This story map requires JavaScript, but running JavaScript is not currently allowed by your web browser. If you wish to view this story, please enable JavaScript in this browser or try a different browser.

Analyzing traffic accidents in space and time

Add your image or video

Can GIS analysis make our roads safer?

You probably didn't wake up today thinking you would lose a loved one in a car crash. Unfortunately, before this day ends, more than 100 people in the United States will have died, and an additional 6000 people will have been injured or disabled, the direct result of a traffic accident (ASIRT, 2002-2006; NSC, 2016).

Dr. Lixin Huang, IT Engineer II, is a GIS analyst for Brevard County, Florida. He knows that Florida's interstates have been ranked among the Nation's deadliest and that the number of traffic accidents in Brevard County is increasing.

Number of car crashes by year in Brevard County, Florida

The cost associated with traffic accidents is staggering. In addition to the devastation of lost lives, highway crashes across the nation are estimated to cost $871 billion dollars each year. The overwhelming majority of these accidents are entirely preventable.  

Lixin hopes that by identifying where and when crashes occur throughout Brevard County, he might be able to help prevent some of them. He begins with a quick exploratory analysis of traffic accident trends around the county.  He then focuses on where accidents occur along the road network.  Finally, he examines temporal cycles and 3D cyclical trends.  His analytical workflow is outlined below.  A step by step tutorial and all of the data used for his analysis are also available.

What data is needed?

Lixin obtains crash data from the University of Florida GeoPlan Center. It includes the location, date, and time for every motor vehicle traffic accident in Brevard County between 2010 and 2015. Each traffic accident is shown as an orange point on the map below.  Click on a point to learn more.

Brevard County traffic accidents 2010 to 2015

Notice that it is difficult to discern any kind of pattern from the point locations alone. Lixin decides to restructure the data so he can examine space-time trends.

Traffic accident trends

Where are traffic accidents increasing?

Lixin performs a quick exploratory space-time pattern analysis to confirm that the number of traffic accidents is increasing overall, and that the increase is statistically significant.   

The number of crashes is different every month, of course. Finding a statistically significant increase in the number of crashes between 2010 and 2015 indicates the increase is not just the result of random fluctuations.

By focusing on different areas around Brevard County, Lixin can interactively explore traffic accident trends and identify broad problem areas.

Space-time traffic accident trends

Is Lixin done?

There are a couple important problems with this quick exploratory analysis of traffic accident trends.

1. The spatial analysis used to assess hot and cold spot areas is based on Euclidean distance rather than the actual road network.

2. The analysis does not consider important temporal cycles such as the workweek rush hour.

Lixin will refine his analyses to address both of these problems.

Road Network Crash Hot Spots

Are there high crash rate hot spots on the road network?

Two crashes separated by a river or by a major highway might be close together as the crow flies (Euclidean distance), but far away from each other on a road network with few bridges or underpasses. Because hot spot analysis is looking for high crash rates that cluster close together, accurate distance measurements are essential.

Lixin aggregates all of the crash and fatality data between 2010 and 2015 onto Brevard County roads so that individual segments of the road network get a count representing the number of crashes and the number of fatalities that have occurred there. For each count, he computes the per mile, per year rate. Next he connects all of the road segment crash and fatality rates using restrictions imposed by the actual road network. When he runs hot spot analysis, he can now see and compare the locations on the road network where high crash rates and high fatality rates cluster spatially. 

The red sections of the road network are locations with statistically significant clustering of high rates.  The map on top shows hot spots for all traffic accidents.  The bottom map shows hot spots for fatal traffic accidents.

Comparing hot spots for all traffic accidents to hot spots for fatal traffic accidents

These maps provide specific target locations where traffic safety can, and should, be evaluated. They indicate locations where remediation measures may help prevent future accidents.

Cyclical Traffic Accident Patterns

When are the most dangerous times to be driving?

The number of car accidents increases with the number of drivers on the road. Lixin decides to look for cyclical patterns in the crash data. He creates a graph showing the number of crashes by day of the week and by hour of the day. Several peaks emerge, but the strongest is associated with the workweek between 3:00 and 5:00 PM (between hours 15 and 17).

The number of vehicle crashes is highest during the workweek between 3:00 and 5:00 PM.

Where do workweek 3:00 to 5:00 PM crashes occur?

Lixin wonders if the locations of traffic accidents associated with the afternoon workweek commute are the same as those on other days and at other times. He compares a map of the crash hot spots for all accidents (left below) to a map of the crash hot spots for accidents occurring between 3:00 and 5:00 PM Monday through Friday (right below). There are some differences in the two maps. 

Comparing hot spots for all traffic accidents to hot spots for traffic accidents between 3:00 and 5:00 PM Monday through Friday

Lixin notices, for example, that US Route 1 just north of Florida State Road 404 (Pineda Causeway) is not a hot spot area for high crashes overall; it is, however, a statistically significant hot spot location on weekdays between 3:00 and 5:00 PM.  He examines the traffic accidents in this area and learns that several involved distracted drivers.  Increased ticketing for cell phone use while driving may help reduce accidents here.

What are the trends for particular peak crash days and times?

Next, Lixin examines weekday 3:00 to 5:00 PM crash trends in space and time using a 3D visualization. By stacking road segment crash hot spots for each year, he can identify locations that are persistent problem areas during the workweek afternoon commute. 

High Workweek 3 to 5 PM Crash Rate Trends

Workflow Summary

What has Lixin accomplished?

Lixin's workflow has answered the following questions.

* Which intersections and roadways in Brevard County have the highest crash rates?

* When and where do most crashes occur?

* How does the spatial pattern of fatalities differ from the spatial pattern of traffic accidents overall?

* How does the spatial pattern of crash rates occurring during the workweek afternoon commute differ from the overall pattern of crash rates?

* Over time, which intersections or roadways are persistent problem areas for traffic accidents?

This same workflow may be extended to answer additional questions.

* Where are the hot spot areas for crashes involving elderly drivers, teenage drivers, or alcohol related accidents?

* When and where do accidents involving elderly drivers, teenage drivers, or alcohol cluster spatially?

By understanding where and when traffic accidents occur throughout the county, Lixin will be able to make more informed recommendations for policies and other measures that can help reduce traffic accidents in the future.

Next steps

Data and a tutorial with a detailed ArcGIS Pro workflow

References and resources

An error has occurred

10%