Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide (2018)

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6 Tools and Strategies for Eliminating Assaults Against Transit Operators: User Guide Assault specific risk factors, listed in Table 1, can be categorized as “system factors,” “route factors,” and “operation factors.” • System factors are factors that potentially impact the risk of driver assault on any route within a given system. • Route factors are tied to a specific route and potentially impact the risk of driver assault on a specific route. • Operation factors are within the purview of the transit company to manage and/or change in a system or on a specific route. Using transit agency data (for example, where information is available through surveys or prior evaluations or reports) and national data on assault rates by region and population density, the estimated risk of driver assaults and other behaviors can be identified for various categories of risk factors. A vulnerability is a physical feature or operational attribute that renders an entity open to exploitation or susceptible to a given hazard. A threat is a natural or man-made occurrence, individual, entity, or action that has or indicates the potential to harm life, information, operations, the environment, and/or property. A consequence is the effect of an event, incident, or occurrence. DHS Risk Lexicon ASSAULT RISK FACTORS CHARACTERISTICS SYSTEM FACTORS REGION South, Midwest, West, Northeast POPULATION DENSITY Metropolitan areas, cities, nonmetropolitan areas ROUTE FACTORS INCIDENT HISTORY Aggravated and simple assault rates, previous driver incidents NUMBER OF BARS/CRIME PRONE SPOTS Bars, sports venues, gang territories, juvenile crime areas KNOWN THREATS Previous/current verbal or other threats to driver, route, system OPERATIONAL FACTORS FARE COLLECTION POLICIES Cash/Cashless fare collection, enforcement responsibilities, transfer policy and processes HOURS OF OPERATION Graveyard, morning/midday, school dismissal times, peak traffic, evenings MANAGEMENT PRACTICES Incident reporting, training, signage/media campaigns, prosecution of offenders, zero tolerance/suspension of service Table 1. Assault specific risk factors. Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide Copyright National Academy of Sciences. All rights reserved.

7 The operator assault risk management toolbox is a practical toolbox developed to support transit agencies in their efforts to prevent, miti- gate, and respond to assaults against operators. The toolbox consists of a series of customizable templates for systematically evaluating and analyzing assault risks, situational factors, technologies, agency efforts, and current countermeasures that assists transit agencies in identifying gaps and improvements that would provide the largest benefits to the agency. The toolbox contains the • Vulnerability self-assessment tool that allows an agency to assess the specific strengths and weaknesses of its operator assault posture, • Route-based risk calculator that produces scores identifying assault risks across the system that is also usable to evaluate risk on a route- based level, • Route-comparison summary table which brings together vulner- ability and risk information in an easy-to-interpret format, and • Detailed step-by-step examples of usage of the tools in the toolbox. Capabilities and Results The operator assault risk management toolbox includes a vulnerability self-assessment tool, a route-based risk calculator, a route-comparison summary table, and examples of usage of the tools in the toolbox. Together, the outputs of the evaluation assist transit agency managers in determining the relationship between assault risks and vulnerabilities on both an agencywide and route-based level. To maximize the scalability and functionality of the toolbox, the vulnerability self-assessment tool is designed to work independent of the route-based risk calculator. For small- to medium- sized agencies that have limited routes to consider it is possible to use the vulnerability self- assessment tool as the means to determine system preparedness and conditions. For larger-sized agencies the use of both tools in concert provides the greatest benefit in terms of relative analysis. Once the evaluation has been completed, transit agencies can use the results to strengthen their approach toward operator assaults in several different ways. The scores generated by the tools allow for the comparison of risks and vulnerabilities across many different routes within a system, enabling agencies to swiftly identify priority areas for new assault countermeasures. Risk and countermeasure scores provide fact-based justification for the transit agency’s future approach to operator assault and identify high-value mitigation measures that will provide the most benefit at the lowest cost. Whether the assessment is agencywide or route-based, the results C H A P T E R 2 Operator Assault Risk Management Toolbox This section includes an overview of the operator assault risk management tool- box and provides transit agencies with straightforward “how to” instructions and guidance for utilization of the toolbox items and methodologies. The toolbox contains the • Vulnerability self-assessment tool, • Route-based risk calculator, • Route-comparison summary table, and • Detailed step-by-step examples of usage. Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide Copyright National Academy of Sciences. All rights reserved.

8 Tools and Strategies for Eliminating Assaults Against Transit Operators: User Guide show that transit agencies can implement a more diverse and balanced countermeasure strategy in response to system-specific risks. Benefits of the Toolbox Agencies that choose to manage operator assault vulnerabilities and risks without a coherent strategy may be forced to make choices with unclear consequences using incomplete informa- tion. The agency evaluation tools within the operator assault risk management toolbox offer many potential benefits for agencies with a need to systematically evaluate and manage operator assault risks and vulnerabilities. The operator assault risk management toolbox provides: • Speed—The agency evaluation can be completed swiftly using data that are readily available. • Clarity—Direct, step-by-step instructions guide agency personnel on completing the assessment. • Simplicity—No extensive research is necessary to make use of the tools. Pre-weighted counter- measure and assault risk inputs to the agency evaluation system reflect the latest industry research on transit operator assaults. • Flexibility—The tools can be implemented on an agencywide or route-based level, and provide actionable results no matter the size of the transit system. Vulnerability Self-Assessment Tool The vulnerability self-assessment tool allows an agency to assess the strength of its operator assault approach and the agency’s unique vulnerabilities systemwide based on specific details of each countermeasure currently in use. Through the use of a self-assessment questionnaire, the tool provides straightforward, easy-to-interpret scores that demonstrate vulnerabilities and opportunities for growth. A transit agency can use these scores, along with information about specific countermeasures, to eliminate vulnerabilities in a systematic and organized manner. Outputs from the analysis can provide guidance on security measures an agency should consider to prevent or mitigate incidents of assault. The complete vulnerability self-assessment tool is found in Appendix A. The vulnerability self-assessment tool is designed to look at countermeasure deployment in transit agencies. Countermeasures are grouped into seven categories. Category 1: Policies, Plans, and Protocols. These are written documents and working protocols that specifically describe the agency’s security approach for preventing, reducing, or mitigat- ing operator assaults. Category 2: Police or Security Staffing. Security personnel or police forces are deployed on board or in transit stations or vehicles. Category 3: Voice Communications Technology. Radios or advanced communications systems such as digital video and voice, computer graphics, and systems or applications are deployed to enable communication. Category 4: Data Communications and Telemetry Systems. Technologies to enable better com- munications between drivers and their stations are mobile data terminals, automatic vehicle locators, GPS units, and emergency alert buttons. Category 5: Surveillance and Observation Systems. Surveillance systems are generally designed to attain complete or nearly complete coverage of an identified space in a defined area using closed-circuit televisions (CCTVs). Digital video surveillance enables embedded Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide Copyright National Academy of Sciences. All rights reserved.

Operator Assault Risk Management Toolbox 9 image-capture capabilities that allow video images or extracted information to be compressed, stored, or transmitted over communication networks or digital data link. Category 6: Driver Protection Systems. Driver protection systems are physical (engineering) controls that are aimed at making it difficult or impossible for an attacker to inflict harm on an operator and are incorporated into the design of the bus itself or added later as an upgrade. Category 7: Training. Training is an organized activity aimed at imparting information and/or instructions to improve performance or to help him or her attain a required level of knowledge or skill. Training can help operators handle situations when they arise. How to Use the Vulnerability Self-Assessment Tool The following steps describe how to use the vulnerability self-assessment tool by using an excerpt from the questionnaire (Figure 1). The complete questionnaire is in Appendix A2. Step 1. Each countermeasure category is followed by one or more questions worth between 0.5 and 5 points; the maximum number of points available for each countermeasure is 5 points. For each question, enter the allotted number of points in the “score” column if the answer to a question is “yes.” If the answer is “no,” award 0 points. Questions included in Category 1 (Policies, Plans, and Protocols) are shown in Figure 1. Step 2. Once the questions have been answered, add the point values recorded in the “score” column to produce a total score for each countermeasure. Total scores for each counter- measure will range from 0 to 5. Step 3. After generating a total score for each countermeasure in the vulnerability self-assessment tool, transfer each total countermeasure score to the vulnerability self-assessment final scores matrix (Figure 2), also found in Appendix A3. Next, multiply each countermeasure score by the countermeasure ranking (from Appendix A1) to produce the transit agency’s counter- measure score. This step will empower transit agencies to act on the results of the vulnerably self-assessment score by evaluating overall performance, strengths, and potential areas for improvement in incorporating assault countermeasures. Countermeasure scores can be understood through two basic measures: the total number of countermeasures in use and the average countermeasure score for countermeasures in use. A robust and balanced approach to operator assault risk depends on a combination of these two factors. Deployment of low-cost, easy-to-implement countermeasures may contribute to a higher average countermeasure score. Understanding Assessment Scores Countermeasure Scores The countermeasure score can be understood through two basic measures—the total number of countermeasures in use and the average countermeasure score for the countermeasures in use. Total Number of Countermeasures in Use. To calculate this value, tally the total number of countermeasures used by your agency. Do not include countermeasures that are not in use or countermeasures that received a self-assessment score of 0. The total number of countermeasures available is 42. Average Countermeasure Score for the Countermeasures in Use. To calculate this value, add the countermeasure scores for all countermeasures used by the transit agency and divide by the total number of countermeasures in use. Do not include countermeasures that are not in use or countermeasures that received a vulnerability self-assessment score of 0 in the average. The end result may vary—the maximum average countermeasure score is 10 (only obtainable when Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide Copyright National Academy of Sciences. All rights reserved.

10 Tools and Strategies for Eliminating Assaults Against Transit Operators: User Guide SELF-ASSESSMENT QUESTIONNAIRE: Category 1: Policies, Plans, and Protocols Countermeasure Questions Points Available Score Fare Collection Policies 3 1 1 TOTAL 3 0.5 0.5 0.5 0.5 TOTAL 3 1 0.5 0.5 TOTAL Communication Protocol for Violent Incidents (Incident Command System) Operator Assaults Zero Tolerance Workplace Violence Policy Coverage Does your agency have a fare collection policy to reduce potential for conflict over payment? Do employees receive training and routine refresher information regarding their responsibilities within the policy? Is the policy strictly enforced (except when enforcing the policy would expose the employee to significant risk)? Does your agency have a communication protocol for violent incidents? Does the protocol incorporate outside emergency responders and like agencies? Are employees trained to their responsibilities within the protocol? Has the protocol been tested in an exercise/drill or a live incident within the past 3 years? Has an after-action report/improvement plan been developed and implemented based on the results of the exercise/drill or incident? Does your agency have a Zero Tolerance Workplace Violence Policy? Are employees required to sign off on receipt of the policy? Is the policy endorsed by upper management? Is the policy enforced? Figure 1. Excerpt from the vulnerability self-assessment questionnaire. very few countermeasures are used) and the average countermeasure score resulting from the perfect vulnerability self-assessment scores on all 42 countermeasures is 7.3 (Figure 3). A robust and balanced approach to operator assault risk depends on a combination of these two factors. Agencies that deploy a large number of countermeasures are generally more capable of addressing risk than agencies that deploy a small number of countermeasures. Likewise, agencies that deploy complete countermeasures, which incorporate training, strict enforcement, drills, after-action reviews, routine updates, or other relevant key criteria identified in the vulnerability self-assessment questionnaire and the Countermeasures Guide are more prepared to address assault risk. Some of the countermeasures may also be more costly, labor Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide Copyright National Academy of Sciences. All rights reserved.

Operator Assault Risk Management Toolbox 11 Figure 2. Vulnerability self-assessment final scores matrix. Total Number of Countermeasures in Use (Out of 42) Average Countermeasure Score for Countermeasures in Use (Maximum of 10. Average Countermeasure Score with perfect Self- Assessment Score on all countermeasures is 7.3) Figure 3. Understanding countermeasure scores. Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide Copyright National Academy of Sciences. All rights reserved.

12 Tools and Strategies for Eliminating Assaults Against Transit Operators: User Guide intensive, or difficult to use than others, impacting their maximum possible score as described in the weighted countermeasure scores section. These factors, while largely beyond agency control, are captured in the evaluation system and deployment of low-cost, easy-to-implement counter- measures may contribute to a higher average countermeasure score. Assessment Scores Figure 4 serves as a visual representation of potential outcomes of understanding the countermeasure scores. Many Countermeasures, High Average Score. The upper right-hand quadrant of the chart reads “Many Countermeasures, High Average Score” and describes a robust approach to risk that includes a large number of well-developed countermeasures. Transit agencies should strive to develop a strategy similar to this. The remaining quadrants of the chart describe various sub-optimal outcomes. If a transit agency currently uses few of the countermeasures, the countermeasures in use are incomplete, or the countermeasures in use rank poorly in terms of cost and ease of implementation, it is likely that these quadrants more accurately describe the current operator assault program. Compare the total number of countermeasures in use and the average countermeasure score of a transit agency to Figure 4. Depending on where the agency falls in the spectrum of outcomes, various strategies are available to improve the approach to operator assault risk. Few Countermeasures, Low Average Score. Diversify the approach by deploying a larger overall number of countermeasures and improve the average score by seeking to deploy high- value countermeasures whenever possible. Few Countermeasures, High Average Score. While the agency likely deploys several com- plete assault countermeasures, a low overall number of countermeasures is an indicator of a narrow approach to assault risks. Diversify the approach by deploying a larger overall number of countermeasures. Many Countermeasures, Low Average Score. The agency may deploy a large number of countermeasures, but those countermeasures may lack key features such as regular training, drills, after-action reviews, and scheduled routine updates to ensure that they are fully effective. To improve, the agency may consider utilizing methods identified in the Countermeasures Guide and vulnerability self-assessment questionnaire to strengthen the countermeasures that are already in use. To quickly identify high-value countermeasures that are underutilized by your agency, Figure 4. Countermeasures chart. Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide Copyright National Academy of Sciences. All rights reserved.

Operator Assault Risk Management Toolbox 13 subtract your countermeasure score from the maximum possible score to calculate the number of remaining points available for each countermeasure. Higher remaining points available values indicate that an agency may significantly improve its score for a particular countermeasure by making improvements identified in the Countermeasures Guide to fulfill more of the criteria outlined in the vulnerability self-assessment questionnaire. Low remaining points available values indicate that the agency is successfully implementing the countermeasure in accordance with this evaluation system. Example: Vulnerability Self-Assessment Tool Worksheet 1 and Worksheet 2 were completed by a transit agency in the South of the United States. The transit agency is in a metropolitan area with services encompassing both the immediate metropolis and its surrounding municipalities and counties, including three law enforcement agencies, and having both bus and light rail facilities and services. The transit agency participated in a pilot test, completing a self-assessment of its implementation of countermeasures across the entire agency. As seen in the excerpt from the vulnerability self-assessment questionnaire, Category 1 (Policy, Plans, and Protocols) and Category 2 (Police or Security Staffing), the agency has implemented a large number of countermeasures systemwide. However, there are notable areas where countermeasures have not been implemented. From the transit agency’s perspective, this self-assessment gives them insights into possible opportunities for improvement. A copy of the agency’s completed vulnerability self-assessment final scores matrix (Worksheet 2) is given below. This allows the agency to act on the results of the vulnerably self-assessment score by evaluating its overall performance, strengths, and potential areas for improvement in incorporating assault countermeasures. Route-Based Risk Calculator The route-based risk calculator (Table 2) allows an agency to compare operator assault risks between routes. For each route, the risk of driver assault can be calculated based upon the risk factors along that route, in conjunction with regional and population density risk factors. The route-based risk calculator provides transit owners and operators with a structured and viable risk management capability that can perform both “what if” and “trade-off” decision making. The route-based risk calculator includes pre-defined and pre-weighted operator assault specific risk factors in an easy-to-use look-up table that makes it possible to develop sound risk estimates on a route-based basis.2 These factors can be categorized as “system factors,” “route factors,” and “operation factors.” • System factors are superordinate factors that potentially impact the risk of driver assault on any route within a given system and include whether the transit operation is within a metro- politan, city, or nonmetropolitan area, and in what geographic section of the country the system resides.3 2TCRP Research Report 193: Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 1: Research Overview, contains a Transit Agency Operator Assault Route Factor Rating Sheet and Weighting Methodology that identified 20 factors that may impact the risk of driver assault within a given transit system and/or along any given route within that system. System and route risk factors used in the route-based risk calculator to calculate the risk of driver assault on any given route in a transit system utilized these 20 factors. 3According to national crime data, there are significant regional differences in crime rates—see Crime in the United States 2011. https://ucr.fbi.gov/crime-in-the-u.s.-2011/offenses-known-to-law-enforcement/standard-links/region. To derive the risk factor ranks in this project, the team normalized the data against population to ensure the likelihood estimates were not distorted by difference in population sizes in the different regions—South, Midwest, West, and Northeast. Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide Copyright National Academy of Sciences. All rights reserved.

VULNERABILITY SELF-ASSESSMENT QUESTIONNAIRE Category 1: Policies, Plans, and Protocols Countermeasure Questions Points Available Score Fare Collection Policies Does your agency have a fare collection policy to reduce potential for conflict over payment? 3 3 Are employees trained to their responsibilities within the policy? 1 1 Is the policy strictly enforced? 1 1 Total 5 Communication Protocol for Violent Incidents (Incident Command System) Does your agency have a communication protocol for violent incidents? 3 3 Does the protocol incorporate outside emergency responders and like agencies? 0.5 .5 Are employees trained to their responsibilities within the protocol? 0.5 .5 Has the protocol been tested in an exercise/drill or a live incident within the past 3 years? 0.5 .5 Has an after-action report/improvement plan been developed and implemented based on the results of the exercise/drill or incident? 0.5 .5 Total 5 Does your agency have an operator assault committee or task force, or are incidents addressed by another committee or task force? 3 3 Are all levels of the agency (operators, supervisors, management, unions, HR, etc.) involved? 1 0 Are the recommendations developed by the committee or task force put into practice? 1 1 Total 4 Post-Incident Action Steps Has your agency developed post-incident action steps (counseling, legal, financial, etc.)? 4 4 Is a process in place to review and update the post-incident action steps at least once every 3 years? 1 1 Total 5 Worksheet 1. Vulnerability self-assessment questionnaire (excerpted from the pilot test). Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide Copyright National Academy of Sciences. All rights reserved.

Operator Assault Risk Management Toolbox 15 Passenger Code of Conduct Does your agency have a passenger code of conduct? 3 3 Is the code of conduct posted on every vehicle in the fleet? 1 0 Is the passenger code of conduct strictly enforced through issuance of notice of violation? 1 1 Total 4 Barring Systems Is there a barring system in place within the agency? 3 0 Do the barring systems include technologies to identify potential or past aggressors? 1 0 Does this system allow the operator the liberty to deny the aggressor entrance into the vehicle? 1 0 Total 0 Passenger Screening Is there a passenger screening system in place? 4 0 Is there training in place to support the passenger screening program 1 0 Total 0 Category 2: Police or Security Staffing Countermeasure Questions Points Available Score Centralized Surveillance with Immediate Force Response Does your agency use centralized surveillance on their vehicles? 3 0 Is there an identified immediate response by a dedicated police/security/supervisor element to what is observed? 1 0 Is there an identified immediate response by a dedicated police/security/supervisor element to what is observed? 1 0 Total 0 Centralized Remote Sensors with Immediate Force Response Does your agency use these devices (pagers/sensors) to detect dangerous substances, such as radioactive or biohazardous material, and alert the operator or dispatch when the vehicle has been contaminated? 4 0 If deployed, does your agency provide immediate response by a dedicated police/security/supervisor element specialized for this type of incident? 1 0 Total 0 Worksheet 1. (Continued). Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide Copyright National Academy of Sciences. All rights reserved.

16 Tools and Strategies for Eliminating Assaults Against Transit Operators: User Guide Worksheet 2. Vulnerability self-assessment final scores matrix (excerpted from the pilot test). RISK FACTOR FACTOR CHARACTERISTICS RISK FACTOR SCORE SYSTEM FACTORS REGION South, Midwest, West, Northeast POPULATION DENSITY Metropolitan areas, cities, nonmetropolitan areas ROUTE FACTORS INCIDENT HISTORY Aggravated and simple assault rates, previous driver incidents NUMBER OF BARS/CRIME PRONE SPOTS Bars, sports venues, gang territories, juvenile crime areas KNOWN THREATS OPERATION FACTORS HOURS OF OPERATION Graveyard, morning/midday, school dismissal times, peak traffic, evenings TERMINALS AND TRANSFER STATIONS TOTAL ROUTE RISK SCORE Table 2. Route-based risk calculator. Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide Copyright National Academy of Sciences. All rights reserved.

Operator Assault Risk Management Toolbox 17 • Route factors are tied to a specific route and potentially impact the risk of driver assault on a specific route. Route factors include the presence of bars, sports venues, gang ter- ritories, schools, homeless shelters, or similar group facilities along any given route within a system.4 • Operation factors are within the purview of the transit company to manage and/or change in a system or on a specific route. The vulnerability self-assessment tool focused on operation factors such as fare collection policies, installing monitoring equipment or drive protection barriers, driver training practices, incident reporting and management practices. The route- based risk calculator includes the hours of operation along routes as a risk factor. In addition to these factors, there is another characteristic common to all transit systems— terminals or route end-points or turnarounds. Each route has an origination and termination terminal (which may or may not be a physical terminal). However, the specific characteristics of those terminals will likely be different for each route although, in some instances, several routes may share terminals (as discussed below, the pilot agency routes that were assessed with the route-based risk calculator all had one route common terminal). Terminals are land uses surrounded by other land uses. Thus each terminal will have a unique combination of risk factors. For large transit systems with multi-modal operations (e.g., light rail, buses, heavy rail), it is also necessary to characterize transfer stations along the route. Similar to terminals, transfer stations are land uses surrounded by other land uses, with unique combinations of risk. The route-based risk calculator provides a simple framework to prepare risk factor ranks for terminals and transfer stations on each route. How to Use the Route-Based Risk Calculator These steps describe how to use the route-based risk calculator by using data from the Look-up table: Risk Factor and Risk Factor Rank. The route-based risk calculator template and the look-up table are included in Appendix B. Step 1: System Factors Enter the system factor risk rank5 found in the look-up table in Appendix B based on the location of the transit agency and the population density of the region. It is assumed that the transit system using the route-based risk calculator is situated in the South of the United States and within a population area designated as cities. In the region row, enter the value 5, cor- responding to the risk factor rank for the South. In the population density row, enter the value 3, corresponding to the risk factor rank for cities. 4It is understood that transit agencies may need to work with local police agencies and planning departments to determine where certain route specific factors may be prevalent (e.g., gang areas, bars). Further, for some transit agencies this may require working with one or more than one police department and planning department. 5Assault is statutorily defined by each state, thus, there are differences between states. To simplify matters, the project team uses four categories of assault—aggravated, simple, repeat crime, and minor incident. In addition, threat is a category for which incident data are collected. Aggravated assault encompasses the intent to do bodily harm, with or without a weapon where a driver is assaulted and sustains injuries sufficient to warrant medical care (this encompasses sexual assault). Simple assault encompasses kicking, punching, etc. wherein the driver may be struck in some manner, but not sufficient to warrant medical care. Minor incidents encompass spitting, throwing projectiles on the bus, unless a driver is struck, in which case it becomes either aggravated or simple depending on whether medical care is required. Repeat crime represents those situations where the same type of criminal activity occurs multiple times and may be captured under the frequency rates by type of crime. Threats are verbal or physical intimidation with or without a weapon. Since transit agencies may collect their assault data in different categories, for purposes of utilizing the route risk lookup tables, those data should be combined into these categories as defined above. Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide Copyright National Academy of Sciences. All rights reserved.

18 Tools and Strategies for Eliminating Assaults Against Transit Operators: User Guide System Factor Region and Population Density Risk Factor Rank South • Metropolitan areas • Cities • Nonmetropolitan areas 5 5 3 1 Midwest • Metropolitan areas • Cities • Nonmetropolitan areas 2 3 2 1 West • Metropolitan areas • Cities • Nonmetropolitan areas 3 3 6 2 Northeast • Metropolitan areas • Cities • Nonmetropolitan areas 2 3 5 3 Step 2: Route Factors Enter the route factors risk rank from the look-up table in Appendix B for each route factor. For example, for the incident history factor, it is assumed that the transit system using the route- based risk calculator has two routes. For Route 1, the aggravated assault rate is less than 1 assault per 60 months and for Route 2, the aggravated assault rate is equal to 1 assault per 18 months. For Route 1, if the aggravated assault rate is less than 1 assault per 60 months, the risk factor rank is 1. For Route 2, with an aggravated assault rate equal to 1 assault per 18 months, the risk factor is 6. There values would be entered in the appropriate cells for aggravated assault for the route. Continue for each of the remaining route factors—number of bars/crime prone spots and known threats—until all appropriate cells have been completed for all the routes being assessed. Route Factor Incident History Factor Risk Factor Rank <1 Aggravated Driver Assault/60 Months 1 1 Aggravated Driver Assault/60 Months 2 1 Aggravated Driver Assault/48 Months 3 1 Aggravated Driver Assault/36 Months 4 1 Aggravated Driver Assault/24 Months 5 1 Aggravated Driver Assault/18 Months 6 1 Aggravated Driver Assault/12 Months 7 Step 3: Operational Factors Enter the operational factors risk rank from the look-up table in Appendix B for the operation factors. It is assumed that Route 1 operates during the morning and early afternoon hours and that Route 2 operates in the evening hours. Route 1 would have a value of 2 (morning to midday— 5:00 a.m. to 2:00 p.m.) and Route 2 would have a value of 5 (evening/late night/early mornings— 7:00 p.m. to 2:00 a.m.). Operation Factor Hours of Operation Risk Factor Rank Graveyard Shift—2:00 a.m. to 5:00 a.m. 1 Morning to Midday—5:00 a.m. to 2:00 p.m. 2 School Dismissal Hours—2:00 p.m. to 4:00 p.m. 3 Peak PM Traffic Period—4:00 p.m. to 7:00 p.m. 4 Evening/Late Night/Early Mornings—7:00 p.m. to 2:00 a.m. 5 Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide Copyright National Academy of Sciences. All rights reserved.

Operator Assault Risk Management Toolbox 19 Table 3 illustrates the completed route-based risk calculator for these two hypothetical routes. Step 4: Terminal/Transfer Stations Calculate the terminal and transfer station risk scores for each route, if applicable. It is assumed that both routes in this system have one common origination/destination and three distinct destination/turn-around terminals. In this example, transfer stations are not factored in, although, such calculations would simply be added to the total route risk score if transfer stations were added. There are essentially two calculations that have to be done to derive the risk ranks for termi- nals. Derive the rank for each terminal on a route (i.e., Terminals A and B), then sum those ranks to derive the route terminal risk which is to be used in the route-based risk calculator (Table 4). Appendix B contains a template. Step 4A: Determine terminal/transfer station risk factors. Use the centroid of the ter- minal to create a one-block radius (if agency operates in an area where it is more appropriate for the radius around a terminal to be time-based, then make the radius a 5-minute walk from the terminal in metropolitan and nonmetropolitan areas and a 10-minute walk in cities) from the terminal to select the appropriate risk factor rank (incident history, number of bars, etc.) from the look-up table in Appendix B. As with the route, the region and population density ranks are constant for all terminals in the system. In some large metropolitan areas, the appropriate radius around the terminal may need to be two blocks or more, depending upon the character of the surrounding area. Again, if the radius selected is greater than one block (5 or 10 minutes), the risk factor rank entered is driven by the appropriate rank found for that particular factor.6 It is assumed that both routes have one common origination/destination (Terminal A) and that Route 1 Terminal B has a low risk rank of 16 and Route 2 Terminal B has high-risk rank of 33. 6Depending upon the area within which the agency operates, it may be more appropriate to use a walking-time radius around the terminal. The time values that should be used are a 5-minute walking radius or a 10-minute walking radius. The specific radius selected should be a reflection of the specific character of land uses and activities surrounding the terminals. RISK FACTOR ROUTE 1 ROUTE 2 ROUTE CHARACTERISTICS RISK FACTOR RANK ROUTE CHARACTERISTICS RISK FACTOR RANK REGION South 5 South 5 POPULATION DENSITY City 3 City 3 INCIDENT HISTORY <1 Simple Driver/Passenger Assault/60 months 1 1 Aggravated Driver/Passenger Assault/18 Months 6 NUMBER OF BARS/CRIME PRONE SPOTS 1/block 1 2/block 2 KNOWN THREAT One Known Threat of Aggravated Assault 1 Three Known Threats of Aggravated Assault 3 HOURS OF OPERATION Morning to Midday 5:00 a.m. to 2:00 p.m. 2 Evening/Late Night/Early Mornings 7:00 p.m. to 2:00 a.m. 5 TERMINALS AND TRANSFER STATIONS ROUTE 1 RISK SCORE 13 ROUTE 2 RISK SCORE 24 Table 3. Completed route-based risk calculator. Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide Copyright National Academy of Sciences. All rights reserved.

20 Tools and Strategies for Eliminating Assaults Against Transit Operators: User Guide The same approach is to be taken for transfer stations, although, in general transfer stations will not require a greater than one-block radius calculation. When several transfer stations are on a route, the total transfer station risk rank is determined by summing the ranks for each transfer station to derive the total transfer station risk rank. This rank is then utilized in the route-based risk calculator. Step 4B: Sum terminal/transfer stations risk rankings. Each terminal or transfer station risk score (e.g., Terminal A and B) is summed to determine the total route terminal/transfer station risk score to be used in the route-based risk calculator. For Route 1, the total is 33 + 16 or 49; for Route 2 the total is 33 + 33 or 66. To illustrate this, two hypothetical routes—Route 1 and Route 2—are used in the following tables. In these examples, transfer stations are not factored in, although, such calculations would simply be added to the total route risk score if transfer stations were added. Example: Route-Based Risk Calculator Step 1. Enter the appropriate system factor risk ranks—the region is the South of the United States and the population density is city. RISK FACTOR ROUTE 1 ROUTE 2 ROUTE CHARACTERISTICS RISK FACTOR RANK ROUTE CHARACTERISTICS RISK FACTOR RANK REGION South 5 South 5 POPULATION DENSITY City 3 City 3 TERMINAL RISK RANK RISK FACTOR TERMINAL A ROUTE TERMINAL CHARACTERISTICS RISK FACTOR RANK REGION POPULATION DENSITY INCIDENT HISTORY NUMBER OF BARS/CRIME PRONE SPOTS KNOWN THREAT HOURS OF OPERATION TERMINAL A RISK SCORE RISK FACTOR TERMINAL B ROUTE TERMINAL CHARACTERISTICS RISK FACTOR RANK REGION POPULATION DENSITY INCIDENT HISTORY NUMBER OF BARS/CRIME PRONE SPOTS KNOWN THREAT HOURS OF OPERATION TERMINAL B RISK SCORE Total Route Terminal Risk Score = A + B Table 4. Terminal risk ranks. Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide Copyright National Academy of Sciences. All rights reserved.

Operator Assault Risk Management Toolbox 21 Step 2. Enter the appropriate route factor risk ranks as derived from the look-up table in Appendix B for incident history, number of bars/crime prone spots, etc. RISK FACTOR ROUTE 1 ROUTE 2 ROUTE CHARACTERISTICS RISK FACTOR RANK ROUTE CHARACTERISTICS RISK FACTOR RANK INCIDENT HISTORY <1 Simple Driver/Passenger Assault/60 months 1 1 Aggravated Driver/Passenger Assault/18 Months 6 NUMBER OF BARS/CRIME PRONE SPOTS 1/block 1 2/block 2 KNOWN THREAT One Known Threat of Aggravated Assault 1 Three Known Threats of Aggravated Assault 3 Step 3. Enter the operation factor risk ranks from the look-up table in Appendix B for the operation factors. RISK FACTOR ROUTE 1 ROUTE 2 ROUTE CHARACTERISTICS RISK FACTOR RANK ROUTE CHARACTERISTICS RISK FACTOR RANK HOURS OF OPERATION Morning to Midday 5:00 a.m. to 2:00 p.m. 2 Night/Early Mornings 7:00 p.m. to 2:00 a.m. 5 Evening/Late Table 5 illustrates the scoring in the route-based risk calculator based on Steps 1, 2, and 3. Step 4A. Determine the terminal/transfer station risk ranks. Use the centroid of the terminal to create a one-block radius or time-based radius from the terminal to select the appropriate risk rank for each factor (incident history, number of bars, etc.) from the look-up table in Appendix B. Step 4B. Sum the terminal/transfer stations risk ranks. Each terminal or transfer station risk score (e.g., Terminals A and B) is summed to determine the total route terminal/transfer station risk score to be used in the route-based risk calculator. The results of these calculations are shown in Tables 6 and 7. Although the two routes share a common terminal (Terminal A), the differences in the remaining route characteristics clearly distinguish the routes in terms of their respective risk RISK FACTOR ROUTE 1 ROUTE 2 ROUTE CHARACTERISTICS RISK FACTOR RANK ROUTE CHARACTERISTICS RISK FACTOR RANK REGION South 5 South 5 POPULATION DENSITY City 3 City 3 INCIDENT HISTORY <1 Simple Driver/Passenger Assault/60 months 1 1 Aggravated Driver/Passenger Assault/18 Months 6 NUMBER OF BARS/CRIME PRONE SPOTS 1/block 1 2/block 2 KNOWN THREAT One Known Threat of Aggravated Assault 1 Three Known Threats of Aggravated Assault 3 HOURS OF OPERATION Morning to Midday 5:00 a.m. to 2:00 p.m. 2 Evening/Late Night/Early Mornings 7:00 p.m. to 2:00 a.m. 5 ROUTE 1 RISK SCORE 13 ROUTE 2 RISK SCORE 24 Table 5. Example of scoring in the route-based risk calculator. Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide Copyright National Academy of Sciences. All rights reserved.

22 Tools and Strategies for Eliminating Assaults Against Transit Operators: User Guide Table 6. Example of terminal and transfer station risk ranks—Route 1. TERMINAL AND TRANSFER STATION RISK RANKS RISK FACTOR TERMINAL A ROUTE 1 TERMINAL CHARACTERISTICS RISK FACTOR RANK REGION South 5 POPULATION DENSITY City 3 INCIDENT HISTORY 1 Aggravated Driver Assault/ 12 Months 7 NUMBER OF BARS/CRIME PRONE SPOTS Bars, Nightclubs, and Entertainment: 2 blocks with 5/block 6 KNOWN THREAT 1 Generalized Driver Threat/Year 7 HOURS OF OPERATION Evening/Late Night/Early Mornings 7:00 p.m. to 2:00 a.m. 5 TERMINAL A RISK SCORE 33 TERMINAL AND TRANSFER STATION RISK SCORE RISK FACTOR TERMINAL B ROUTE 1 TERMINAL CHARACTERISTICS RISK FACTOR RANK REGION South 5 POPULATION DENSITY City 3 INCIDENT HISTORY <1 Simple Driver Assault/ 60 months 1 NUMBER OF BARS/CRIME PRONE SPOTS 1/block 1 KNOWN THREAT One Known Threat of Aggravated Assault 1 HOURS OF OPERATION Evening/Late Night/Early Mornings 7:00 p.m. to 2:00 a.m. 5 TERMINAL B RISK 16 Table 6A: Example of Terminal and Transfer Station Risk Ranks – Route 1 ranks. Thus, for this hypothetical agency, it would then look at the countermeasures that have been implemented along those routes and could then make more informed decisions regarding whether additional countermeasures should be implemented on Route 2. Example: Route-Based Risk Calculator for the Three Pilot Agency Routes Building on the calculations shown in Tables 6 and 7, this section depicts the actual deployment of the tool during a field test/pilot study of the route-based risk calculator. The following data were compiled by a transit agency in the South of the United States. The agency is in a metro- politan area with services encompassing both the immediate metropolis and its surrounding municipalities and counties, including three law enforcements agencies, and having both bus and light rail facilities and services. As mentioned previously, the pilot agency tested the methodology for three routes (A, B, and C) within its system. Because of data aggregation—three-year totals—the data for the pilot agency are Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide Copyright National Academy of Sciences. All rights reserved.

Operator Assault Risk Management Toolbox 23 Table 6. (Continued). TERMINAL AND TRANSFER STATION RISK RANKS RISK FACTOR TERMINAL A – TRANSIT CENTER ROUTE 1 RISK FACTOR RANK REGION South 5 POPULATION DENSITY City 3 INCIDENT HISTORY Multiple Simple Assaults/ 36 Months 7 NUMBER OF BARS/CRIME PRONE SPOTS Multiple Bars/Gang Issues 2 blocks with 5/block 7 KNOWN THREAT Multiple Known Threats 7 HOURS OF OPERATION Evening/Late Night/Early Mornings 7:00 p.m. to 2:00 a.m. 5 TERMINAL A RISK 34 TERMINAL AND TRANSFER STATION RISK RANKS RISK FACTOR TERMINAL B ROUTE 1 TERMINAL CHARACTERISTICS TERMINAL CHARACTERISTICS RISK FACTOR RANK REGION South 5 POPULATION DENSITY City 3 INCIDENT HISTORY Zero Assaults/36 months 0 NUMBER OF BARS/CRIME PRONE SPOTS None 0 KNOWN THREAT Zero Known Threats 0 HOURS OF OPERATION Evening/Late Night/Early Mornings 7:00 p.m. to 2:00 a.m. 5 TERMINAL B RISK 13 Total Route 1 Terminal Risk Score = A + B 34 + 13 = 47 Table 6B: Example of Terminal and Transfer Station Risk Ranks – Route 1 SCORE SCORE attached to the terminals and transfer stations (Tables 8, 9, and 10). For discussion purposes, the three routes are contrasted in terms of what the data suggest regarding driver assault risks. In Table 8, Route A has a transit center terminal (Terminal A) that has a high density of bars and entertainment establishments within a 10-minute walking radius of the terminal. This location is the primary entertainment center for the metropolitan area and has high incidents of assaults, crime prone spots, and multiple known threats to drivers. (This terminal is shared by all three routes in the pilot study.) Terminal B on Route A has no reported incidents but shares the same operating hours as Terminal A, which increases the risk to drivers during those hours. Thus, the score for this route indicates high risk in and around Terminal A, reduced risk along the remainder of the route (imputed from the aggregated data), and no incident history at Terminal B. There is some elevated risk because of the hours of operation and because it is in a metropolitan area in the South. Route A total terminal and transfer station risk score is 51. Table 9 provides the calculated risk rank for Route B. This route includes Terminal A, which experiences high incidence of criminal activity and risk prone areas. Terminal B also has elevated risk levels—not quite as high as Terminal A, but close in ranking. For Route B, a transfer sta- tion was added (see Table 9B). This transfer station experiences significant levels of risk, both in terms of incidents as well as being associated with gang areas. Thus Route B’s total route risk score is 95, substantially higher than Route A. Given the data associated with the terminals and transfer station on this route, it can be imputed that risks along the route are likely to be higher Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide Copyright National Academy of Sciences. All rights reserved.

24 Tools and Strategies for Eliminating Assaults Against Transit Operators: User Guide Table 7. Example of terminal and transfer station risk ranks—Route 2. TERMINAL AND TRANSFER STATION RISK RANKS RISK FACTOR TERMINAL A ROUTE 2 TERMINAL CHARACTERISTICS RISK FACTOR RANK REGION South 5 POPULATION DENSITY City 3 INCIDENT HISTORY 1 Aggravated Driver Assault/ 12 Months 7 NUMBER OF BARS/CRIME PRONE SPOTS Bars, Nightclubs, and Entertainment: 2 blocks with 5/block 6 KNOWN THREAT 1 Generalized Driver Threat/Year 7 HOURS OF OPERATION Evening/Late Night/Early Mornings 7:00 p.m. to 2:00 a.m. 5 TERMINAL A RISK SCORE 33 TERMINAL AND TRANSFER STATION RISK RANKS RISK FACTOR TERMINAL B ROUTE 2 TERMINAL CHARACTERISTICS RISK FACTOR RANK REGION South 5 POPULATION DENSITY City 3 INCIDENT HISTORY 1 Aggravated Driver Assault/ 12 Months 7 NUMBER OF BARS/CRIME PRONE SPOTS Bars, Nightclubs, and Entertainment: 2 blocks with 5/block 6 KNOWN THREAT 1 Generalized Driver Threat/Year 7 HOURS OF OPERATION Evening/Late Night/Early Mornings 7:00 p.m. to 2:00 a.m. 5 TERMINAL B RISK SCORE 33 Total Route 2 Terminal Risk Score = A + B 33 + 33 = 66 Table 7A: Example of Terminal and Transfer Station Risk Ranks – Route 2 than will be found along the other two routes. In short, simply based upon these data, that clearly demonstrates that Route B has the highest risk levels for driver assaults, a detailed look at possible increased countermeasure implementation on this route would be warranted. Table 10 shows the data for Route C. Again, this route shares Terminal A with the other two routes and thus has an elevated risk for driver assault that is associated with that terminal. The second terminal on this route, Terminal B, has a high incident history as well. The hours of operation increase the likelihood of driver assault, particularly at Terminal A. Thus, the combined risk rank for the terminals on Route C is 64. Again imputing from the aggregated terminal data, it is likely there are increased driver assault risks along the route compared to Route A, although not as high as are likely to be the case along Route B. Given the data associated with the terminals and transfer station on this route, it can be imputed that risks along Route B are likely to be higher than along the other two routes. In short, simply based upon these data that clearly demonstrates that Route B has the highest risk levels for driver assaults, a detailed look at possible increased countermeasure implementation on this route would be warranted. How an agency chooses to address these differential risks becomes Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide Copyright National Academy of Sciences. All rights reserved.

Table 7. (Continued). TERMINAL AND TRANSFER STATION RISK RANKS RISK FACTOR TERMINAL A – TRANSIT CENTER ROUTE 2 TERMINAL CHARACTERISTICS RISK FACTOR RANK REGION South 5 POPULATION DENSITY City 3 INCIDENT HISTORY Multiple Simple Assaults/ 36 Months 7 NUMBER OF BARS/CRIME PRONE SPOTS Multiple Bars, Nightclubs/ Gang Issues 7 KNOWN THREAT Multiple Known Threats 7 HOURS OF OPERATION Evening/Late Night/Early Mornings 7:00 p.m. to 2:00 a.m. 5 TERMINAL A RISK SCORE 34 TERMINAL AND TRANSFER STATION RISK RANKS RISK FACTOR TERMINAL B ROUTE 2 TERMINAL CHARACTERISTICS RISK FACTOR RANK REGION South 5 POPULATION DENSITY City 3 INCIDENT HISTORY Three Minor to Simple Driver Assaults/36 Months 7 NUMBER OF BARS/CRIME PRONE SPOTS Gang Issues 5 KNOWN THREAT Three Known Threats 3 HOURS OF OPERATION Evening/Late Night/Early Mornings 7:00 p.m. to 2:00 a.m. 5 TERMINAL B RISK SCORE 28 Total Route 2 Terminal Risk Score = A + B 34 + 28 = 62 Table 7B: Example of Terminal and Transfer Station Risk Ranks – Route 2 TERMINAL AND TRANSFER STATION RISK RANKS RISK FACTOR TERMINAL A – TRANSIT CENTER ROUTE A RISK FACTOR RANK REGION South 5 POPULATION DENSITY Metropolitan 5 INCIDENT HISTORY Multiple Simple Assaults/36 Months 7 NUMBER OF BARS/CRIME PRONE SPOTS Multiple Bars/Gang Issues 2 blocks with 5/block 7 KNOWN THREAT Multiple Known Threats 7 HOURS OF OPERATION Evening/Late Night/Early Mornings 7:00 p.m. to 2:00 a.m. 5 TERMINAL A RISK SCORE SCORE 36 TERMINAL AND TRANSFER STATION RISK RANKS RISK FACTOR TERMINAL B ROUTE A TERMINAL CHARACTERISTICS RISK FACTOR RANK REGION South 5 POPULATION DENSITY Metropolitan 5 INCIDENT HISTORY Zero Assaults/36 months 0 NUMBER OF BARS/CRIME PRONE SPOTS None 0 KNOWN THREAT Zero Known Threats 0 HOURS OF OPERATION Evening/Late Night/Early Mornings 7:00 p.m. to 2:00 a.m. 5 TERMINAL B RISK Total Route A Terminal Risk Score = A + B 36 + 15 = 51 Table 8. Example of terminal and transfer station risk ranks—Route A. Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide Copyright National Academy of Sciences. All rights reserved.

26 Tools and Strategies for Eliminating Assaults Against Transit Operators: User Guide TERMINAL AND TRANSFER STATION RISK RANKS TERMINAL AND TRANSFER STATION RISK RANKS RISK FACTOR TERMINAL A – TRANSIT CENTER ROUTE B TERMINAL CHARACTERISTICS RISK FACTOR RANK REGION South 5 POPULATION DENSITY Metropolitan 5 INCIDENT HISTORY Multiple Simple Assaults/36 Months 7 NUMBER OF BARS/CRIME PRONE SPOTS Multiple Bars, Nightclubs/Gang Issues 7 KNOWN THREAT Multiple Known Threats 7 HOURS OF OPERATION Evening/Late Night/Early Mornings 7:00 p.m. to 2:00 a.m. 5 TERMINAL A RISK SCORE 36 RISK FACTOR TERMINAL B ROUTE B TERMINAL CHARACTERISTICS RISK FACTOR RANK REGION South 5 POPULATION DENSITY Metropolitan 5 INCIDENT HISTORY Three Minor to Simple Driver Assaults/36 Months 7 NUMBER OF BARS/CRIME PRONE SPOTS Gang Issues 5 KNOWN THREAT Three Known Threats 3 HOURS OF OPERATION Evening/Late Night/Early Mornings 7:00 p.m. to 2:00 a.m. 5 TERMINAL B RISK SCORE 30 Total Route B Terminal Risk Score = A + B 36 + 30 = 66 Table 9A: Example Terminal and Transfer Station Risk Ranks – Route B Table 9B: Example Terminal and Transfer Station Risk Ranks – Route B RISK FACTOR TRANSFER STATION 1 ROUTE B TERMINAL CHARACTERISTICS RISK FACTOR RANK REGION South 5 POPULATION DENSITY Metropolitan 5 INCIDENT HISTORY Four Simple Assaults/36 Months 7 NUMBER OF BARS/CRIME PRONE SPOTS Gang Issues 3 KNOWN THREAT Four Known Threats 4 HOURS OF OPERATION Evening/Late Night/Early Mornings 7:00 p.m. to 2:00 a.m. 5 TRANSFER STATION 1 RISK SCORE 29 Total Route B Terminal Risk Score = A + B + TS1 36 + 30 + 29 = 95 TERMINAL AND TRANSFER STATION RISK RANKS Table 9. Example of terminal and transfer station risk ranks—Route B. Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide Copyright National Academy of Sciences. All rights reserved.

Operator Assault Risk Management Toolbox 27 TERMINAL AND TRANSFER STATION RISK RANKS RISK FACTOR TERMINAL A – TRANSIT CENTER ROUTE C TERMINAL CHARACTERISTICS RISK FACTOR RANK REGION South 5 POPULATION DENSITY Metropolitan 5 INCIDENT HISTORY Multiple Simple Assaults/ 36 Months 7 NUMBER OF BARS/CRIME PRONE SPOTS Multiple Bars, Nightclubs/ Gang Issues 7 KNOWN THREAT Multiple Known Threats 7 HOURS OF OPERATION Evening/Late Night/Early Mornings 7:00 p.m. to 2:00 a.m. 5 TERMINAL A RISK SCORE 36 TERMINAL AND TRANSFER STATION RISK RANKS RISK FACTOR TERMINAL B ROUTE C TERMINAL CHARACTERISTICS RISK FACTOR RANK REGION South 5 POPULATION DENSITY Metropolitan 5 INCIDENT HISTORY Five Simple Assaults/36 Months One Aggravated Assaults/ 36 Months 7 NUMBER OF BARS/CRIME PRONE SPOTS Multiple Bars/Low Income Housing 3 KNOWN THREAT Three Known Threats 3 HOURS OF OPERATION Evening/Late Night/Early Mornings 7:00 p.m. to 2:00 a.m. 5 TERMINAL B RISK SCORE 28 Total Route C Terminal Risk Score = A + B 36 + 28 = 64 Table 10. Example of terminal and transfer station risk ranks—Route C. a matter of comparing existing countermeasures that have been implemented on a route basis, as well as systemwide. Further, such decisions potentially raise policy issues regarding agency matters as well as more general community-based policy issues. The route-based risk calculator clearly distinguishes between the three routes in terms of bus driver assault risks. In the next section, the three routes utilized for the pilot study will be com- pared in the route-comparison summary table. Route-Comparison Summary Table The route-comparison summary table (see Table 11 and 12) assists transit agencies in making decisions regarding resource deployment across all sectors with the agency and providing a means to determine which routes within its system have the greatest risk of driver assault. Routes within a system may be compared so that a transit agency can identify routes that may require more imme- diate attention with regard to implementing countermeasures and those that may be addressed at a later stage. In the route-comparison summary table, the transit agency staff and decisionmakers can view the risk rate of driver assault by route and then use these comparisons for determining which routes should receive what kinds of measures to result in the greatest reduction of assaults with the most efficient and effective implementation of those countermeasures. Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide Copyright National Academy of Sciences. All rights reserved.

7This table can be reproduced to cover as many routes in a transit agency’s system as denoted by n in the last row. See Appendix C. ROUTE-COMPARISON SUMMARY TABLE—PART A7 ROUTES REGION SELECT RISK FACTOR RANK FROM LOOK-UP TABLE IN APPENDIX B (ENTER SAME VALUE FOR ALL ROUTES) POPULATION DENSITY SELECT RISK FACTOR RANK FROM LOOK-UP TABLE IN APPENDIX B (ENTER SAME VALUE FOR ALL ROUTES) ROUTE INCIDENT FREQUENCY SELECT RISK FACTOR RANK FROM LOOK-UP TABLE IN APPENDIX B ROUTE RISK SCORE— PART A AGG ASLT SIMP ASLT MIN INC THR SIMP TO AGG MIN TO SIMP REP AGG REP SIMP REP MIN REP THR THR AGG THR SIMP THR MIN 1 2 3 4 5 6 7 8 9 10 .. .. .. n Table 11. Route-comparison summary table—Part A. T ools and S trategies for E lim inating A ssaults A gainst T ransit O perators, V olum e 2: U ser G uide C opyright N ational A cadem y of S ciences. A ll rights reserved.

8This table can be reproduced to cover as many routes in a transit agency’s system as denoted by n in the last row. See Appendix C. ROUTE-COMPARISON SUMMARY TABLE—PART B8 ROUTES ROUTE FACTOR SELECT RISK FACTOR RANK FROM LOOK-UP TABLE IN APPENDIX B OPERATION FACTOR SELECT RISK FACTOR RANK FROM LOOK-UP TABLE IN APPENDIX B ROUTE RISK SCORE— PART B TOTAL ROUTE RISK SCORE— A + B BARS, NIGHTCLUBS, AND ENTERTAINMENT HIGH INCIDENT GANG AREAS HIGH JUVENILE CRIME AREAS KNOWN THREATS TERMINALS AND TRANSFER STATIONS HOURS OF OPERATION 1 2 3 4 5 6 7 8 9 10 .. .. .. n HIGH INCIDENT VENUES—TAVERNS, BARS, NIGHTCLUBS, AND SPORTS BARS/STADIUMS Table 12. Route-comparison summary table—Part B. T ools and S trategies for E lim inating A ssaults A gainst T ransit O perators, V olum e 2: U ser G uide C opyright N ational A cadem y of S ciences. A ll rights reserved.

30 Tools and Strategies for Eliminating Assaults Against Transit Operators: User Guide How to Use the Route-Comparison Summary Table For every route in the entire system, a route risk score would be derived and the scores for each route would be entered into a route-comparison summary table (see Tables 11 and 12). Use of the summary table will assist the transit agency in making the decisions regarding resource deployment across all sectors with the company. In short, in a single table, the transit agency staff and decision makers can view the risk rate of bus driver assault by route and then use these comparisons for determining which routes should receive what kinds of measures to result in the greatest reduction of assaults with the most efficient and effective implementation of those countermeasures. Understanding the Route-Comparison Summary Table To better understand how the route-comparison summary table works, examples were devel- oped using the route-risk scores on the three routes from the pilot agency. These three routes illustrate how the agency can utilize the route-comparison summary table in its analysis of the risks of driver assault along different routes in a system. As seen in route-comparison summary table (Tables 13 and 14), the three routes (A, B, and C) have some risk levels in common but have distinctly different overall driver risk profiles. In this particular case, the three routes share a common terminus that happens to have a conglomeration of high incident venues (bars, nightclubs, sporting venues, etc.) within a 10-minute walk from the terminal. Thus, all three share the same risk score for that terminal. However, Terminal B for the three routes is different (Route A in particular has a distinctly different B terminus in terms of risk profile), as well as the route characteristics of each route. For example, Terminal B for Routes B and C indicates relatively high-risk scores as a result of incident data and surrounding activities. Thus, Routes B and C have similar risk scores in terms of incident frequency rankings. However, Route B has an additional factor of a transfer station that experiences high levels of incidents and surrounding activities that dramatically increase the risks along the route. So, on the basis of route risk scoring Steps 1 and 2 captured in Table 13 (recall the data aggre- gation issues mentioned previously), all three routes have the same route risk score because all three routes share the same region and city scores. However, when the Step 3 scores are factored in (see Table 14), particularly the bars, nightclubs, and entertainment category, and the B terminus category and inclusion of the transfer station, Route B has much higher route risk scores. Thus, overall, Route B comes out as a much more likely route for a driver to experience assault—as borne out by the incident data. Route A, in contrast, other than sharing Terminal A and the riskier operating hours, is a relatively low risk route compared to Routes B and C. In conversations with the pilot agency personnel, the data driven results confirm their intuitive sense of the risks to their drivers on the three different routes. When considering these routes vis-à-vis countermeasures that have been implemented, the pilot agency can assess whether there are deficiencies in countermeasure deployment versus the risk profiles of the routes. Thus, these analyses can inform decisions regarding the deployment of additional countermeasures, as well as the likely effectiveness of additional countermeasures by type of incident being addressed. Calculating Total Route Risk with Countermeasures After completing the vulnerability self-assessment and calculating the route risks, the next step in the process is to determine the reduction of route risks associated with the counter- measures currently in place and then calculate the reduction in risk to be derived from imple- menting additional countermeasures—in short the total route risk with countermeasures. Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide Copyright National Academy of Sciences. All rights reserved.

ROUTE-COMPARISON SUMMARY TABLE – PART B – ROUTES 1, 2, AND 3 ROUTES ROUTE FACTOR: SELECT RISK FACTOR RANK FROM LOOK-UP TABLE IN APPENDIX B OPERATION FACTOR SELECT RISK FACTOR RANK FROM LOOK-UP TABLE IN APPENDIX B ROUTE RISK SCORE – PART B TOTAL ROUTE RISK SCORE – A + B BARS, NIGHTCLUBS, AND ENTERTAINMENT HIGH INCIDENT VENUES—TAVERNS, BARS, NIGHTCLUBS, AND SPORTS BARS/STADIUMS HIGH INCIDENT GANG AREAS HIGH JUVENILE CRIME AREAS KNOWN THREATS TERMINALS AND TRANSFER STATIONS HOURS OF OPERATION A 0 0 0 0 0 31 10 31 51 B 0 0 0 0 0 70 15 70 95 C 0 0 0 0 0 44 10 44 64 Table 14. Route-comparison summary table—Part B: pilot study Routes 1, 2, and 3. ROUTE-COMPARISON SUMMARY TABLE – PART A – ROUTES 1,2, AND 3 ROUTES REGION SELECT RISK FACTOR RANK FROM LOOK- UP TABLE IN APPENDIX B (ENTER SAME VALUE FOR ALL ROUTES) POPULATION DENSITY SELECT RISK FACTOR RANK FROM LOOK-UP TABLE IN APPENDIX B (ENTER SAME VALUE FOR ALL ROUTES) ROUTE INCIDENT FREQUENCY SELECT RISK FACTOR RANK FROM LOOK-UP TABLE IN APPENDIX B ROUTE RISK SCORE – PART A AGG ASLT SIMP ASLT MIN INC THR SIMP TO AGG MIN TO SIMP REP AGG REP SIMP REP MIN REP THR THR AGG THR SIMP THR MIN A 5 5 0 0 0 0 0 0 0 0 0 0 0 0 0 10 B 5 5 0 0 0 0 0 0 0 0 0 0 0 0 0 10 C 5 5 0 0 0 0 0 0 0 0 0 0 0 0 0 10 Table 13. Route-comparison summary table—Part A: pilot study Routes 1, 2, and 3. T ools and S trategies for E lim inating A ssaults A gainst T ransit O perators, V olum e 2: U ser G uide C opyright N ational A cadem y of S ciences. A ll rights reserved.

32 Tools and Strategies for Eliminating Assaults Against Transit Operators: User Guide This section adds countermeasures to the calculus. Table 15 groups the countermeasures accord- ing to the risk components [i.e., threat (T), vulnerability (V), or consequence (C) and weight or effectiveness]. As noted above, in some cases, the countermeasure may impact some combination of risk components and these are shown as combined countermeasure weights or effectiveness. However, similar to the risk factors, there are no baseline data that can be used to develop and test the assigned weights. In short, the weights shown in Table 15 represent the consensus estimates of the project team based upon what is available in the literature. However, should an agency/ authority have either data or alternative hypotheses as to the weightings, those can be substituted in Table 15 and the method of calculation remains the same.9 This is illustrated by two hypothetical cities, one in the Northeast and one in the Midwest to examine the impact of introducing off-board fare collection in the Northeast city and bus stop lighting in the Midwest city. So, for example, the following facts are assumed: Route 1—Northeast City, route with one assault in the past 48 months and two bars along the route. If off-board fare collection policies are implemented, how much will this reduce the risk of bus driver assault on that route? Example: Risk Reduction on Northeast City Route after Implementing Off-Board Fare Collection Step 1: Sum of risk factor rank is 7 (Northeast City) + 3 (two bars) + 3 (one assault over 48 months) = 13. The estimated probability of an assault is 0.59 (13/22)1 The implementation of off-board fare collection could affect the likelihood of an assault. Without detailed data at hand, the following assumptions can be made: • The transit agency may implement a maximum of three countermeasures for V, T, C, respectively • The maximum effectiveness score for each countermeasure is 5, so the maximum total score for each risk component (V, T, C) is 15. • It is assumed that the reductions of likelihood of an assault (T), the probability that an assault is successful (V), and the consequence (C) are linearly correlated with the total effectiveness score. • Under the maximum effectiveness score (i.e., 15), each risk component would be reduced by 80 percent.1 Based on these assumptions, off-board fare collection has an estimated effective- ness score 5, so the projected reduction in assault likelihood is 0.8 × (5/15) = 0.27. So the updated estimated assault probability is 0.59 × (1 – 0.27) = 0.431 1The Research Team makes the simplifying assumption that there is a linear relationship with effectiveness. While it is entirely arguable there are non-linear relationships among these variables, we have no a priori information as to what form such relationships might have and further, from a computational standpoint, introducing non-linearity introduces computational complexity in an arena where there remains a great deal of uncertainty regarding these phenomena, with no further gain in insights and understanding. 9For example, an agency may have sufficient experience with assault legislation in its legislative district over several years to believe that the effectiveness weight should be 2 instead of 3, as has been found in the literature and shown in Table 15. In that case, the weight of 2 would be substituted for the weight of 3 in Table 15 and would be used in subsequent calculations of that particular countermeasure effectiveness. Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide Copyright National Academy of Sciences. All rights reserved.

Operator Assault Risk Management Toolbox 33 Countermeasure TVC Effectiveness Weight Countermeasures Impacting Threats Policies, Plans, Protocols Fare Collection Policy and Procedures T 5 Passenger Screening T 5 Operator Assaults Zero Tolerance Workplace Violence Policy Coverage T 4 Passenger Code of Conduct T 4 Assault Legislation T 3 Barring Systems T 3 Passenger Awareness Programs T 3 Surveillance and Observation Systems Visible Surveillance Systems - Cameras in Plain Sight T 4 Driver Protection Services Public Address System and Signage T 3 Countermeasures Impacting Vulnerabilities Police or Security Staffing Staffing On Board Conveyance V 5 Driver Protection Services Physical Barriers/Compartment Barriers or Shielding—Full or Partial V 5 Driver-Side Exit Doors V 4 Defensive Weapons V 3 Training Driver/Operator Security V 5 Driver/Operator Self-Defense V 3 Countermeasures Impacting Consequences Policies, Plans, Protocols Communication Protocol for Violent Incidents C 5 Violent Incident Emergency Response Plan C 4 Police or Security Staffing Centralized On-Board Alarms, Panic Buttons with Immediate Force Response C 5 Data Communications and Telemetry Systems Vehicle Disabling C 4 Anti-Theft—Secure Driver Sign On C 4 Anti-Theft—Enroute C 4 Electronic Distress Signs C 3 Countermeasures Impacting Combined T/V Surveillance and Observation Systems Bus Stop Lighting T/V 3 Driver Protection Services On-Board Vehicle Fire Suppression Equipment T/V 5 Countermeasures Impacting Combined T/C Policies, Plans, Protocols Post-Incident Action Steps T/C 4 Driver Protection Services DNA Swipe Kits T/C 2 Table 15. Threat (T), vulnerability (V), and consequence (C) countermeasure effectiveness. Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide Copyright National Academy of Sciences. All rights reserved.

34 Tools and Strategies for Eliminating Assaults Against Transit Operators: User Guide Route 2—Midwest city, route with one assault over the last 60 months, one bar along the route. If bus stop lighting is implemented on the route in a Midwest city that has one bar and one assault over the last 60 months, how much will this reduce the risk of bus driver assault on that route? Countermeasure TVC Effectiveness Weight Countermeasures Impacting Combined V/C Policies, Plans, Protocols Operator Assault Committees/Task Forces V/C 5 Police or Security Staffing Centralized Surveillance with Immediate Force Response V/C 4 Shadowing Vehicles V/C 3 Centralized Remote Sensors with Immediate Force Response V/C 3 Voice Communications Technology Two-Way Radio - 3G/4G/LTE/ V/C 4 Satellite Mobile Broadband Least Cost Routing V/C 4 Cellular Telephone—Texting and Emailing V/C 2 Real-Time Audio V/C 2 Data Communications and Telemetry Systems Mobile Data Terminals with DVRs V/C 4 Vehicle Locator Systems (AVLs)—GPS V/C 4 Tracking and Monitoring—GPS V/C 4 Surveillance and Observation Systems Video Surveillance Using On-board Computer/DVR V/C 2 Training Driver/Operator Handbook V/C 5 Driver/Operator Security Awareness V/C 5 Driver/Operator Security Communications V/C 4 Table 15. (Continued). Example: Risk Reduction on Midwest City Route after Implementing Bus Stop Lighting Step 1: Sum of risk factor rank is 2 (Midwest City) + 2 (one bar) + 2 (one assault over 48 months) = 6. The estimated probability of an assault is 0.35 (6/17) Bus stop lighting could reduce both the likelihood of an assault and the probability that an assault is successful. It is assumed that the former is reduced by 30 percent, and the latter is reduced by 20 percent. So the updated estimated assault probability is 0.35 × (1 – 0.3) = 0.245 Step 2: Probability that the assault is successful, P = 1 (worst case), thus, the updated estimated probability of success is 1 × (1 – 0.2) = 0.8 Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide Copyright National Academy of Sciences. All rights reserved.

Operator Assault Risk Management Toolbox 35 Integrating the Route-Based Risk Calculator and Vulnerability Self-Assessment Tool By comparing the results of the route-based risk calculator with the route-based scores from the vulnerability self-assessment tool, agencies may gain a deeper understanding of the value of specific countermeasures relative to the risks present along each route—information that helps maximize the value of investment in individual countermeasures. To compare the relative risks along each route with corresponding countermeasure scores for the route, populate Table 16 with the route risk score, average countermeasure score, and the total number of countermeasures for each route being evaluated. Using the data for Routes A, B, and C for the pilot agency, the route-comparison summary table with the countermeasure score (Table 17) was constructed. Because the pilot agency has deployed all countermeasures systemwide, each route has the same average countermeasure score, as well as the total number of countermeasures. While it is clear that the pilot agency has deployed a large number of countermeasures (see Figure 4 Worksheet), it is equally clear from Table 17 that this may not be the most cost-effective deployment, or, more importantly, that deployment of further countermeasures, for example, barring systems on buses should be pri- oritized by route risk. This point directly raises the agency policy question mentioned previously. The pilot agency’s current policy is that countermeasures are deployed systemwide. However, notwithstanding the large number of deployed countermeasures for this agency, there are still routes—as illustrated for routes B and C, as well as a shared terminus for all three routes—where the risks may still remain above an acceptable level for the agency. Thus, an agency policy issue is potentially raised, as well as whether there may be communitywide policies—or some combination of both—that should be addressed, as discussed below. Route-Comparison Summary Table Route 1 2 3 4 5 6 7 8 9 10 … nᶦ Route Risk Score Average Countermeasure Score Total Number of Countermeasures Table 16. Route-comparison summary table with countermeasure score. Route A B C Route Risk Score 51 95 64 Average Countermeasure Score 6.95 6.95 6.95 Total Number of Countermeasures 30 30 30 Table 17. Illustration of the route-comparison summary table with countermeasure Score—3 pilot agency routes. Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide Copyright National Academy of Sciences. All rights reserved.

36 Tools and Strategies for Eliminating Assaults Against Transit Operators: User Guide Interpreting the Results Resources to combat operator assaults are limited, most agencies depend upon agency pol- icy target routes with the highest risk for the deployment of new countermeasures. Agencies should seek to deploy a large number of countermeasures that fulfill the vulnerability self- assessment tool criteria along each high-risk route. Route-specific scores on the vulnerability self-assessment tool can be classified into one of the several potential outcomes corresponding to the four quadrants of the diagram in Figure 5. Similar to the agencywide countermeasure assessment, transit providers may follow two dis- tinct strategies to improve route-specific countermeasure scores. If few countermeasures are currently in use, the agency should develop a more diverse approach by deploying a larger over- all number of countermeasures. If many countermeasures are already in use, the agency should consider utilizing methods identified in the Countermeasures Guide and Self-Assessment criteria to strengthen and improve the countermeasures that are already being implemented. Again, using the pilot agency example, Figure 5 suggests that the agency should consider countermeasures deployment to strengthen those that have already been implemented with a focus particularly upon Route B and then Route C, with Route A being largely well-served by the existing countermeasures—recognizing that countermeasures addressing the common terminus (the Transit Center) will be beneficial to route A as well. As all transit agencies know, there will be local circumstances that may dictate less than optimal countermeasure deployment beyond the obvious of limited resources. For example, if a local transit agency has experienced widely publicized assaults of drivers, there may be pressure from the press and elected officials to “know” which the most dangerous routes are and what is being Route-Comparison Summary Table Route 1 2 3 Route Risk Score Average Countermeasure Score Total Number of Countermeasures On routes with high risk relative to their countermeasure scores… … reduce vulnerability by improving countermeasure scores using the key described below. Figure 5. Countermeasure outcome categories. Tools and Strategies for Eliminating Assaults Against Transit Operators, Volume 2: User Guide Copyright National Academy of Sciences. All rights reserved.