The graph illustrates the number of car accidents in the United States from 2013 to 2023. The x-axis represents the years, abbreviated from '13 to '23, while the y-axis displays the annual number of crashes. Over this 11-year period, the number of accidents ranges from a low of 5,251,006 in 2020 to a high of 6,821,129 in 2016. Other notable figures include 6,756,084 crashes in 2019 and 5,686,891 in 2013. The data exhibits significant fluctuations, with a peak in 2016, a sharp decline in 2020, and subsequent variations in the following years. This information is presented in a line graph format, effectively highlighting the yearly changes and overall variability in car accidents across the United States.

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Category: TransportationTraffic fatality and serious injury crashes from 2014-2018 in the city of Houston. Crashes reported to Texas Department of Transportation. Across all road users (motor vehicle-motor vehicle, motor vehicle-pedestrians, motor vehicle-bicyclist). Local, major, and frontage roads according to STAR*Map (Southeast Texas Addressing and Referencing Map). Excludes major highways. Minimum 4.5 severe crashes (deaths and serious injuries) per half-mile segment. Corridor-level data.

We use RideAustin OD trips to examine the effect of ridesourcing exposure on road safety outcomes, such as road crashes, injuries, fatalities, and DWI offenses. Spatial fixed effects panel data models are employed to establish that RideAustin use is significantly associated with a decrease in total road crashes, injuries, and DWI offenses in Travis County, Texas. On the contrary, our findings do not demonstrate significant relationships between ridesourcing use and road fatalities. Given the significant costs associated with road safety outcomes and DWI offenses [57], ridesourcing services can be a low-cost option that could assist cities and counties with meeting goals for road injuries and DWI offenses decrease. At the same time, the magnitude of the road safety externalities reduction associated with ridesourcing trips is smaller compared to the effectiveness that has been documented after the application of other interventions, including seatbelt laws, reduced speeds, and traffic calming design. This outlines the need for determining population segments that ridesourcing-related solutions or policies could be more impactful for improving road safety, like focusing on younger ridesourcing demographics). Our analysis augments existing work in this field by accounting for spatial distributions of ridesourcing use, road safety outcomes, and other socio-economic characteristics in the given region. Instead of testing associations of the launch of ridesourcing with road injuries and the rest of safety outcomes, we account for spatio-temporal characteristics and capture actual ridesourcing use via real-time trip data analytics in Travis County. The spatial panel data modeling outcomes show that spatial dependence is of significance. Thus, granular longitudinal travel, road safety, and socio-demographic panel data can provide transportation and traffic safety agencies that opportunity to uncover associations and plan for appropriate safety interventions.