TECHBRIEF Surrogate Safety 

Assessment Model (SSAM) 

FHWA Publication No.: FHWA-HRT-08-049 

FHWA Contact: Joe Bared, HRDS-05, (202) 493-3314, 

joe.bared@fhwa.dot.gov

This document is a technical summary of the Federal Highway 

Administration report, Surrogate Safety Assessment Model 

and Validation: Final Report, FHWA-HRT-08-051.

Objective

The safety of intersections, interchanges, and other traffic facilities 

is most often assessed by tracking and analyzing police-reported 

motor vehicle crashes over time. Given the infrequent and random 

nature of crashes, this process is slow to reveal the need for 

remediation of either the roadway design or the flow-control 

strategy. This process is also not applicable to assess the safety of 

roadway designs that have yet to be built or flow-control strategies 

that have yet to be applied in the field. 

This TechBrief summarizes the research and development of 

the Surrogate Safety Assessment Model (SSAM), a technique 

combining microsimulation and automated conflict analysis, 

which analyzes the frequency and character of narrowly averted 

vehicle-to-vehicle collisions in traffic, to assess the safety of traffic 

facilities without waiting for a statistically above-normal number of 

crashes and injuries to actually occur. 

Research, Development, and 

Technology

Turner-Fairbank Highway 

Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

www.tfhrc.gov

Figure 1. Conflict scenario resulting from a lane change maneuver.
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Introduction

A conflict is a scenario where two road users 

will likely collide without evasive action. Figure 1 

illustrates an example of a conflict, where a vehicle is angling across two lanes to the left-turn bay 

and has abruptly cut in front of another vehicle 

that must decelerate to avoid collision.

Traffic conflicts have been studied since the late 

1960s as a technique to assess the safety of a 

location, with the understanding that conflict 

frequency is correlated with the risk of actual 

collision.

Conflict studies traditionally utilize personnel 

trained to identify and record conflicts observed 

at an intersection. In this research, the SSAM 

software application was developed to automate 

conflict analysis by directly processing vehicle 

trajectory data. Researchers specified an openstandard, “universal” vehicle trajectory data 

format designed to provide the location and 

dimensions of each vehicle approximately 

every tenth of a second. It is hoped that video 

processing technology will, in the coming years, 

be capable of automatically extracting vehicle 

trajectory data adequate for SSAM processing. 

However, the trajectory file format is currently 

supported as an export option by four traffic 

microsimulation models: VISSIM, AIMSUN, 

Paramics, and TEXAS.

Assessment Method

To assess a traffic facility with SSAM, the facility 

is first modeled in one of the aforementioned 

simulation models and then simulated with 

desired traffic conditions (typically simulating 

several replications with different random 

number seeds). Each simulation run results in a 

corresponding trajectory file, referred to as a TRJ 

file corresponding to the .trj filename extension. 

Then, SSAM is used as a post-processor to 

analyze the batch of TRJ files.

SSAM analyzes vehicle-to-vehicle interactions 

to identify conflict events and catalogs all 

events found. For each such event, SSAM also 

calculates several surrogate safety measures, 

including the following:

•	 Minimum time-to-collision (TTC).

•	 Minimum post-encroachment (PET).

•	 Initial deceleration rate (DR).

•	 Maximum deceleration rate (MaxD).

•	 Maximum speed (MaxS).

•	 Maximum speed differential (DeltaS).

•	 Classification as lane-change, rear-end, or 

path-crossing event type.

•	 Vehicle velocity change had the event proceeded to a crash (DeltaV)

SSAM provides the following features:

•	 A table of all conflicts identified in the batch 

of analyzed TRJ files, including file, time, 

location, vehicles identifications, and several 

measures of conflict severity.

•	 A summary of conflict counts by type and file, 

with average values of surrogate measures 

over all conflicts.

•	 A filtering mechanism that allows the isolation 

of subsets of conflicts by ranges of surrogate 

safety measures, conflict type, network link, 

or a rectangular region of the network.

•	 A facility for statistical comparisons of the 

conflict frequencies and values of surrogate 

safety measures for two alternative cases or 

designs using the Student t distribution for 

hypothesis testing.

•	 A display of the location of conflicts on the 

network map, with icons of different shapes 

and colors assignable to different conflict 

types or severities.

Figure 2 is a screenshot of the map display in 

SSAM, with conflict icons color coded according 

to their time-to-collision values.

Validation

To assess the capabilities of SSAM, researchers 
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conducted theoretical validation, field validation, 

and sensitivity analysis.

The theoretical validation effort considered 

11 pairs of intersection designs (e.g., right-turn 

bay versus no right-turn bay; SPUI versus diamond interchange; roundabout versus diamond 

interchange). The relative safety assessments 

of SSAM were compared to assessments using 

traditional, theoretical crash-prediction equations. The results yielded interesting insights, 

though it was often the case that design A had 

more conflicts than design B, while design B had 

higher severity conflicts than design A, pointing 

to the need for further research in interpreting 

and comparing surrogate safety measures.

The field validation effort was concerned with 

the absolute accuracy of surrogate safety 

assessment, in contrast to the relative safety 

assessments of the theoretical validation. A set 

of 83 field sites were selected—all were fourleg, urban, signalized intersections—and were 

modeled in VISSIM and simulated and assessed 

with SSAM. The conflict analysis results of these 

intersections were compared to actual crash 

histories (based on historical insurance claims 

records) using five statistical tests. This effort 

also provided an opportunity for benchmark 

comparison of surrogate safety estimates versus traditional crash-prediction models based 

on average daily traffic volumes (ADT).

The simulation-based intersection conflicts data 

provided by SSAM were significantly correlated 

with the crash data collected in the field, with 

the exception of conflicts during path-crossing 

maneuvers (e.g., left turns colliding with 

opposing through-traffic), which were underrepresented in the simulation. Intersection 

rankings based on total conflict frequency 

correlated with intersection rankings based on 

total crash frequency with a Spearman rank 

coefficient of 0.463 (and similarly for rear-end 

and lane-change incidents). The relationship 

between total conflicts and total crashes in this 

study (shown in figure 3) exhibits a correlation 

(R-squared) value of 0.41.

This correlation of conflicts to crashes is 

consistent with the range of correlations reported 

in several studies between ADT and crashes for 

urban, signalized intersections. This result was 

achieved despite simulating only morning peakhour volumes. Crash prediction models based 

on a yearly average of 24-hour ADT volumes 

exhibited a correlation (R-squared) value of 0.68 

with actual crash frequencies. This study also 

found a conflict-to-crash ratio of approximately 

20,000 to 1, though that ratio varied by conflict 

type.

The sensitivity analysis effort compared 

an assessment of five intersections (of the 

aforementioned 83) conducted separately 

using each of the four microsimulation models: 

VISSIM, AIMSUN, Paramics, and TEXAS. 

Crashes (vehicles driving through each other) 

were found in all simulations, and SSAM proved 

particularly useful in revealing questionable 

simulated behavior (due to user-configuration 

of the model in some cases and underlying 

simulation logic in other cases). This has notably 

prompted some revisions (so far by TEXAS and 

Figure 2. SSAM user interface displaying conflict 

locations on map.

Figure 3. Relationship between conflicts and crashes.
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Researchers—This study was performed by the Intelligent Transportation Systems Business Unit of 

Siemens Energy & Automation, Inc. (Siemens ITS); Principal Investigators Douglas Gettman, and Steven 

Shelby. For more information about this research, contact Joe Bared, FHWA Project Manager, HRDS at 

(202) 493-3314, joe.bared@fhwa.dot.gov.

Distribution—This TechBrief is being distributed according to a standard distribution. Direct distribution 

is being made to the Divisions and Resource Center.

Availability—This TechBrief may be obtained from the FHWA Product Distribution Center by e-mail to 

report.center@fhwa.dot.gov, fax to (301) 577-1421, phone to (301) 577-0818, or online at http://www.tfhrc

.gov/safety/intersect.htm.

Key Words—Surrogate Safety Measures; Traffic Conflicts; Intersection Safety; Simulation.

Notice—This document is disseminated under the sponsorship of the U.S. Department of Transportation 

in the interest of information exchange. The U.S. Government assumes no liability for the use of the information contained in this document. The U.S. Government does not endorse products or manufacturers. 

Trademarks or manufacturers’ names appear in this report only because they are considered essential to 

the objective of the document.

Quality Assurance Statement—The Federal Highway Administration (FHWA) provides high-quality 

information to serve the Government, industry, and public in a manner that promotes public understanding. Standards and policies are used to ensure and maximize the quality, objectivity, utility, and integrity 

of its information. FHWA periodically reviews quality issues and adjusts its programs and processes to 

ensure continuous quality improvement.

May 2008 FHWA-HRT-08-049

HRDI-05/05-08(3M)E

VISSIM) to improve the behavior of the underlying 

simulation models.

Recommendations

SSAM provides a compelling new option to 

assess the safety of traffic facilities using 

popular microsimulation software. This 

approach circumvents the need to wait for 

“abnormally high” crashes to actually occur, 

allows assessments of hypothetical designs 

and control alternatives, and is applicable 

to facilities where traditional, volume-based 

crash-prediction models (and norms) have 

not been established. Research is ongoing in 

this area, and as simulation models and video 

technology improve, this technique is expected 

to grow in use.

Additional Information

SSAM documentation is available in two FHWA 

reports: Surrogate Safety Assessment Model and 

Validation: Final Report, FHWA-HRT-08-051 and 

Surrogate Safety Assessment Model (SSAM): 

Software User Manual, FHWA-HRT-08-050 (http://

www.tfhrc.gov/safety/intersect.htm). The SSAM 

software and open source code is available from 

Siemens Energy and Automation (http://www

.itssiemens.com/research/ssam/ ).