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 6,000 people will have been injured or disabled, the direct result of a traffic accident (ASIRT, 2021; NSC, 2021).
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 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. His analytical workflow is outlined below.
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. 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.
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.
From the space-time trend map below, Lixin can identify broad problem areas.
The bright red hexagons (, New Hot Spot) on the map are locations with statistically significant high crash rate values during the final four months of 2015. The deep red hexagons (, Consecutive Hot Spot) are locations that have had a run of high crash rate values over the past couple of years only. The pink and white hexagons (, Sporadic Hot Spot) are locations with intermittent statistically significant high crash rate values.
Examining the trends in three dimensions makes these temporal patterns clearer.
In the 3D map on the right below, 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 of high crash rate values. The blue bins are statistically significant space-time clusters of low crash rate values.
By focusing on different areas around the county, Lixin can interactively explore traffic accident trends.
Notice in the 3D map below, for example, that the column labeled is a New Hot Spot. It is symbolized using gray for all time periods except the top one (the most recent time period), which is red. The gray coloring indicates that the crash rate pattern is not statistically significant (it isn't higher or lower than expected). The column labeled is gray for all of the earlier time periods with red shaded bins for all of the most recent time periods. This is the definition of a Consecutive Hot Spot. The column labeled has red bins at the bottom and at the top of the column, separated by gray bins. It is significant (red), then not significant (gray), then significant again. This is the definition for a Sporadic Hot Spot.
Is Lixin done?
There are a couple of important problems with this quick exploratory analysis of traffic accident trends.
- The spatial analysis used to assess hot and cold spot areas is based on Euclidean distance rather than the actual road network.
- 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.
Are there high crash rate hot spots on the road network?
Two crashes separated by a river or 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 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 that have occurred there. For each count, he computes the crash rate per mile, per year. Next he connects all of the road segment crash rates using restrictions imposed by the actual road network. When he runs hot spot analysis, he can now see the locations on the road network where high crash rates cluster spatially. The red sections of the road network below are locations with statistically significant clustering of high crash rates.
This map provides some specific target locations where traffic safety can be evaluated and remediation measures can be implemented to prevent future accidents.
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 p.m. and 5:00 p.m. (between hours 15 and 17).
Where do workweek, 3:00 to 5:00 p.m. 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 to a map of the crash hot spots for accidents occurring between 3:00 and 5:00 p.m. Monday through Friday. There are some differences in the two maps. He notices, for example, US Route 1 just north of Florida State Road 404 (Pineda Causeway) is not a hot spot area for high crash rates overall; it is, however, a statistically significant hot spot location on weekdays between 3:00 and 5:00 p.m.
Lixin examines the accidents that occurred along US Route 1 during the afternoon workweek commute to see if there are any patterns. Several accidents in this area involved distracted drivers. Billboards or increased ticketing for using cell phones while driving may help reduce accidents in this location.
What are the trends for particular peak crash days and times?
Next, Lixin examines weekday 3:00 to 5:00 p.m. 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 brightest red hot spot ribbons. A blue arrow in the map below points to a wall of red. This is a persistent problem area for traffic accidents Monday through Friday during the afternoon commute.
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 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.
- How does the spatial pattern of fatalities differ from the spatial pattern of traffic accidents overall? See the workflow provided with this case study.
- 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.