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.
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.
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.
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.
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.
New Hot Spots are locations that have had a large number of crashes during the final four months of 2015. Consecutive Hot Spots are locations that have consistently had a large number of crashes over the last year or two. Sporadic Hot Spots are locations that sometimes have a high number of crashes and sometimes don't. Click on the map to see a definition for each type of hot spot trend.
In three dimensions, each hexagon becomes a column of stacked bins. Each bin represents a four month time period with the most recent time period at the top of the column.
The red bins are statistically significant space-time clusters where a large number of crashes occurred. The blue bins are statistically significant space-time clusters with very few crashes.
See if you can find new, consecutive, and sporadic hot spot trends.
Use your mouse to navigate around the 3D map. Tilt the map, for example, by pressing and holding the right mouse button.
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.
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.
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.
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).
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.
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.
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.
The bottom layer of red ribbons reflects crash hot spots for 2010. The top layer of ribbons reflects crash hot spots for 2015. Lighter red ribbons are still statistically significant (road segments where high crash rates cluster), but they are less intense than the darkest red hot spot ribbons.
Use the mouse to navigate around the map and explore other high crash areas.
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.
This case study would not have been possible without input and assistance from Lixin Huang, PhD, GISP, IT Engineer II (GIS), and support from Lois Boisseau, IT Assistant Director for Brevard County, Florida. Thank you!
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