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Surrogate Safety Measures From Traffic Simulation Models

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2. Literature Review

For the purpose of this study, the safety of a traffic facility is defined as follows:

The expected number of crashes, by type, expected to occur at an entity in a certain period, per unit of time.

In this study, crashes will be treated as unintended collisions between two or more motor vehicles of the canonical types specified in (1). Note that single-vehicle crashes are excluded from this definition. In addition, the bulk of crash research and the available literature on surrogate measures neglects collisions involving more than two vehicles. Those events are much less prevalent than collisions involving a pair of vehicles (e.g., see table 1 in [2]).

To estimate the safety of various traffic facilities, including facilities that have not yet been built, research in safety has focused on the establishment of safety performance functions that relate the number of crashes or crash rate to a number of "operational" (e.g., Average annual daily traffic (AADT), average speed) and "nonoperational" independent variables via a (typically complex) regression equation(s), including AADT, occupancy, Volume to capacity (V/C) ratios, products of crossing volumes, etc. (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13). Calibration is then required to choose the equation parameters for the best statistical fit to the available data (2, 14, 15).

Research has also been done on Bayesian methods and advanced statistical techniques (e.g., Classification And Regression Trees) for revising crash estimates based on observations as a way to develop safety estimates for facilities with no crash data (16, 17, 18, 19, 20). Various other methods for combining crash rates and other measures into "safety level of service" measures (21, 22) or common indices based on one type of crash (e.g., property damage only) have also been proposed (23). Both methods proposed in (21) and (23) rely on macroscopic measurements of total flows rather than recording individual vehicle movements or events to develop safety level-of-service estimates.

Despite the large body of safety modeling research, absolute numbers of crashes and crash rates are still difficult to predict accurately. This has led to increased interest over time in obtaining surrogate measures that reflect the safety of a facility or at least the increased probability of higher than average crash rates for a facility. The most prevalent literature in surrogate measures is related to the traffic conflicts technique (24, 25, 26, 27).

Conflicts

A conflict is defined as:

An observable situation in which two or more road users approach each other in time and space to such an extent that there is risk of collision if their movements remain unchanged (28).

The traffic conflicts technique is a methodology for field observers to identify conflict events at intersections by watching for strong braking and evasive maneuvers. The method has a long history of development, including research on:

• Recommended data collection methods (27, 29).
• Definitions of various types of conflicts (26, 30).
• Severity measures (31, 32).
• How conflict measures are related to crash counts (27, 33).
• How conflicts are related to specific crash types (23).
• Standards for data collection (34).
• Standard definitions of conflict indices that can be used to compare the performance of multiple facilities (33).

There is, however, still some debate regarding the connection between conflict measures and crash predictions (30). This includes the fact that the subjectivity of field observers induces additional uncertainty into the collection of accurate data on conflicts. Conflict studies are, however, still continuing to be used to rank locations with respect to safety to identify construction upgrades (35, 36, 37). There is general consensus that higher rates of traffic conflicts can indicate lower levels of safety for a particular facility, given that conflicts generally result from a lack or misunderstanding of communication between the different road users (38, 39).

Conflict Severity

Tabulation of total numbers of traffic conflicts indicates one part of the safety issue (frequency). The other element of the safety issue is the severity of the conflicts that occur. The primary conflict severity measure that has been proposed is the time to collision (TTC) (31, 40). Some researchers have indicated that TTC is the surrogate measure of safety, while others refute that lower TTC indicates higher severity of crashes, primarily because speed is not included in the measure (41, 42). That is to say that lower TTC certainly indicates a higher probability of collision, but cannot be directly linked to the severity of the collision. Some research indicates deceleration rate (DR) as the primary indicator of severity instead of TTC (43, 44). Other proposed measures defining and characterizing a conflict are presented in table 1 below (29, 45).

Table 1. Table of surrogate safety conflict measures.

Allen, et al., specify these measures primarily for left-turn conflict events (encroaching vehicle crossing in front of traffic with the right-of-way) and rank the above measures in "overall desirability" in the following way:

1. GT.
2. PET.
3. DR.
4. ET.
5. IAPT.
6. PSD.

This ranking by Allen, et al., takes into consideration the relation to crash history, relations among the other measurements, consistency over time, relation to braking application, ease of measurement, and application to other conflict types. TTC is similar to GT and would be ranked accordingly. The measures in table 1 are computed for each conflicting event (as appropriate for the measure, e.g., GT does not apply to rear-end conflicting events). More evaluation of each of these measures will be provided in the algorithm development report.

Other Surrogate Measures

Other surrogate safety measures proposed in the literature for intersections include fairly standard measures of effectiveness: delay, travel time, approach speed, percent stops, queue length, stop-bar encroachments, red-light violations, percent left turns, spot speed, speed distribution, and deceleration distribution (46, 47, 48). No attempt was made to relate these measures quantitatively to crash rates, but rather to assert such rules-of-thumb as "more stop-bar encroachments indicates higher probability of crashes," "longer queues indicate higher probability of crashes," and so on. A similar list of surrogates for two-lane roads has also been published, although more non-operational variables appear in the list for two-lane roads (e.g., superelevation, curvature, distance since last curve) (12, 46, 47, 48).

The above statistics, as well as conflict measures, require field observer crews to collect the data. This is expensive and includes the problem of unreliable subjective observers. Collection of TTC measures and the other measures in table 1 requires instrumented vehicles and/or high-resolution, multi-view video footage and extensive human analysis. Some additional surrogate measures proposed include:

• DR distributions.
• Required braking power distributions.
• Distribution of merge points (freeway travel).
• Merge area encroachments (freeway on-ramp merging in weaving areas).
• Gap-acceptance distributions.
• Number of vehicles caught in dilemma zones.
• Speed differential between crossing movements.
• Speed variance.
• Red- and yellow-light violations by phase.
• Time-integrated and time-exposed TTC measures (TET and TIT—duration of time that the TTC is less than a threshold and the integrated total TTC summation during that time, respectively) (49).

As indicated by the above measures, microscopic simulations are generally required for generating and collecting conflict severity statistics and/or other surrogate measures that require detailed information on vehicle acceleration, deceleration, position, etc. as a substitute for field studies. Some simulation models have been built specifically for simulation of particular conflict types and based on varying approaches to the computation of conflicts (22, 45, 50, 51, 52, 53, 54, 55). In particular, (45) contains a comprehensive treatment of conflict types and surrogate measures for both signalized and unsignalized intersections. The level of detail and variety of modeling variables available to the user are typically compromised in special-purpose simulations.

General-Purpose Microscopic Models

Some previous efforts have also focused on modification of multipurpose traffic simulation models to include conflict statistics or other surrogates (32, 43, 56, 57, 58, 59). This category includes the Helsinki Urban Traffic Simulation (HUTSIM), Transportation Analysis and Simulation System (TRANSIMS), Integrated Traffic Simulator (INTRAS - now FRESIM - Freeway Simulation, part of CORSIM - Corridor Simulation), NETSIM (Network Simulation - also now part of CORSIM), Texas, Advanced Interactive Microscopic Simulator for Urban and Non-urban Networks (AIMSUN), and Integration (no acronym meaning).

The SINDI project specifies including a more detailed driver behavior model (i.e., "nanoscopic" simulation) into the HUTSIM microscopic simulation for representation of lapses in driver reaction time and errors in response. The project is still in development (58). The paper on TRANSIMS is a discussion of the potential uses of microsimulation for safety analysis (59). TRANSIMS uses macroscopic representation of vehicle movements to simulate large-scale network (e.g., entire cities) transportation behavior and is therefore probably not detailed enough for the level of analysis required for this effort.

CORSIM currently outputs "conflict" statistics by movement (left, right, through/diagonal), conflicting movement (left, right, through/diagonal), and approach for intersections when micronode analysis is enabled (60, 61). Micronode analysis is an approach to simulation of the vehicles within the intersection "box." CORSIM normally operates by considering the intersection as a point. The vehicle movement logic determines whether the vehicle is clear to enter the intersection and then places the vehicle on the next link after a delay time based on the speed of the vehicle and the width of the intersection (or path distance of the left or right turn). The animation element of CORSIM (Traffic Visualization - TRAFVU) "fills in" the movements of the vehicles within the intersection for visualization. The micronode module, although based on reasonable approximation principles, is not considered a viable model for intersection vehicle movements. Also, the FRESIM component of CORSIM was modified to output merging conflicts for freeway weaving sections (when the model was called INTRAS) at an earlier time (32).

Texas uses the concept of conflicts to determine acceptance of gaps and lane changes by checking for conflicts and then avoiding conflicts. At each check and avoidance step, TTC and distance proximity values, as well as the relevant acceleration, deceleration, velocity, position, etc. of the two conflicting vehicles, can be exported to a file (62).

A recent study (63) illustrates the use of AIMSUN for collecting a surrogate measure of safety for ramp junctions. This study extracts the speed differential, maximum speed of the follower, and the DR of the follower vehicle for all ramp-merging events in a test case with and without ramp metering. The "un-safety" measure is the product of the three values. The study illustrates the effectiveness of ramp metering in decreasing the cumulative "un-safety" during rush-hour peak periods.

Integration has also been augmented to produce estimates of the safety impact of traffic signal coordination (64). The hypothesis is that reducing the number of vehicle-to-vehicle interactions by reducing total stops would result in fewer total crashes. A module for calculating total crashes based on mean free speed (using previously developed nonlinear regression functions for safety performance) of each intersection approach was added to Integration. In addition, lookup tables for type of crash based on speed were added to the simulation model.

Literature Summary

There is limited quantitative research to date on surrogate measures for safety assessment. The main difficulty is illustrating the correlation between any proposed surrogates with crashes, since crashes are rare events. The available literature is focused mainly on various aspects of traffic conflicts and related field studies for obtaining surrogate measures. Given the technical difficulty and cost of field studies, use of simulation models has been proposed and some previous work has been done to develop specific models for simulating conflicts.

The most notable surrogate measure of the severity of a conflict is the TTC, although other surrogates (e.g., PET, DR) have been proposed to measure other characteristics of conflict situations. Only limited effort has been expended to modify or enhance existing, general-purpose microscopic simulations to obtain conflict or other surrogate measures for intersections and two-lane roads. The primary difficulty is defining:

• A set of surrogate measures that can be extracted from simulations that were specifically designed to be "crash-free."
• A set of surrogate measures that have reasonable connectivity to safety assessment of particular facilities (e.g., the frequency and severity of resulting crashes).

It is desirable to have a general-purpose simulation that can produce surrogate measures. The next section will discuss various attributes and features of general-purpose, commercially available microscopic simulation models that are required for obtaining or enhancing the ability to obtain surrogate measures of safety.

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