed to the changes in exposure (i.e., people travel less during the COVID-19 pandemic) or other issues. These required attributes include, but are not limited to, vehicle miles traveled (VMT), road network configuration, vehicle type, temporary local traffic management, land use, and trip generation. Further investigation of these additional attributes enables us to establish a cross-section model, including all counties in Florida in different time periods and considering all relevant built environment, transport system, population profile, and traffic factors.
There are several future research directions. For example, the proposed approach can be extended to evaluate the crash severities instead of counts to answer the following questions: How would a decrease in the total number of trips affect the severity of crashes? Would the drivers tend to drive at a higher speed in this case? Moreover, some findings of this research may be site-specific. Therefore, another interesting area of research is to expand this research into other counties. The current research disregarded the rural census block groups that experienced a negligible increase in the number of crashes compared to urban groups. It would be interesting to focus on these regions in more detail. The temporal results can also be utilized to interpret three-dimensional mapping of crash density differences in future research [
77]. Furthermore, applying more advanced methods like propensity score matching (PSM) and empirical Bayes (EB) could provide reliable findings for a before-and-after comparison. Moreover, several researchers incorporated land use variables and assigning their contributions to the various types of crashes [
78,
79,
80], so further investigation is required to assess how land use correlates with crash density patterns during the COVID-19 pandemic.