This is a topic I became very interested in before my untimely retirement from DGR, UNM in 2012. Much of the traffic safety data reported by DGR in New Mexico was based on aggregate counts of crashes at various scale locations (statewide, counties, cities and towns, road segments and intersections). The calculation of crash rates based on traffic volume was only readily available for major roadways that were part of the Interstate, US, or New Mexico road systems. Although traffic volume data was available for local road segments and intersections, the problems related to combining traffic volume and crash data for these roads had yet to be adaquetely resolved.

A cooperative pilot project was undertaken with the Mid-Region Council of Governments (MRCOG) and DGR that resulted in the assignment of MRCOG intersection identification numbers (IDs) to crashes that were geocoded by DGR. This allowed both DGR and MRCOG to calculate crash rates by traffic volume at local intersections. Both organizations were able to produce more informative maps and to begin a more rigorous process of statistical analyses of problem locations based on these crash rates over time. This process was developed using DGR's SAS based GIS (GRNDB - Geographic Road Network Database) used for the mapping and analysis of traffic crash locations (linear referenced and georeferenced to intersections). As future GRNDB development and maintenance has been discontinued in favor of reliance solely on ESRI's ArcGIS, I'm not sure if crash data will be assigned MRCOG's intersection IDs after the 2011 data was processed. It is important to note that maintaining a uniquely assigned intersection and segment IDs was essential for this work. This underlying data structure has been clearly illustrated by several national mapping initiatives such as Great Britain's Ordnance Survey.

Unfortunately, I can not present some of the maps (PDF's and Web based) prepared by DGR as part of the MRCOG cooperative project and other projects as I did not have permission from the New Mexico Department of Transportation, Traffic Safety Bureau (NMDOT, TSB) to release this information before my retirement and UNM's GPS has shut down the web servers with ArcGIS Server that were hosting these services. Regardless, there will be some examples of previously available (public) interactive web based crash maps on my NM web based traffic crash plus census and demographic data page (just screen captures for now). Also, MRCOG has done an excellent job of compiling crash rate related data that resulted from this pilot project. Their Safety Analysis and Reporting page has link to a PDF report that contains this information. Another excellent and more analytical study on the Albuquerque's Red Light Camera Program also used crash rate data provided by DGR that was possible due to this cooperative project (study conducted by UNM's Institute for Social Research).

I hope to be able to use crash rate data in the future to take a step beyond mapping and focus on the statistical analyses of this data to help determine which local intersections and road segments are long-term problem locations for various types of crashes. We were just at the point to be able to do these types of studies at DGR before the reorganization. I will explore the possibilities of obtaining this data in the future and conducting cooperative studies with other organizations in New Mexico who share an interest in improving the quality of traffic safety related suudies.

I recently worked on a special project with UNM's Earth Data Analysis Center during the spring and summer of 2013 preparing an extensive intersection based dataset for New Mexico. This dataset used as sources both the New Mexico E911 county files combined with the intersections (GRNDB - Geographic Road Network Database) that DGR maintained for the New Mexico Traffic Saferty Bureau (NM TSB). SAS was used to combine these files and derive a multitude of aliases for each intersection that were present in both sources throughout New Mexico. This resulted in a very comprehensive database that can be used for law enforcement and emergency computer aided dispatch plus various other georeferencing applications. As this intersection database is derived using a combination of SAS and ArcGIS it provides an excellent foundation for further analytical studies of traffic crashes such as problem identification.

Also see NM web based traffic crash plus census and demographic data for some examples of DGR's previous work for the access and display of New Mexico's traffic crash data.

Esri has recently made individual non-commercial organizational accounts available for ArcGIS Online that are part of their ArcGIS for Personal Use program. Esri has also been encouraging government agencies to participate in their Open Data program. Hopefully more New Mexico government agencies such as the New Mexico Department of Transportation, Traffic Safety Bureau (NMDOT, TSB) will eventually make their data available to the public as have other states and communities (see Vision Zero and the Vision Zero Network). Currently GPS-TRU, UNM performs work for the NMDOT TSB, but there does not appear to be any Open Data available from them at this time. However, the Mid-Region Council of Governments (MRCOG) has taken the initiative and is currently preparing interactive web based maps (see the High Fatality and Injury Network) using the newer versions of Arc GIS Server that provides more functionality and makes it easier to create web maps. These improvements have allowed them to make traffic crash information more accessible to the public (see MRCOG Safety Analysis and Reporting). Depending on future availability, I hope to use open data resources to prepare more analytical examples of traffic crash and other spatial analysis applications (see my Open Data).

I have not done much work in this area since my retirment from UNM in 2012. At that time I was starting to develop more analytical projects with the traffic crash and related data. Most of the work related to traffic crash analysis in New Mexico is still mostly descriptive although improved reports, web pages, and interactive maps are routinely prepared. Currently these data are not publicly available as open data such as at the City of Denver and from other places. This good government initiative makes these data more accessible for students and other researchers to use and promotes public participation. As I am learning more about Geographic Artificial Intelligence ( GeoAI and AI-ML Class Project, YouTube) I am aware that there have been significant developments using this technology that have proven very useful for traffic safety applications. I will be conducting more research and plan to demonstrate some of these recent analytical methods with a focus on the Albuquerque metropolitian area in the future. I am hopeful that recent traffic crash and related data will be made more accessible. Update: I have not been involved in any traffic safety research since my retirement in 2012 and have been keeping very busy with other research projects. However, I happened to see this recent article Spatial pattern identification and crash severity of road traffic crash hot spots in Ohio that is a good example of the various types of GIS and statistical analysis that could be conducted in New Mexico. Also see the references as there are other useful examples of related studies.

Below are some useful background publications and specific studies that illustrate recent analytical developments in the area of traffic safety problem identification. As my research continues, this section will be expandended with links to useful studies and demonstarations.

Some Traffic (Road) Safety Resources and Examples:

Address and Contact Information

     Larry Spear, Sr. Research Scientist (Ret.) 
     Division of Government Research
     University of New Mexico 
     
     Email: lspear@unm.edu  lspearnm@gmail.com 
     WWW: https://www.unm.edu/~lspear
     LinkedIn https://www.linkedin.com/in/larry-spear-93371970
UNM UNM's Home Page

This page has been accessed 4905 times since 8/9/2012.
You are the 4906th person to access this page.

Last Revised: 1/10/2024 Larry Spear (lspear@unm.edu)