Decline in Traffic Congestion Increased Crash Severity in the Wake of COVID-19

Abstract

Highway fatalities are a leading cause of death in the U.S. and other industrialized countries. Using highly detailed crash, speed, and flow data, we show highway travel and motor vehicle crashes fell substantially in California during the response to the COVID-19 pandemic. However, we also show the frequency of severe crashes increased owing to lower traffic congestion and higher highway speeds. This “speed effect” is largest in counties with high pre-existing levels of congestion, and we show it partially or completely offsets the “VMT effect” of reduced vehicle miles traveled on total fatalities. During the first eleven weeks of the COVID-19 response, highway driving decreased by approximately 22% and total crashes decreased by 49%. While average speeds increased by a modest 2 to 3 mph across the state, they increased between 10 and 15 mph in several counties. The proportion of severe crashes increased nearly 5 percentage points, or 25%. While fatalities decreased initially following restrictions, increased speeds mitigated the effect of lower vehicle miles traveled on fatalities, yielding little to no reduction in fatalities later in the COVID period.

Keywords

data and data science statistical methods analysis urban transportation data and information systems speed data safety safety performance and analysis crash data crash frequency crash severity hazard analysis modeling and forecasting

Each year more than 30,000 motor vehicle fatalities occur in the U.S. ( 1 ), with 1.35 million deaths worldwide ( 2 ). Fatal crashes reflect a tremendous amount of driving, over 3 trillion vehicle miles traveled (VMT) ( 3 ), and many crashes, over 6 million, each year in the U.S. ( 4 ). Understanding the determinants of fatal crashes is an important public policy issue. Further, government policies that affect VMT, congestion, and speed may indirectly affect vehicle crashes. Therefore, quantifying the relationships between these factors and fatalities is important for designing and evaluating public policies.

While increased VMT and higher speeds should clearly affect motor vehicle fatalities, isolating and quantifying their individual effects is empirically challenging owing to the interplay between the demand for driving, traffic congestion, speeds, and crashes. Cross-sectional analysis of the effect of these factors on fatalities is prone to omitted variable bias (e.g., differences in road conditions, funding, policing, underlying attitudes, etc.). Time-series analysis is complicated by reverse causality in the timing of crashes and congestion-related speed reductions—that is, crashes that cause congestion, or time-varying trends in driving, crashes, and fatalities (e.g., changes in vehicle technologies and safety).

We overcome these challenges by exploiting the reduction in travel due COVID-19 restrictions to estimate the causal effects of VMT and average vehicle speeds on crashes and motor vehicle fatalities. Decreased driving during COVID-19 led to large reductions in VMT and higher average vehicle speeds in congested urban areas. We measure these shifts using hourly data on VMT and traffic speeds at thousands of locations across California from the Freeway Performance Measurement System (PeMS) ( 5 ). We combine these data with reports from the California Highway Patrol (CHP) Incident Report System, also collected through PeMS. Using a text analysis of these detailed incident reports (several tens of millions of individual entries at the minute time-scale), we categorize crashes by severity and whether a fatality occurs. Because weather plays a role in many crashes, we collect hourly weather data from the NOAA ISD-lite system ( 6 ) and match station-level observations to California counties based on proximity. Combined, these are the most comprehensive micro-level data on motor vehicle-related fatalities available.

Regression analysis shows travel restrictions decreased VMT by approximately 22% and total crashes by approximately 49%. Average highway speeds increased by 2 to 3 mph across all counties, but increased as much as 10 to 15 mph during peak hours in some counties. The share of severe crashes increased nearly 5 percentage points, or approximately 25%, during the COVID period. We find a 1% increase in VMT increases fatalities by about 1% and cannot reject an elasticity of one. A 1% increase in average speed increases fatalities by about 4%. These parameter estimates imply reduced VMT during the COVID period would have reduced fatalities in California by approximately 50%. However, higher average speeds owing to reduced congestion mitigate this effect by half, such that the total decrease in fatalities was approximately 25%.

Using detailed data on the characteristics of drivers and crashes in California during this period, we investigate the potential role of compositional shifts in increased fatalities. During the COVID period, younger drivers and male drivers make up a larger share of crashes. Vehicles involved in crashes are older, and alcohol and poor weather are more likely to contribute to crashes during the COVID period. An analysis of the fatality age distributions for single car crashes indicates an increase in fatalities among both younger and middle-age drivers. This suggests the increase in crash severity we document is not isolated to younger and potentially riskier drivers.

Our results have policy implications beyond the current COVID crisis. State and local policies that reduce congestion and increase average highway speeds are likely to experience similar increases in crash severity. In contrast to earlier studies that identified the effects of higher speed limits on fatalities on rural highways, the effects identified here are largest on congested urban highways, which carry a substantial fraction of total vehicles. Further, since the effects we estimate move in opposite directions (decreased VMT reduces fatalities, increased speed increases fatalities), we note that our results have important implications for the choice of congestion relief policy. For example, highway expansions that increase both the amount of driving and vehicle speeds will increase fatalities through both channels. By contrast, congestion charges that reduce VMT but increase speeds can either increase or decrease fatalities depending on the relative strength of these channels.

Literature Review

We contribute to a large literature that investigates the determinants of motor vehicle crashes and fatalities resulting from a variety of factors (e.g., Loeb [ 7 ], Loeb and Clarke [ 8 ], Welki and Zlatoper [ 9 ], Burger et al. [ 10 ], and de Vries et al. [ 11 ]). Earlier cross-sectional studies have explored the relationship between VMT and fatalities ( 12 , 13 ). However, an important contrast is that our approach exploits exogenous variation in travel demand and is therefore less susceptible to bias that may occur in cross-sectional studies. The relationship between vehicle speeds and fatalities has been studied in the context of increases in speed limits on rural interstates during the 1980s and 1990s following changes to U.S. national speed limits. While a 10 mph higher speed limit increases average speed between 2 and 4 mph ( 14 – 17 ), fatalities increase substantially, between 15% and 60% ( 14 , 17 – 21 ). However, extending these results to urban areas or metro-area highways has been challenging in part because urban vehicle speeds are often limited by congestion rather than speed limits ( 22 ). Further, systematic differences in factors such as hospital access, emergency vehicle response times, vehicle fleet composition, and the prevalence of divided highways imply the effect of speed on fatalities is likely different in urban areas. Understanding effects in urban areas is especially important as over 80% of U.S. population live in urban areas ( 23 ).

Our VMT results are most comparable to causal estimates using exogenous changes in traffic demand from Israeli drivers observing the Jewish Sabbath ( 24 ). They note the potential concerns related to omitted variable bias and take advantage of the astronomically defined and time-varying start and end of the Sabbath over the course of the year. They find that a 10% increase in VMT leads to a 10% increase in severe crashes. This figure is comparable to earlier panel data estimates from the U.S. ( 25 ). We find a similar VMT effect and also investigate crash severity. Specifically, the decrease in VMT in California during the initial COVID-19 period increased the proportion of severe crashes. Similarly, panel data have been used to characterize the relationship between crashes and traffic flows in London ( 26 ), though the study does not investigate crash severity. One trade-off of note is that the causal estimates in the Sabbath study are necessarily limited to specific times and traffic flow levels (e.g., after sundown on Saturdays), while the panel data approaches are able to examine a broader set of driving conditions, at the cost of potentially biased estimates. The COVID-19 traffic restrictions used in our analysis provide exogenous variation similar to the Sabbath study, but we are able to look at a much wider set of driving conditions, comparable to earlier panel data efforts.

Compositional changes may also be an important factor in the relationships between VMT, crashes, and fatalities. For instance, policies or changes in economic conditions may increase the proportion of risky drivers on the road. A study of Ohio drivers finds risky drivers reduced their VMT more during the Great Recession leading to a larger overall reduction in fatalities than from the drop in VMT alone ( 27 ). Here, we investigate both compositional shifts and whether the reduction VMT in California during COVID increased the risk posed by riskier drivers. Because congestion can constrain speeding and other risky behavior, alleviating congestion my increase accident severity.

Finally, given interest in policies to alleviate congestion ( 28 ), our results speak to the potential effects of congestion pricing and highway construction on crashes and fatalities. Earlier studies analyzed the effects of the London congestion charge ( 29 ) on congestion, VMT, crashes, and fatalities. The congestion charge reduced the total number of fatalities ( 30 ) and the number of accidents ( 31 ). Importantly, Green et al. ( 31 ) note the increased speed owing to the congestion charge may increase accidents and their severity. Here, we use the reduction in VMT in California during COVID-19 to investigate the hypothesis that lower congestion increases accident severity. Because policies such as highway construction reduce congestion without lowering VMT, while others such as congestion charges reduce both VMT and congestion, understanding the contributions of both components is essential for understanding the effects of policies on fatalities and severe accidents.

Data

We combine detailed data on motor vehicle travel and fatalities from several sources. Vehicle miles traveled and average speeds are from the PeMS ( 5 ). PeMS reports hourly traffic data for major highways in 42 of California’s 58 counties (additional information on PeMS monitoring network is provided in the Supplemental Information). Hourly data are collected for the period from March 1, 2015 through May 31, 2020. Observations are county-level VMT totals and mean speeds calculated from thousands of traffic sensors (loop detectors) throughout the state. We sum hourly VMT to the daily total within each county. We calculate mean speed as the average across all detectors within a given county.

Detailed crash data are collected by the CHP Incident Report System and made available through PeMS ( 5 ). Each record contains the time, location, duration, and a description of the crash. The CHP data also include police dispatch codes that we use to classify crashes as minor, severe, or unknown. Severe crashes are those where the dispatch code reports a fatality (1144), requests an ambulance (1179, 1141), or reports a major injury (1180). Minor crashes are those with dispatch codes reporting minor injuries (1181) or no injuries (1182, 20002) and crashes classified as unknown are those reported with unknown injuries (1183, 20001).

Fatality data are also derived from a text analysis of the CHP incident reports. Dispatch codes reported in CHP incident reports denote probable fatalities (1144). However, many incidents with different initial dispatch codes ultimately result in a fatality. More detailed notes accompanying each incident report indicate whether a fatality subsequently occurred. Therefore, we scrape CHP’s detailed incident notes and perform a text analysis to determine an accurate fatality count. Specifically, we search the detailed incident notes for words such as “coroner” and “veh 1144” to determine whether a fatality has occurred.

Because weather, in particular rainfall, is a key factor in many traffic crashes ( 32 ), we collect weather data from the National Oceanic and Atmospheric Administration ( 6 ). We collect hourly precipitation, cloud cover, wind speed, wind direction, temperature, and pressure. Hourly data are collapsed to daily average precipitation, wind speed, wind direction, cloud cover, temperature, and pressure. Stations are matched to counties based on the shortest distance between each station and each county’s population-weighted centroid. Because the effect of rainfall on crashes may be non-linear, our main empirical results include an indicator variable that equals one if the daily total rainfall in a county exceeds 5 mm. In specifications using weekly data, heavy rainfall is defined as weeks with weekly total rainfall greater than 10 mm. Robustness checks presented in the Supplemental Information include additional weather controls.

Finally, for our analysis of compositional changes we obtain detailed data on the characteristics of drivers involved in crashes from the California Highway Patrol Statewide Integrated Traffic Records System ( 33 ). These data report driver characteristics such as sex, age, and ethnicity as well as crash characteristics such as vehicle type, weather, crash severity, and whether alcohol was involved. We calculate the mean values of driver and crash characteristics immediately before and then during the initial COVID period and interpret differences in these values as evidence of compositional shifts during COVID-19 driving restrictions.

Traffic Changes During Initial COVID-19 Related Restrictions

Figure 1 plots daily VMT, average highway speeds, weekly crashes, and crash severity for Los Angeles, Sacramento, San Diego, San Francisco, and Santa Clara counties (these represent approximately 44% of highway VMT in the state—authors’ calculations using PeMS data). We note three dates: First, March 4, 2020, the day California Governor Gavin Newsom declared a state of emergency related to the COVID-19 pandemic. Second, March 12, 2020, the date of the Governor’s executive order limiting large gatherings and enacting social distancing measures. Third, March 19, 2020, the beginning of the California stay-at-home order. Each of these events likely had a different effect on driving within the state and their relative importance is not clear a priori.

Figure 1.

Time-series of traffic patterns in six California counties before and after COVID-19 related travel restrictions: (a) vehicle miles traveled, (b) weekly crash totals, (c) average highway speeds, and (d) changes in the share of severe crashes.

The VMT and crash data in Figure 1 are normalized to account for differences in scale across cities. For VMT, Figure 1a, we account for daily traffic patterns by first regressing VMT on day-of-week fixed-effects, using observations from 2020 before the Governor’s executive order. We estimate the model separately for each city to account for differences in daily traffic patterns and mean VMT levels across cities, and then plot the ratio of observed VMT to predicted VMT. For crashes, Figure 1b, we aggregate crashes to the weekly level to smooth day-to-day variability and better illustrate the county-level trends. We again estimate separate models for each county and plot the ratio of observed crashes to model predictions based on rainfall and week-of-year fixed effects. For crash severity, Figure 1d, we expand the sample to five years before March 2020 to preserve statistical power. We predict the mean severe crash share for each week and county during 2020 and plot the difference between the observed and predicted shares, based on week-of-year fixed effects and an indicator for heavy rainfall. This measure gives the change in the share of severe crashes, in percentage points, over time.

Figure 1a shows VMT trends for the six counties, which are essentially constant through the state of emergency declaration and are decreasing only slightly before the March 12 executive order. However, following the executive order, VMT decreased sharply for several weeks, falling as much as 50% by the beginning of April. The largest decreases are for Santa Clara and San Diego counties, while the decline in VMT is smaller in Alameda and Los Angeles Counties. Following several weeks of declining VMT, driving begins to increase during the month of April. By the end of May, VMT rises to near 75% of pre-COVID levels. Figure 1b shows similar crash trends for the six counties. Crash totals are noisy but essentially constant during the first part of 2020. Crashes begin to decrease around the time of the executive order, falling to below 50% of pre-COVID levels. However, by week 16, 4 to 5 weeks following the executive order, crashes begin to increase slightly.

Figure 1c shows average speeds for the six counties increased as VMT declined. Before COVID-19 restrictions, average speeds in counties such as Los Angeles do not reach free flow levels, even on weekends, and range between 55 mph and 60 mph. Speeds begin to increase in the week before the Governor’s executive order. Following the order, average speeds are 5 to 10 mph higher across the six counties, which suggests that when crashes do occur, they are likely more severe. Throughout the month of April, speeds remain high despite the increase in VMT, as highways were still largely uncongested. However, by May average speeds begin to decrease, indicating a return to congested conditions.

Figure 1d shows the share of severe crashes in the six counties over time, coded based on police dispatch codes from the CHP incident reports. The share of severe crashes post COVID-19 restrictions increases between 5 and 10 percentage points, providing evidence of a substantial increase in crash severity, as this represents a doubling of severe crash share in some counties. The largest effects are in San Francisco and Santa Clara, counties that saw the largest speed increases in Figure 1c. Overall, the trends illustrated in Figure 1 suggest COVID-19 restrictions had large effects on vehicle travel and crashes in California. We quantity the average effects across all PeMS counties in the section below.

Empirical Analysis

To quantify the mean effects of COVID-19 travel restrictions on traffic, crashes, and fatalities across California, we estimate a series of models of the form:

y_{it} = β_{0} + E O_{t} + δ_{it}^{hr} + ϵ_{i} + ϵ_{it},

(1)

where $y_{it}$ is an outcome of interest (VMT, average speed, crashes, or crash severity) in county $i$ on date $t$ . We account for mean differences across counties using county fixed-effects $ϵ_{i}$ . We model the effect of rainfall on traffic patterns and crashes with an indicator variable $δ_{it}^{hr}$ that is equal to 1 if rainfall is heavy, as described above. We show in the Supplemental Information that the results presented below are robust to alternate specifications. The main parameter of interest is an indicator variable for the start of COVID-19 travel restrictions $E O_{t}$ . Observations occurring after Governor Newsom’s March 12, 2020 executive order are coded as 1, based on the timing of the VMT decline in Figure 1a. Therefore, $E O_{t}$ measures the mean effect of the COVID-19 travel restrictions across all counties during the treated period.

For VMT, we specify the dependent variable as the natural logarithm of VMT to account for differences in scale in the treatment effect across counties with widely varying levels of driving. We account for changes in the size of the PeMS monitoring network over time by including the number of PeMS “lane-points” (or lanes-monitoring locations) in each county on each day as an additional explanatory variable in our VMT model.

We model average highway speed in miles per hour. Because the effect of changes in speed on fatalities also varies with the number of drivers exposed to these changes in speed, we estimate a weighted average treatment effect using weighted least-squares where the weights are county-level VMT. Crash severity is modeled as the share of severe crashes as indicated by CHP dispatch codes on each day in each county. We model the number of crashes and fatalities per day in each county as count variables and estimate Equation 1 using Poisson regression.

Table 1 presents results for our estimates of Equation 1. Column 1 shows that log daily VMT decreases by −0.249 across all counties, or about 22%, after implementation of the COVID restrictions. Column 2 presents results for crashes, which fall by −0.647 or approximately 48%. While crashes decrease overall, Column 3 shows that the share of severe crashes increases by 4.8 percentage points, or about 25% during this period. In column 4 we see average speeds increase by about 2.0 mph as a result of COVID-19 restrictions. However, this figure ignores the fact speed increases are greater when more drivers are affected, that is in the more congested counties with greater traffic volumes. Column 5 presents the estimated speed increase from a weighted least squares regression where counties are weighted by daily VMT, whereby the estimated effect is over 50% larger, approximately 3.1 mph.

Table 1.

Regression Analysis of COVID-19 Related Travel Restrictions on VMT, Crashes, Highway Speeds, and Fatalities

	ln(VMT)	ln(Crashes)	Severe share	Avg. speed	Avg. speed wgt.	ln(Minor)	ln(Fatalities)	ln(Fatalities)
Post E.O.	−0.249*** (0.0480)	−0.647*** (0.0370)	0.048*** (0.0060)	2.033*** (0.2710)	3.066*** (0.5520)	−0.917*** (0.0500)	0.072 (0.1130)
Rain > 5 mm	−0.040** (0.0160)	0.475*** (0.0360)	−0.017*** (0.0040)	−0.508*** (0.1640)	−0.583*** (0.0940)	0.189*** (0.0330)	0.182** (0.0890)	−0.352 (0.5750)
ln(VMT)								1.048*** (0.1080)
ln(Speed)								4.352*** (1.8980)
Lanepoints	Yes	No	No	No	No	No	No	No
County
Fixed-effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Observations	78,990	77,488	58,048	78,990	78,990	77,488	70,341	6,074
Adj. R-sq.	0.97		0.05	0.41	0.48

Note: Vehicle miles traveled (VMT) measured in millions of miles per county per day for Freeway Performance Measurement System (PeMS) counties. Crashes are the sum of California Highway Patrol (CHP) severe, minor, and unknown incidents by CA county and day. The severe share is the share of all crashes classified as severe according to CHP dispatch codes. Average speed is the average speed on PeMS highways over all hours of the day. Weights are total county level daily VMT. Minor crashes and fatalities are CHP reported totals by county and date for 2020. Standard errors clustered at the date level. Post E.O. is the period following the Governor's executive order. The abbreviation na indicates the corresponding table entry in not applicable.

***

and ** denote significance at the 1% and 5% levels.

The decrease in total crashes is driven by a decrease in minor crashes. Column 6 shows a reduction in log daily minor accidents of 0.917, about 60%. However, while minor accidents decrease, the increases in speed and crash severity likely increase fatalities. We see evidence of this in column 7 that shows the net effect of these factors could be slightly positive, about 7%, though the estimate is not statistically significant.

To identify the relationships between driving, vehicle speeds, and traffic deaths we model fatalities as:

\begin{matrix} \ln (Fatalitie s_{it}) = β_{0} + β_{1} lnVM T_{it} \\ + β_{2} lnSpee d_{it} + δ_{it}^{hr} + ϵ_{i} + ϵ_{it}, \end{matrix}

(2)

where $lnVM T_{it}$ is the natural logarithm of VMT and $lnSpee d_{it}$ is the natural logarithm of average speed in county $i$ on date $t$ . Again, $δ_{it}^{hr}$ is an indicator variable for days with heavy rainfall and $ϵ_{i}$ are county fixed-effects. A common challenge in modeling fatalities is that unobserved factors that are correlated with VMT and speeds may also be correlated with fatalities leading to omitted variable bias. Here, we exploit the COVID-19 travel restrictions to isolate exogenous variation in VMT and highway speeds. We focus on a narrow window of time, approximately ten weeks before and ten weeks following the implementation of travel restrictions to isolate plausibly exogenous shifts in travel behavior. We estimate Equation 2 using Poisson regression. Parameter estimates are reported in column 7 of Table 1. The coefficient $β_{1}$ can be interpreted as the “VMT effect” on fatalities, while the coefficient $β_{2}$ can be interpreted as the “speed effect'' on fatalities.

The relationship between vehicle speed and flow (VMT) is well known to be backward bending; that is, at high levels of congestion, speed and flow decrease simultaneously. Here, we use flow and VMT interchangeably because county-level daily VMT is proportional to daily flow when network size is fixed. Figure A1 in the Supplemental Information shows speed–flow relationships for six representative counties. The reduction in driving during the COVID period shifted many California counties out of these highly congested “hypercongestion” conditions to less-congested travel. As a result, average traffic speeds increase substantially. In the most congested regions and periods, initial reductions in driving increased both speeds and flows. Ultimately, large reductions in travel demand led to free-flow (or nearly free-flow) speeds and lower vehicle flows. Because of these shifts, the data have sufficient variation in both VMT and speed to allow separate estimation of both effects. Specifically, we find a 1% increase in VMT increases fatalities by about 1%. A 1% increase in average speed increases fatalities by approximately 4%.

To gauge whether these estimates are reasonable, consider a conceptual model for traffic fatalities where the probability a driver is involved in an crash is a constant $ρ$ (per mile driven), such that the product $ρ \times VM T_{it}$ is the expected number of crashes in county $i$ and day $t$ . Some fraction of these crashes will be severe enough to result in a fatality. For simplicity, assume the likelihood of a fatal crash is proportional to the amount of kinetic energy in the collision (proportional to vehicle speed squared). Under these assumptions, the expected number of fatalities is:

Fatalitie s_{it} = α ρ VM T_{it} \times {Speed}_{it}^{2},

(3)

where $α$ is a constant. Taking the natural logarithm yields the following equation, where $γ = \ln (α ρ)$ :

\begin{matrix} \ln Fatalitie s_{it} = γ + 1 \times \ln VM T_{it} \\ + 2 \times \ln Spee d_{it} . \end{matrix}

(4)

Under this model, the VMT coefficient would be 1, and if one could measure each vehicle’s speed, the speed coefficient would be approximately 2. However, since the relative infrequency of fatal crashes requires some amount of aggregation, our regression analysis only measures changes in average speed at the daily level. This average reflects relatively larger increases in speed in congested counties during hours with the most driving and a near-zero change during uncongested evening and early morning hours. Therefore, we expect the speed coefficient to be somewhat larger than 2, as found in Table 1.

Heterogeneity and Aggregate Effects

The mean effects presented in Table 1 hide important heterogeneity in the data, as counties with different baseline levels of driving and congestion have different impacts from COVID-19 restrictions. Figure 2 decomposes the effect of COVID-19 restrictions on speed into different periods of the day (AM peak, mid-day, PM peak, and night) for county quintiles based on historical (5 year) measures of congestion. In the most congested counties (top row, Q5), there are large increases in average speeds during the daytime periods, ranging from 5 to over 15 mph. The largest increases occur during the afternoon peak. Less congested counties experience smaller speed increases, on the order of 5 to 10 mph for counties in the fourth quintile of congestion and 0 to 5 mph for counties in the third quintile. The least-congested counties (bottom row, Q1) see little-to-no change in average speeds, and there are no nighttime effects outside of the fifth quintile.

Figure 2.

Average COVID-19 related speed effects (change in mph) by time of day: AM Peak is 6 to 9 a.m., Mid-day is 9 a.m. to 4 p.m., PM Peak is 4 to 7 p.m. and Night is 7 p.m. to 6 a.m. The counties are grouped into quintiles of traffic congestion defined as historical average delay using a free-flow speed of 65 mph. Q5 is the most congested quintile, Q1 is the least.

Figure 3 uses the estimates from Equation 2 to decompose county-level changes in fatalities into a VMT effect and a speed effect. To facilitate comparisons across counties, the VMT effect is the decrease in fatalities solely attributable to VMT reductions under COVID-19 restrictions normalized relative to a no-COVID counterfactual. The speed effect is defined as the increase in fatalities owing to higher speeds under COVID-19 restrictions relative to the no-COVID baseline counterfactual. The x-axis shows percentage reductions in fatalities owing to lower driving and the y-axis shows percentage increases owing to higher speeds, such that the 45-degree line is where the two effects exactly cancel, implying no net effect on fatalities. In most counties, reduced VMT lowers fatalities between 20 and 40%. In uncongested counties (green), the speed effect is essentially zero. In moderately congested counties, higher speeds increase fatalities between 10 and 20%, negating about half of the VMT effect. In the most congested counties (red), the speed effect increases to between 20 and 35%. This implies San Francisco and Alameda counties experience a small reduction in fatalities, while Los Angeles experiences a small increase in fatalities owing to COVID-19 restrictions.

Figure 3.

Decomposition of total fatalities into decreases from lower vehicle miles traveled and increases owing to higher speeds. County-level estimates are shown based on Equation 2. The 45-degree line indicates no net change in fatalities. Color coding is based on quintiles in Figure 2.

Figure 4 shows the total fatalities and model predictions for all California PeMS counties over time. We compare predicted fatalities under COVID-19 restrictions that include both the speed and VMT effects (red), with the no-COVID counterfactual (dark gray) and a counterfactual excluding the speed effect (orange). Fatalities fall by approximately 30% during the initial COVID-period relative to the counterfactual. By May, as VMT increases but speeds remain high, the COVID fatality rate increases to a level comparable with the no-COVID counterfactual. Importantly, without the COVID-19 speed effects (orange) the fatality rate during the COVID period would have been substantially lower, by approximately 25%.

Figure 4.

Estimated effects of COVID-19 related travel restrictions on California motor vehicle fatalities. Observed fatalities are shown in light-gray. The estimated no-COVID counterfactual based on Equation 2 is plotted in dark gray. Plotted in red are (smoothed) fatalities under COVID restrictions, that is taking into account the combined effects of reduced vehicle miles traveled (VMT) and increased speed. Plotted in orange is the counterfactual prediction assuming no average speed increase owing to COVID-19 restrictions. The difference between the red and orange plots shows the estimated increase in daily fatalities owing to higher speeds from lower traffic congestion during the treated period.

Driver and Crash Characteristics

In this section, we exploit detailed crash and driver data from the California Highway Patrol Statewide Integrated Traffic Records System to explore potential mechanisms for the crash and fatality effects. We focus on characteristics that may be associated with riskier drivers or more dangerous driving conditions. For example, one hypothesis is that the composition of drivers changed, as riskier drivers were relatively less likely to stay home during the COVID period. Given that a reduction in congestion had previously constrained these drivers’ speeding or risky behavior, a reduction in congestion might now enable more speeding thereby increasing crash severity.

Our analysis focuses on two periods: the ten-week period in 2020 immediately before the California Executive order and the ten-week period immediately following the order. Our sample includes all crashes on all types of roadways during the period. Table 2 presents the mean of each characteristic in each period.

Table 2.

Crash Characteristics During the Pre-COVID and COVID Periods

	Pre-COVID	COVID period	Change	p-value
Driver age (years)	40.06	39.00	−1.07	0.000
Driver over 65	0.080	0.65	−0.014	0.000
Driver under 25	0.197	0.211	0.014	0.000
Driver male	0.614	0.674	0.060	0.000
Young male driver	0.119	0.140	0.021	0.000
Alcohol involved	0.089	0.113	0.024	0.000
Speeding (fatal crash)	0.169	0.194	0.025	0.244
Rain	0.021	0.060	0.039	0.000
Wet roadway	0.059	0.140	0.081	0.000
Darkness	0.371	0.276	−0.095	0.000
Fatal crash	0.0048	0.0082	0.0034	0.000

Beginning with driver age, we see the mean age of drivers involved in and crash in the pre-COVID period is approximately 40 years. The mean age falls by approximately 1 year during the COVID period. This change is largely driven by a decrease in the proportion of older drivers and an increase in younger drivers. In the pre-COVID period, approximately 8% of drivers involved in crashes were over the age of 65. The share of older drivers involved in crashes decreased to 6.5% during the COVID period. For drivers under the age of 25, the share grew from 19.7% to 21.1%. Drivers involved in crashes are 6 percentage points more likely to be male during the COVID period. Overall, these shifts mean the proportion of crashes involving young males increases from approximately 12% to 14%.

There is some evidence these shifts led to riskier driving behavior. During COVID, the percentage of crashes where alcohol use is indicated increased from 8.9% to 11.3%. There is also suggestive evidence speed was more likely a factor in fatal crashes, increasing from 16.9% to 19.4%. Though this effect is not statistically significant, it is consistent with our regression results above indicating increases in highway speeds led to increases in fatalities. Further, one would expect increases in speed to be less of a factor in the statistics reported in Table 2, since this sample included crashes on all California roadways—many of which may have seen less congestion relief during COVID than the PeMS sample of major highways used in the analysis above.

If the shifts above imply riskier drivers are more likely to be on the road and involved in crashes during COVID, we would expect an increase in fatalities for these groups. Figure 5 plots the distribution of fatalities by driver age in the pre-COVID and COVID periods. We limit our sample to single car crashes to remove the influence of drivers of other vehicles on fatalities, though effects for crashes involving multiple vehicles are quite similar to those presented here. During the COVID period we see increases in the number of fatalities among younger drivers. However, we also see similar increases in fatalities among middle-aged drivers. There is a small decrease in fatalities for older drivers during the COVID period.

Figure 5.

The distribution of driver ages involved in fatal single car crashes in the 10 weeks before and 10 weeks following the California Executive Order.

Overall, these trends suggest substantial compositional shifts in the types of drivers involved in crashes in California during this period. However, the net effect on crash severity and fatalities is less clear. While we do observe shifts toward drivers typically considered risky, mainly young drivers, fatalities increase across a wide range of ages. While age is an imperfect proxy for riskiness, this result suggests to us the speed effects identified above apply more broadly than simply to a subset of risky drivers.

As a final check we investigate weather and daylight hours, as they are conditions typically associated with crash risk. These measures reflect the change in weather during the spring of 2020, namely rainfall increases later in the sample and the sun rises earlier and sets later thereby increasing hours of daylight. Since these effects are offsetting, wet roadways increase risk and better lighting decreases risk, it is difficult to sign the overall affect. However, the weather statistics underscore the need to account for rainfall as we do in our estimates above. Since the speed increases are greatest during uncongested mid-day hours, the effect of longer days seems unlikely to bias our results.

Discussion and Conclusions

While we acknowledge our point estimates for California during the COVID period may not translate directly to other settings, our results are roughly consistent with earlier studies. However, an important contrast is that our approach exploits exogenous variation in travel demand and is therefore less susceptible to bias as in cross-sectional studies. More generally, since many parts of the U.S. experienced similar reductions in VMT as California ( 34 ), our results likely generalize to congested urban highways outside our sample.

Our results have potentially important policy implications. Policies aimed at reducing highway fatalities should focus on the causes of severe crashes. Our estimates show reductions in driving led to a substantial drop in total crashes, due in large part to a reduction in minor crashes. However, speed increases from lower congestion led to a greater proportion of severe crashes and increased fatalities. Policies aimed at reducing all types of crashes could have the unintended effect of increasing fatal crashes.

More broadly, highway expansion and congestion pricing are oft-discussed policies to manage traffic demand ( 28 ), and given they reduce congestion and increase traffic speeds, they are susceptible to the effects highlighted here. For instance, in the fall of 2010, a new 11-mi stretch of carpool (HOV) lane opened on route CA-60 east of Los Angeles. In the months following the opening, peak-hour mainline speeds increased between 10 and 20 mph (authors’ estimates from PeMS data). Such an expansion can increase fatalities both through increased VMT and faster speeds—in this case the 16% increase in average VMT would increase fatalities by 16% while the average speed increase of 8% would increase fatalities by 32%. Taken together, our estimates imply that the HOV lane expansion on CA-60 and corresponding increase in speeds increased the crash risk by nearly 50% on that route in the short run. In the long run, the well-known phenomenon of induced demand would likely return speeds to near pre-HOV lane levels.

By contrast, congestion pricing can also increase speeds, but it does so by reducing VMT. For example, beginning in 2003 London levied a £ 5.00 daily charge on vehicles entering central London, which reduced car VMT by 34% and correspondingly increased traffic speeds by 17% ( 29 ). Directly applying our estimates suggests that fatalities would increase, on net, by 34% as the increase in fatalities from the traffic speed effect would exceed the reduction via the VMT effect. Noting these potentially offsetting effects of VMT reductions and speed increases, Green et al. ( 31 ) empirically estimate the impact of the London Congestion Charge on severe crashes and fatalities using monthly data and find that they actually fell by 25% and 35% respectively. This discrepancy is likely attributable to the type of roads under consideration—the 17% increase in speeds was from 8.9 mph to 10.4 mph on the surface streets of central London, which is a rather different context than the much higher speed urban highways of California considered here. As such, while the London congestion charge appears to have provided substantial social benefits in the form of reduced fatalities on the surface streets of central London, our findings (e.g., Los Angeles county in Figure 3) suggest this would be less likely to be true in the context of urban highways.

Finally, our analysis highlights an important secondary effect of COVID-19 travel restrictions. In addition to supporting public health goals, COVID-19 restrictions led to improvements in air quality, and reductions in greenhouse gas emissions and energy use ( 34 – 36 ). Here, we show how COVID-19 restrictions had dramatic impacts on VMT, highway speeds, crashes, and fatalities. While there was speculation that the sharp decline in driving could lead to a substantial reduction in traffic fatalities, the speed rebound effect we highlight here mitigated those benefits to some extent.

Supplemental Material

sj-pdf-1-trr-10.1177_03611981221103239 – Supplemental material for Decline in Traffic Congestion Increased Crash Severity in the Wake of COVID-19

Supplemental material, sj-pdf-1-trr-10.1177_03611981221103239 for Decline in Traffic Congestion Increased Crash Severity in the Wake of COVID-19 by Jonathan E. Hughes, Daniel Kaffine and Leah Kaffine in Transportation Research Record

Footnotes

Acknowledgements

The authors thank Matthew Butner for helpful comments on an earlier version of this paper.

Author Contributions

The authors confirm contribution to the paper as follows: study conception and design: J. Hughes, D. Kaffine; data collection: J. Hughes, D. Kaffine, L. Kaffine; analysis and interpretation of results: J. Hughes, D. Kaffine; draft manuscript preparation: J. Hughes, D. Kaffine. All authors reviewed the results and approved the final version of the manuscript.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Daniel Kaffine

Supplemental Material

Supplemental material for this article is available online.

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